It is to be understood that both the following general description and the following detailed description are illustrative and explanatory only and are not restrictive.
In one embodiment, the disclosure provides a computer-implemented method. The computer-implemented method includes determining, based on a first drag-and-drop interaction with a user interface, a selection of a first element defining a data model; and determining, based on a second drag-and-drop interaction with the user interface, a selection of a second element defining a data context. The computer-implemented method also includes generating, based on the data model and the data context, a query. The computer-implemented method further includes sending the query to a computing platform configured to execute the query against a database; and receiving, from the computing platform, data responsive to the query. The computer-implemented method still further includes causing presentation of the data within a viewport pane of the user interface.
Additional elements or advantages of this disclosure will be set forth in part in the description which follows, and in part will be apparent from the description, or may be learned by practice of the subject disclosure. The advantages of the subject disclosure can be attained by means of the elements and combinations particularly pointed out in the appended claims.
This summary is not intended to identify critical or essential features of the disclosure, but merely to summarize certain features and variations thereof. Other details and features will be described in the sections that follow. Further, both the foregoing general description and the following detailed description are illustrative and explanatory only and are not restrictive of the embodiments of this disclosure.
The annexed drawings are an integral part of the disclosure and are incorporated into the subject specification. The drawings illustrate example embodiments of the disclosure and, in conjunction with the description and claims, serve to explain at least in part various principles, elements, or aspects of the disclosure. Embodiments of the disclosure are described more fully below with reference to the annexed drawings. However, various elements of the disclosure can be implemented in many different forms and should not be construed as limited to the implementations set forth herein. Like numbers refer to like elements throughout.
The disclosure recognizes and addresses, among other technical challenges, the issue of composition of queries and visualization of data responsive to a query. Embodiments of this disclosure, include systems, devices, computer-implemented methods, and computer program products that, individually or in combination, permit generating a query using a user interface and also permit visualizing data responsive to a query. The query can be generated interactively, where by one or more selections of defined elements that define a data model and/or a data context. Interaction with the user interface can define a desired data context and/or a data model. The query can be automatically composed by including query criteria representing the data context and/or data model. The automated composition isolates the structure of a database from the construction of the query. Because such a structure defines query grammar, the isolation permits composing a query without information on the applicable query grammar. The query that is generated can be executed against the database to generate one or several data views. Visualization of a data view can dynamic, in response to the query being resolving against data identified by the data model.
Subsequent interactions with the user interface can update the data model or the data context, or both. In some cases, instead of generating an updated query, an existing query prior to the update(s) can be resolved against a different database consistent the updated data model. An updated data view can be visualized in response to the updated query being resolved.
In sharp contrast to existing technologies, the query composition and data visualization described herein decouple a client domain from a query-resolution domain, providing a straightforward and efficient mechanism for data exploration that avoids complex and difficult to maintain client-domain parsers.
In an aspect, the extraction of the data can comprise extracting an initial dataset or scope from the data source 102, e.g. by reading the initial dataset into the primary memory (e.g. RAM) of a computer. The initial dataset can comprise the entire contents of the data source 102, or a subset thereof. The internal database 120 can comprise the extracted data and symbol tables. Symbol tables can be created for each field and, in one aspect, can only contain the distinct field values, each of which can be represented by their clear text meaning and a bit filled pointer. The data tables can contain said bit filled pointers.
In the case of a query of the data source 102, a scope can be defined by the tables included in a SELECT statement (or equivalent) and how these are joined. In an aspect, the SELECT statement can be SQL (Structured Query Language) based. For an Internet search, the scope can be an index of found web pages, for example, organized as one or more tables. A result of scope definition can be a dataset.
Once the data has been extracted, a user interface can be generated to facilitate dynamic display of the data. By way of example, a particular view of a particular dataset or data subset generated for a user can be referred to as a state space or a session. Embodiments of this disclosure can dynamically generate one or more visual representations of the data to present in the state space.
A user can make a selection in the dataset, causing a logical inference engine 106 to evaluate a number of filters on the dataset. For example, a query on a database that holds data of placed orders, could be requesting results matching an order year of ‘1999’ and a client group be ‘Nisse.’ The selection may thus be uniquely defined by a list of included fields and, for each field, a list of selected values or, more generally, a condition. Based on the selection, the logical inference engine 106 can generate a data subset that represents a part of the scope. The data subset may thus contain a set of relevant data records from the scope, or a list of references (e.g. indices, pointers, or binary numbers) to these relevant data records. The logical inference engine 106 can process the selection and can determine what other selections are possible based on the current selections. In an aspect, flags can enable the logical inference engine 106 to work out the possible selections. By way of example, two flags can be used: the first flag can represent whether a value is selected or not, the second can represent whether or not a value selection is possible. For every click in an application, states and colors for all field values can be calculated. These can be referred to as state vectors, which can allow for state evaluation propagation between tables.
The logical inference engine 106 can utilize an associative model to connect data. In the associative model, all the fields in the data model have a logical association with every other field in the data model. An example, data model 501 is shown in
An example database, as shown in
Queries that compare for equality to a string can retrieve values very fast using a hash index. For instance, referring to the tables of
As shown in
The logical inference engine 106 can scan one or more of BTI 502a, 502b, 502c, 502d, and/or 502e and create the BAI 503a, 503b, 503c and/or 503d. The BAI 503a, 503b, 503c, and/or 503d can comprise a hash index. The BAI 503a, 503b, 503c, and/or 503d can comprise an index configured for connecting attributes in a first table to common columns in a second table. The BAI 503a, 503b, 503c, and/or 503d thus allows for identification of rows in the second table which then permits identification of other attributes in other tables. For example, referring to the tables of
Using the BTI 502a, 502b, 502c, 502d, and/or 502e and the BAI 503a, 503b, 503c, and/or 503d, the logical inference engine 106 can generate an index window 504 by taking a portion of the data model 501 and mapping it into memory. The portion of the data model 501 taken into memory can be sequential (e.g., not random). The result is a significant reduction in the size of data required to be loaded into memory.
In an aspect, bidirectional indexing using BTIs can have limits as to how much parallelization can be applied when processing the data model 501. To improve parallelization applied to the data model 501, the logical inference engine 106 can generate bidirectional indexes for partitions for a table in the data model 501. Such bidirectional indexes are hereinafter referred to as “indexlets.” In an aspect, the logical inference engine 106 can generate indexlets for a given table by partitioning the table into blocks of rows. In an aspect, the blocks of rows can be of a same size. In an aspect, a last block of rows can be of a size less than the remaining blocks of rows. In an aspect, after partitioning the blocks of rows, the logical inference engine can generate an indexlet for each of the blocks of rows. In an aspect, generating an indexlet for a given block of rows comprises generating a bidirectional index as described above, but limited in scope to the given block of rows.
Provided the input data sources, the logical inference engine 106 can implement an indexation process (e.g., symbol indexation) to generates the indexlets. Indexlets thus generated can serve as a foundation for providing bi-directional indexing information for the both inferencing and/or hypercube domain calculation techniques.
Given an input data source 102 in an interpretable format, e.g., comma-separated values (CSV) format, the indexation process can begin with partitioning the data source 102 into disjoint, same-sized blocks of rows. In some aspects, the indexation process will not partition the last row (e.g., the size of the last block might be smaller than the size of the other blocks). These “slices” of the data can be then processed independently to generate intermediate indexlet structures. Intermediate indexlet structures can be processed sequentially to generate a global symbol map. In addition to bi-directional information (symbol to row and row to symbol), a mapping between the symbols can reside locally in the indexlet and in the global symbol map. This mapping enables a simple yet fast and efficient transformation between symbols in an indexlet and in global symbol maps and vice versa through select and rank operations on bit vectors.
There are two main challenges to the indexation process: parallelization of the creation of intermediate indexlet structures and the creation and handling of large global symbol maps that contain potentially billions of symbols.
The indexation process can be divided into two components: an indexer service and a global symbol service. While the indexer service handles an indexation request as well as distributing tasks of creating the intermediate indexlet structures, the global symbol service enables splitting global symbol maps across machines. Even in good hash map implementations, there is always overhead in memory consumption due to the management of the internal data structure. As result, the ability to split global symbol maps across machines helps to share the load as well as supporting both horizontal and vertical scaling when dealing with large dataset.
To achieve the maximum parallelization of the creation of intermediate indexlet structures, the indexer service can utilize a distributed computing environment. A master node can comprise information regarding the capability of worker nodes registered during their initialization. On receiving an indexation request, the master node distributes tasks to worker nodes based on the registered capability. In this setup, more worker nodes can be dynamically added and registered with the master node to reduce the required creation time of the intermediate indexlet structures. Moreover, if a worker node dies during the process, a new worker node can be instantiated and registered to the master node to take over the corresponding tasks. The master node can also communicate with a global symbol master node to get global symbol maps initialized and ready for the global symbol service.
When dealing with large datasets, global symbol maps can comprise billions of symbols. Naturally, an in-memory hash map can provide better performance on both look up and insert operations in comparison to file-based hash map implementations. Unfortunately, it is not practical to have an unlimited amount of physical memory available. Although virtual memory can help to elevate the limitation of physical memory, the performance of lookup and insert operations degrades dramatically.
A global symbol service is provided in which global symbol maps are split across machines to share the load as well as the stress on memory requirements while achieving the desired performance.
The indexer service and the global symbol service can generate intermediate indexlet structures and process the intermediate indexlet structures sequentially to generate the global symbol maps together with bi-directional indexing information. This constraint on processing order permits fast and efficient mappings between symbols that reside locally in an indexlet and the global symbol maps. The global symbol service allows parallelism to improve indexation performance.
For example, a state, S, can be introduced into the global symbol maps 416a, 416b, and 416c on the worker nodes 414a, 414b, and 414c as follows
S={standing_by,serving,closed}
The creation of the global symbol map can start with inserting symbols into a serving hash map on the corresponding worker node. When the optimal capacity of the hash map is reached, the corresponding worker node informs the global symbol master node and changes its state to closed. The global symbol master node can then request another worker node to handle the upcoming tasks, e.g., changing the state of a hash map from “standing_by” to “serving.” On subsequent processes, lookup operations can be carried out in a bulk and in a parallelized manner on a closed hash map to maximize the performance. The remaining new symbols can then be inserted into the serving hash map on the corresponding worker node. If a worker node in “standing_by” state dies during the process, it can be replaced by instantiating another worker node that registers itself to the master node. If a worker node in “closed” or “serving” state dies, it can be replaced by either another worker node in “standing_by” state or a newly instantiated worker node. In this case, the master node informs the indexer service and the range of the corresponding data will be indexed again to reconstruct the corresponding hash map.
In an aspect, a Bloom filter 418a, 418b, and 418c can be used to further optimize lookup performance. A Bloom filter is a probabilistic data structure that can indicate whether an element either definitely is not in the set or may be in the set. In other words, false-positive matches are possible, but false negatives are not. The base data structure of a Bloom filter is a bit vector. On a very large hash map that contains several billion symbols, the performance of the lookup operation can degrade dramatically as the size increases. The Bloom filter is a compact data structure that can represent a set with an arbitrarily large number of elements. The Bloom filter enables fast querying of the existence of an element in a set. Depending on the registered resource information, the false positive rate can be specified to achieve both the compactness of the Bloom filter and the minimum access to the hash map. A Bloom filter can improve the performance of lookup operation on closed hash map by 3 to 5 times. The constructed Bloom filter 418a, 418b, and 418c can be used to minimize the amount of communication required in the inferencing as well as hypercube domain construction process. Particularly, by performing lookup operations in the Bloom filters 418a, 418b, and 418c first, the number of hash maps that possibly contain the desired information will be minimized, and, thus, reduce the number of requests that need to be transferred through the network
The indexer service and the global symbol service allows both local as well as cloud-based deployment of symbol indexation. With the cloud-based deployment, more resources can be added to improve the indexation process. The indexation process is bounded by the amount of resources and the available bandwidth. In large-scale deployment, direct communication between indexer worker nodes 404a, 404b, and 404c and global symbol worker nodes 414a, 414b, and 414c can be setup to reduce the load on the global symbol master node 412.
A representation of a data structure for indexlets is shown in
Thus, the logical inference engine 106 can determine a data subset based on user selections. The logical inference engine 106 automatically maintains associations among every piece of data in the entire dataset used in an application. The logical inference engine 106 can store the binary state of every field and of every data table dependent on user selection (e.g., included or excluded). This can be referred to as a state space and can be updated by the logical inference engine 106 every time a selection is made. There is one bit in the state space for every value in the symbol table or row in the data table, as such the state space is smaller than the data itself and faster to query. The inference engine will work associating values or binary symbols into the dimension tuples. Dimension tuples are normally needed by a hypercube to produce a result.
The associations thus created by the logical inference engine 106 means that when a user makes a selection, the logical inference engine 106 can resolve (quickly) which values are still valid (e.g., possible values) and which values are excluded. The user can continue to make selections, clear selections, and make new selections, and the logical inference engine 106 will continue to present the correct results from the logical inference of those selections. In contrast to a traditional join model database, the associative model provides an interactive associative experience to the user.
The inverted index 521 can be generated such that each position in the inverted index 521 corresponds to a row of Table 2 (e.g., first position corresponds to row 1, second position corresponds to row 2, etc. . . . ). A value can be entered into each position that reflects the corresponding position 522 for each attribute. Thus, in the inverted index 521, position 1 comprises the value “1” which is the corresponding position 522 value for the attribute “Nisse”, position 2 comprises the value “2” which is the corresponding position 522 value for the attribute “Gullan”, position 3 comprises the value “3” which is the corresponding position 522 value for the attribute “Kalle”, position 4 comprises the value “3” which is the corresponding position 522 value for the attribute “Kalle”, position 5 comprises the value “4” which is the corresponding position 522 value for the attribute “Pekka”, and position 6 comprises the value “1” which is the corresponding position 522 value for the attribute “Nisse”.
A BTI 524 can be generated for the “Product” attribute of Table 2. In an aspect, the BTI 524 can comprise an inverted index 525. In other aspect, the inverted index 525 can be considered a separate structure. The BTI 524 can comprise a row for each unique attribute in the “Product” column of Table 2. Each unique attribute can be assigned a corresponding position 526 in the BTI 524. In an aspect, the BTI 524 can comprise a hash for each unique attribute. The BTI 524 can comprise a column 527 for each row of Table 2. For each attribute, a “1” can indicate the presence of the attribute in the row and a “0” can indicate an absence of the attribute from the row. “0” and “1” are merely examples of values used to indicate presence or absence. Thus, the BTI 524 reflects that the attribute “Toothpaste” is found in row 1 of Table 2, the attribute “Soap” is found in rows 2, 3, and 5 of Table 2, and the attribute “Shampoo” is found in rows 4 and 6 of Table 2.
By way of example, the inverted index 525 can be generated such that each position in the inverted index 525 corresponds to a row of Table 2 (e.g., first position corresponds to row 1, second position corresponds to row 2, etc.). A value can be entered into each position that reflects the corresponding position 526 for each attribute. Thus, in the inverted index 525, position 1 comprises the value “1” which is the corresponding position 526 value for the attribute “Toothpaste”, position 2 comprises the value “2” which is the corresponding position 526 value for the attribute “Soap”, position 3 comprises the value “2” which is the corresponding position 526 value for the attribute “Soap”, position 4 comprises the value “3” which is the corresponding position 526 value for the attribute “Shampoo”, position 5 comprises the value “2” which is the corresponding position 526 value for the attribute “Soap”, and position 6 comprises the value “3” which is the corresponding position 526 value for the attribute “Shampoo.”
By way of example, a BTI 528 can be generated for the “Product” attribute of Table 1. In an aspect, the BTI 528 can comprise an inverted index 529. In other aspect, the inverted index 529 can be considered a separate structure. The BTI 528 can comprise a row for each unique attribute in the “Product” column of Table 1. Each unique attribute can be assigned a corresponding position 530 in the BTI 528. In an aspect, the BTI 528 can comprise a hash for each unique attribute. The BTI 528 can comprise a column 531 for each row of Table 1. For each attribute, a “1” can indicate the presence of the attribute in the row and a “0” can indicate an absence of the attribute from the row. “0” and “1” are merely examples of values used to indicate presence or absence. Thus, the BTI 528 reflects that the attribute “Soap” is found in row 1 of Table 1, the attribute “Soft Soap” is found in row 2 of Table 1, and the attribute “Toothpaste” is found in rows 3 and 4 of Table 1.
By way of example, the inverted index 529 can be generated such that each position in the inverted index 529 corresponds to a row of Table 1 (e.g., first position corresponds to row 1, second position corresponds to row 2, etc. . . . ). A value can be entered into each position that reflects the corresponding position 530 for each attribute. Thus, in the inverted index 529, position 1 comprises the value “1” which is the corresponding position 530 value for the attribute “Soap”, position 2 comprises the value “2” which is the corresponding position 530 value for the attribute “Soft Soap”, position 3 comprises the value “3” which is the corresponding position 530 value for the attribute “Toothpaste”, and position 4 comprises the value “3” which is the corresponding position 530 value for the attribute “Toothpaste”.
By way of example, a BAI 532 can be generated as an index between the product attribute of Table 2 and Table 1. The BAI 532 can comprise a row for each unique attribute in the BTI 524 by order of corresponding position 526. The value in each row can comprise the corresponding position 530 of the BTI 528. Thus, position 1 of the BAI 532 corresponds to “Toothpaste” in the BTI 524 (corresponding position 526 of 1) and comprises the value “3” which is the corresponding position 530 for “Toothpaste” of the BTI 528. Position 2 of the BAI 532 corresponds to “Soap” in the BTI 524 (corresponding position 526 of 2) and comprises the value “1” which is the corresponding position 530 for “Soap” of the BTI 528. Position 3 of the BAI 532 corresponds to “Shampoo” in the BTI 524 (corresponding position 526 of 3) and comprises the value “−1” which indicates that the attribute “Shampoo” is not found in Table 1.
By way of example, a BAI 533 can be created to create an index between the product attribute of Table 1 and Table 2. The BAI 533 can comprise a row for each unique attribute in the BTI 528 by order of corresponding position 530. The value in each row can comprise the corresponding position 526 of the BTI 524. Thus, position 1 of the BAI 533 corresponds to “Soap” in the BTI 528 (corresponding position 530 of 1) and comprises the value “2” which is the corresponding position 526 for “Soap” of the BTI 524. Position 2 of the BAI 533 corresponds to “Soft Soap” in the BTI 528 (corresponding position 530 of 2) and comprises the value “−1” which indicates that the attribute “Soft Soap” is not found in Table 2. Position 3 of the BAI 533 corresponds to “Toothpaste” in the BTI 528 (corresponding position 530 of 3) and comprises the value “1” which is the corresponding position 526 for “Toothpaste” of the BTI 524.
The BTI 520 can be consulted to determine that the attribute “Kalle” has a value of “1” in the column 523 corresponding to rows 3 and 4. In an aspect, the inverted index 521 can be consulted to determine that the user selection 534 relates to the position 522 value of “3” which is found in the inverted index 521 at positions 3 and 4, implicating rows 3 and 4 of Table 1. Following path 535, a row state 536 can be generated to reflect the user selection 534 as applied to the rows of Table 2. The row state 536 can comprise a position that corresponds to each row and a value in each position reflecting whether a row was selected. Thus, position 1 of the row state 536 comprises the value “0” indicating that row 1 does not contain “Kalle”, position 2 of the row state 536 comprises the value “0” indicating that row 2 does not contain “Kalle”, position 3 of the row state 536 comprises the value “1” indicating that row 3 does contain “Kalle”, position 4 of the row state 536 comprises the value “1” indicating that row 4 does contain “Kalle”, position 5 of the row state 536 comprises the value “0” indicating that row 5 does not contain “Kalle”, and position 6 of the row state 536 comprises the value “0” indicating that row 6 does not contain “Kalle”.
Following path 537, the row state 536 can be compared with the inverted index 525 to determine the corresponding position 526 contained in the inverted index 525 at positions 3 and 4. The inverted index 525 comprises the corresponding position 526 value of “2” in position 3 and the corresponding position 526 value of “3” in position 4. Following path 538, the corresponding position 526 values of “2” and “3” can be determined to correspond to “Soap” and “Shampoo” respectively in the BTI 524. Thus, the logical inference engine 106 can determine that both “Soap” and “Shampoo” in Table 2 are associated with “Kalle” in Table 2. The association can be reflected in an inferred state 539 in the BTI 524. The inferred state 539 can comprise a column with a row for each attribute in the BTI 524. The column can comprise a value indicated the selection state for each attribute. The inferred state 539 comprises a “0” for “Toothpaste” indicating that “Toothpaste” is not associated with “Kalle”, the inferred state 539 comprises a “1” for “Soap” indicating that “Soap” is associated with “Kalle”, and inferred state 539 comprises a “1” for “Shampoo” indicating that “Shampoo” is associated with “Kalle”.
Following path 540, the inferred state 539 can be compared to the BAI 532 to determine one or more associations between the selection of “Kalle” in Table 2 and one or more attributes in Table 1. As the inferred state 539 comprises a value of “1” in both position 2 and position 3, the BAI 532 can be assessed to determine the values contained in position 2 and position 3 of the BAI 532 (following path 541). Position 2 of the BAI 532 comprises the value “1” which identifies the corresponding position 530 of “Soap” and position 3 of the BAI 532 comprises the value “−1” which indicates that Table 1 does not contain “Shampoo”. Thus, the logical inference engine 106 can determine that “Soap” in Table 1 is associated with “Kalle” in Table 2. The association can be reflected in an inferred state 542 in the BTI 528. The inferred state 542 can comprise a column with a row for each attribute in the BTI 528. The column can comprise a value indicated the selection state for each attribute. The inferred state 542 comprises a “1” for “Soap” indicating that “Soap” is associated with “Kalle”, the inferred state 542 comprises a “0” for “Soft Soap” indicating that “Soft Soap” is not associated with “Kalle”, and the inferred state 542 comprises a “0” for “Toothpaste” indicating that “Toothpaste” is not associated with “Kalle”. Based on the current state of BTIs and BAIs, if the data sources 102 indicate that an update or delta change has occurred to the underlying data, the BTIs and BAIs can be updated with corresponding changes to maintain consistency.
In aspects implementing indexlets, the logical inference engine 106 can apply query language by first performing intra-table inferencing on respective tables. Intra-table inferencing comprises transferring the imposed state of one field to other fields within the same table. In an aspect, intra-table inferencing can comprise computing the union of the index of the active attributes in a user input 504. The intersection of the result of the union operation and record states (i.e. row states 510) is then determined. This result is then intersected with the attribute states 514 of other columns using the inverted index 512. If other selection vectors from a previously provided user input vector 504 has zero active entries, a conflict can be detected. In an aspect, the logical inference engine 106 can resolve the detected conflict. In an aspect, resolving a conflict can include deleting or otherwise eliminating one or more incompatible selections. In another aspect, resolving a conflict can include reverting the data model 501 or a portion of the data model 501, e.g. a table, record, or attribute, to a previous state.
In an aspect, after performing intra-table inferencing, the logical inference engine 106 can perform inter-table inferencing based on the intra-table inferencing output of a plurality of tables, as is depicted in
Based on current selections and possible rows in data tables a calculation/chart engine 108 can calculate aggregations in objects forming transient hypercubes in an application. The calculation/chart engine 108 can further build a virtual temporary table from which aggregations can be made. The calculation/chart engine 108 can perform a calculation (e.g., evaluate an expression in response to a user selection/de-selection) via a multithreaded operation. The state-space can be queried to gather all of the combinations of dimensions and values necessary to perform the calculation. In an aspect, the query can be on one thread per object, one process, one worker, combinations thereof, and the like. The expression can be calculated on multiple threads per object. Results of the calculation can be passed to a rendering engine 116 and/or optionally to an extension engine 110.
In an aspect, the calculation/chart engine 108 can receive dimensions, expressions, and sorting parameters and can compute a hypercube data structure containing aggregations along the dimensions. For example, a virtual record can be built with a placeholder for all field values (or indices) needed, as a latch memory location. When all values are assigned, the virtual record can be processed to aggregate the fields needed for computations and save the dimension values in a data structure per row of the resulting hypercube. In such a way, the traversal of the database can be done in an arbitrary way, just depending on requirements provided by memory consumption and indexing techniques used for the particular case at hand.
Optionally, the extension engine 110 can be implemented to communicate data via an interface 112 to an external engine 114. In another aspect, the extension engine 110 can communicate data, metadata, a script, a reference to one or more artificial neural networks (ANNs), one or more commands to be executed, one or more expressions to be evaluated, combinations thereof, and the like to the external engine 112. The interface 112 can comprise, for example, an Application Programming Interface (API). The external engine 114 can comprise one or more data processing applications (e.g., simulation applications, statistical applications, mathematical computation applications, database applications, combinations thereof, and the like). The external engine 114 can be, for example, one or more of MATLAB®, R, Maple®, Mathematica®, combinations thereof, and the like.
In an aspect, the external engine 114 can be local to the associative data indexing engine 100 or the external engine 114 can be remote from the associative data indexing engine 100. The external engine 114 can perform additional calculations and transmit the results to the extension engine 110 via the interface 112. A user can make a selection in the data model of data to be sent to the external engine 114. The logical inference engine 106 and/or the extension engine 110 can generate data to be output to the external engine 114 in a format to which the external engine 114 is accustomed to processing. In an example application, tuples forming a hypercube can comprise two dimensions and one expression, such as (Month, Year, Count (ID)), ID being a record identification of one entry. Then said tuples can be exchanged with the external engine 114 through the interface 112 as a table. If the data comprise births there can be timestamps of the births and these can be stored as month and year. If a selection in the data model will give a set of month-year values that are to be sent out to an external unit, the logical inference engine 106 and/or the extension engine 110 can ripple that change to the data model associatively and produce the data (e.g., set and/or values) that the external engine 114 needs to work with. The set and/or values can be exchanged through the interface 112 with the external engine 114. The external engine 114 can comprise any method and/or system for performing an operation on the set and/or values. In an aspect, operations on the set and/or values by the external engine 114 can be based on tuples (aggregated or not). In an aspect, operations on the set and/or values by the external engine 114 can comprise a database query based on the tuples. Operations on the set and/or values by the external engine 114 can be any transformation/operation of the data as long as the cardinality of the result is consonant to the sent tuples/hypercube result.
In an aspect, tuples that are transmitted to the external engine 114 through the interface 112 can result in different data being received from the external engine 114 through the interface 112. For example, a tuple consisting of (Month, Year, Count (ID)) should return as 1-to-1, m-to-1 (where aggregations are computed externally) or n-to-n values. If data received are not what were expected, association can be lost. Transformation of data by the external engine 114 can be configured such that cardinality of the results is consonant to the sent tuples and/or hypercube results. The amount of values returned can thus preserve associativity.
Results received by the extension engine 110 from the external engine 114 can be appended to the data model. In an aspect, the data can be appended to the data model without intervention of the script engine 104. Data model enrichment is thus possible “on the fly.” A natural work flow is available allowing clicking users to associatively extend the data. The methods and systems disclosed permit incorporation of user implemented functionality into a presently used work flow. Interaction with third party complex computation engines, such as MATLAB® or R, is thus facilitated.
The logical inference engine 106 can couple associated results to the external engine 114 within the context of an already processed data model. The context can comprise tuple or tuples defined by dimensions and expressions computed by hypercube routines. Association is used for determination of which elements of the present data model are relevant for the computation at hand. Feedback from the external engine 114 can be used for further inference inside the inference engine or to provide feedback to the user.
In an aspect, one or more handles that were generated prior to receiving results from the external engine 114 can be updated after the results have been appended to the data model. For example, if one or more values in one or more tables changes because of the results from the external engine 114, an UPDATE operation can be performed to refresh the handles to reflect changes in the underlying data.
A rendering engine 116 can produce a desired graphical object (charts, tables, etc) based on selections/calculations. When a selection is made on a rendered object there can be a repetition of the process of moving through one or more of the logical inference engine 106, the calculation/chart engine 108, the extension engine 110, the external engine 114, and/or the rendering engine 116. The user can explore the scope by making different selections, by clicking on graphical objects to select variables, which causes the graphical object to change. At every time instant during the exploration, there exists a current state space, which is associated with a current selection state that is operated on the scope (which always remains the same).
In an aspect, any aggregation function processed by the associative data indexing engine 100 can be qualified to operate on a subset of records (rather than a current selection of data records and/or all data records). The associative data indexing engine 100 can define alternative aggregation sets based on set analysis (e.g., set expression, etc.). Using set analysis, the associative data indexing engine 100 can support methods to define an aggregation set. The exact compositions of defined aggregation sets may not only depend on desired conditions but also the chart (analysis) they are used in. The associative data indexing engine 100 may execute/perform set analysis (e.g., set expression analysis, etc.) for one or more set expressions determined/extracted from a query, such as an undetermined query (e.g., an imprecise query, an undefined query, an incomplete query, a partially expressed query, a portioned query, etc.), to determine and/or define an aggregation set.
To define an aggregation set for an undetermined query (e.g., an imprecise query, an undefined query, an incomplete query, a partially expressed query, a portioned query, etc.), the associative data indexing engine 100 may consider and/or account for items (e.g., compositional elements, predicates, etc.), constraints (e.g., data constraints, logical constraints, etc.) of the query, and one or more data analysis models (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.).
For example, the associative data indexing engine 100 may determine how each input item and/or computational element fits a data analysis model based on the data analysis model's capacity and/or projectability of an item (e.g., whether it has any condition, whether the condition results on one or multiple values, etc.). For example, the associative data indexing engine 100 may determine an optimal data analysis model from one or more data analysis models determined (e.g., via the calculation/chart engine 108, etc.) from a query (e.g., an undetermined query or another type of query) that best fits each input item and/or computational element.
For example, the associative data indexing engine 100 may define an aggregation set for each of the following undetermined business-related queries:
Query 1, Query 2, and Query 3, each include similar (e.g., conceptually similar, etc.) items (e.g., compositional elements, predicates, etc.), such as “sales,” “products,” “>2000,” and/or the like. The associative data indexing engine 100 may, for example, use natural language parsing and/or metadata analysis to determine the items and/or any constraints of the query, such as a default analysis period, a required data/element selection, and/or the like. The computing device may determine/perform a different set analysis for the Query 1, the Query 2, the Query 3, and/or any other undetermined query based on, for example, an order/arrangement of items (and/or computational elements/constraints) of the query and/or the composition (e.g., dimensions, measures, etc.) of one or more data analysis models (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.). The computing device may determine/perform a different set analysis for Query 1, Query 2, and Query 3 (and/or any other query) according to novel algorithms described herein.
Compositional elements (e.g., predicates, conditions, data constraints, etc.) of a query and/or query data may be determined. Compositional elements (e.g., predicates, conditions, data constraints, etc.) of the query data may include and/or be based on text/items from the query and corresponding conditional predicate(s). For example, for Query 1, Query 2, and Query 3, the associative data indexing engine 100 may determine example compositional elements shown below:
Metadata for and/or associated with a query (e.g., an undetermined query or another type of query) and/or any compositional element for the undetermined query may be determined. For example, semantic data types may uniformly represent standard data types, compositional elements, validations, formatting rules, and other business logic that may be further used to determine and/or define an aggregation set. Semantic types may be stored as metadata structures that may be used and reused during the process of query analysis.
A set of input items and/or computational elements may be adjusted, for example by the associative data indexing engine 100, to ensure there is no conflict. A query, such as a natural question, may include an explicit time frame, for example, an undetermined query may be “Sales by product, where sales >2000 in 2019,” where the year 2019 is the explicit time frame for the query. The associative data indexing engine 100 may determine that any default time period is unwarranted and/or if the global selections already satisfy any metadata-driven preconditions to use a measure.
The best data analysis model for a query may be determined. For example, the best data analysis model may be a data analysis model most relevant to a query—determined based on how aggregated related data may potentially fit and/or apply to elements, fields, constraints, components, and/or the like of a data analysis model. For example, input items and/or computational elements associated with a rank and/or ranking may be best fitted to a bar chart and/or related data analysis model, input items and/or computational elements associated with values may be best fitted to a table, input items and/or computational elements associated with facts may be best fitted to a KPI and/or related data analysis model.
The associative data indexing engine 100 may determine, for example, a data analysis model most relevant to a query based on the analysis' capacity and also the projectability of an item and/or compositional element and of a query, such as whether the item and/or compositional element is associated with any condition, and/or whether the condition results on one or multiple values. For example, the associative data indexing engine 100 may determine that a rank analysis may accommodate one measure and one dimension. For example, a rank analysis and/or associated data analysis model may be determined for Query 1 (Sales by product where sales >2000) because a rank analysis and/or associated data analysis model may include “sales and measure,” and “product” as dimensions. However, for a slightly modified query such as:
The methods provided can be implemented by means of a computer program as illustrated in a flowchart of a method 300 in
To increase evaluation speed, each unique value of each data variable in said database can be assigned a different binary code and the data records can be stored in binary-coded form. This can be performed, for example, when the program first reads the data records from the database. For each input table, the following operations can be carried out. The column names, e.g. the variables, of the table can be read (e.g., successively). Every time a new data variable appears, a data structure can be instantiated for the new data variable. An internal table structure can be instantiated to contain some or all the data records in binary form, whereupon the data records can be read (e.g., successively) and binary-coded. For each data value, the data structure of the corresponding data variable can be checked to establish if the value has previously been assigned a binary code. If so, that binary code can be inserted in the proper place in the above-mentioned table structure. If not, the data value can be added to the data structure and assigned a new binary code, for example the next binary code in ascending order, before being inserted in the table structure. In other words, for each data variable, a unique binary code can be assigned to each unique data value.
After having read some or all data records in the database, the method 300 can analyze the database in a operation 304 to identify all connections between the data tables. A connection between two data tables means that these data tables have one variable in common. In an aspect, operation 304 can comprise generation of one or more bidirectional table indexes and one or more bidirectional associative indexes. In an aspect, generation of one or more bidirectional table indexes and one or more bidirectional associative indexes can comprise a separate operation. In another aspect, generation of one or more bidirectional table indexes and one or more bidirectional associative indexes can be on-demand. After the analysis, all data tables are virtually connected. In
After this initial analysis, the user can explore the database and/or define a mathematical function. Assume that the user wants to extract the total sales per year and client from the database in
Optionally, a mathematical function may be determined for an undetermined query (e.g., an imprecise query, an undefined query, an incomplete query, a partially expressed query, a portioned query, etc.), for example, “Sales by product where sales >2000.” Calculation variables for the undetermined query may include “Product,” “Price,” “Date,” and “Year.”
At operation 306, a mathematical function may be determined. The mathematical function may be, for example, a combination of mathematical expressions. For example, an undetermined query such as “Sales by product where sales >2000,” can be used to extract the total sales of a product where the number (e.g., sale amount, etc.) exceeds 2000. A corresponding mathematical function may be defined, for example by a user as:
=sum({<Set1,Set2>}Sales),
where:
Calculation variables to be included in this function may include “Product” and “Number.” The classification variable “Year” may also be set, for example, by a user.
The method 300 then identifies in operation 308 all relevant data tables, e.g. all data tables containing any one of the selected calculation and classification variables, such data tables being denoted boundary tables, as well as intermediate data tables in the connecting path(s) between these boundary tables in the snowflake structure, such data tables being denoted connecting tables. There are no connecting tables in the present example. In an aspect, one or more bidirectional table indexes and one or more bidirectional associative indexes can be accessed as part of operation 308.
In the present example, all occurrences of every value, e.g. frequency data, of the selected calculation variables can be included for evaluation of the mathematical function. In
Then, a starting table can be selected in operation 310, for example, among the data tables within subset (B). In an aspect, the starting table can be the data table with the largest number of data records in this subset. In
Thereafter, a conversion structure can be built in operation 312. This conversion structure can be used for translating each value of each connecting variable (“Date,” “Product”) in the starting table (Table 2) into a value of a corresponding selected variable (“Year,” “Price”) in the boundary tables (Table 3 and 1, respectively). A table of the conversion structure can be built by successively reading data records of Table 3 and creating a link between each unique value of the connecting variable (“Date”) and a corresponding value of the selected variable (“Year”). It can be noted that there is no link from value 4 (“Date: 1999 Jan. 12”), since this value is not included in the boundary table. Similarly, a further table of the conversion structure can be built by successively reading data records of Table 1 and creating a link between each unique value of the connecting variable (“Product”) and a corresponding value of the selected variable (“Price”). In this example, value 2 (“Product: Toothpaste”) is linked to two values of the selected variable (“Price: 6.5”), since this connection occurs twice in the boundary table. Thus, frequency data can be included in the conversion structure. Also note that there is no link from value 3 (“Product: Shampoo”).
When the conversion structure has been built, a virtual data record can be created. Such a virtual data record accommodates all selected variables (“Client,” “Year,” “Price,” “Number”) in the database. In building the virtual data record, a data record is read in operation 314 from the starting table (Table 2). Then, the value of each selected variable (“Client”, “Number”) in the current data record of the starting table can be incorporated in the virtual data record in a operation 316. Also, by using the conversion structure each value of each connecting variable (“Date”, “Product”) in the current data record of the starting table can be converted into a value of a corresponding selected variable (“Year”, “Price”), this value also being incorporated in the virtual data record.
In operation 318 the virtual data record can be used to build an intermediate data structure. Each data record of the intermediate data structure can accommodate each selected classification variable (dimension) and an aggregation field for each mathematical expression implied by the mathematical function. The intermediate data structure can be built based on the values of the selected variables in the virtual data record. Thus, each mathematical expression can be evaluated based on one or more values of one or more relevant calculation variables in the virtual data record, and the result can be aggregated in the appropriate aggregation field based on the combination of current values of the classification variables (“Client,” “Year”).
The above procedure can be repeated for one or more additional (e.g., all) data records of the starting table. In an operation 320 it can be checked whether the end of the starting table has been reached. If not, the process can be repeated from operation 314 and further data records can be read from the starting table. Thus, an intermediate data structure can be built by successively reading data records of the starting table, by incorporating the current values of the selected variables in a virtual data record, and by evaluating each mathematical expression based on the content of the virtual data record. If the current combination of values of classification variables in the virtual data record is new, a new data record can be created in the intermediate data structure to hold the result of the evaluation. Otherwise, the appropriate data record is rapidly found, and the result of the evaluation is aggregated in the aggregation field.
Thus, data records can be added to the intermediate data structure as the starting table is traversed. The intermediate data structure can be a data table associated with an efficient index system, such as an AVL or a hash structure. The aggregation field can be implemented as a summation register, in which the result of the evaluated mathematical expression is accumulated.
In some aspects, e.g. when evaluating a median, the aggregation field can be implemented to hold all individual results for a unique combination of values of the specified classification variables. It should be noted that only one virtual data record is needed in the procedure of building the intermediate data structure from the starting table. Thus, the content of the virtual data record can be updated for each data record of the starting table. This can minimize the memory requirement in executing the computer program.
After traversing the starting table, the intermediate data structure can contain a plurality of data records. If the intermediate data structure accommodates more than two classification variables, the intermediate data structure can, for each eliminated classification variable, contain the evaluated results aggregated over all values of this classification variable for each unique combination of values of remaining classification variables.
In an aspect, operation 322 can involve any of the processes described previously with regard to
In an aspect, when the intermediate data structure has been built, a final data structure(s), e.g., data analysis model(s) (e.g., data charts, data tables, data graphs, data maps, key performance indicators (KPIs), etc.), may be created by evaluating the mathematical function based on the results of the mathematical expression contained in the intermediate data structure. In doing so, the results in the aggregation fields for each unique combination of values of the classification variables may be combined.
The data analysis model may be a best fit data analysis model, for example, a data analysis model that best fits compositional elements (e.g., predicates, conditions, data constraints, etc.) of an undetermined query and/or any other query. As explained, a data analysis model most relevant to a query may be based on the analysis' capacity and also the projectability of an item and/or compositional element and of the query, such as whether the item and/or compositional element is associated with any condition, and/or whether the condition results on one or multiple values.
In the example, the creation of the final data structure is straightforward, due to the trivial nature of the present mathematical function. At operation 324, the content of the final data structure may be presented to the user.
At operation 326, input from the user can be received. For example, input from the user can be a selection and/or de-selection of the presented results.
Optionally, input from the user at operation 326 can comprise a request for external processing. In an aspect, the user can be presented with an option to select one or more external engines to use for the external processing. Optionally, at operation 328, data underlying the user selection can be configured (e.g., formatted) for use by an external engine. Optionally, at operation 330, the data can be transmitted to the external engine for processing and the processed data can be received. The received data can undergo one or more checks to confirm that the received data is in a form that can be appended to the data model. For example, one or more of an integrity check, a format check, a cardinality check, combinations thereof, and the like. Optionally, at operation 332, processed data can be received from the external engine and can be appended to the data model as described herein. In an aspect, the received data can have a lifespan that controls how long the received data persists with the data model. For example, the received data can be incorporated into the data model in a manner that enables a user to retrieve the received data at another time/session. In another example, the received data can persist only for the current session, making the received data unavailable in a future session.
Query 1, Query 2, and Query 3, each include similar (e.g., conceptually similar, etc.) items (e.g., compositional elements, predicates, etc.), such as “sales,” “products,” “>2000,” and/or the like. Natural language parsing and/or metadata analysis may be used to determine the items and/or any constraints of the query, such as a default analysis period, a required data/element selection, and/or the like. A different set analysis may be performed for Query 1, Query 2, Query 3, and/or any other undetermined query based on, for example, an order/arrangement of items (and/or constraints) of the query and/or the composition (e.g., dimensions, measures, etc.) of one or more data analysis models (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.).
One or more items and/or data constraints of a query (e.g., an undetermined query or another type of query) may be used to determine one or more data analysis models. An application can be designed to host a number of data analysis models (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.) that evaluate one or more mathematical functions (also referred to as an “expression”) on the data subset 54 for one or more dimensions (classification variables). The result of this evaluation creates a data analysis model result 56.
As illustrated in
As shown in
Mathematical functions together with calculation variables and classification variables (dimensions) can be used to calculate the chart result 56, and both of these information sets can be also used to generate identifier ID3 for the input to the chart calculation. ID2 can be generated already in the previous operation, and ID3 can be generated as the first operation in the chart calculation procedure
The identifier ID3 can be formed from ID2 and the relevant chart properties. ID3 can be seen as an identifier for a specific chart generation instance, which can include all information needed to calculate a specific chart result. In addition, a chart result identifier ID4 can be created from the chart result definition, for example, a bit sequence that defines the chart result 56. ID4 can be put in the cache using ID3 as a lookup identifier. Likewise, the chart result definition can be put in the cache using ID4 as a lookup identifier.
Optionally, further calculations, transforming, and/or processing can be included through an extension engine 62. Optionally, associated results from the inference engine 18 and further computed by hypercube computation in said calculation/chart engine 58 can be coupled to an external engine 64 (or, in some cases, an external engine 114) that can comprise one or more data processing applications (e.g., simulation applications, statistical applications, mathematical computation applications, database applications, combinations thereof, and the like). Context of a data model processed by the inference engine 18 can comprise a tuple or tuples of values defined by dimensions and expressions computed by hypercube routines. Data can be exchanged through an interface 66.
The associated results coupled to the external engine 64 can be intermediate. Further results that can be final hypercube results can also be received from the external engine 64. Further results can be fed back to be included in the Data/Scope 52 and enrich the data model. The further results can also be rendered directly to the user in the chart result 56. Data received from and computed by the external engine 64 can be used for further associative discovery.
Each of the data elements of the database shown in Tables 1-5 of
Additional database structures can be included within the database illustrated as an example herein, with such structures including additional information pertinent to the database such as, in the case of products for example; color, optional packages, etc. Each table can comprise a header row which can identify the various data element types, often referred to as the dimensions or the fields, that are included within the table. Each table can also have one or more additional rows which comprise the various records making up the table. Each of the rows can contain data element values (including null) for the various data element types comprising the record.
The database as referred to in Tables 1-5 of
The graphical objects (or visual representations) can be substantially any display or output type including graphs, charts, trees, multi-dimensional depictions, images (computer-generated or digital captures), video/audio displays describing the data, hybrid presentations where output is segmented into multiple display areas having different data analysis in each area and so forth. A user can select one or more default visual representations; however, a subsequent visual representation can be generated on the basis of further analysis and subsequent dynamic selection of the most suitable form for the data.
In an aspect, a user can select a data point and a visualization component can instantaneously filter and re-aggregate other fields and corresponding visual representations based on the user's selection. In an aspect, the filtering and re-aggregation can be completed without querying a database. In an aspect, a visual representation can be presented to a user with color schemes applied meaningfully. For example, a user selection can be highlighted in green, datasets related to the selection can be highlighted in white, and unrelated data can be highlighted in gray. A meaningful application of a color scheme provides an intuitive navigation interface in the state space.
The result of a standard query can be a smaller subset of the data within the database, or a result set, which is comprised of the records, and more specifically, the data element types and data element values within those records, along with any calculated functions, that match the specified query. For example, as indicated in
Optionally, in this application, external processing can also be requested by ticking “External” in the user interface of
In a further aspect, external processing can also be optionally requested by ticking “External” in a box as shown in
SUM(ExtFunc(Price*Number))
can be evaluated. Data sent out are (Nisse, 1999, Jan, {19.5, null}). In this case, the external engine 64 will process data in accordance with Function (1) as shown below and in
A further optional embodiment is shown in
Should a user instead select “Gullan,” “1999,” “Jan,” and “External,” the feedback signal would include “VG” based on the content shown in qualification table 68. The computations actually performed in the external engine 62 are not shown or indicated, since they are not relevant to the inference engine.
In
A result of the various embodiments disclosed herein is a business analytic solution. The business analytic solution operates on the data stored and/or generated (e.g., hypercube/multidimensional cube, various indexes, etc. . . . ) by the disclosed embodiments. Users of the business analytic solution can query the data to obtain insight into the data. The query can be made, for example, by specifying data element types and data element values of interest and by further specifying any functions to apply to the data contained within the specified data element types of the database. The functions which can be used within a query can include, for example, expressions using statistics, sub-queries, filters, mathematical formulas, and the like, to help a user to locate and/or calculate the specific information wanted from the database. Once located and/or calculated, the results of a query can be displayed to the user with various visualization techniques and objects such as list boxes or various charts of a user interface. In another aspect, a result of the query can be displayed not only as visualizations but in the form of natural language, providing the user an insight overview across data sources and/or data tables.
Provided herein, among other things, is a “smart” DSS and related analytic techniques. The DSS and related techniques form a business analytic solution. For example, the business analytic solution can make reasonable defaults at various operations of an analysis, from data preparation, to building the data model, and preparing visual and/or text analyses. In an aspect, the business analytic solution can guide users to make sensible choices in order to quickly get to both expected answers and new answers (e.g., new insights). The business analytic solution enables a user to find unknown insights from the data and presents it to the user with the use of the systems and methods disclosed herein.
Domain experts, such as data architects, or visualization experts, are sources to provide rules (e.g., defaults and guidelines, usually in the form of generic best practices) for data analysis. Similarly, specific precedents that are established by users or a community of users who actually use the data are also sources of rules (e.g., defaults and guidelines) for data analysis. The disclosed embodiments, individually or in particular combinations, can provide an optimized technique to capture and represent such rules. Given the heuristic nature of such rules, the disclosed embodiments can utilize precedents to capture both types of rules (e.g., domain expert rules and user rules). Such precedents can then be utilized in a system that, given a specific context, can locate applicable precedents (for example, by similarity and/or generalization) and use those precedents to enable smart data analysis behavior.
Execution of the client application 1414 can cause the client device 1410 to present a sequence of user interfaces, including a user interface 1420a and a user interface 1420b. The user interface 1420a permits interactively configuring a data model or data context, or both. The user interface 1420a provides, via the client application 1410, drag-and-drop functionality that permits generating a query based at least on the data model or the data context, or both. The user interface 1420b can be a redrawn or otherwise updated version of the user interface 120a, and permits the visualization of data responsive to the query. A display device (not depicted in
More specifically, the client device 1410 can execute the client application 1414 and, in response, the client device 1410 can cause presentation of the user interface 1420a. The user interface 1420a can include a configuration pane 1422 and a viewport pane 1424. In some cases, as is shown in
The configuration pane 1422 can include multiple selectable visual elements that can be selected, individually or in combination, in order to define a data model and/or data context. The defined data model and/or data context can be used to generate a query 1430. To that end, the client device 1410 can determine, based on a first interaction with the user interface 1420a, a selection of a first element defining a data model. The first interaction with the UI can be a drag-and-drop action, which is represented by a curved arrow in the user interface 1420a. The drag-and-drop action can originate in the configuration pane 1422 and can terminate at the viewport pane 1424. In some cases, a section represented by a UI element 1426 can serve as terminal point. To determine such a selection, the client device 1410 can execute, or can continue executing, the presentation module 1524 (
The client device 1410 can generate the query 1430 based on the data model and the data context that have been selected. To that end, the client device 1410 can execute, or can continue executing, a query generation module 1526 (
The query generation module 1526 can format the query 130 according to JavaScript object notation (JSON) format. As such, the query 130 can be cast as an update to a prior query 130, in some cases. Accordingly, subsequent changes to a prior query can be accomplished by sending a small amount of data to the database engine. In that way, computational efficiency can be improved relative to existing technologies.
The client device 1410 can send the query 1430 to a computing platform configured to execute the query 1430 against one or more databases. To that end, the client device 1410 can execute, or can continue executing, an interface module 1528 (
The computing platform can be remotely located relative to the client device 1410, and includes a database engine 1450 functionally coupled to one or more data repositories 1460 (referred to as data repository 1460). The database engine 1450 can be embodied in, or can include, the data indexing engine 100, in some embodiments. The database engine 1450 can execute the query 1430 against one or more particular tables of the multiple tables 1466. The particular table(s) can be defined by the data context. As a result, the database engine 1450 can generate data 1470 responsive to the query 1430. The database engine 1450 can send the data 1470 to the client device 1410. As is illustrated in
In response to receiving the data 1470, the client device 1410 can cause presentation of the data 1470 within the viewport pane 1424 of the user interface 1420b. As mentioned, the user interface 1420b can be an updated version of the user interface 1420a. Causing presentation of the data can include causing presentation of a graphical object 1480 within the viewport pane 1424 of user interface 1420b. Thus, in response to receiving the data 1470, the client device 1410, via the presentation module 1524 (
In further response to receiving the data 1470, the client device 1410, via the presentation module 1524 (
Updates to a defined data model and/or a defined data context also can be defined interactively. In some cases, instead of generating an updated query, the query 1430 can be resolved against a different database consistent the updated data model. More specifically, the client device can determine, based on a new interaction with the user interface 1420b, a selection of a third element updating one of the defined data model and/or the defined data context. Similar to other interactions described herein, the new interaction with the user interface 1420b also can be a drag-and-drop action. As mentioned, rather than generating an updated query based on the updated data model and/or updated data context, the client device can send the query 1430 to the computing platform. The interface module 1528 (
The database engine 1450 can execute the query 1430 against a database according to the updated data model, using one or more tables of the multiple tables 1466. Such table(s) can be identified by the multiple tables 1466. The database engine 1450 can send updated data 1470 responsive to the query 1430 to the client device 1410. The interface module 1528 (
The client device 1410 can cause presentation of the updated data 1470 within the viewport pane 1424 of the user interface 1420b. In some embodiments, causing presentation of the updated data 1470 can include updating the graphical object 1480, and causing presentation of the updated graphical object 1480 within the viewport pane 1424 of the user interface 1420b. The updated graphical object 1480 depicts the updated data 1470. In some cases, the client device can receive, via the presentation module 1524, for example, input data indicative of selection of a particular visualization format for the updated graphical object 1480. In response, the client device 1410 can redraw the updated graphical object 1480 according to that particular visualization format.
The user interface 1420b can include, in some cases, a defined selectable visual element that, in response to being selected, permits editing the query 1430 that has been generated. Accordingly, in some embodiments, the client device 1410 can receive input data indicative of selection of the defined selectable visual element (not shown in
The first section 1610 also can include selectable elements that provide other functionalities. For example, the first section 1610 can include a selectable visual element that, in response to being selected, cause the client device 1410 to search for available tables. The tables, individually or in combination, can define, at least partially, a data model. To perform a search, the client device 1410 can send a request for one or more available tables from the tables 1466. As another example, the first section 1410 also can include a selectable visual elements that, in response to being selected, cause the client device 1410 to obtain additional data. The data can be obtained from the data repository 1460, for example. Again, the client device 1410 can access a utility module to perform a search and/or add data. The utility module can be part of the application 1418 or can be accessed as a service via an API, for example.
The configuration pane 1422 also includes a second section 1620 that permits defining, at least partially, a data model. To that end, the second section 1620 includes a Data subsection having a first UI element that permits selection of a dimension and a second UI element that permits selection of a measure. It is noted that in some cases, a dimension can coincide with a field and, in other cases, a dimension can included multiple fields. Further, in yet other cases, a dimension can be derived from a set of one or more fields, which dimension can be referred to as a calculated dimension. Markings, such as text (e.g., “Dimensions”) can indicate the type of the first UI element. The first UI element includes a selectable visual element 1626 that, in response to being selected, cause presentation of menu of dimension options (not depicted in
The second section 1620 also includes a Filters subsection that can permit defining a data context for the data model defined by a field, a dimension, a measure, or a combination thereof. Selection of the selectable visual element 1626 within the Filters subsection can cause presentation of a menu of filtering options (not depicted in
In response to an element being selected and dragged-and-dropped into the section 1426, the client device 1410 can redraw the user interface 1420a as user interface 1420b, to present a graphical object 1640 conveying data responsive to the query 1430 generated in response to the drag-and-drop operation. The element can be a dimension, a measure, or a filter. The graphical object 1640 can be presented according to a particular data visualization format. As mentioned, the particular data visualization format can be selected from a group of data visualization formats. The group of data visualization formats can be conveyed, in some cases, by multiple selectable UI elements corresponding to respective ones of the data visualization formats. The multiple UI elements can be contained in a Visualization subsection 1630 within the second section 1620, for example. Selection of one of the multiple selectable UI elements causes a particular data visualization format to be configured for presentation, and also causes the selected UI element to be presented according to indicia that distinguishes the selected UI element from other selectable UI elements in the Visualization subsection 1630.
In further response to the element being dragged-and-dropped into the section 1426, records within a table or a portion of the table (e.g., particular rows) can be presented in the review pane 1428. Indicia providing a description of the table and/or identifying the table can be presented in a section 1650 within the review pane 1428.
As is described herein, after the graphical object 1640 has been presented, further selections can be made within the configuration pane 1422. For example, a filter can be selected. To that end, the selectable visual element 1626 within the Filters subsection can be selected. Such a selection can cause presentation of an overlay element 1660 that overlays the graphical object 1640 within the viewport pane 1424, as is shown in
In addition, also after the graphical object 1640 has been presented, another data visualization format may be selected from the Visualization subject 1630. By permitting various selections of data visualization formats after the presentation of the graphical object 1640, the data 1470 can be readily explored in numerous ways, without relying on direct manipulation of the data 1470 by an end-user.
As is indicated in
The client device 1410, via the presentation module 1524 (
The particular selection and movement constitute an interaction with the viewport pane 1424 and define a modification action that can change the structure of the layout. For example, as is illustrated in
Other interactions with the graphical objects can be performed. Each one of those interactions can define respective modification actions. For example, as is illustrated in
Some of the interactions can be directed to a particular graphical object presented in the view port 1424 and can be performed in sequence. For example, the resize action shown in
Besides resize actions, the presentation module 1524 can permit performing transpose actions. A transpose action is an operation that changes the position of a graphical object to a new position between other adjacent objects, while preserving the geometry and size of the graphical object. Such a transpose action also can change the geometry and/or size of those other adjacent objects. To that end, the presentation module 1524 can determine that a graphical object shown in the viewport pane 1424 has been selected. Again, in response to the selection, the presentation module 1524 can detect a drag operation that moves the graphical object from an initial position to a terminal position (that is, the position of the graphical object after movement has ceased). As an example,
The computing device can embody, or can include, the client device 1410 (
At block 1910, the computing device can determine, based on a first interaction with a user interface (e.g., user interface 1420a), a selection of a first element defining a data model. The first interaction with the UI can be a drag-and-drop action. Such a determination can be implemented by performing the example method 1900 shown in
At block 1920, the computing device can determine, based on a second interaction with the user interface (e.g., user interface 1420a), a selection of a second element defining a data context. The second interaction with the UI can be a drag-and-drop action. Such a determination can be implemented by performing the example method 1900 shown in
At block 1930, the computing device can generate, based on the data model and the data context, a query. In some cases, generating the query can include generating a set expression including an outer expression and an inner expression. The outer expression defines a scope of data available to the inner expression. The query that is generated can be formatted according to JSON format.
The user interface can include, in some cases, a defined selectable visual element that, in response to being selected, permits editing the query that has been generated. Accordingly, although not illustrated in
At block 1940, the computing device can send the query to a computing platform configured to execute the query against a database. As mentioned, in one example, the computing platform can include the database engine 1450 and the data repository 1460.
At block 1950, the computing device can receive, from the computing platform, data responsive to the query. The data can be received by means of a network architecture (at least one of the network(s) 1440 (
At block 1960, the computing device can cause presentation of the data within a viewport pane of the user interface. In one example, the second data can be presented within the viewport pane 1424 within the user interface 1420b, as is shown in
At block 1970, the computing device can determine, based on a third interaction with the user interface, a selection of a third element updating one of the defined data model or the defined data context. The third interaction with the UI can be a drag-and-drop action. Rather than generating an updated query based on the updated data model and/or updated data context, the computing device can send the query to the computing platform at block 1980.
At block 1990, the computing device can receive, from the computing platform, second data responsive to the query.
At block 1995, the computing device can cause presentation of the second data within the viewport pane of the user interface. In one example, the second data can be presented within the view portpane 1424 within the user interface 1420b, as is shown in
The computing device can be embodied in, or can include, the client device 1410 (
The user interface can include a configuration pane and a viewport pane. For example, the configuration pane can be the configuration pane 1422 (
At block 2020, the computing device can cause presentation of an overlay element overlaying a section of the user interface. The overlay element includes one or more second selectable visual elements. The second selectable visual element(s) can include a defined element that defines, at least partially, the data model and/or the data context.
At block 2030, the computing device can receive input data indicative of selection of the defined element.
At block 2040, the computing device can detect a drag action that moves the defined element from the configuration pane to the viewport pane.
In order to provide some context, the computer-implemented methods, devices, and systems of this disclosure can be implemented on the computing environment illustrated in
The computer-implemented methods and systems in accordance with this disclosure can be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that can be suitable for use with the systems and methods comprise, but are not limited to, personal computers, server computers, laptop devices, and multiprocessor systems. Additional examples comprise set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that comprise any of the above systems or devices, and the like.
The processing of the disclosed computer-implemented methods and systems can be performed by software components. The disclosed systems and computer-implemented methods can be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers or other devices. Generally, program modules comprise computer code, routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The disclosed computer-implemented methods can also be practiced in grid-based and distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.
Further, the systems and computer-implemented methods disclosed herein can be implemented via a general-purpose computing device in the form of a computing device 2101. The components of the computing device 2101 can comprise, but are not limited to, one or more processors 2103, a system memory 2112, and a system bus 2113 that functionally couples various system components including the one or more processors 2103 to the system memory 2112. The system can utilize parallel computing.
The system bus 2113 represents one or more of several possible types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, or local bus using any of a variety of bus architectures. The bus 2113, and all buses specified in this description can also be implemented over a wired or wireless network connection and each of the subsystems, including the one or more processors 2103, one or more mass storage devices 2104 (referred to as mass storage 2104), an operating system 2105, software 2106, data 2107, a network adapter 2108, the system memory 2112, an Input/Output Interface 2110, a display adapter 2109, a display device 2111, and a human-machine interface 2102, can be contained within one or more remote computing devices 2114a,b,c at physically separate locations, connected through buses of this form, in effect implementing a fully distributed system.
The computing device 2101 typically comprises a variety of computer-readable media. Exemplary readable media can be any available media that is accessible by the computing device 2101 and comprises, for example and not meant to be limiting, both volatile and non-volatile media, removable and non-removable media. The system memory 2112 comprises computer readable media in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read only memory (ROM). The system memory 2112 typically contains data such as the data 2107 and/or program modules such as the operating system 2105 and the software 2106 that are immediately accessible to and/or are presently operated on by the one or more processors 2103.
The computing device 2101 can also comprise other removable/non-removable, volatile/non-volatile computer storage media. As an example,
Optionally, any number of program modules can be stored on the mass storage 2104, including by way of example, the operating system 2105 and the software 2106. Each of the operating system 2105 and the software 2106 (or some combination thereof) can comprise elements of the programming and the software 2106. The data 2107 can also be stored on the mass storage 2104. The data 2107 can be stored in any of one or more databases known in the art. Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases can be centralized or distributed across multiple systems. The software 2106 can include, in some cases, the presentation module 1524, the query generation module 1526, and the interface module 1528. In some embodiments, the software 2106 also can include the utility component described herein. Execution of the software 2106 by the processor(s) 2103 can cause the computing device 2101 to provide at least some of the functionality described herein connection with query composition using a user interface and/or visualization of data responsive to the query as is described herein.
In another aspect, the user can enter commands and information into the computing device 2101 via an input device (not shown). Examples of such input devices comprise, but are not limited to, a keyboard, pointing device (e.g., a “mouse”), a microphone, a joystick, a scanner, tactile input devices such as gloves, and other body coverings, and the like These and other input devices can be connected to the one or more processors 2103 via the human-machine interface 2102 that is coupled to the system bus 2113, but can be connected by other interface and bus structures, such as a parallel port, game port, an IEEE 1394 Port (also known as a Firewire port), a serial port, or a universal serial bus (USB).
In yet another aspect, the display device 2111 can also be connected to the system bus 2113 via an interface, such as the display adapter 2109. It is contemplated that the computing device 2101 can have more than one display adapter 2109 and the computing device 2101 can have more than one display device 2111. For example, the display device 2111 can be a monitor, an LCD (Liquid Crystal Display), or a projector. In addition to the display device 2111, other output peripheral devices can comprise components such as speakers (not shown) and a printer (not shown) which can be connected to the computing device 2101 via the Input/Output Interface 2110. Any operation and/or result of the methods can be output in any form to an output device. Such output can be any form of visual representation, including, but not limited to, textual, graphical, animation, audio, tactile, and the like. The display device 2111 and computing device 2101 can be part of one device, or separate devices.
The computing device 2101 can operate in a networked environment using logical connections to one or more remote computing devices 2114a,b,c and/or one or multiple storage server devices 2120. By way of example, a remote computing device can be a personal computer, portable computer, smartphone, a server, a router, a network computer, a peer device or other common network node, and so on. Logical connections between the computing device 2101 and a remote computing device 2114a,b,c and a server storage device of the server storage device(s) 2120 can be made via a network 2115, such as a local area network (LAN) and/or a general wide area network (WAN). Such network connections can be through the network adapter 2108. The network adapter 2108 can be implemented in both wired and wireless environments. In an aspect, one or more of the remote computing devices 2114a,b,c can comprise an external engine and/or an interface to the external engine.
For purposes of illustration, application programs and other executable program components such as the operating system 2105 are illustrated herein as discrete blocks, although it is recognized that such programs and components reside at various times in different storage components of the computing device 2101, and are executed by the one or more processors 2103 of the computer. An implementation of the software 2106 can be stored on or transmitted across some form of computer-readable media. Any of the disclosed methods can be performed by computer readable instructions embodied on computer-readable media. Computer-readable media can be any available media that can be accessed by a computer. By way of example and not meant to be limiting, computer-readable media can comprise “computer storage media” and “communications media.” “Computer storage media” comprise volatile and non-volatile, removable and non-removable media implemented in any methods or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Exemplary computer storage media comprises, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
It is to be understood that the computer-implemented methods and systems described here are not limited to specific operations, processes, components, or structure described, or to the order or particular combination of such operations or components as described. It is also to be understood that the terminology used herein is for the purpose of describing exemplary embodiments only and is not intended to be restrictive or limiting.
As used herein the singular forms “a,” “an,” and “the” include both singular and plural referents unless the context clearly dictates otherwise. Values expressed as approximations, by use of antecedents such as “about” or “approximately,” shall include reasonable variations from the referenced values. If such approximate values are included with ranges, not only are the endpoints considered approximations, the magnitude of the range shall also be considered an approximation. Lists are to be considered exemplary and not restricted or limited to the elements comprising the list or to the order in which the elements have been listed unless the context clearly dictates otherwise.
Throughout the specification and claims of this disclosure, the following words have the meaning that is set forth: “comprise” and variations of the word, such as “comprising” and “comprises,” mean including but not limited to, and are not intended to exclude, for example, other additives, components, integers, or operations. “Include” and variations of the word, such as “including” are not intended to mean something that is restricted or limited to what is indicated as being included, or to exclude what is not indicated. “May” means something that is permissive but not restrictive or limiting. “Optional” or “optionally” means something that may or may not be included without changing the result or what is being described. “Prefer” and variations of the word such as “preferred” or “preferably” mean something that is exemplary and more ideal, but not required. “Such as” means something that serves simply as an example.
Operations and components described herein as being used to perform the disclosed methods and construct the disclosed systems are illustrative unless the context clearly dictates otherwise. It is to be understood that when combinations, subsets, interactions, groups, etc. of these operations and components are disclosed, that while specific reference of each various individual and collective combinations and permutation of these may not be explicitly disclosed, each is specifically contemplated and described herein, for all methods and systems. This applies to all aspects of this application including, but not limited to, operations in disclosed methods and/or the components disclosed in the systems. Thus, if there are a variety of additional operations that can be performed or components that can be added, it is understood that each of these additional operations can be performed and components added with any specific embodiment or combination of embodiments of the disclosed systems and methods.
Embodiments of this disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the methods and systems may take the form of a computer program product on a computer-readable storage medium having computer-readable program instructions (e.g., computer software) embodied in the storage medium. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices, whether internal, networked, or cloud-based.
Embodiments of this disclosure have been described with reference to diagrams, flowcharts, and other illustrations of computer-implemented methods, systems, apparatuses, and computer program products. Each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by processor-accessible instructions. Such instructions can include, for example, computer program instructions (e.g., processor-readable and/or processor-executable instructions). The processor-accessible instructions can be built (e.g., linked and compiled) and retained in processor-executable form in one or multiple memory devices or one or many other processor-accessible non-transitory storage media. These computer program instructions (built or otherwise) may be loaded onto a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine. The loaded computer program instructions can be accessed and executed by one or multiple processors or other types of processing circuitry. In response to execution, the loaded computer program instructions provide the functionality described in connection with flowchart blocks (individually or in a particular combination) or blocks in block diagrams (individually or in a particular combination). Thus, such instructions which execute on the computer or other programmable data processing apparatus create a means for implementing the functions specified in the flowchart blocks (individually or in a particular combination) or blocks in block diagrams (individually or in a particular combination).
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including processor-accessible instruction (e.g., processor-readable instructions and/or processor-executable instructions) to implement the function specified in the flowchart blocks (individually or in a particular combination) or blocks in block diagrams (individually or in a particular combination). The computer program instructions (built or otherwise) may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process. The series of operations can be performed in response to execution by one or more processor or other types of processing circuitry. Thus, such instructions that execute on the computer or other programmable apparatus provide operations for implementing the functions specified in the flowchart blocks (individually or in a particular combination) or blocks in block diagrams (individually or in a particular combination).
Accordingly, blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions in connection with such diagrams and/or flowchart illustrations, combinations of operations for performing the specified functions and program instruction means for performing the specified functions. Each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware-based computer systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
The methods and systems can employ artificial intelligence techniques such as machine learning and iterative learning. Examples of such techniques include, but are not limited to, expert systems, case-based reasoning, Bayesian networks, behavior-based AI, neural networks, fuzzy systems, evolutionary computation (e.g. genetic algorithms), swarm intelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g. expert inference rules generated through a neural network or production rules from statistical learning).
While the computer-implemented methods, apparatuses, devices, and systems have been described in connection with preferred embodiments and specific examples, it is not intended that the scope be limited to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.
Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its operations be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its operations or it is not otherwise specifically stated in the claims or descriptions that the operations are to be limited to a specific order, it is in no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of operations or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification.
It will be apparent to those skilled in the art that various modifications and variations can be made without departing from the scope or spirit. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims.
This application claims priority to U.S. Provisional Patent Application No. 63/329,617, which was filed on Apr. 11, 2022, and the entirety of which is incorporated by reference herein.
Number | Date | Country | |
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63329617 | Apr 2022 | US |