SYSTEM AND METHOD FOR PROVIDING A DATA ANALYTICS WORKBOOK ASSISTANT AND INTEGRATION WITH DATA ANALYTICS ENVIRONMENTS

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TECHNICAL FIELD

Embodiments described herein are generally related to computer data analytics, and are particularly related to a system and method for providing a data analytics workbook assistant and integration with data analytics environments.

BACKGROUND

Data analytics enables computer-based examination of large amounts of data, for example to derive conclusions or other information from the data. For example, business intelligence tools can be used to provide users with business intelligence describing their enterprise data, in a format that enables the users to make strategic business decisions.

SUMMARY

In accordance with an embodiment, described herein are systems and methods for providing a data analytics workbook assistant and integration with data analytics environments. A data analytics system or environment can be integrated with a provider operating as an implementation of a provider framework which provides natural language processing, for purposes of leveraging a user's text or speech input, within a data analytics or data visualization project, for example while generating, modifying, or interacting with data visualizations. The method can, upon receiving the input, process, by the selected provider, a text input or a speech input of the input, to generate, modify, or interact with a data analytics information or visualization.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 2 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 3 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 4 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 5 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 6 illustrates a use of the system to transform, analyze, or visualize data, in accordance with an embodiment.

FIG. 7 illustrates the preparation of a data visualization for use with a data analytics environment, in accordance with an embodiment.

FIG. 8 further illustrates the preparation of a data visualization for use with a data analytics environment, in accordance with an embodiment.

FIG. 9 illustrates a system for providing digital assistant integration with a data analytics assistant, in accordance with an embodiment.

FIG. 10 illustrates the use of natural language to support digital assistant integration, in accordance with an embodiment.

FIG. 11 illustrates an example user interaction with a data analytics environment, to generate analytic information or visualizations, in accordance with an embodiment.

FIG. 12 illustrates the use of a provider framework to support digital assistant integration, in accordance with an embodiment.

FIG. 13 further illustrates the use of a provider framework to support digital assistant integration, in accordance with an embodiment.

FIG. 14 further illustrates the use of a provider framework to support digital assistant integration, in accordance with an embodiment.

FIG. 15 illustrates another process diagram of an example digital assistant integration with an analytics workbook assistant, in accordance with an embodiment.

FIG. 16 illustrates a method for providing digital assistant integration with an analytics workbook assistant, in accordance with an embodiment.

FIG. 17A illustrates an example user interaction with a data analytics environment, in accordance with an embodiment.

FIG. 17B further illustrates an example user interaction with a data analytics environment, in accordance with an embodiment.

FIG. 17C further illustrates an example user interaction with a data analytics environment, in accordance with an embodiment.

FIG. 17D further illustrates an example user interaction with a data analytics environment, in accordance with an embodiment.

FIG. 17E further illustrates an example user interaction with a data analytics environment, in accordance with an embodiment.

FIG. 18 illustrates a method for providing digital assistant integration with an analytics workbook assistant, in accordance with an embodiment.

DETAILED DESCRIPTION

Generally described, within an organization, data analytics enables computer-based examination of large amounts of data, for example to derive conclusions or other information from the data. For example, business intelligence (BI) tools can be used to provide users with business intelligence describing their enterprise data, in a format that enables the users to make strategic business decisions.

Increasingly, data analytics can be provided within the context of enterprise software application environments, such as, for example, an Oracle Fusion Applications environment; or within the context of software-as-a-service (SaaS) or cloud environments, such as, for example, an Oracle Analytics Cloud or Oracle Cloud Infrastructure environment; or other types of analytics application or cloud environments.

Examples of data analytics environments and business intelligence tools/servers include Oracle Business Intelligence Server (OBIS), Oracle Analytics Cloud (OAC), and Fusion Analytics Warehouse (FAW), which support features such as data mining or analytics, and analytic applications.

FIG. 1 illustrates an example data analytics environment, in accordance with an embodiment.

The example embodiment illustrated in FIG. 1 is provided for purposes of illustrating an example of a data analytics environment in association with which various embodiments described herein can be used. In accordance with other embodiments and examples, the approach described herein can be used with other types of data analytics, database, or data warehouse environments. The components and processes illustrated in FIG. 1, and as further described herein with regard to various other embodiments, can be provided as software or program code executable by, for example, a cloud computing system, or other suitably-programmed computer system.

As illustrated in FIG. 1, in accordance with an embodiment, a data analytics environment 100 can be provided by, or otherwise operate at, a computer system having a computer hardware (e.g., processor, memory) 101, and including one or more software components operating as a control plane 102, and a data plane 104, and providing access to a data warehouse, data warehouse instance 160 (database 161, or other type of data source).

In accordance with an embodiment, the control plane operates to provide control for cloud or other software products offered within the context of a SaaS or cloud environment, such as, for example, an Oracle Analytics Cloud environment, or other type of cloud environment. For example, in accordance with an embodiment, the control plane can include a console interface 110 that enables access by a customer (tenant) and/or a cloud environment having a provisioning component 111.

In accordance with an embodiment, the console interface can enable access by a customer (tenant) operating a graphical user interface (GUI) and/or a command-line interface (CLI) or other interface; and/or can include interfaces for use by providers of the SaaS or cloud environment and its customers (tenants). For example, in accordance with an embodiment, the console interface can provide interfaces that allow customers to provision services for use within their SaaS environment, and to configure those services that have been provisioned.

In accordance with an embodiment, a customer (tenant) can request via the console interface, a number of attributes associated with the data warehouse instance, including required attributes (e.g., login credentials), and optional attributes (e.g., size, or speed). The provisioning component can then provision the requested data warehouse instance, including a customer schema of the data warehouse; and populate the data warehouse instance with the appropriate information supplied by the customer. The provisioning component can also be used to update or edit a data warehouse instance, and/or an ETL process that operates at the data plane, for example, by altering or updating a requested frequency of ETL process runs, for a particular customer (tenant).

In accordance with an embodiment, the data plane can include a data pipeline or process layer 120 and a data transformation layer 134, that together process operational or transactional data from an organization's enterprise software application or data environment, such as, for example, business productivity software applications provisioned in a customer's (tenant's) SaaS environment. The data pipeline or process can include various functionalities that extracts transactional data from business applications and databases that are provisioned in the SaaS environment, and then load a transformed data into the data warehouse.

In accordance with an embodiment, the data transformation layer can include a data model, such as, for example, a knowledge model (KM), or other type of data model, that the system uses to transform the transactional data received from business applications and corresponding transactional databases provisioned in the SaaS environment, into a model format understood by the data analytics environment.

In accordance with an embodiment, the data plane is responsible for performing extract, transform, and load (ETL) operations, including extracting transactional data from an organization's enterprise software application or data environment, such as, for example, business productivity software applications and corresponding transactional databases offered in a SaaS environment, transforming the extracted data into a model format, and loading the transformed data into a customer schema of the data warehouse.

For example, in accordance with an embodiment, each customer (tenant) of the environment can be associated with their own customer tenancy within the data warehouse, that is associated with their own customer schema; and can be additionally provided with read-only access to the data analytics schema, which can be updated by a data pipeline or process, for example, an ETL process, on a periodic or other basis.

In accordance with an embodiment, a data pipeline or process can be scheduled to execute at intervals (e.g., hourly/daily/weekly) to extract transactional data from an enterprise software application or data environment, such as, for example, business productivity software applications and corresponding transactional databases 106 that are provisioned in the SaaS environment.

In accordance with an embodiment, an extract process 108 can extract the transactional data, whereupon extraction the data pipeline or process can insert extracted data into a data staging area, which can act as a temporary staging area for the extracted data. The data quality component and data protection component can be used to ensure the integrity of the extracted data. For example, in accordance with an embodiment, the data quality component can perform validations on the extracted data while the data is temporarily held in the data staging area.

In accordance with an embodiment, when the extract process has completed its extraction, the data transformation layer can be used to begin the transform process, to transform the extracted data into a model format to be loaded into the customer schema of the data warehouse.

In accordance with an embodiment, the data pipeline or process can operate in combination with the data transformation layer to transform data into the model format. The mapping and configuration database can store metadata and data mappings that define the data model used by data transformation. The data and configuration user interface (UI) can facilitate access and changes to the mapping and configuration database.

In accordance with an embodiment, the data transformation layer can transform extracted data into a format suitable for loading into a customer schema of data warehouse, for example according to the data model. During the transformation, the data transformation can perform dimension generation, fact generation, and aggregate generation, as appropriate. Dimension generation can include generating dimensions or fields for loading into the data warehouse instance.

In accordance with an embodiment, after transformation of the extracted data, the data pipeline or process can execute a warehouse load procedure 150, to load the transformed data into the customer schema of the data warehouse instance. Subsequent to the loading of the transformed data into customer schema, the transformed data can be analyzed and used in a variety of additional business intelligence processes.

Different customers of a data analytics environment may have different requirements with regard to how their data is classified, aggregated, or transformed, for purposes of providing data analytics or business intelligence data, or developing software analytic applications. In accordance with an embodiment, to support such different requirements, a semantic layer 180 can include data defining a semantic model of a customer's data; which is useful in assisting users in understanding and accessing that data using commonly-understood business terms; and provide custom content to a presentation layer 190.

In accordance with an embodiment, a semantic model can be defined, for example, in an Oracle environment, as a BI Repository (RPD) file, having metadata that defines logical schemas, physical schemas, physical-to-logical mappings, aggregate table navigation, and/or other constructs that implement the various physical layer, business model and mapping layer, and presentation layer aspects of the semantic model.

In accordance with an embodiment, a customer may perform modifications to their data source model, to support their particular requirements, for example by adding custom facts or dimensions associated with the data stored in their data warehouse instance; and the system can extend the semantic model accordingly.

In accordance with an embodiment, the presentation layer can enable access to the data content using, for example, a software analytic application, user interface, dashboard, key performance indicators (KPI's); or other type of report or interface as may be provided by products such as, for example, Oracle Analytics Cloud, or Oracle Analytics for Applications.

In accordance with an embodiment, a query engine 18 (e.g., an OBIS instance) operates in the manner of a federated query engine to serve analytical queries or requests from clients within, e.g., an Oracle Analytics Cloud environment, directed to data stored at a database.

In accordance with an embodiment, the OBIS instance can push down operations to supported databases, in accordance with a query execution plan 56, wherein a logical query can include Structured Query Language (SQL) statements received from the clients; while a physical query includes database-specific statements that the query engine sends to the database to retrieve data when processing the logical query. In this way the OBIS instance translates business user queries into appropriate database-specific query languages (e.g., Oracle SQL, SQL Server SQL, DB2 SQL, or Essbase MDX). The query engine (e.g., OBIS) can also support internal execution of SQL operators that cannot be pushed down to the databases.

In accordance with an embodiment, a user/developer can interact with a client computer device 10 that includes a computer hardware 11 (e.g., processor, storage, memory), user interface 12, and client application 14. A query engine or business intelligence server such as OBIS generally operates to process inbound, e.g., SQL, requests against a database model, build and execute one or more physical database queries, process the data appropriately, and then return the data in response to the request.

To accomplish this, in accordance with an embodiment, the query engine or business intelligence server can include various components or features, such as a logical or business model or metadata that describes the data available as subject areas for queries; a request generator that takes incoming queries and turns them into physical queries for use with a connected data source; and a navigator that takes the incoming query, navigates the logical model and generates those physical queries that best return the data required for a particular query.

For example, in accordance with an embodiment, a query engine or business intelligence server may employ a logical model mapped to data in a data warehouse, by creating a simplified star schema business model over various data sources so that the user can query data as if it originated at a single source. The information can then be returned to the presentation layer as subject areas, according to business model layer mapping rules.

In accordance with an embodiment, the query engine (e.g., OBIS) can process queries against a database according to a query execution plan. During operation the query engine or business intelligence server can create a query execution plan which can then be further optimized, for example to perform aggregations of data necessary to respond to a request. Data can be combined together and further calculations applied, before the results are returned to the calling application.

In accordance with an embodiment, a request for data analytics or visualization information can be received via a client application and user interface as described above, and communicated to the analytics system (in the example of a cloud environment, via a cloud service). The system can retrieve an appropriate dataset to address the user/business context, for use in generating and returning the requested data analytics or visualization information to the client, as a data visualization 196.

In accordance with an embodiment, a client application can be implemented as software or computer-readable program code executable by a computer system or processing device, and having a user interface, such as, for example, a software application user interface or a web browser interface. The client application can retrieve or access data via an Internet/HTTP or other type of network connection to the analytics system, or in the example of a cloud environment via a cloud service provided by the environment.

FIG. 2 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 2, in accordance with an embodiment, the analytics system enables a dataset to be retrieved, received, or prepared from one or more data source(s) 198, for example via one or more data source connections. Examples of the types of data that can be transformed, analyzed, or visualized using the systems and methods described herein include HCM, HR, or ERP data, e-mail or text messages, or other of free-form or unstructured textual data provided at one or more of a database, data storage service, or other type of data repository or data source.

For example, in accordance with an embodiment, a request for data analytics or visualization information can be received via a client application and user interface as described above, and communicated to the analytics system (in the example of a cloud environment, via a cloud service). The system can retrieve an appropriate dataset to address the user/business context, for use in generating and returning the requested data analytics or visualization information to the client. For example, the data analytics system can retrieve a dataset using, e.g., SELECT statements or Logical SQL instructions.

In accordance with an embodiment, the system provides functionality that allows a user to generate datasets, analyses, or visualizations for display within a user interface, for example to explore datasets or data sourced from multiple data sources.

In accordance with an embodiment, the provisioning component can also comprise a provisioning application programming interface (API), a number of workers, a metering manager, and a data plane API, as further described below. The console interface can communicate, for example, by making API calls, with the provisioning API when commands, instructions, or other inputs are received at the console interface to provision services within the SaaS environment, or to make configuration changes to provisioned services.

In accordance with an embodiment, the data plane API can communicate with the data plane. For example, in accordance with an embodiment, provisioning and configuration changes directed to services provided by the data plane can be communicated to the data plane via the data plane API.

In accordance with an embodiment, the metering manager can include various functionality that meters services and usage of services provisioned through control plane. For example, in accordance with an embodiment, the metering manager can record a usage over time of processors provisioned via the control plane, for particular customers (tenants), for billing purposes. Likewise, the metering manager can record an amount of storage space of data warehouse partitioned for use by a customer of the SaaS environment, for billing purposes.

In accordance with an embodiment, the data pipeline or process, provided by the data plane, can include a monitoring component, a data staging component, a data quality component, and a data projection component, as further described below.

In accordance with an embodiment, the data transformation layer can include a dimension generation component, fact generation component, and aggregate generation component, as further described below. The data plane can also include a data and configuration user interface, and mapping and configuration database.

In accordance with an embodiment, the data warehouse can include a default data analytics schema (referred to herein in accordance with some embodiments as an analytic warehouse schema) 162 and, for each customer (tenant) of the system, a customer schema 164.

In accordance with an embodiment, to support multiple tenants, the system can enable the use of multiple data warehouses or data warehouse instances. For example, in accordance with an embodiment, a first warehouse customer tenancy for a first tenant can comprise a first database instance, a first staging area, and a first data warehouse instance of a plurality of data warehouses or data warehouse instances; while a second customer tenancy for a second tenant can comprise a second database instance, a second staging area, and a second data warehouse instance of the plurality of data warehouses or data warehouse instances.

In accordance with an embodiment, based on the data model defined in the mapping and configuration database, the monitoring component can determine dependencies of several different datasets (data sets) to be transformed. Based on the determined dependencies, the monitoring component can determine which of several different datasets should be transformed to the model format first.

For example, in accordance with an embodiment, if a first model dataset incudes no dependencies on any other model dataset; and a second model dataset includes dependencies to the first model dataset; then the monitoring component can determine to transform the first dataset before the second dataset, to accommodate the second dataset's dependencies on the first dataset.

For example, in accordance with an embodiment, dimensions can include categories of data such as, for example, “name,” “address,” or “age”. Fact generation includes the generation of values that data can take, or “measures.” Facts can be associated with appropriate dimensions in the data warehouse instance. Aggregate generation includes creation of data mappings which compute aggregations of the transformed data to existing data in the customer schema of data warehouse instance.

In accordance with an embodiment, once any transformations are in place (as defined by the data model), the data pipeline or process can read the source data, apply the transformation, and then push the data to the data warehouse instance.

In accordance with an embodiment, data transformations can be expressed in rules, and once the transformations take place, values can be held intermediately at the staging area, where the data quality component and data projection components can verify and check the integrity of the transformed data, prior to the data being uploaded to the customer schema at the data warehouse instance. Monitoring can be provided as the extract, transform, load process runs, for example, at a number of compute instances or virtual machines. Dependencies can also be maintained during the extract, transform, load process, and the data pipeline or process can attend to such ordering decisions.

In accordance with an embodiment, after transformation of the extracted data, the data pipeline or process can execute a warehouse load procedure, to load the transformed data into the customer schema of the data warehouse instance. Subsequent to the loading of the transformed data into customer schema, the transformed data can be analyzed and used in a variety of additional business intelligence processes.

FIG. 3 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 3, in accordance with an embodiment, data can be sourced, e.g., from a customer's (tenant's) enterprise software application or data environment (106), using the data pipeline process; or as custom data 109 sourced from one or more customer-specific applications 107; and loaded to a data warehouse instance, including in some examples the use of an object storage 105 for storage of the data.

In accordance with embodiments of analytics environments such as, for example, Oracle Analytics Cloud (OAC), a user can create a dataset that uses tables from different connections and schemas. The system uses the relationships defined between these tables to create relationships or joins in the dataset.

In accordance with an embodiment, for each customer (tenant), the system uses the data analytics schema that is maintained and updated by the system, within a system/cloud tenancy 114, to pre-populate a data warehouse instance for the customer, based on an analysis of the data within that customer's enterprise applications environment, and within a customer tenancy 117. As such, the data analytics schema maintained by the system enables data to be retrieved, by the data pipeline or process, from the customer's environment, and loaded to the customer's data warehouse instance.

In accordance with an embodiment, the system also provides, for each customer of the environment, a customer schema that is readily modifiable by the customer, and which allows the customer to supplement and utilize the data within their own data warehouse instance. For each customer, their resultant data warehouse instance operates as a database whose contents are partly-controlled by the customer; and partly-controlled by the environment (system).

For example, in accordance with an embodiment, a data warehouse (e.g., ADW) can include a data analytics schema and, for each customer/tenant, a customer schema sourced from their enterprise software application or data environment. The data provisioned in a data warehouse tenancy (e.g., an ADW cloud tenancy) is accessible only to that tenant; while at the same time allowing access to various, e.g., ETL-related or other features of the shared environment.

In accordance with an embodiment, to support multiple customers/tenants, the system enables the use of multiple data warehouse instances; wherein for example, a first customer tenancy can comprise a first database instance, a first staging area, and a first data warehouse instance; and a second customer tenancy can comprise a second database instance, a second staging area, and a second data warehouse instance.

In accordance with an embodiment, for a particular customer/tenant, upon extraction of their data, the data pipeline or process can insert the extracted data into a data staging area for the tenant, which can act as a temporary staging area for the extracted data. A data quality component and data protection component can be used to ensure the integrity of the extracted data; for example, by performing validations on the extracted data while the data is temporarily held in the data staging area. When the extract process has completed its extraction, the data transformation layer can be used to begin the transformation process, to transform the extracted data into a model format to be loaded into the customer schema of the data warehouse.

FIG. 4 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 4, in accordance with an embodiment, the process of extracting data, e.g., from a customer's (tenant's) enterprise software application or data environment, using the data pipeline process as described above; or as custom data sourced from one or more customer-specific applications; and loading the data to a data warehouse instance, or refreshing the data in a data warehouse, generally involves three broad stages, performed by an ETP service 160 or process, including one or more extraction service 163; transformation service 165; and load/publish service 167, executed by one or more compute instance(s) 170.

For example, in accordance with an embodiment, a list of view objects for extractions can be submitted, for example, to an Oracle BI Cloud Connector (BICC) component via a REST call. The extracted files can be uploaded to an object storage component, such as, for example, an Oracle Storage Service (OSS) component, for storage of the data. The transformation process takes the data files from object storage component (e.g., OSS), and applies a business logic while loading them to a target data warehouse, e.g., an ADW database, which is internal to the data pipeline or process, and is not exposed to the customer (tenant). A load/publish service or process takes the data from the, e.g., ADW database or warehouse, and publishes it to a data warehouse instance that is accessible to the customer (tenant).

FIG. 5 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 5, which illustrates the operation of the system with a plurality of tenants (customers) in accordance with an embodiment, data can be sourced, e.g., from each of a plurality of customer's (tenant's) enterprise software application or data environment, using the data pipeline process as described above; and loaded to a data warehouse instance.

In accordance with an embodiment, the data pipeline or process maintains, for each of a plurality of customers (tenants), for example customer A 180, customer B 182, a data analytics schema that is updated on a periodic basis, by the system in accordance with best practices for a particular analytics use case.

In accordance with an embodiment, for each of a plurality of customers (e.g., customers A, B), the system uses the data analytics schema 162A, 162B, that is maintained and updated by the system, to pre-populate a data warehouse instance for the customer, based on an analysis of the data within that customer's enterprise applications environment 106A, 106B, and within each customer's tenancy (e.g., customer A tenancy 181, customer B tenancy 183); so that data is retrieved, by the data pipeline or process, from the customer's environment, and loaded to the customer's data warehouse instance 160A, 160B.

In accordance with an embodiment, the data analytics environment also provides, for each of a plurality of customers of the environment, a customer schema (e.g., customer A schema 164A, customer B schema 164B) that is readily modifiable by the customer, and which allows the customer to supplement and utilize the data within their own data warehouse instance.

As described above, in accordance with an embodiment, for each of a plurality of customers of the data analytics environment, their resultant data warehouse instance operates as a database whose contents are partly-controlled by the customer; and partly-controlled by the data analytics environment (system); including that their database appears pre-populated with appropriate data that has been retrieved from their enterprise applications environment to address various analytics use cases. When the extract process 108A, 108B for a particular customer has completed its extraction, the data transformation layer can be used to begin the transformation process, to transform the extracted data into a model format to be loaded into the customer schema of the data warehouse.

In accordance with an embodiment, activation plans 186 can be used to control the operation of the data pipeline or process services for a customer, for a particular functional area, to address that customer's (tenant's) particular needs.

For example, in accordance with an embodiment, an activation plan can define a number of extract, transform, and load (publish) services or steps to be run in a certain order, at a certain time of day, and within a certain window of time.

In accordance with an embodiment, each customer can be associated with their own activation plan(s). For example, an activation plan for a first Customer A can determine the tables to be retrieved from that customer's enterprise software application environment (e.g., their Fusion Applications environment), or determine how the services and their processes are to run in a sequence; while an activation plan for a second Customer B can likewise determine the tables to be retrieved from that customer's enterprise software application environment, or determine how the services and their processes are to run in a sequence.

FIG. 6 illustrates a use of the system to transform, analyze, or visualize data, in accordance with an embodiment.

As illustrated in FIG. 6, in accordance with an embodiment, the systems and methods disclosed herein can be used to provide a data visualization environment 192 that enables insights for users of an analytics environment with regard to analytic artifacts and relationships among the same. A model can then be used to visualize relationships between such analytic artifacts via, e.g., a user interface, as a network chart or visualization of relationships and lineage between artifacts (e.g., User, Role, DV Project, Dataset, Connection, Dataflow, Sequence, ML Model, ML Script).

In accordance with an embodiment, the user interface can include or provide access to various dataflow action types, as described in further detail below, that enable self-service text analytics, including allowing a user to display a dataset, or interact with the user interface to transform, analyze, or visualize the data, for example to generate graphs, charts, or other types of data analytics or visualizations of dataflows.

In accordance with an embodiment, the analytics system enables a dataset to be retrieved, received, or prepared from one or more data source(s), for example via one or more data source connections. Examples of the types of data that can be transformed, analyzed, or visualized using the systems and methods described herein include HCM, HR, or ERP data, e-mail or text messages, or other of free-form or unstructured textual data provided at one or more of a database, data storage service, or other type of data repository or data source.

In accordance with an embodiment, the system can create a model or dataflow that reflects an understanding of the dataflow or set of input data, by applying various algorithmic processes, to generate visualizations or other types of useful information associated with the data. The model or dataflow can be further modified within a dataset editor 193 by applying various processing or techniques to the dataflow or set of input data, including for example one or more dataflow actions 194, 195 or steps that operate on the dataflow or set of input data. A user can interact with the system via a user interface, to control the use of dataflow actions to generate data analytics, data visualizations 196, or other types of useful information associated with the data.

In accordance with an embodiment, datasets are self-service data models that a user can build for data visualization and analysis requirements. A dataset contains data source connection information, tables, and columns, data enrichments and transformations. A user can use a dataset in multiple workbooks and in dataflows.

In accordance with an embodiment, when a user creates and builds a dataset, they can, for example: choose between many types of connections or spreadsheets; create datasets based on data from multiple tables in a database connection, an Oracle data source, or a local subject area; or create datasets based on data from tables in different connections and subject areas.

For example, in accordance with an embodiment, a user can build a dataset that includes tables from an Autonomous Data Warehouse connection, tables from a Spark connection, and tables from a local subject area; specify joins between tables; and transform and enrich the columns in the dataset.

In accordance with an embodiment, additional artifacts, features, and operations associated with datasets can include, for example:

View available connections: a dataset uses one or more connections to data sources to access and supply data for analysis and visualization. A user list of connections contains the connections that they built and the connections that they have permission to access and use.

Create a dataset from a connection: when a user creates a dataset, they can add tables from one or more data source connections, add joins, and enrich data.

Add multiple connections to a dataset: a dataset can include more than one connection. Adding more connections allows a user to access and join all of the tables and data that they need to build the dataset. The user can add more connections to datasets that support multiple tables.

Create dataset table joins: joins indicate relationships between a dataset's tables. If the user is creating a dataset based on facts and dimensions and if joins already exist in the source tables, then joins are automatically created in the dataset. If the user is creating a dataset from multiple connections and schemas, then they can manually define the joins between tables.

In accordance with an embodiment, a user can use dataflows to create datasets by combining, organizing, and integrating data. Dataflows enable the user to organize and integrate data to produce curated datasets that either they or other users can visualize.

For example, in accordance with an embodiment, a user might use a dataflow to: Create a dataset; Combine data from different source; aggregate data; and train a machine learning model or apply a predictive machine learning model to their data.

In accordance with an embodiment, a dataset editor as described above allows a user to add actions or steps, wherein each step performs a specific function, for example, add data, join tables, merge columns, transform data, or save the data. Each step is validated when the user adds or changes it. When they have configured the dataflow, they can execute it to produce or update a dataset.

In accordance with an embodiment, a user can curate data from datasets, subject areas, or database connections. The user can execute dataflows individually or in a sequence. The user can include multiple data sources in a dataflow and specify how to join them. The user can save the output data from a dataflow in either a dataset or in a supported database type.

In accordance with an embodiment, additional artifacts, features, and operations associated with dataflows can include, for example:

Add columns: add custom columns to a target dataset.

Add data: add data sources to a dataflow. For example, if the user is merging two datasets, they add both datasets to the dataflow.

Aggregate: create group totals by applying aggregate functions; for example, count, sum, or average.

Branch: creates multiple outputs from a dataflow.

Filter: select only the data that the user is interested in.

Join: combine data from multiple data sources using a database join based on a common column.

Graph Analytics: perform geo-spatial analysis, such as calculating the distance or the number of hops between two vertices.

The above are provided by way of example; in accordance with an embodiment, other types of steps can be added to a dataflow to transform a dataset or provide data analytics or visualizations.

Dataset Analyses and Visualizations

FIGS. 7-8 illustrate the preparation of a data visualization for use with a data analytics environment, in accordance with an embodiment.

The user interfaces and features shown in FIGS. 7-8 and elsewhere herein are provided by way of example, for purposes of illustration of the various features described herein; in accordance with various embodiments, alternative examples of user interfaces and features can be provided.

As illustrated in FIGS. 7-8, in accordance with an embodiment, the system allows a user to prepare a data visualization for use with a dataset. A panel of visualization options allows users to add data visualization elements to a workbook or canvas, to create a dashboard or data visualization. The user can create a workbook, add a dataset, and then drag and drop its columns onto a canvas to create visualizations.

In accordance with an embodiment, the system can automatically generate a visualization based on the contents of the canvas, with one or more visualization types automatically selected for selection by the user.

In accordance with an embodiment, the user can continue adding data elements directly to the canvas to build the visualization. In this manner, a dataset operates as a self-service data model from which the user can build a data analysis or visualization. The user can then use the data visualization to access the data analytics environment, for example to submit analyses or queries against an organization's data, or explore datasets or data sourced from multiple data sources. In accordance with an embodiment, dataflows can be used to merge datasets, cleanse data, and output the results to a new dataset.

In accordance with an embodiment, the system can provide automatically generated data visualizations (automatically-generated insights, auto-insights), by suggesting visualizations which are expected to provide the best insights for a particular dataset. The user can review an insight's automatically generated summary, for example by hovering over the associated visualization in the workbook canvas.

Data Analytics Assistant

In accordance with an embodiment, a data analytics system or environment can be integrated with a digital assistant which provides natural language processing capabilities, for purposes of leveraging the natural language processing of a user's text or speech input, within a data analytics or data visualization project, for example while generating, modifying, or interacting with data visualizations, or generating a story or script that includes or is descriptive of data visualizations.

For example, in accordance with an embodiment a data analytics system or environment, for example an Oracle Analytics Cloud (OAC) environment, can be integrated with a digital assistant system or environment, for example an Oracle Digital Assistant (ODA) environment, which provides natural language processing (NLP) and speech processing capabilities, for purposes of leveraging the natural language (NL) processing of a user's text or speech input, within a data analytics or data visualization project, for example while generating, modifying, or interacting with data visualizations.

FIG. 9 illustrates a system for providing digital assistant integration with a data analytics assistant, in accordance with an embodiment.

As illustrated in FIG. 9, in accordance with an embodiment, at (1) a data analytics system or environment, for example an Oracle Analytics Cloud (OAC) environment, receives as input from a user via a user interface (e.g., data analytics assistant) a natural language expression, or request to prepare a data visualization 1010.

At (2), the input natural language can be associated with a context where appropriate, for example an instruction to create a project, e.g., visualization, story, script.

At (3), a relevant dataset can be determined (e.g., by a search component 1020 such as BI Search) based on the parsed data visualization request (context supplied with input and/or based on keywords in input). Based upon the determination of the relevant dataset, the natural language input can be sent to a digital assistant environment 1030 (for example an Oracle Digital Assistant (ODA) environment, which provides natural language processing (NLP) and speech processing capabilities, for purposes of leveraging the natural language (NL) processing of the natural language expression, e.g., a user's text or speech input.

At (4), a data visualization request format (e.g., JSON) can be prepared with resolved intent and entities.

At (5), an (e.g., ODA) JSON data is prepared with resolved intent and entities and returned to the data visualization (DV) environment for rendering.

At (6), the data analytics or data visualization project is rendered in the user interface (UI).

Natural Language Input

In accordance with an embodiment, the system supports the use of natural language input, to generate simple and insightful natural language text for a given visualization. A simple text explains the data behind the visualization, whereas an insightful text is meant to provide related but useful insights about the columns and the data surrounding them in the visualization. The data analytics assistant can then use the insights generation feature to fetch and display related insights for the visualization.

FIG. 10 illustrates the use of natural language to support digital assistant integration, in accordance with an embodiment.

As illustrated in FIG. 10, in accordance with an embodiment, a user can interact with a user interface 1102 of a search environment 1100 (e.g., BI Search) via a natural language utterance/input. A request 1101 can be passed to a natural language parser 1103 (e.g., ODA) and parsed for use by a visualization generator 1104 and natural language text generator 1120 comprising a data collector 1121 and data to text converter 1124. Responses (for example insights 1122, 1123) can be collated 1105 in order to provide a response/visualization 1140 to the user interface.

For example, a user request received at the user interface, such as for example “What are the top performing products in Asia?” is parsed by a natural language parser and passed to a visualization generator. A natural language text generator can then be used to generate a response associated with a visualization, such as for example “The top 3 products by sales were . . . . Here's a visualization of the totals sales for the top 20 products . . . ”.

User Interaction with Data Analytics Environments

FIG. 11 illustrates an example user interaction with a data analytics environment, to generate analytic information or visualizations, in accordance with an embodiment.

As illustrated in FIG. 11, in accordance with an embodiment, when the user starts an exploration, using a computer device in communication with a server API 212, the data analytics system can, based on natural language processing 200, present the user with dynamic insights about their data which they can review and choose from. The user can then continue adding data elements directly to the canvas, for example using a chat-like interface, to continue building their visualization.

Provider Framework

In accordance with an embodiment, the system supports a user's chat-like conversations utilizing a semantic search based provider framework. The provider framework provides a flexible approach to having chat conversations, as opposed to the limited scope of flowchart based alternatives.

FIGS. 12-14 illustrate the use of a provider framework 1200 to support digital assistant integration, in accordance with an embodiment.

In accordance with an embodiment, a BI Search environment and user interface. Such as BI Ask, supports chat-like interactions for a given dataset with measure and dimension columns. The scope is not limited to datasets but can expand to include any artifact within the BI Search or data analytics environment.

In accordance with an embodiment, an index 1202 operates as a repository of documents, with each document containing fields that describe the various facets of a single item in a BI System catalog. The index can contain the items in the BI Search or data analytics environment (including datasets) thereby acting as a global dictionary, and can be seeded with additional metadata/keywords that provide contextual support for processing a user's utterance or chat input.

In accordance with an embodiment, examples of keywords that get seeded into the index are shown in Table 1 below:

TABLE 1

Analytical View Grammar

pivot table
View: Pivot

Table
View: Table

bar chart
View: Bar

horizontal bar chart
View: Bar

vertical bar chart
View: Bar

horizontal stacked bar chart
View: Bar

vertical stacked bar chart
View: Bar

line chart
View: Line

pie chart
View: Pie

scatter plot chart
View: Scatter

bubble chart
View: Bubble

radar chart
View: Radar

tree map
View: TreeMap

geo map
View: GeoMap

stacked bar chart
View: Bar

area chart
View: Area

stacked area chart
View: Area

In accordance with an embodiment, in order for synonyms to provide the ability to express in natural language, the system can be adapted to understand synonyms for words. Synonym information can be pre-seeded into the system via a knowledgebase, or can be curated by the user community. An exemplary synonym metadata is provided below in Table 2:

TABLE 2

Abbreviation/Synonym
Root

Nbr
Number

No.
Number

Num
Number

#
Number

Pct
Percent

%
Percent

$
Dollars

Cnt
Count

Avg
Average

Mth
Month

Mon
Month

Qtr
Quarter

Wk
Week

Yr
Year

Dt
Date

QTD
Quarter To Date

YTD
Year to Date

MTD
Month to Date

Yago
Year Ago

Qago
Quarter Ago

Mago
Month Ago

Act
Account

Fscl
Fiscal

ID
Identifier

In accordance with an embodiment, the provider framework operates as an abstraction over various implementations of the provider.

In accordance with an embodiment, a selected provider 1203 (i.e., selected from a number of optional providers 1203, 1204, 1205) operates as an implementation of the provider framework to interpret a user's utterance or chat input along with the hits from the index. Such providers can leverage as simple as a regular expression interpretation of the user's utterance or chat input, or as complex as a large language model.

For example, in accordance with an embodiment, a regular expression (regex) provider leverages regular expressions to match a given utterance or chat input against a set of given rules, and extracts parts of the input.

The extracted parts can then be evaluated against index lookup terms passed to the provider, thereby effectively resolving valid column names (including synonyms) of a dataset and ignore invalid ones.

The index provides support to determine a column name for a column value specified in the user's utterance or chat input, even if the column name itself is not present in the utterance. The chart type specified in the input can also be inferred from the index.

This technique enables effective resolution of the utterance or chat input to generate an appropriate response. The ability to combine metadata (including synonyms, chart types) from the index along with sets rules of regular expression make this a powerful technique for chat input resolution and response. This can be made more natural to the user if the UI elements based on user selection generates an utterance or chat input which potentially matches one of the regular expression rules.

In accordance with an embodiment, sample utterance patterns for resolution by a regex grammar are illustrated below in Table 3, including example positive use cases:

TABLE 3

SI No
Utterance pattern
Sample utterance
Observations

1
Show me <column1>
Show me profit
The prefix of “show

Show me region
me” and “by” stop

words are required

The column can be a

dimension or a measure

2
[show me | what is | what are |
Show me profit by

what is the | what are the]
region

<column1> by <column2>
what is sales by

product

3
Show me <aggr_function>

aggr_function is one of:

<column1> by <column2>

average

avg

min

minimum

max

maximum

count

count of

sum

sum of

4
Show me <column1>,
Show me sales, profit
columns are extracted

<column2> by
by region, product
based on the comma

<column3>, <column4>
category
delimiter

5
Show me a <chartType> of
Show me a table of
columns are extracted

<column1> and <column2> by
sales and profit by
based on the “and”

<column3> and <column4>
region and product
keyword separator

category

6
Show me a <chartType> of
Show me a table of
The columnValue1 and

<column1> and <column2> by
sales and profit by
columnValue2 can belong

<column3> for
product category for
to different columns. This

<columnValue1> and
furniture
is feasible due to

<columnValue2>
Show me a table of
combined power of regexp

sales and profit by
matching + index lookup

product category for

furniture and quebec

7
Show me a <chartType> of
Show me a table of

<column1> and <column2> by
sales and profit by

<column3> IN
product category in

<columnValue1> and
quebec and manitoba

<columnValue2>

8
Show me a <chartType> of
Show me a table of

<column1> and <column2> by
sales and profit by

<column3> and <column4>
region and product

where <column5> is
category where

<column5_value1> and
province is Quebec

<column5_value2>

9
Show me a <chartType> of
Show me a table of
support for basic number

<column1> and <column2> by
sales and profit by
expressions of > < >= <=

<column3> and <column4>
region and product
“of”, “by”,

where <column5> > 10000
category where
“and”, “where”

profit > 10000
are required

10
Show me top <N>
Show me top 3

<dimension1> by <measure1>
product category by

sales

11
Show me bottom <N>
Show me bottom 3

<dimension1> by <measure1>
product segment by

profit

12
Show me <column1> by
Show me sales by

<column2> in year <year1>,
region in year 2012,

<year2> and <year3>
2013 and 2015

13
Show me <column1> by
Show me sales by
in <quarterX> will mean it

<column2> in <quarterX>
region in Q1 2012 and
is referring to quarter and

<year1> and <quarterY>
Q1 2015
the subsequent term is a

<year2>

year

14
Show me <column1> by
Show me sales by
in <month> will mean it is

<column2> in <month1>
region in may 2012
referring to month and the

<year1> and <month2>
and june 2012
subsequent term is a year.

<year2>

15
Show me <column1> by
Show me sales and
The term FOR

<column2> for year <year1>,
profit by region for
YEAR needs to precede

<year2> and <year3>
year 2013 and 2015
the actual year numbers

16
Show me <column1> by
Show me sales and
for <quarterX> will mean it

<column2> for <quarterX>
profit for q2 2013 and
is referring to quarter and

<year1> and <quarterY>
q3 2013
the subsequent term is a

<year2>

year

17
Show me <column1> by
Show me sales by
for<month> will mean it is

<column2> for <month1>
region for december
referring to month and the

<year1> and <month2>
2009 and january 2010
subsequent term is a year

<year2>

The list of accepted month

values are:

Jan/January . . . /dec/December

18
Show me <column1> by
Show me sales and
The

<column2> from year
profit by product
terms FROM and TO are

<year1> to year <year2>
category from 2011 to
required.

2016
The keyword “year” as a

prefix before the year

value is optional here.

19
Show me <column1> by
Show me profit by
The

<column2> from <quarterX>
product category and
terms FROM and TO are

<year1> to <quarterY>
region from q3 2012 to
required.

<year2>
q1 2014
<quarterX> will mean it is

referring to quarter and the

subsequent term is a year

20
Show me <column1> by
Show me profit by
The

<column2> from <month1>
product category and
terms FROM and TO are

<year1> to <month2> <year2>
region from May 2012
required.

to November 2014
<month> will mean it is

referring to month and the

subsequent term is a year

21
Show me <column1> by
Show me profit by
Keywords year Or month

<year/month/quarter>
product category,
Or quarter needs to be

month
specified to get added to

projections

22
Show
Show me sales by
Expression: <col> <op>

me <column1> by <column2>
product
<colVal>

in
category in manitoba
<op>: =, <, >, >=, <=

<columnValue1> where
where profit >
Multiple filters are being

<expression> for
1000 for furniture
expressed in a single

<columnValue2>

statement

expression is only

supported after

a where clause and not

after in or for

In accordance with an embodiment, sample utterance patterns for resolution by a regex grammar are illustrated below in Table 4, including example negative use cases:

TABLE 4

SI

Sample

No
Utterance pattern
utterance
Observations

1
Show me <valid_column>
Show me
Unknown is not available

by <invalid_term>
sales by
from index lookup and are

unknown
ignored

2
Show me <invalid_term1>
Show me
Unknown and xyz are not

by <invalid_term2>
unknown
available from the index

by xyz
lookup and are ignored

In accordance with an embodiment, the provider framework includes an Oracle Digital Assistant (ODA) model provider 1204, backed by an ODA model that supports handling expressive forms of utterance in chat. The ODA model can be created for a dataset by using an ODA API to create a skill, creating a schema for the dataset within the skill, and subsequently training the skill which builds a model associated with the skill. When trained, the skill can then handle utterance or chat inputs that are directed to that provider.

In accordance with an embodiment, a deep learning model provider 1205 (e.g., provider plugin) can be backed by a large language model (LLM) thus enabling handling of more expressive/colloquial forms of utterance in chat. The LLM itself can be any model, including, for example, any of the available open source models.

In accordance with an embodiment, as a provider response, the provider generates a chart grammar in response to a given utterance containing chart type, projections, filter clauses, sort orders which gets consumed by BI Ask component to generate a visualization to the end user.

FIG. 15 illustrates a process diagram of an example digital assistant integration with an analytics workbook assistant, in accordance with an embodiment.

As illustrated in FIGS. 15, in accordance with an embodiment, when a user provides an utterance or chat input via a user interface 1301, for example by typing into a BI Ask search interface or speaking a command, a search component 1401, e.g., BI Search, sends the natural language input to an ODA skill 1302 (e.g., data analytics workbook assistant) for processing.

In accordance with an embodiment, since there may be different skills for different interactions (e.g., the dataset may be associated with a C2SQL skill, and/or a canvas Intents skill), the natural language input needs to be sent to the correct skill. The skill that understands the input can determine how BI Search proceeds: for example, it may either create the visualization JSON from a returned representation (e.g., in an Oracle environment, as an Oracle Meaning Representation Language, OMRL, representation); or pass to a data visualization component the returned canvas action intent and entities, for use in generating, modifying, or interacting with a data analytics information or visualization.

The example illustrated in FIG. 15 is provided by way of example, for purposes of illustration of the various features described herein; in accordance with various embodiments, alternative examples of search components, representations, and features can be provided.

FIG. 16 illustrates a method for providing digital assistant integration with an analytics workbook assistant, in accordance with an embodiment.

As illustrated in FIG. 16, in accordance with an embodiment, at 1510, an analytics environment can be provided, wherein a user can create data analytics or visualizations based on data from one or more data sets.

In accordance with an embodiment, at 1520, a data analytics system or environment, for example an Oracle Analytics Cloud (OAC) environment, can receive as input a natural language (NL) expression.

In accordance with an embodiment, at 1530, the input NL expression can be associated with a context, for example a homepage Oracle Ask input bar, or a create project Ask bar.

In accordance with an embodiment, at 1540, a search environment (e.g., BISearch) can determine a most relevant dataset based on inputs (e.g., context supplied within input), as well as search index hits on keywords in the input NL.

In accordance with an embodiment, at 1550, upon determining the most relevant dataset, the search environment (e.g., BISearch) can send the NL to a digital assistant environment (e.g., ODA, other skill) that corresponds to the determined dataset, which provides natural language processing (NLP) and speech processing capabilities, for purposes of leveraging the natural language (NL) processing of the NL expression, e.g., a user's text or speech input . . . .

In accordance with an embodiment, at 1560, an, e.g., ODA JSON data can be prepared with resolved intent and entities.

In accordance with an embodiment, at 1570, the ODA JSON can be utilized to generate a project JSON, which is returned to data visualization (DV) for rendering

In accordance with an embodiment, at 1580, the data analytics or data visualization project can be rendered in the user interface (UI).

FIGS. 17A-17E illustrate an example user interaction with a data analytics environment, in accordance with an embodiment.

As illustrated in FIGS. 17A-17E, for example, the above-described approach allows a search environment and user interface to supports chat-like interactions for a given dataset with measure and dimension columns, for example to allow a user to provide an utterance or chat input to “Create a calculation for GDP Exp % which is GDP Exposed divided by Total GSP; and then “Show me the relationship between population and gdp exposure % and add the top 20 by GDP”. The system can process the user's input, by the selected provider, to generate, modify, or interact with a data analytics information or visualization.

FIG. 18 illustrates a method for providing digital assistant integration with an analytics workbook assistant, in accordance with an embodiment.

In accordance with an embodiment, at step 1801, the method can provide a computer comprising a microprocessor.

In accordance with an embodiment, at step 1802, the method can run a data analytics system or environment on the computer.

In accordance with an embodiment, at step 1803, the method can operate the data analytics system or environment with a selected provider operating as an implementation of a provider framework to interpret an input along and provide natural language processing capabilities.

In accordance with an embodiment, at step 1804, the method can, upon receiving the input, process, by the selected provider operating as the implementation of the provider framework, a text input or a speech input of the input, to generate, modify, or interact with a data visualization.

In accordance with various embodiments, the teachings herein can be implemented using one or more computer, computing device, machine, or microprocessor, including one or more processors, memory and/or computer readable storage media programmed according to the teachings herein. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those skilled in the software art.

In some embodiments, the teachings herein can include a computer program product which is a non-transitory computer readable storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the present teachings. Examples of such storage mediums can include, but are not limited to, hard disk drives, hard disks, hard drives, fixed disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, or other types of storage media or devices suitable for non-transitory storage of instructions and/or data.

The foregoing description has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the scope of protection to the precise forms disclosed. Further modifications and variations will be apparent to the practitioner skilled in the art.

The embodiments were chosen and described in order to best explain the principles of the teachings herein and their practical application, thereby enabling others skilled in the art to understand the various embodiments and with various modifications that are suited to the particular use contemplated. It is intended that the scope be defined by the following claims and their equivalents.

SYSTEM AND METHOD FOR PROVIDING A DATA ANALYTICS WORKBOOK ASSISTANT AND INTEGRATION WITH DATA ANALYTICS ENVIRONMENTS

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