Patent Grant
10754822
This disclosure relates to approaches for ontology migration.
Under some approaches, developers may need to utilize multiple stacks when developing software. For example, a developer may develop software on a developer stack, and then deploy the software to a production stack. As development progresses on a stack (e.g., a development stack), ontologies associated with the stack may be modified. Updating ontologies of corresponding stacks (e.g., a production stack) can be time consuming and computationally expensive. These and other drawbacks exist with conventional data management systems.
A claimed solution rooted in computer technology overcomes problems specifically arising in the realm of computer technology. In various embodiments, a computing system is configured to migrate one or more ontologies between multiple stacks. For example, a source stack may have source data sets and a source ontology, and a destination stack may have destination data sets and a destination ontology. The source data sets and the destination data sets may correspond. In some implementations, however, the source data set identifiers for the source data sets may be different from the destination data set identifiers for the destination data sets. As such, migrating the source ontology to the destination stack may require translation of the destination data set identifiers.
In some embodiments, the source ontology for a source stack may be received. The destination data set identifiers may be translated to correspond to the source data set identifiers. For example, the destination data set identifiers may be translated into updated destination data set identifiers. In some embodiments, the source data set identifiers may be translated into updated source data set identifiers based on the relative locations of the source data sets on the source stack and/or the relative locations of the destination data sets on the destination stack. The source ontology may be migrated to the destination stack. In some embodiments, the migration may be based on translation of the destination data set identifiers, user input, and/or other information. The user input may indicate which source objects should be migrated to the destination stack, and/or which source objects should be excluded from the migration to the destination stack. In some embodiments, the source ontology may be migrated to the destination stack on a continuous or semi-continuous basis, responsive to user input, and/or responsive to a trigger event.
Migration of an ontology from a source stack to a destination stack is a problem that arises specifically in the realm of computer based programming (e.g., hardware programming and/or software programming). Software developers may be required to maintain multiple stacks (e.g., solution stacks or software stacks) when developing a software program, application, and/or platform. Applying an ontology for one stack to another stack is a tedious process that requires the developer to manually re-create the ontology by re-defining the definitions of data objects and potential links between the data objects defined by the ontology.
A source stack and a destination stack may have corresponding datasets with one or more different data set identifiers. Migration of an ontology from a source stack to a destination stack may require translation of the destination data set identifiers into updated destination data set identifiers that correspond to source data set identifiers. Implementations of the technology described herein may enable migration of a source ontology for a source stack to a destination stack. In some implementations, links between the source objects in the source ontology may be translated and/or migrated to the destination stack.
The technology may further provide a user interface through which a user may provide user input indicating which source objects should or should not be migrated to the destination stack. The technology may provide an auto-migration feature that may trigger migration of a source ontology to a destination stack based on one or more trigger events (e.g., updates to the source ontology, a user input initiating migration, the passage of an interval of time, the arrival of a predetermined date and/or time, and/or other trigger events). In some implementations, the system may provide a dataset correspondence feature that may determine and/or apply an offset to a destination stack such that the relative locations of the destination data sets on the destination stack correspond to the relative locations of the source data sets on the source stack. Applying an offset may ensure that the source ontology can be migrated to the destination stack responsive to their datasets not being identical.
In an implementation, a system for migrating an ontology is provided. The system may comprise one or more processors and a memory storing instructions. When executed by the one or more processors, the instructions may cause the system to receive a source ontology for a source stack that defines source objects associated with source datasets that correspond to destination datasets having destination data set identifiers that are different from source data set identifiers for the source datasets, receive the destination data set identifiers, translate the destination data set identifiers into updated destination data set identifiers that correspond to the source data set identifiers, and migrate the source ontology to the destination stack based on translation of the destination data set data set identifiers.
In another implementation, a computer implemented method of migrating an ontology is provided. The method may be performed on a computer system having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the computer system to perform the method. The method may comprise receiving, by the computer system, a source ontology for a source stack that defines source objects associated with source datasets that correspond to destination datasets having destination data set identifiers that are different from source data set identifiers for the source datasets, receiving, by the computer system, the destination data set identifiers, translating, by the computer system, the destination data set identifiers into updated destination data set identifiers that correspond to the source data set identifiers, and migrating, by the computer system, the source ontology to the destination stack based on translation of the destination data set data set identifiers
These and other objects, features, and characteristics of the system and/or method disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.
Certain features of various embodiments of the present technology are set forth with particularity in the appended claims. A better understanding of the features and advantages of the technology will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the technology are utilized, and the accompanying drawings of which
A claimed solution rooted in computer technology overcomes problems specifically arising in the realm of computer technology. In various implementations, the technology may facilitate an ontology migration between a source stack and a destination stack. Developers (e.g., software and/or hardware developers) may develop programs, applications, and/or platforms (e.g., software and/or hardware), using multiple stacks (e.g., solution stacks or software stacks). Accordingly, developers are not required to develop/test and produce/publish on the same stack. This enables developers to have one or more backup stacks, test on a different stack they publish on, perform updates and/or changes before publishing to customers, and/or the like. Migrating an ontology from one stack to another stack may be necessary to eliminate the requirement that developers manually re-create the same ontology, and/or manually update an ontology, on another stack with corresponding datasets. Due to the technical nature and structure of ontologies, developers cannot merely copy and paste from one stack to another as they would to migrate code. Instead, applying an ontology from a source stack to a destination stack is a tedious process that typically requires the developer to manually re-create the ontology on the destination stack by re-defining the definitions of data objects and potential links between the data objects. The technology described herein provides systems and methods for migrating an ontology from a source stack to a destination stack.
In various embodiments, a computing system is configured to migrate one or more ontologies between multiple stacks. For example, a source stack may have source data sets and a source ontology, and a destination stack may have destination data sets and a destination ontology. The source data sets and the destination data sets may correspond. In some implementations, however, the source data set identifiers for the source data sets may be different from the destination data set identifiers for the destination data sets. As such, migrating the source ontology to the destination stack may require translation of the destination data set identifiers.
In some embodiments, the destination data set identifiers of the source ontology may be translated to correspond to the source data set identifiers. For example, the destination data set identifiers may be translated into updated destination data set identifiers. In some embodiments, the source data set identifiers may be translated into updated source data set identifiers based on the relative locations of the source data sets on the source stack and/or the relative locations of the destination data sets on the destination stack. The source ontology may then be migrated to the destination stack. In some embodiments, the migration may be based on translation of the destination data set identifiers, user input, and/or other information. User input may indicate which source objects should be migrated to the destination stack, and/or which source objects should be excluded from the migration to the destination stack. In some embodiments, the source ontology may be migrated to the destination stack on a continuous and/or semi-continuous basis, responsive to user input, and/or responsive to a trigger event.
In some embodiments, a source stack and a destination stack may have corresponding datasets with one or more different data set identifiers. Migration of an ontology from a source stack to a destination stack may require translation of the destination data set identifiers into updated destination data set identifiers that correspond to source data set identifiers. Implementations of the technology described herein may enable migration of a source ontology for a source stack to a destination stack. In some implementations, links between the source objects in the source ontology may be translated and/or migrated to the destination stack. For example, links may each be associated with a link identifier, and the link identifiers may be translated (e.g., in the same and/or similar manner as the dataset identifiers).
In some embodiments, the technology may further provide a user interface through which a user may provide user input indicating which source objects should or should not be migrated to the destination stack. The technology may provide an auto-migration feature that may trigger migration of a source ontology to a destination stack based on one or more trigger events (e.g., updates to the source ontology, a user input initiating migration, the passage of an interval of time, the arrival of a predetermined date and/or time, and/or other trigger events). In some implementations, the system may provide a dataset correspondence feature that may determine and/or apply an offset to a destination stack such that the relative locations of the destination data sets on the destination stack correspond to the relative locations of the source data sets on the source stack. Applying an offset may ensure that the source ontology can be migrated to the destination stack responsive to their datasets not being identical.
Migration of an ontology from a source stack to a destination stack is a problem that arises specifically in the realm of computer based programming (e.g., hardware programming and/or software programming). For example, software developers may be required to maintain multiple stacks (e.g., solution stacks or software stacks) when developing a software program, application, and/or platform. Applying an ontology for one stack to another stack is a tedious process that requires the developer to manually re-create the ontology by re-defining the definitions of data objects and potential links between the data objects defined by the ontology.
Ontologies 101 may define the data objects and how the data objects are linked to one or more other data objects associated via links 108. The ontology may be dynamic, updated to match evolving needs of the system and analysts. The ontology may define types of data objects 106, object properties, and data links 108. The ontology may further define which data types may be associated with each other. Each data types may have a URI (uniform resource identifier) that identifies it.
Object types may define features that may be represented in the system, and provide structure for data objects 104. Object types may be derived from, for example, entity types, event types, document types, and multimedia types. Event and document types may have temporal and geospatial data directly included within the data object 104 itself. An object type may define the number and composition of properties, notes, and/or media components of a data object(s) 104. The object type may further define what other types of objects that data links 108 may permit association with. For example, an entity object type may define a data object 104 used to store data about a person, and may include data properties for storing name, address, occupation, e-mail address, phone number, etc. Data links 108 of an entity object 104 may permit the entity object 104 to be linked to other entity objects (e.g., friends or business associates), linked to event objects (e.g., events attended or invited to), linked to document objects (e.g., authored), etc.
Property types may define the type and behavior of input data. Property types may define the structure of the data stored in an object property. The property type may define one or more data fields, the type of data associated with the field, as well as tools that may operate on the data fields. Property types may be simple, including a single data field, and/or may be composite, including multiple data fields. For example, an e-mail property type may define an e-mail object property. For example, the e-mail address john@acmeinc.com may be stored in an e-mail object property as follows: URI: com.property.Email, Base Type: Composite, with these components: EMAIL_USERNAME with the value “john,” EMAIL_DOMAIN with the value “acmeinc.com.” Further, the e-mail property type may define tools for parsing and concatenating the username and the domain, depending on what is required.
Link types may define the types of data links 108 that can exist between two objects 104. Links may be symmetric or asymmetric. All links may have one object that is considered the “parent” object, and the other that is the “child.” In the case of symmetric links, e.g., “Spouse Of,” which the parent and child objects are not contextually important. In the case of asymmetric links, like “Manager Of/Managed By,” the parent and child may reflect the direction of the link.
Thus, the ontologies 101 of the object based data systems may define the way in which data is organized in the object based data systems. The ontology may define the types of objects that may be stored and the components of the defined data objects 104 as well as the manner in which the defined data objects may link to one another via data links 108.
Source ontology 101A may be migrated to a destination stack corresponding to destination data source 103. In some implementations, links 108 may be migrated to the destination stack with source ontology 101A. Destination ontology 101B may be updated and/or replaced responsive to the migration of destination ontology 101A.
Destination data set identifiers 206 for destination data sets on destination stack 210 may be received and/or obtained. The source ontology 204 may define one or more source objects that are associated with source data sets having source data set identifiers. The source data sets may correspond to destination data sets on destination stack 210. The destination data sets may have destination data set identifiers that are different from the source data set identifiers. In some embodiments, the source data sets may be identical to the destination data sets, and/or the source data sets may be located in the same relative location on source stack 202 as the destination data sets are located on destination stack 210. The source data set identifiers may be different from the destination data set identifiers 206 even though their datasets correspond.
The destination data set identifiers 206 may be translated to generate updated destination data set identifiers 208. Updated destination data set identifiers 208 may correspond to the source data set identifiers for source data sets on source stack 202. In some implementations, the translation of destination data set identifiers 206 may be based on relative locations of source data sets on source stack 202 compared to the relative locations of the destination data sets on destination stack 210.
Source ontology 204 may be migrated to destination stack 210 responsive to translating the destination data set identifiers 206 into updated destination data set identifiers 208. In one embodiment, source ontology 204 may be used as a basis for updating a destination ontology for destination stack 210. Source ontology 204 may be processed and compared to the destination ontology for destination stack 210. Migrating source ontology 204 to the destination stack 210 may include identifying one or more differences between source ontology 204 and the destination ontology. Migrating source ontology 204 to destination stack 210 may include updating a destination ontology for destination stack 210 based on source ontology 204. Foy example, one or more of the differences identified may be updated responsive to migrating source ontology 204 to destination stack 210 such that the destination ontology matches the source ontology 204.
In one embodiment, source ontology 204 may replace at least one or more portions of the destination ontology for destination stack 210 such that at least one or more portions of the destination ontology for destination stack 210 match the source ontology 204 for the source stack 202. In some embodiments, one or more links between the source objects and/or the source ontology may be migrated to destination stack 210 with source ontology 204.
Tabular data module 304 may be a computer memory configured to store data. Tabular data module 304 may store a data set formatted with a tabular structure. A tabular data structure may be defined by a data schema, encompassing data schema related information including at least the names of the columns of the table, the data types of the columns, user descriptions of the columns, etc.
Object based data module 306 may be a computer memory configured to store data. Object based data module 306 may store a data set formatted with an object based structure according to an ontology, as described, e.g., with respect to
Computer system 310 may be configured as a server (e.g., having one or more server blades, processors, etc.), a personal computer (e.g., a desktop computer, a laptop computer, etc.), a smartphone, a tablet computing device, and/or other device that can be programmed to receive tabular data, generate script editing tools, and transform the tabular data into object based data.
Computer system 310 may include one or more processors 332 (also interchangeably referred to herein as processors 332, processor(s) 332, or processor 332 for convenience), one or more storage devices 334, and/or other components. Processors 332 may be programmed by one or more computer program instructions stored on storage device 334. For example, processors 332 may be programmed by source ontology module 318, destination data set identifier module 320, dataset comparison module 322, translation module 324, trigger module 326, migration module 328, and/or other instructions that program computer system 310 to perform various operations, each of which are described in greater detail herein. As used herein, for convenience, the various instruction modules will be described as performing an operation, when, in fact, the various instructions program the processors 332 (and therefore computer system 310) to perform the operation. Further details and features of a computer system 310 configured for implementing features of the described technology may be understood with respect to computer system 500 as illustrated in
User device 340 may be configured as a server device, a gaming console, a handheld gaming device, a personal computer (e.g., a desktop computer, a laptop computer, etc.), a smartphone, a tablet computing device, and/or other device that can be programmed to receive tabular data, generate script editing tools, and transform the tabular data into object based data.
User device 340 may include one or more processors 342 (also interchangeably referred to herein as processors 342, processor(s) 342, or processor 342 for convenience), one or more storage devices 344, and/or other components. Processors 342 may be programmed by one or more computer program instructions. For example, processors 342 may be programmed by source ontology module 318, destination data set identifier module 320, dataset comparison module 322, translation module 324, trigger module 326, migration module 328, and/or other instructions that program computer systems 310 to perform various operations, each of which are described in greater detail herein. As used herein, for convenience, the various instruction modules will be described as performing an operation, when, in fact, the various instructions program the processors 342 (and therefore user device 340) to perform the operation.
Various aspects of the transform facilitation system may operate on computer system 310 and/or on user device 340. That is, the various modules described herein may each operate on one or both of computer system 310 and/or user device 340. For example, in one implementation, a user device 340 comprising a client side laptop computer may run a trigger module 326 and migration module 328, while other aspects of the system are run on computer system 310, acting as a server.
Source ontology module 318 may be a software module in operation on computer system 310 and/or on user device 340. Source ontology module 318 may include programming instructions that cause the system to receive, acquire, obtain, or otherwise gain access to a source ontology for a source stack. The source ontology may define one or more source objects for the source stack. The one or more source objects may be associated with source datasets having source dataset identifiers. The source datasets may correspond to destination datasets on a destination stack. Corresponding datasets may be identical and/or have the same relative locations within their respective stacks. The destination datasets may have destination dataset identifiers that are different from the source dataset identifiers and/or do not match the source dataset identifiers despite the datasets corresponding.
In some embodiments, the source stack may comprise a development stack, and the destination stack may comprise a production stack. By way of example, a developer may be able to develop, update, and/or test a software solution, application, and/or platform on a development stack and/or publish the software solution, application, and/or platform via the production stack. By way of another example, the destination stack may comprise a backup stack in case something goes wrong with the source stack. As such, the source ontology created on the source stack may need to be migrated to the destination stack. In some implementations, responsive to migration of the source ontology to the destination stack, all, or one or more portions of the source ontology may match the destination ontology for the destination stack.
Destination dataset identifier module 320 may be a software module in operation on computer system 310 and/or on user device 340. Destination dataset identifier module 320 may include programming instruction that cause computer system 310 and/or user device 340 to receive, acquire, obtain, or otherwise gain access to the destination dataset identifiers for the destination datasets.
The destination dataset identifiers for the destination datasets may be different from the source dataset identifiers for the source datasets. Dataset identifiers may reference one or more datasets behind an object. In some embodiments, the identifiers may be stack-specific such that they are unique to a given stack. As such, the source ontology defining the source objects cannot merely be transferred to another stack because the one or more datasets behind the objects defined by the source ontology will be referenced incorrectly for the destination stack.
In some embodiments, destination dataset identifier module 320 may be configured to receive secondary destination dataset identifiers. The secondary destination dataset identifiers may be for secondary destination datasets on a second destination stack. The source ontology may be migrated to the second destination stack and/or other destination stacks responsive to the datasets corresponding.
Dataset comparison module 322 may further be configured to translate the destination data set identifiers. The destination dataset identifiers may be translated into updated destination data set identifiers that correspond to the source data identifiers. The destination dataset identifiers may be translated based on relative locations of the datasets within the stacks. For example, the destination dataset identifiers may be translated based on the relative locations of the source datasets within the source stack based on the fact that the destination datasets correspond to the source datasets and/or are located within the same relative location within the destination stack.
In some embodiments, dataset comparison module 322 may be configured to identify a first relative location of a source data set on the source stack. The second relative location of a destination dataset on the destination stack may be identified. Dataset comparison module 322 may further be configured to compare the first relative location and the second relative location to determine whether or not the relative locations of the datasets within the stacks correspond to each other. If the source datasets and the destination datasets are the same and the relative locations of the source datasets correspond to the relative locations of the destination datasets, it may be possible to migrate the source ontology to the destination stack.
In some embodiments, dataset comparison module 322 may be configured determine an offset of the second relative location to the first relative location based on the comparison. Responsive to a determination that the second relative location is different from the first relative location (in relation to the destination stack and the source stack respectively), an offset may be determined and/or applied to the destination stack and or the destination datasets to move the second relative location such that it corresponds with the first relative location. The offset may be determined based on the difference between the first relative location in relation to the source stack and the second relative location in relation to the destination stack.
Translation module 324 may be a software module in operation on computer system 310 and/or on user device 340. Translation module 324 may include programming instruction that cause computer system 310 to translate the destination dataset identifiers into updated destination dataset identifiers.
The updated destination data set identifiers may correspond to the source dataset identifiers. The updated destination dataset identifiers may include one or more of the same references to the one or more datasets behind an object as the source dataset identifiers such that the source ontology can be migrated to the destination stack and will reference the datasets behind the destination objects corresponding to the source objects correctly.
In some embodiments, translation module 324 may be configured to translate one or more links between the source objects and/or the source ontology. The one or more links may be separate from the dataset identifiers. The links may define how one or more objects linked to one another and/or within the ontology. The one or more links may be translated from the source stack to the destination stack based on the source ontology.
In other embodiments, translation module 324 may further be configured to translate the secondary destination dataset identifiers into updated secondary destination dataset identifiers. The secondary destination dataset identifiers may be translated based on relative locations of the datasets within the stacks. For example, the secondary destination dataset identifiers may be translated based on the relative locations of the source datasets within the source stack responsive to the secondary destination datasets corresponding to the source datasets and/or are being located within the same relative location within the second destination stack.
In some embodiments, translation module 324 may further be configured to apply the offset determined by dataset comparison module 322. The offset may be applied to the destination stack to move the second relative location. Moving the second relative location may include actually moving the location of the destination dataset on the destination stack, or applying an offset such that translation of the destination dataset identifiers reflects the offset.
Trigger module 326 may be a software module in operation on computer system 310 and/or on user device 340. Trigger module 326 may include programming instruction that cause computer system 310 to trigger migration of the source ontology to the source stack.
In some embodiments, translation of the destination dataset identifiers into the updated destination dataset identifiers may be responsive to identification of a trigger event by trigger module 326. A trigger event may include one or more of user input initiating migration of the source ontology to the destination stack; one or more updates to the source ontology, the source stack, and/or the destination stack; the passage of an interval of time; the arrival of a predetermined date and/or time; and/or other trigger events.
Trigger module 326 may further be configured to trigger and/or initiate migration of the source ontology to the destination stack based on one or more predetermined settings defining the trigger event(s). Developers and/or users may be able to define and/or indicate what constitutes a trigger event. By way of example, trigger module 326 may be configured to initiate migration of the source ontology to the destination stack on a continuous and/or semi-continuous basis (e.g., every hour, day, week, and/or other interval of time). By way of another example, trigger module 326 may be configured to initiate migration of the source ontology to the destination stack responsive to the developer updating and/or changing the source ontology.
Migration module 328 may be a software module in operation on computer system 310 and/or on user device 340. Migration module 328 may include programming instruction that cause computer system 310 and/or user device 340 to migrate the source ontology to the destination stack. The source ontology may be migrated to the destination stack based on translation of the destination dataset identifiers. The source ontology being migrated to the destination stack based on translation of the destination dataset identifiers may include migrating the source ontology to the destination stack based on the relative locations of the datasets within the given stacks.
In some embodiments, migration module 328 may be configured to receive user input indicating one or more source objects to be migrated to the destination stack. By way of example, the source ontology may be migrated to the destination stack responsive to determining (by dataset comparison module 322) the first relative location of the source dataset on the source stack is the same as the second relative location of the destination dataset on the destination stack. In other words, the source ontology may be migrated to the destination stack responsive to determining the source datasets within the source stack of the same locations within the source stack as the destination datasets have within the destination stack. The source ontology may be migrated to the destination stack responsive to the relative locations of the datasets in the source stack corresponding to the relative locations of the datasets in the destination stack.
In some embodiments, migration module 328 may be configured to receive user input indicating one or more source objects to be migrated to the destination stack. The user input may be received via a graphical user interface displaying one or more source objects or indications of source objects. The graphical user interface may be configured to receive user input indicating one or more of the source objects to be migrated to the destination stack via migrating the source ontology.
In some embodiments, migration module 328 may be configured to receive user input indicating one or more source objects to be excluded from migration to the destination stack. The user input may be received via a graphical user interface displaying one or more source objects or indications of source objects. The graphical user interface may be configured to receive user input indicating one or more of the source objects to be excluded from migration to the destination stack via migrating the source ontology.
In some embodiments, migration module 328 may further be configured to migrate the source ontology to the second destination stack, and/or other destination stacks based on translation of the secondary destination dataset identifiers. The source ontology may be migrated from the source stack to any number of destination stacks responsive to translating the dataset identifiers associated with the datasets for a given stack.
In some implementations, the source ontology may be migrated to the destination stack responsive to identification of a trigger event by trigger module 326. As such, the source ontology may be migrated to one or more destination stacks automatically, or on a continuous or semi-continuous basis.
Although illustrated in
Furthermore, it should be appreciated that although the various instructions are illustrated in
Additionally, the modular software breakdown as illustrated in
The description of the functionality provided by the different instructions described herein is for illustrative purposes, and is not intended to be limiting, as any of instructions may provide more or less functionality than is described. For example, one or more of the instructions may be eliminated, and some or all of its functionality may be provided by other ones of the instructions. As another example, processor(s) 332 may be programmed by one or more additional instructions that may perform some or all of the functionality attributed herein to one of the instructions.
The various instructions described herein may be stored in a storage device 334, which may comprise random access memory (RAM), read only memory (ROM), and/or other memory. The storage device may store the computer program instructions (e.g., the aforementioned instructions) to be executed by processor 332 as well as data that may be manipulated by processor 332. The storage device may comprise floppy disks, hard disks, optical disks, tapes, or other storage media for storing computer-executable instructions and/or data.
The various components illustrated in
In an operation 402, ontology migration process 400 may include receiving a source ontology for a source stack. The source ontology may be received by source ontology module 318. The source ontology may define source objects associated with for status. The source datasets may correspond to destination dataset on a destination stack. The source datasets may have source dataset identifiers that are different from destination dataset identifiers for the destination datasets. In some implementations, receiving the source ontology may include obtaining and/or obtaining access to the source ontology. A source ontology module the same as or similar to source ontology module 318 may be configured to perform operation 402.
In an operation 404, ontology migration process 400 may include receiving the destination dataset identifiers. Receiving the destination dataset identifiers may include obtaining, accessing, and/or otherwise acquiring the destination dataset identifiers for the destination datasets on the destination stack. A destination dataset identifier module the same as or similar to destination dataset identifier module 320 may be configured to perform operation 404.
In an operation 406, ontology migration process 400 may include translating the destination dataset identifiers into updated destination dataset identifiers. The updated destination dataset identifiers may correspond to the source dataset identifiers. The destination doesn't identifiers may be translated into updated destination dataset identifiers based on the relative locations of the source datasets within the source stack and/or the relative locations of the destination data sets within the destination stack. A translation module the same as or similar to translation module 324 may be configured to perform operation 406.
In an operation 408, ontology migration process 400 may include migrating the source ontology to the destination stack. Source ontology may migrated to the destination stack based on translation of the destination dataset identifiers (e.g., the relative location of the source datasets within the source stack and/or the relative locations of the destination datasets within the destination stack). In some embodiments, the source ontology may be migrated to destination stack based on user input. The source ontology may be migrated to the destination stack responsive to identification of a trigger event by trigger module 326. A migration module the same as or similar to migration module 328 may be configured to perform operation 408.
The computer system 500 also includes a main memory 506, such as a random access memory (RAM), cache and/or other dynamic storage devices, coupled to bus 502 for storing information and instructions to be executed by processor 504. Main memory 506 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 504. Such instructions, when stored in storage media accessible to processor 504, render computer system 500 into a special-purpose machine that is customized to perform the operations specified in the instructions.
The computer system 500 further includes a read only memory (ROM) 508 or other static storage device coupled to bus 502 for storing static information and instructions for processor 504. A storage device 510, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc., is provided and coupled to bus 502 for storing information and instructions.
The computer system 500 may be coupled via bus 502 to a display 512, such as a cathode ray tube (CRT) or LCD display (or touch screen), for displaying information to a computer user. An input device 514, including alphanumeric and other keys, is coupled to bus 502 for communicating information and command selections to processor 504. Another type of user input device is cursor control 516, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 504 and for controlling cursor movement on display 512. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections as cursor control may be implemented via receiving touches on a touch screen without a cursor.
The computing system 500 may include a user interface module to implement a GUI that may be stored in a mass storage device as executable software codes that are executed by the computing device(s). This and other modules may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices may be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and may be originally stored in a compressed or installable format that requires installation, decompression or decryption prior to execution). Such software code may be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors. The modules or computing device functionality described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.
The computer system 500 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 500 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 500 in response to processor(s) 504 executing one or more sequences of one or more instructions contained in main memory 506. Such instructions may be read into main memory 506 from another storage medium, such as storage device 510. Execution of the sequences of instructions contained in main memory 506 causes processor(s) 504 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
The term “non-transitory media,” and similar terms, as used herein refers to any media that store data and/or instructions that cause a machine to operate in a specific fashion. Such non-transitory media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 510. Volatile media includes dynamic memory, such as main memory 506. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, and networked versions of the same.
Non-transitory media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between non-transitory media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 502. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 504 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 500 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 502. Bus 502 carries the data to main memory 506, from which processor 504 retrieves and executes the instructions. The instructions received by main memory 506 may retrieves and executes the instructions. The instructions received by main memory 506 may optionally be stored on storage device 510 either before or after execution by processor 504.
The computer system 500 also includes a communication interface 518 coupled to bus 502. Communication interface 518 provides a two-way data communication coupling to one or more network links that are connected to one or more local networks. For example, communication interface 518 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 518 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to communicated with a WAN). Wireless links may also be implemented. In any such implementation, communication interface 518 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
A network link typically provides data communication through one or more networks to other data devices. For example, a network link may provide a connection through local network to a host computer or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet”. Local network and Internet both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link and through communication interface 518, which carry the digital data to and from computer system 500, are example forms of transmission media.
The computer system 500 can send messages and receive data, including program code, through the network(s), network link and communication interface 518. In the Internet example, a server might transmit a requested code for an application program through the Internet, the ISP, the local network and the communication interface 518.
The received code may be executed by processor 504 as it is received, and/or stored in storage device 510, or other non-volatile storage for later execution.
Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware. The processes and algorithms may be implemented partially or wholly in application-specific circuitry.
The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. In addition, certain method or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states relating thereto can be performed in other sequences that are appropriate. For example, described blocks or states may be performed in an order other than that specifically disclosed, or multiple blocks or states may be combined in a single block or state. The example blocks or states may be performed in serial, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The example systems and components described herein may be configured differently than described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.
Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
Any process descriptions, elements, or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those skilled in the art.
It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure. The foregoing description details certain embodiments of the invention. It will be appreciated, however, that no matter how detailed the foregoing appears in text, the invention can be practiced in many ways. As is also stated above, it should be noted that the use of particular terminology when describing certain features or aspects of the invention should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the invention with which that terminology is associated. The scope of the invention should therefore be construed in accordance with the appended claims and any equivalents thereof.
Engines, Components, and Logic
Certain embodiments are described herein as including logic or a number of components, engines, or mechanisms. Engines may constitute either software engines (e.g., code embodied on a machine-readable medium) or hardware engines. A “hardware engine” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware engines of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware engine that operates to perform certain operations as described herein.
In some embodiments, a hardware engine may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware engine may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware engine may be a special-purpose processor, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A hardware engine may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware engine may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware engines become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware engine mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
Accordingly, the phrase “hardware engine” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented engine” refers to a hardware engine. Considering embodiments in which hardware engines are temporarily configured (e.g., programmed), each of the hardware engines need not be configured or instantiated at any one instance in time. For example, where a hardware engine comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware engines) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware engine at one instance of time and to constitute a different hardware engine at a different instance of time.
Hardware engines can provide information to, and receive information from, other hardware engines. Accordingly, the described hardware engines may be regarded as being communicatively coupled. Where multiple hardware engines exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware engines. In embodiments in which multiple hardware engines are configured or instantiated at different times, communications between such hardware engines may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware engines have access. For example, one hardware engine may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware engine may then, at a later time, access the memory device to retrieve and process the stored output. Hardware engines may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented engines that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented engine” refers to a hardware engine implemented using one or more processors.
Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented engines. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)).
The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented engines may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented engines may be distributed across a number of geographic locations.
Language
Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
Although an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or concept if more than one is, in fact, disclosed.
The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
It will be appreciated that an “engine,” “system,” “data store,” and/or “database” may comprise software, hardware, firmware, and/or circuitry. In one example, one or more software programs comprising instructions capable of being executable by a processor may perform one or more of the functions of the engines, data stores, databases, or systems described herein. In another example, circuitry may perform the same or similar functions. Alternative embodiments may comprise more, less, or functionally equivalent engines, systems, data stores, or databases, and still be within the scope of present embodiments. For example, the functionality of the various systems, engines, data stores, and/or databases may be combined or divided differently.
“Open source” software is defined herein to be source code that allows distribution as source code as well as compiled form, with a well-publicized and indexed means of obtaining the source, optionally with a license that allows modifications and derived works.
The data stores described herein may be any suitable structure (e.g., an active database, a relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, and the like), and may be cloud-based or otherwise.
As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, engines, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.
Other implementations, uses and advantages of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. The specification should be considered exemplary only, and the scope of the invention is accordingly intended to be limited only by the following claims.
This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application Ser. No. 62/659,574, filed Apr. 18, 2018, the content of which is incorporated by reference in its entirety into the present disclosure.
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| 2014250678 | Feb 2016 | AU |
| 2666364 | Jan 2015 | CA |
| 102546446 | Jul 2012 | CN |
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| 2560134 | Feb 2013 | EP |
| 2778913 | Sep 2014 | EP |
| 2778914 | Sep 2014 | EP |
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| 2835745 | Feb 2015 | EP |
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| 2851852 | Mar 2015 | EP |
| 2858014 | Apr 2015 | EP |
| 2858018 | Apr 2015 | EP |
| 2863326 | Apr 2015 | EP |
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| WO 2002065353 | Aug 2002 | WO |
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| WO 2005104736 | Nov 2005 | WO |
| WO 2005116851 | Dec 2005 | WO |
| WO 2008064207 | May 2008 | WO |
| WO 2009061501 | May 2009 | WO |
| WO 2010000014 | Jan 2010 | WO |
| WO 2010030913 | Mar 2010 | WO |
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| WO 2011017289 | May 2011 | WO |
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| WO 2012079836 | Jun 2012 | WO |
| WO 2013010157 | Jan 2013 | WO |
| WO 2013067077 | May 2013 | WO |
| WO 2013102892 | Jul 2013 | WO |
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| Number | Date | Country | |
|---|---|---|---|
| 62659574 | Apr 2018 | US |
