SYSTEM AND METHOD FOR GENERATING AUTOMATED INSIGHTS IN ANALYTICAL DATA - Patent application

Abstract

Described herein are systems and methods for generating automated insights into analytical data. Typically, when data is uploaded, linked or made accessible to an analytical environment, a skilled user is required to generate meaningful data visualizations. The systems and methods described herein provide an automated mechanism for generating a set of meaningful data visualizations for display and selection, said generation being based on a determined set of indicators, scored data columns, and scored visualizations.

Description

[Technical field]

[0001] Copyright Notice A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction of the patent document or the patent disclosure as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights.

[0002] Claiming priority This application claims the benefit of priority to U.S. provisional patent application Ser. No. 63 / 243,012, filed on Sep. 10, 2021, entitled "SYSTEM AND METHOD FOR GENERATING AUTOMATED INSIGHTS IN ANALYTICAL DATA," the contents of which are incorporated herein by reference.

[0003] Technical Field FIELD OF THE DISCLOSURE The embodiments described herein relate generally to systems and methods for use in computer data analysis, computer-based methods for providing business intelligence data, and analytical application environments for generating automated insights into the analytical data. [Background technology]

[0004] background Data analytics allows for the 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 provide users with business intelligence that describes enterprise data in a form that enables users to make strategic business decisions. Summary of the Invention [Means for solving the problem]

[0005] overview According to one embodiment, a system and method for generating automated insights of analytical data is described herein. Typically, when data is uploaded, linked or made accessible to an analytical environment, an experienced user is required to generate meaningful data visualizations. The system and method described herein provides an automated mechanism for generating a set of meaningful data visualizations for display and selection, the generation being based on a determined set of indicators, scored data columns, and scored visualizations. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 illustrates an example of a data analysis environment according to one embodiment. [Diagram 2] FIG. 2 further illustrates an example of a data analytics environment according to one embodiment. [Diagram 3] FIG. 2 further illustrates an example of a data analytics environment according to one embodiment. [Figure 4] FIG. 2 further illustrates an example of a data analytics environment according to one embodiment. [Diagram 5] FIG. 2 further illustrates an example of a data analytics environment according to one embodiment. [Figure 6] FIG. 1 illustrates the use of the system to transform, analyze, or visualize data, according to one embodiment. [Figure 7] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 8] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 9] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 10] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 11]FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 12] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 13] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 14] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 15] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 16] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 17] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 18] FIG. 1 illustrates an exemplary user interface for use in a data analysis environment, according to one embodiment. [Figure 19] FIG. 2 illustrates an overall flow diagram of the automatic insight feature, according to one embodiment. [Figure 20] 1 is a flowchart of a user experience of the auto-insight feature, according to one embodiment. [Figure 21] 1 is a flowchart of a user experience of the auto-insight feature, according to one embodiment. [Figure 22] FIG. 1 illustrates an exemplary data visualization of the automated insight feature, according to one embodiment. [Diagram 23] 1 is a flowchart of a method for generating automated insights into analytical data, according to one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Detailed Description Typically, data analytics within an organization involves computer-based exploration of large amounts of data to derive conclusions or other information from the data. For example, business intelligence (BI) tools can provide users with business intelligence that describes enterprise data in a format that enables users to make strategic business decisions.

[0008] Examples of such business intelligence tools / servers include Oracle Business Intelligence Applications (OBIA), Oracle Business Intelligence Enterprise Edition (OBIEE), or Oracle Business Intelligence Server (OBIS), which provide a query, reporting, and analytics server that can interface with databases to support functions such as data mining, analysis, and analytical applications.

[0009] Data analytics is increasingly being delivered within the context of enterprise software application environments, such as Oracle Fusion Applications environments, or within the context of software-as-a-service (SaaS) offerings, or in cloud environments, such as Oracle Analytics Cloud or Oracle Cloud Infrastructure environments, or other types of analytical applications or cloud environments.

[0010] introduction According to one embodiment, a data warehouse environment or component (such as Oracle Autonomous Data Warehouse (ADW), Oracle Autonomous Data Warehouse Cloud (ADWC), or other type of data warehouse environment or component suitable for storing large amounts of data) can provide a central repository for storing data collected by one or more business applications.

[0011] For example, according to one embodiment, a data warehouse environment or component may be provided as a multidimensional database that uses online analytical processing (OLAP) or other techniques to generate business-related data from multiple disparate data sources. An organization can extract such business-related data from one or more vertical and / or horizontal business applications and inject the extracted data into a data warehouse instance associated with the organization.

[0012] Examples of horizontal business applications may include ERP, HCM, CX, SCM, and EPM as described above, providing broad functionality across various enterprise organizations.

[0013] Vertical business applications are typically narrower in scope than horizontal business applications, but provide access to data further up or down the data chain within a defined scope or industry. Examples of vertical business applications might include medical software or banking software for use within a particular organization.

[0014] While other enterprise software products or components, such as Oracle ADWC, can be delivered as one or more of SaaS, platform-as-a-service (PaaS), or hybrid subscriptions, and software vendors are increasingly offering their enterprise software products or components as SaaS or cloud-oriented offerings (such as Oracle Fusion Applications), enterprise users with traditional business intelligence applications and processes are typically faced with the task of extracting data from horizontal and vertical business applications and injecting the extracted data into a data warehouse, a process that can be both time and resource intensive.

[0015] According to one embodiment, the analytical application environment enables a customer (tenant) to develop computer-executable software analytical applications for use with a BI component (e.g., an OBIS environment or other type of BI component suitable for examining large amounts of data sourced from the customer (tenant) itself or from multiple third-party entities).

[0016] As another example, according to one embodiment, the analytical application environment can be used to pre-populate the reporting interface of a data warehouse instance with relevant metadata that describes business-related data objects in the context of various business productivity software applications, for example, to include pre-defined dashboards, key performance indicators (KPIs), or other types of reports.

[0017] Data analysis Generally, data analytics involves the computer-based examination or analysis of large amounts of data in order to derive conclusions or other information from the data, while business intelligence tools (BI) provide an organization's business users with information that describes their enterprise data in a form that allows them to make strategic business decisions.

[0018] Examples of data analysis environments and business intelligence tools / servers include Oracle Business Intelligence Server (OBIS), Oracle Analytics Cloud (OAC), and Fusion Analytics Warehouse (FAW), which support functions such as data mining, analysis, and analytical applications.

[0019] FIG. 1 illustrates an example of a data analysis environment according to one embodiment. The exemplary embodiment shown in Figure 1 is provided for the purpose of illustrating an example of a data analysis environment in which various embodiments described herein may be used. According to other embodiments and examples, the approaches described herein may be used in other types of data analysis, database, or data warehouse environments. The components and processes shown in Figure 1 and further described herein with respect to various other embodiments may be provided as software or program code executable by, for example, a cloud computing system or other suitably programmed computer system.

[0020] As shown in FIG. 1, according to one embodiment, a data analysis environment 100 may be provided by or operate on a computer system having computer hardware (e.g., processor, memory) 101, 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).

[0021] According to one embodiment, the control plane operates to provide control of a cloud or other software product 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, according to one embodiment, the control plane may include a cloud environment having a console interface 110 that allows access by customers (tenants), and / or a provisioning component 111.

[0022] According to one embodiment, the console interface may enable access by customers (tenants) operating a graphical user interface (GUI) and / or a command-line interface (CLI) or other interface, and / or may include an interface used by a provider of a SaaS or cloud environment and its customers (tenants). For example, according to one embodiment, the console interface may provide an interface that allows a customer to provision services for use within its SaaS environment and configure the provisioned services.

[0023] According to one embodiment, a customer (tenant) can request provisioning of a customer schema in a data warehouse. Through a console interface, the customer can also provide a number of attributes associated with the data warehouse instance, including required attributes (e.g., login credentials) and optional attributes (e.g., size, speed, etc.). A provisioning component can then provision the requested data warehouse instance, including the data warehouse customer schema, and populate the data warehouse instance with the appropriate information provided by the customer.

[0024] According to one embodiment, the provisioning component may also be used to update or edit a data warehouse instance or ETL processes running on the data plane, for example, by changing or updating the requested frequency of execution of the ETL processes for a particular customer (tenant).

[0025] According to one embodiment, the data plane may include a data pipeline or processing layer 120 and a data transformation layer 134 that together process operational or transactional data from an organization's enterprise software applications or data environments (e.g., business productivity software applications provisioned in a customer's (tenant's) SaaS environment, etc.). The data pipeline or processing may include various functions to extract transactional data from business applications and databases provisioned in the SaaS environment and load the transformed data into a data warehouse.

[0026] According to one embodiment, the data transformation layer may include data models, such as knowledge models (KMs) or other types of data models that the system uses to transform transactional data received from business applications and corresponding transactional databases provisioned in the SaaS environment into a model format that the data analytics environment can understand. The model format may be provided in any data format suitable for storage in a data warehouse. According to one embodiment, the data plane may also include data and configuration user interfaces, and mapping and configuration databases.

[0027] According to one embodiment, the data plane is responsible for performing extract, transform, and load (ETL) operations, including extracting transactional data from an organization's enterprise software applications and data environments (e.g., 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 customer schemas in a data warehouse.

[0028] For example, according to one embodiment, each customer (tenant) of the environment may be associated with its own customer tenant in the data warehouse that is associated with its own customer schema, and may further be provided with read-only access to a data analysis schema that may be updated periodically or on another basis by a data pipeline or process (such as an ETL process).

[0029] According to one embodiment, a data pipeline or process may be scheduled to run at regular intervals (e.g., hourly, daily, weekly) to extract transactional data from an enterprise software application or data environment, such as, for example, a business productivity software application and corresponding transactional database 106 provisioned in the SaaS environment.

[0030] According to one embodiment, an extraction process 108 can extract transactional data, and a data pipeline or extraction process can insert the extracted data into a data staging area, which can serve as a temporary staging area for the extracted data. To ensure the integrity of the extracted data, data quality and data protection components can be used. For example, according to one embodiment, a data quality component can perform validation of the extracted data while the data is temporarily held in the data staging area.

[0031] According to one embodiment, once the extraction process completes the extraction, a transformation process can begin using a data transformation layer to convert the extracted data into a model format that is loaded into a customer schema in the data warehouse.

[0032] According to one embodiment, the data pipeline or process may work in conjunction with a data transformation layer to transform data into a model format. A mapping and configuration database may store metadata and data mappings that define the data models used in data transformation. A data and configuration user interface (UI) facilitates access to and modification of the mapping and configuration database.

[0033] According to one embodiment, the data transformation layer can transform the extracted data into a format suitable for loading into a data warehouse customer schema, for example according to a data model. During transformation, the data transformation can perform dimension generation, fact generation, and aggregation generation as needed. Dimension generation can include generating dimensions or fields for loading into the data warehouse instance.

[0034] According to one embodiment, after transforming the extracted data, the data pipeline or process may execute a warehouse load step 150 to load the transformed data into a customer schema of a data warehouse instance. After the transformed data is loaded into the customer schema, the transformed data may be analyzed and used in a variety of additional business intelligence processes.

[0035] Different customers of the data analytics environment may have different requirements for how data is classified, aggregated, or transformed for the purposes of data analysis, providing business intelligence data, or developing software analytics applications. According to one embodiment, to support such varying requirements, semantic layer 180 may include data that defines a semantic model of customer data, which helps users understand and access that data using commonly understood business terms, and provides custom content to presentation layer 190.

[0036] According to one embodiment, a semantic model can be specified, for example in an Oracle environment, as a BI repository (RPD) file having metadata that specifies logical schemas, physical schemas, physical-to-logical mappings, aggregate table navigation, and / or other components that implement various physical layer, business model and mapping layer, and presentation layer aspects of the semantic model.

[0037] According to one embodiment, customers can make changes to the data source model, such as adding custom facts or dimensions associated with the data stored in the data warehouse instance, to support their specific requirements, and the system can extend the semantic model accordingly.

[0038] According to one embodiment, the presentation layer enables access to the data content, for example, using software analytics applications, user interfaces, dashboards, key performance indicators (KPIs), or other types of reports or interfaces provided by products, such as, for example, Oracle Analytics Cloud, Oracle Analytics for Applications, etc.

[0039] Business Intelligence Server According to one embodiment, query engine 18 (e.g., an OBIS instance) operates in the manner of a federated query engine, responding to analytical queries or requests from clients within the Oracle Analytics Cloud environment, e.g., directed to data stored in a database.

[0040] According to one embodiment, the OBIS instance can push down operations to supported databases according to a query execution plan 56, where a physical query includes database-specific statements that the query engine sends to a database to retrieve data when processing a logical query, while a logical query can include Structured Query Language (SQL) statements received from a client. In this manner, the OBIS instance translates business user queries into the appropriate database-specific query language (e.g., Oracle SQL, SQL Server SQL, DB2 SQL, Essbase MDX, etc.). The query engine (e.g., OBIS) may also support internal execution of SQL operators that cannot be pushed down to the database.

[0041] According to one embodiment, a user / developer can interact with a client computing device 10 that includes computing hardware 11 (e.g., processor, storage, memory), a user interface 12, and an application 14. A query engine or business intelligence server, such as OBIS, typically operates to handle inbound, e.g., SQL, requests against a database model, construct and execute one or more physical database queries, process the data appropriately, and return the data in response to the request.

[0042] To accomplish this, according to one embodiment, a query engine or business intelligence server may include various components or functions, such as a logical or business model or metadata that describes the data available as the subject area of ​​a query, a request generator that takes incoming queries and converts them into physical queries for use with connected data sources, and a navigator that receives incoming queries, navigates the logical model, and generates physical queries that optimally return the data needed for a particular query.

[0043] For example, according to one embodiment, a query engine or business intelligence server can create a simplified star-schema business model for various data sources, allowing users to query the data as if it were from a single source, using a logical model that is mapped to the data in the data warehouse. Information can then be returned to the presentation layer as subject areas according to the mapping rules in the business model layer.

[0044] According to one embodiment, a query engine (e.g., OBIS) can process queries against a database according to a query execution plan, which can include various child (leaf) nodes, e.g., generally referred to herein in various embodiments as RqList below. Execution plan: [[ RqList < <191986> > [for database 0:0,0] D102.c1 as c1 [for database 0:0,0], sum(D102.c2 by [ D102.c1] ) as c2 [for database 0:0,0] Child Nodes (RqJoinSpec): < <192970> > [for database 0:0,0] RqJoinNode < <192969> > [] ( RqList < <193062> > [for database 0:0,0] D2.c2 as c1 [for database 0:0,0], D1.c2 as c2 [for database 0:0,0] Child Nodes (RqJoinSpec): < <193065> > [for database 0:0,0] RqJoinNode < <193061> > [] ( RqList < <192414> > [for database 0:0,118] T1000003.Customer_ID as c1 [for database 0:0,118], T1000003.TARGET as c2 [for database 0:0,118] Child Nodes (RqJoinSpec): < <192424> > [for database 0:0,118] RqJoinNode < <192423> > [] [users / administrator / dv_joins / multihub / input::##dataTarget] as T1000003 ) as D1 LeftOuterJoin (Eager) < <192381> > On D1.c1 = D2.c1; actual join vectors:

[0000] =

[0000] ( RqList < <192443> > [for database 0:0,0] D104.c1 as c1 [for database 0:0,0], nullifnotunique(D104.c2 by [ D104.c1] ) as c2 [for database 0:0,0] Child Nodes (RqJoinSpec): < <192928> > [for database 0:0,0] RqJoinNode < <192927> > [] ( RqList < <192852> > [for database 0:0,118] T1000006.Customer_ID as c1 [for database 0:0,118], T1000006.Customer_City as c2 [for database 0:0,118] Child Nodes (RqJoinSpec): < <192862> > [for database 0:0,118] RqJoinNode < <192861> > [] [users / administrator / dv_joins / my_customers / input::data] as T1000006 ) as D104 GroupBy: [ D104.c1] [for database 0:0,0] sort OrderBy: c1, Aggs:[ nullifnotunique(D104.c2 by [ D104.c1] ) ] [for database 0:0,0] ) as D2 ) as D102 GroupBy: [ D102.c1] [for database 0:0,0] sort OrderBy: c1 asc, Aggs:[ sum(D102.c2 by [ D102.c1] ) ] [for database 0:0,0] Within a query execution plan, each execution plan component (RqList) represents a block of queries in the query execution plan and is typically translated into a SELECT statement. An RqList can contain nested child RqLists, similar to how a SELECT statement can select from a nested SELECT statement.

[0045] According to one embodiment, the query engine can interact with different databases and can use data source specific code generators for each of these databases. A common strategy is to send as many SQL statements as possible to the database by sending them as part of the physical query, thereby reducing the amount of information returned to the OBIS server.

[0046] According to one embodiment, during operation, the query engine or business intelligence server can create a query execution plan that can be further optimized, for example to perform aggregations of data necessary to respond to the request. Data can be joined and further calculations can be applied before the results are returned to the calling application, such as via an ODBC interface.

[0047] According to one embodiment, complex multi-pass requests involving multiple data sources may require a query engine or business intelligence server to decompose the query, determine which sources, multi-pass calculations and aggregations can be used, and generate a logical query execution plan that spans multiple databases and physical SQL statements; the results may then be returned and further combined or aggregated by the query engine or business intelligence server.

[0048] FIG. 2 further illustrates an example of a data analysis environment according to one embodiment. 2, according to one embodiment, the provisioning component may also include a provisioning application programming interface (API) 112, a number of workers 115, a metering manager 116, and a data plane API 118, as further described below. The console interface may communicate with the provisioning API by making API calls, for example, when commands, instructions, or other inputs are received at the console interface to provision services within the SaaS environment or to change the configuration of a provisioned service.

[0049] According to one embodiment, a data plane API can communicate with the data plane. For example, according to one 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.

[0050] According to one embodiment, the metering manager can include various functions for metering the usage of services and services provisioned via the control plane. For example, according to one embodiment, the metering manager can record the usage of processors provisioned via the control plane over time for a particular customer (tenant) for billing purposes. Similarly, the metering manager can record the amount of data warehouse storage space carved out for use by a customer of a SaaS environment for billing purposes.

[0051] According to one embodiment, the data pipeline or processing provided by the data plane may include a monitoring component 122, a data staging component 124, a data quality component 126, and a data projection component 128, as described further below.

[0052] According to one embodiment, the data transformation layer may include a dimension generation component 136, a fact generation component 138, and an aggregation generation component 140, as further described below. The data plane may also include a data and configuration user interface 130, and a mapping and configuration database 132.

[0053] According to one embodiment, the data warehouse may include a default data analysis schema (referred to herein as an analysis warehouse schema according to some embodiments) 162 and, for each customer (tenant) of the system, a customer schema 164.

[0054] According to one embodiment, to support multiple tenants, the system may enable the use of multiple data warehouses or data warehouse instances. For example, according to one embodiment, a first warehouse customer tenant for a first tenant may include a first database instance, a first staging area, and a first data warehouse instance of the multiple data warehouse or data warehouse instances, while a second customer tenant for a second tenant may include a second database instance, a second staging area, and a second data warehouse instance of the multiple data warehouse or data warehouse instances.

[0055] According to one embodiment, based on the data model defined in the mapping and configuration database, the monitoring component can determine dependencies of several different data sets (also referred to herein as "data sets") to be converted. Based on the determined dependencies, the monitoring component can determine which of the several different data sets needs to be converted to the model format first.

[0056] For example, according to one embodiment, if a first model dataset does not include dependencies on other model datasets, and if a second model dataset includes a dependency on the first model dataset, then the monitoring component can determine to transform the first dataset before the second dataset to accommodate the dependency of the second dataset on the first dataset.

[0057] For example, according to one embodiment, dimensions may include categories of data, such as, for example, "Name," "Address," or "Age." Fact generation involves generating values ​​or "measurements" that the data can take. Facts can be associated with appropriate dimensions in the data warehouse instance. Aggregation generation involves creating data mappings that calculate aggregates of the transformed data against existing data in a customer schema in the data warehouse instance.

[0058] According to one embodiment, if any transformations are performed (as specified by the data model), a data pipeline or process can read the source data, apply the transformations, and then push the data to a data warehouse instance.

[0059] According to one embodiment, data transformations can be expressed in rules and once transformations have occurred, values ​​can be held in an intermediate staging area where data quality and data projection components can validate and verify the integrity of the transformed data before it is uploaded to the customer schema of the data warehouse instance. For example, monitoring can be provided as the extract, transform and load processes run across multiple compute instances or virtual machines. Dependencies can also be maintained during the extract, transform and load processes and the data pipeline or process can accommodate such ordering decisions.

[0060] According to one embodiment, after transforming the extracted data, the data pipeline or process may execute a warehouse load procedure to load the transformed data into a customer schema of a data warehouse instance. After loading the transformed data into the customer schema, the transformed data may be analyzed and used in a variety of additional business intelligence processes.

[0061] FIG. 3 further illustrates an example of a data analysis environment according to one embodiment. As shown in FIG. 3, according to one embodiment, data may be sourced from a customer's (tenant's) enterprise software application or data environment (106), for example, using data pipeline processing, or as custom data 109 derived from one or more customer-specific applications 107, and loaded into the data warehouse instance, in some examples including using object storage 105 for storing the data.

[0062] In an embodiment of an analytics environment such as Oracle Analytics Cloud (OAC), a user can create a dataset that uses tables from different connections and schemas, and the system uses the relationships defined between these tables to create relationships or joins in the dataset.

[0063] According to one embodiment, for each customer (tenant), the system uses a data analysis schema maintained and updated by the system in the system / cloud tenant 114 to pre-populate the customer's data warehouse instance based on an analysis of data in the customer's enterprise application environment and in the customer tenant 117. The data analysis schema maintained by the system thus enables a data pipeline or process to retrieve data from the customer's environment and load it into the customer's data warehouse instance.

[0064] According to one embodiment, the system also provides, for each customer of the environment, a customer schema that is easily modifiable by the customer and allows the customer to supplement and utilize the data in their own data warehouse instance. For each customer, the resulting data warehouse instance acts as a database, the contents of which are partly controlled by the customer and partly controlled by the environment (system).

[0065] For example, according to one embodiment, a data warehouse (e.g., ADW) may include data analysis schemas and customer schemas sourced from enterprise software applications or data environments on a per customer / tenant basis. Data provisioned in a data warehouse tenant (e.g., ADW cloud tenant) is accessible only to that tenant while allowing access to various functions (e.g., ETL-related functions and other functions) of the shared environment.

[0066] According to one embodiment, the system allows for the use of multiple data warehouse instances to support multiple customers / tenants; for example, a first customer tenant may include a first database instance, a first staging area, and a first data warehouse instance, and a second customer tenant may include a second database instance, a second staging area, and a second data warehouse instance.

[0067] According to one embodiment, for a particular customer / tenant, upon extraction of that data, the data pipeline or process can insert the extracted data into the tenant's data staging area, which can serve as a temporary staging area for the extracted data. For example, data quality and data protection components can be used to ensure the integrity of the extracted data by performing validation of the extracted data while the data is temporarily held in the data staging area. Once the extraction process completes the extraction, a data transformation layer can be used to initiate a transformation process to convert the extracted data into a model format for loading into the customer schema in the data warehouse.

[0068] FIG. 4 further illustrates an example of a data analysis environment according to one embodiment. As shown in FIG. 4 , according to one embodiment, the process of extracting data from a customer's (tenant's) enterprise software applications or data environment, for example using the data pipeline process described above or as custom data obtained from one or more customer-specific applications, loading the data into a data warehouse instance, or updating the data in the data warehouse generally involves three broad stages performed by the ETP services 160 or processes including one or more extract services 163, transform services 165, and load / publish services 167 performed by one or more compute instances 170.

[0069] For example, according to one embodiment, a list of view objects for extraction can be sent to an Oracle BI Cloud Connector (BICC) component, e.g., via a REST call. The extracted files can be uploaded to an object storage component, such as an Oracle Storage Service (OSS) component, for storing the data. A transform process retrieves the data files from the object storage component (e.g., OSS) and applies business logic as it loads the data into a target data warehouse (e.g., an ADW database) that is internal to the data pipeline or process and not exposed to the customer (tenant). A load / publish service or process retrieves the data from the ADW database, warehouse, etc., and publishes it to a data warehouse instance that is accessible to the customer (tenant).

[0070] FIG. 5 further illustrates an example of a data analysis environment according to one embodiment. As shown in FIG. 5, which illustrates operation of a multiple tenant (customer) system in accordance with one embodiment, data can be obtained from each of the multiple customer (tenant) enterprise software applications or data environments and loaded into the data warehouse instance, for example, using the data pipeline process described above.

[0071] According to one embodiment, the data pipeline or process maintains a data analysis schema that is periodically updated for each of multiple customers (tenants), e.g., Customer A 180, Customer B 182, by the system following best practices for a particular analytical use case.

[0072] According to one embodiment, for each of multiple customers (e.g., Customer A, Customer B), the system populates the customer's data warehouse instance based on an analysis of data within that customer's enterprise application environment 106A, 106B and within each customer's tenant (e.g., Customer A's tenant 181, Customer B's tenant 183) using data analysis schemas 162A, 162B maintained and updated by the system such that data is obtained from the customer's environment via a data pipeline or process and loaded into the customer's data warehouse instance 160A, 160B.

[0073] According to one embodiment, the data analysis environment also provides, for each of the environment's multiple customers, customer schemas (e.g., customer A schema 164A, customer B schema 164B) that can be easily modified by the customer, allowing the customers to supplement and utilize data in their own data warehouse instance.

[0074] As described above, according to one embodiment, for each of multiple customers of the data analytics environment, the resulting data warehouse instance acts as a database whose contents are partially controlled by the customer and partially controlled by the data analytics environment (system), including appearing to be pre-populated with appropriate data obtained from the enterprise application environment to support various analytical use cases. Once the extraction process 108A, 108B for a particular customer has completed its extraction, a transformation process can be initiated using a data transformation layer to convert the extracted data into a model format that is loaded into the customer schema of the data warehouse.

[0075] According to one embodiment, activation plans 186 can be used to control the operation of data pipelines or processing services for a customer (tenant) for a particular functional area to address the customer's (tenant's) specific needs.

[0076] For example, according to one embodiment, an activation plan may prescribe a number of extract, transform, load (publish) services or steps to be executed in a particular order, at a particular time, and within a particular time frame.

[0077] According to one embodiment, each customer may be associated with a unique activation plan. For example, an activation plan for a first customer A may determine which tables to retrieve from the customer's enterprise software application environment (e.g., a Fusion Applications environment) and may determine the services and how their operations are sequenced. Meanwhile, an activation plan for a second customer B may similarly determine which tables to retrieve from the customer's enterprise software application environment and may determine the services and how their operations are sequenced.

[0078] FIG. 6 illustrates the use of the system to transform, analyze, or visualize data, according to one embodiment.

[0079] 6, according to one embodiment, the systems and methods disclosed herein can be used to provide a data visualization environment 192 that enables insights to a user of the analytical environment regarding analytical artifacts and the relationships between them. The model can then be used to visualize, for example, via a user interface, the relationships between such analytical artifacts as network charts and visualizations of the relationships and lineage between the artifacts (e.g., users, roles, DV projects, datasets, connections, data flows, sequences, ML models, ML scripts).

[0080] According to one embodiment, the client application may be implemented as software or computer readable program code executable by a computer system or processing device and has a user interface, such as a software application user interface or a web browser interface. The client application may obtain or access data via an Internet / HTTP or other type of network connection to the analysis system or, in the example of a cloud environment, via cloud services provided by the environment.

[0081] According to one embodiment, the user interface may include or provide access to various data flow action types that enable self-service text analysis including allowing a user to view data sets and manipulate the user interface to transform, analyze, and visualize data to generate, for example, graphs, charts, and other types of data analysis and data flow visualizations, as described in more detail below.

[0082] According to one embodiment, the analytics system is capable of retrieving, receiving, or preparing datasets from one or more data sources, e.g., via one or more data source connections. Examples of types of data that can be transformed, analyzed, or visualized using the systems and methods described herein include HCM, HR, or ERP data, email or text messages, or other free-form or unstructured text data provided in one or more databases, data storage services, or other types of data repositories or data sources.

[0083] For example, according to one embodiment, a request for data analysis or visualization information may be received via a client application and user interface as described above and communicated to an analysis system (via a cloud service in an example cloud environment). The system may retrieve an appropriate data set corresponding to the user / business context for use in generating and returning the requested data analysis or visualization information to the client. For example, the data analysis system may retrieve the data set using a SELECT statement, a logical SQL instruction, or the like.

[0084] According to one 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 the dataset editor 193 by applying various processes 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 analysis, data visualization 196, or other types of useful information associated with the data.

[0085] According to one embodiment, a dataset is a self-service data model that users can build for their data visualization and analysis requirements. A dataset contains data source connection information, tables, columns, data enrichment and transformation. Users can use a dataset in multiple workbooks and data flows.

[0086] According to one embodiment, when a user creates and builds a dataset, the user can, for example, select from different types of connections or spreadsheets, create a dataset based on data from multiple tables in a database connection, an Oracle data source, or a local subject area, or create a dataset based on data from tables in different connections and subject areas.

[0087] For example, according to one 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, can specify joins between the tables, and can transform and enrich columns in the dataset.

[0088] According to one embodiment, additional deliverables, features and operations related to the dataset include, for example:

[0089] Viewing available connections: A dataset uses one or more connections to data sources to access and provide data for analysis and visualization. A user's list of connections includes connections that the user has built and connections that they have permission to access and use.

[0090] Creating a dataset from a connection: When a user creates a dataset, they can add tables, add joins, and enrich the data from one or more data source connections.

[0091] Adding multiple connections to a dataset: A dataset can contain multiple connections. Adding more connections allows the user to access and join all the tables and data required to build the dataset. User can add more connections to a dataset that supports multiple tables.

[0092] Creating Dataset Table Joins: Joins indicate relationships between tables in a dataset. If a user is creating a dataset based on facts and dimensions and joins already exist in the source tables, then joins are automatically created in the dataset. If a user is creating datasets from multiple connections and schemas, then the user can manually specify joins between tables.

[0093] According to one embodiment, a dataflow allows a user to create a dataset by combining, organizing, and integrating data. Dataflow allows a user to organize and integrate data to create a curated dataset that can be visualized by themselves or other users.

[0094] For example, according to one embodiment, a user may use a dataflow to create datasets, combine data from different sources, aggregate data, train machine learning models, and apply predictive machine learning models to data.

[0095] According to one embodiment, the dataset editor described above allows a user to add actions or steps, where each step performs a specific function, such as, for example, adding data, joining tables, merging columns, transforming data, or saving data. Each step is validated as the user adds or modifies the step. Once the dataflow is configured, it can be executed to generate or update a dataset.

[0096] According to one embodiment, users can curate data from a dataset, subject area, or database connection. Users can run dataflows individually or sequentially. Users can include multiple data sources in a dataflow and specify how they are joined. Users can save output data from a dataflow to a dataset or supported database type.

[0097] According to one embodiment, additional artifacts, functions and operations related to data flow include, for example:

[0098] Add Column: Add a custom column to the target dataset. Add Data: Add a data source to the dataflow. For example, if a user is joining two datasets, add both datasets to the dataflow.

[0099] Aggregation: Applying an aggregation function, for example count, sum, average, etc., to create totals for groups.

[0100] Branch: Creating multiple outputs from a dataflow. Filters: Users select only the data that interests them.

[0101] Join: Combining data from multiple data sources using database joins based on common columns.

[0102] Graph Analysis: Performing geospatial analysis such as calculating the distance or number of hops between two vertices. The above are provided as examples, and according to one embodiment, other types of steps can be added to the dataflow to transform data sets or provide data analysis and visualization.

[0103] Analyzing and visualizing the data set According to one embodiment, the system provides functionality that enables a user to generate datasets, analyses, or visualizations for display within a user interface, e.g., to explore datasets or data sourced from multiple data sources.

[0104] 7-18 show various examples of user interfaces for use in a data analysis environment, according to one embodiment.

[0105] The user interfaces and functionality illustrated in FIGS. 7-18 are provided as examples for purposes of illustrating the various features described herein, and alternative examples of user interfaces and functionality may be provided in accordance with various embodiments.

[0106] As shown in FIGS. 7-8, according to one embodiment, a user may access a data analysis environment, for example, to submit an analysis or query on an organization's data.

[0107] For example, according to one embodiment, a user can select from various types of connections to create a dataset based on data from tables such as database connections, Oracle subject areas, Oracle ADW connections, or from spreadsheets, files, or other types of data sources. In this manner, the dataset acts as a self-service data model from which users can build data analyses and visualizations.

[0108] As shown in Figures 9-10, according to one embodiment, the dataset editor can display a list of connections to which the user has access and enable the user to create or edit a dataset containing tables, joins, and / or enriched data. The editor can display the schemas and tables of the data source connections from which the user can drag and drop into the dataset diagram. If the particular connection itself does not provide a list of schemas and tables, the user can use a manual query for the appropriate tables. Once a connection is added, the relevant tables and data can be accessed and joined to build the dataset.

[0109] According to one embodiment, in the Dataset Editor, a join diagram displays the tables and joins in a dataset, as shown in Figures 11-12. Joins specified in a data source can be automatically created between tables in a dataset, for example, by creating joins based on column name matches found between tables.

[0110] According to one embodiment, when a user selects a table, a preview data area displays a sample of the table's data. Join links and icons are displayed to indicate which tables are joined and the type of join used. A user can create joins by dragging and dropping one table onto another, click on a join to view or update its configuration, or click on the type attribute of a column to change the type, for example, from measure to attribute.

[0111] According to one embodiment, the system can generate source-specific optimized queries for the visualization, where the dataset is treated as a data model and only the tables necessary to satisfy the visualization are used in the query.

[0112] By default, the granularity of a dataset is determined by the least granular table. Users can create measurements on any table in the dataset. However, this can result in duplicate measurements on one side of a one-to-many or many-to-many relationship. To address this, according to the embodiment shown in Figure 13, users can maintain granularity by setting a table on one side of the cardinality to maintain that level of detail.

[0113] As shown in FIG. 14, according to one embodiment, dataset tables can be associated with data access settings that determine whether the system loads the table into the cache or whether the table receives its data directly from the data source.

[0114] According to one embodiment, when auto-cache mode is selected for a table, the system loads or reloads the table data into the cache, which improves performance when the table's data is updated from a workbook, etc., and provides reload menu options at the table and dataset level.

[0115] According to one embodiment, when live mode is selected for a table, the system retrieves table data directly from the data source and the source system manages the data source queries for the table. This option is useful when data is stored in a high performance data warehouse such as Oracle ADW and ensures that the latest data is used.

[0116] According to one embodiment, if a dataset uses multiple tables, some tables may use automatic caching and other tables may contain live data, during a reload of multiple tables using the same connection, if reloading data for one table fails, the table currently set to use automatic caching is switched to retrieving data using live mode.

[0117] According to one embodiment, the system allows users to enrich and transform data before it is made available for analysis. When a workbook is created and a dataset is added to the workbook, the system performs column-level profiling on a representative sample of the data. After profiling the data, the user can implement the transformation and enrichment recommendations provided for recognizable columns in the dataset, such as, for example, GPS enrichment of city latitude and longitude, zip code, etc.

[0118] According to one embodiment, data transformation and enrichment changes applied to a dataset impact workbooks and data flows that use that dataset, for example, when a user opens a workbook that shares a dataset, they receive a message indicating that the workbook is using updated or refreshed data.

[0119] According to one embodiment, dataflows provide a means to organize and integrate data to generate curated data sets that users can visualize. For example, users can use dataflows to create data sets, combine data from various sources, aggregate data, train machine learning models, or apply predictive machine learning models to data.

[0120] 15, according to one embodiment, within a dataflow, each step performs a specific function, such as, for example, adding data, joining tables, merging columns, transforming data, or saving data. Once configured, a dataflow can be run to perform operations that generate or update a data set, such as using SQL operators such as BETWEEN, LIKE, IN, conditional expressions, and functions.

[0121] According to one embodiment, data flows can be used to merge data sets, cleanse data, and output results to new data sets. Data flows can be run individually or in sequence. If a data flow in a sequence fails, all changes made in the sequence are rolled back.

[0122] As shown in Figures 16-18, according to one embodiment, visualizations can be displayed within a user interface, for example to explore and add insight into a data set or data sourced from multiple data sources.

[0123] For example, according to one embodiment, a user can create a workbook, add a dataset, and drag and drop its columns onto a canvas to create a visualization. The system can automatically generate the visualization based on the contents of the canvas, and automatically select one or more visualization types for the user to choose from. For example, if a user adds an income measure to the canvas, the data element is placed in the value area of ​​the grammar panel and a tile visualization type is selected. The user can continue to add data elements directly to the canvas to build the visualization.

[0124] According to one embodiment, the system can provide automatically generated data visualizations (auto-generated insights, auto-insights) by suggesting visualizations that are expected to provide the best insights for a particular data set. A user can see an automatically generated summary of insights, such as by hovering over the relevant visualization in the workbook canvas.

[0125] automatic insights According to one embodiment, the systems and methods disclosed herein provide an easy-to-use way to build data visualizations (also referred to herein as "visualizations" or "vises") for a dataset connected to an analytics environment. For example, upon uploading or connecting a previously uploaded dataset to an analytics environment (such as Oracle Analytics Cloud, previously mentioned), the systems and methods can automatically provide a user with multiple data visualizations to select from, or select and drag into, an analytics pane of a user interface.

[0126] According to one embodiment, the systems and methods described herein can analyze a provided dataset (e.g., an uploaded dataset or a dataset already present in the analysis environment) to identify key columns that can be displayed in a data visualization. Upon such analysis, the systems and methods can further provide a number of selected data visualizations for selection by a user of the analysis environment, e.g., via a user interface.

[0127] According to one embodiment, such a system and method has the advantage of automatically providing a large number of highly descriptive and desirable data visualizations for selection and analysis that are useful to an end-user.

[0128] FIG. 19 is a diagram of the overall flow of the auto-insight feature, according to one embodiment. Figure 19 shows a dataset, such as a CSV or EXCEL file, or data previously uploaded to a database that is imported into the analytics cloud data store. Connection to such a dataset allows a number of data visualizations to be automatically displayed to the user, such as through a user interface.

[0129] More specifically, according to one embodiment, one or more datasets 1901 and 1902 can be connected to the data analysis environment 1903, as described above. These datasets can include any number of data formats, including but not limited to CSV, EXCEL, or other file types or formats. Such datasets can be newly uploaded (e.g., by a user or via automatic / scheduled upload) or can already exist within the data analysis environment via a linked database.

[0130] According to one embodiment, the data analysis environment can display multiple data visualizations via a user interface 1904, e.g., a graphical user interface, based on the analysis of the linked data. Such data visualizations can include, for example, selectable forms that can receive input indicating a desired data visualization to be selected for display and / or analysis.

[0131] FIG. 20 is a flow chart of a user experience of the auto-insight feature, according to one embodiment. According to one embodiment, from a user's perspective, a user creates a dataset or connects to an analytical environment such as an OAC 2010, and an artificial intelligence / machine learning process can introspect the dataset 2020 and, from such introspection, provide the user with a view (canvas) of optional data visualizations 2030. The user can then select which visualizations to populate the user interface 2040 workspace.

[0132] FIG. 21 is a flow chart of the auto-insight feature, according to one embodiment. According to one embodiment, the system and method may calculate various statistics related to the stored, linked, or uploaded 2110 dataset. The system and method may then utilize one or more scoring mechanisms or rules to identify a set of columns of data (or rows, depending on how the dataset is structured - although the remainder of the description will use the term "columns" for ease of reference) that are determined 2120 to score highest for data visualization insights (e.g., data columns that are not sparse, data columns that constitute relationships with other data columns, etc.). Additionally, this scoring stage may also generate and score calculations between two or more columns of data (e.g., cost / profit ratios, total sales to number of employees, etc.).

[0133] According to one embodiment, the system and method then generates and scores a number M of data visualizations based on the number N of columns identified in step 2120 as likely to be particularly important or useful among the data visualizations to select 2130 a set Y of M data visualizations likely to display meaningful data visualizations.

[0134] According to one embodiment, the system and method can then render the set of top scoring data visualizations for presentation via the user interface 2140.

[0135] FIG. 22 is a flow chart of the auto-insight feature, according to one embodiment. According to one embodiment, a dataset 2200 may be uploaded, linked, or accessed by an analytics environment. From this dataset, various statistics of the dataset may be calculated 2210. These may include, for example, basic statistics 2211, date and / or time statistics such as time index 2212, frequency statistics 2213, correlations between columns 2214 (e.g., calculating profit margins by dividing profit by revenue), and profiler statistics 2215. These dataset statistics are then utilized in column scoring 2220 (e.g., may be referred to as scores calculated for column scoring, multiple column scores, or column scores).

[0136] According to one embodiment, the score generated for each of the multiple columns may be based on utilizing a set of configurable rules that operate on each set of statistics for each of the multiple columns of the dataset.

[0137] From these statistics, according to one embodiment, the system and method can proceed to score the columns via a column rule scoring engine.

[0138] According to one embodiment, the column rule scoring engine 2223 can calculate distribution statistics for columns of the dataset 2200 given the dataset and its associated statistics. Using the distribution statistics, the system and method can identify which columns are representative of the dataset, e.g., dense. The system and method does this for a set of columns, dimensions, and metrics. Similarly, the system and method can select dates to analyze trends in the data of the dataset, taking into account time fields. This time field can be determined to determine which time fields are most useful for data visualization. The system and method generates from this step a set of columns / sets that are generally smaller than the original number of columns in the dataset. In addition to extracting columns, the system and method can also build ratios between columns - for example, extracting cost and sales from the dataset, then automatically calculating the ratio of cost to sales and displaying it in a visualization, such as a ratio, index, normalized data, etc., if the calculated column scores well.

[0139] According to one embodiment, below is a chart illustrating exemplary column scoring rules, for example when columns have medium cardinality.

[0140] [Table 1] TIFF2024533389000003.tif89162

[0141] According to one embodiment, various rules may be utilized to score columns of data, as shown above in Table 1. These rules include, but are not limited to, column selection, user interest flag, NULL percentage, cardinality, NULL penalty, integer, keyword, text length, etc.

[0142] According to one embodiment, the system and method may score a data string according to, for example, the following scale:

[0143] · Interested User - When you make a change to a column, the column is marked as being of interest to the user Null percentage - Dense columns will have increased scores Cardinality / Boolean - Columns with appropriate cardinality will have increased scores Decimal - Decimal value score is decreased Null penalty - A high percentage of NULLs reduces the score Integer - If the value is an integer, the score is decreased Keywords - columns containing certain keywords will get a lower score Text Length - Columns with a good average text length will have increased scores Frequency - if the frequency is low, the score is decreased (item member) ·index - Uniformity - If uniformity is high, the score increases - Skewness - if it has a normal bell shape, the score increases - Kurtosis - for skewed indicators, the score increases - Keywords - Time / ID percent, average, ranking - Indicator density - if the indicator density is high, the score increases - Metric ID - columns like ID will get lower scores - IQR - Mean and ratio are lower scores - Attribute conflict - Profiles that identified columns as attributes - Cardinality tiebreaker - tiebreaker for low cardinality strings - Negative penalty - negative columns will result in lower scores - Latitude and longitude score According to one embodiment, the above rules in Table 1 may be applied to columns in a dataset with medium cardinality, however, other rule sets may be provided for every column of data in the dataset such that each column of data in the dataset is scored. Scoring data columns may also include MS Density 2221, Distribution IQR 2222, and Clean Timespan Score 2224.

[0144] According to one embodiment, the system and method can then score the columns of data in the dataset and select the set of N columns of data (indicators and measurements) that are most meaningful to use in visualization scoring 2230 (e.g., which can be referred to as scores calculated for visualization scoring, multiple visualization scores, or visualization scores).

[0145] According to one embodiment, given the scored and selected N columns of data from the dataset, the system and method can generate M data visualizations 2231 based on the N columns of data based on a set of rules. These rules can include rules for determining which type of data visualization to utilize for different types of data columns. For example, the rule set can include rules for selecting which type of data visualization to utilize depending on various statistics calculated for the columns of data (e.g., selecting a bar chart visualization for some N columns of data and selecting a scatter plot for some other N columns of data).

[0146] According to one embodiment, it is noted that although N data strings are described, the N data strings are not necessarily correlated with the data strings directly from the dataset 2200, such as when ratios between the data strings are calculated.

[0147] According to one embodiment, in generating these M data visualizations based on the determined N data columns, a structured language query (LSQL) 2223 may be utilized to pull the requested data from the dataset. Additionally, when generating these M data visualizations, the system and method may perform additional operations on the columns of data to generate potentially more valuable data visualizations. These operations may include calculating ratios between the data columns, normalizing the data, indexing the data, etc.

[0148] According to one embodiment, once the M data visualizations are generated, a data visualization scoring engine 2232 may be utilized to score each of the M generated data visualizations. For each type of data visualization (e.g., bar graph, scatter plot, pie chart, line graph, etc.), some or all of the rules may be utilized to generate a score for each of the M data visualizations. For example, scoring may be based on the variability within each generated data visualization. Outliers, data visualizations that exhibit clear trends (e.g., the slope of a line graph), and data visualizations that generally exhibit greater visual contrast (e.g., data visualizations that are deemed more informative to users of the system, such as by data displays that display higher visual contrast between plotted data) may be scored higher than those that do not.

[0149] According to one embodiment, for scatter plots, for example, the system and method may utilize an analysis of variance (the average distance from each point to a trend line that is fitted to the data points on the scatter plot). Scatter plots of columns of a data set with higher analysis of variance scores (meaning there are more points that are further away from the fitted trend line) will score higher than scatter plots of other columns of the data set with lower analysis of variance scores (meaning plots with tightly grouped data points).

[0150] According to one embodiment, when scoring the M visualizations, certain factors may be considered to determine how the M visualizations are scored by the scoring engine 2232. These include, for example, high contrast, obvious trend lines. Data visualizations that show contrast in data plots or data points are generally better / more useful / interesting to users than data visualizations with low contrast (e.g., data visualizations that show flat lines). For example, a data visualization that relies on a high density of time levels may have multiple trend graphs associated with it plotted. The system and method may then display graphs with obvious trend lines when looking for trend data in the data visualization.

[0151] According to one embodiment, once the system and method obtains M visualizations, from the M visualizations created, the top Y (highest scoring) visualizations 2234 are selected and displayed via user interface rendering 2240, which displays a finite number of visualizations in a user interface selectable manner 2241.

[0152] According to one embodiment, while the top Y visualizations are displayed via the user interface, the system and method may continue to track the other high scores of the M visualizations such that if any or all of the top Y visualizations are rejected or discarded via the user interface, the system and method may continue to display the next highest score of the M visualizations via the user interface.

[0153] According to one embodiment, the above description utilizes some integers, such as the N top data columns from a dataset utilized in generating the M top visualizations in a topic for which Y visualizations receive the highest scores. These integer values ​​can be set automatically by the systems and methods of a data analysis environment or by a user of such an environment. Examples of these values ​​are N=10 columns of (indicators and measures), 250 number of visualizations in a topic, and the top 10 visualizations of these 250 visualizations.

[0154] FIG. 23 is a flowchart of a method for generating automated insights of analytical data, according to one embodiment.

[0155] According to one embodiment, in step 2310, the method may provide a computer including one or more processors to provide access to the data warehouse by the analytical application environment for storage of data by tenants.

[0156] According to one embodiment, in step 2320, the method may receive, in an analytical application environment, a data set including multiple columns.

[0157] According to one embodiment, in step 2330, the method may calculate a set of statistics for each of a number of columns of the data set.

[0158] According to one embodiment, in step 2340, the method may generate a score for each of the plurality of columns based on a respective set of statistics for each of the plurality of columns.

[0159] According to one embodiment, in step 2350, the method may select a set of multiple columns, where the selection is based on the score of each column.

[0160] According to one embodiment, in step 2360, the method may generate multiple data visualizations for a selected set of multiple columns.

[0161] According to one embodiment, in step 2370, the method may select a set of multiple data visualizations based on a set of rules for display via a user interface.

[0162] According to one embodiment, the score generated for each of the multiple columns may utilize a set of configurable rules that operate on each set of statistics for each of the multiple columns of the dataset.

[0163] According to one embodiment, a set of rules utilized to select multiple data visualizations for display via a user interface may include a rule that scores data visualizations with high visual contrast higher than data visualizations with low visual contrast.

[0164] According to various embodiments, the teachings herein may be conveniently implemented using one or more conventional general-purpose or special-purpose computers, computing devices, machines, or microprocessors, including one or more processors, memory, and / or computer-readable storage media programmed according to the teachings of the present disclosure. As will be apparent to those skilled in the software art, appropriate software coding may be readily produced by skilled programmers based on the teachings of the present disclosure.

[0165] In some embodiments, the teachings herein may include a computer program product that is a non-transitory computer-readable storage medium(s) having stored thereon instructions that can be used to program a computer to perform any of the processes of the present teachings. Examples of such storage media include, but are not limited to, hard disk drives, hard disks, hard drives, fixed disks or other electromechanical data storage devices, floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems, or other types of storage media or devices suitable for non-transitory storage of instructions and / or data.

[0166] The foregoing description has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the scope of protection to the precise form disclosed. Many modifications and variations will be apparent to those skilled in the art.

[0167] For example, although some of the examples provided herein illustrate operation of an analytics application environment with an enterprise software application or data environment (e.g., such as an Oracle Fusion Applications environment) or within the context of a Software as a Service (SaaS) or cloud environment (such as an Oracle Analytics Cloud or Oracle Cloud Infrastructure environment), according to various embodiments, the systems and methods described herein may be used with other types of enterprise software application or data environments, cloud environments, cloud services, cloud computing, or other computing environments.

[0168] The embodiments have been chosen and described in order to best explain the principles of the present teachings and their practical application, so that others skilled in the art can appreciate various embodiments with various modifications suited to the particular uses contemplated. It is intended that the scope be defined by the following claims and their equivalents.

Claims

**Claim 1** A system for generating automatic insights from analysis data, comprising a computer including one or more processors that provide access to a data warehouse by an analysis application environment for storing data by a tenant, wherein the computer receives a data set including a plurality of columns in the analysis application environment, a set of statistics is calculated for each of the plurality of columns of the data set, a score is generated for each column of the plurality of columns based on each set of statistics for each column of the plurality of columns, a set of the plurality of columns is selected, the selection being made based on the score of each column, a plurality of data visualizations are generated for the selected set of the plurality of columns, and the set of the plurality of data visualizations is selected based on a set of rules for display via a user interface. **Claim 2** The system according to claim 1, wherein the score generated for each column of the plurality of columns utilizes a configurable set of rules that manipulate each set of statistics for each of the plurality of columns of the data set. **Claim 3** The system according to claim 1, wherein the set of rules utilized to select the plurality of data visualizations for display via the user interface includes a rule that scores data visualizations with high visual contrast higher than data visualizations with low visual contrast. **Claim 4** The system according to claim 1, wherein each of the selected set of the plurality of columns includes one of a measurement and an indicator. **Claim 5** The system according to claim 1, wherein the cardinality of each of the plurality of columns is utilized when generating the score for each of the plurality of columns. **Claim 6** The system according to claim 5, wherein a NULL percentage is further utilized when generating the score for each of the plurality of columns. **Claim 7** The system according to claim 1, wherein the generation of the plurality of data visualizations includes a plurality of data visualization types. **Claim 8** The system according to claim 7, wherein at least one of the generated plurality of data visualizations includes a data visualization type selected based on a set of statistics calculated for a column represented by at least one of the generated plurality of data visualizations. **Claim 9** A method for generating automatic insights from analysis data, Providing a computer including one or more processors to provide access to a data warehouse by an analysis application environment for storing data by a tenant; Receiving, in the analysis application environment, a data set including a plurality of columns; Calculating a series of statistics for each of the plurality of columns of the data set; Generating a score for each column of the plurality of columns based on each set of statistics for each of the plurality of columns; Selecting a set of the plurality of columns, the selection being made based on the scores of the respective columns; The method further includes Generating a plurality of data visualizations for the selected set of the plurality of columns; Selecting a set of the plurality of data visualizations based on a series of rules for display via a user interface.

10. The method according to claim 9, wherein the score generated for each column of the plurality of columns utilizes a set of configurable rules that manipulate each set of statistics for each of the plurality of columns of the data set.

11. The method according to claim 9, wherein the set of rules utilized to select the plurality of data visualizations for display via the user interface includes rules that score data visualizations with high visual contrast higher than data visualizations with low visual contrast.

12. The method according to claim 9, wherein each of the selected sets of the plurality of columns includes one of a measurement value and an indicator.

13. The method according to claim 9, wherein the cardinality of each of the plurality of columns is utilized when generating the score for each of the plurality of columns.

14. The method according to claim 13, wherein a NULL percentage is further utilized when generating the score for each of the plurality of columns.

15. The method according to claim 9, wherein the generation of the plurality of data visualizations includes a plurality of data visualization types.

16. The method according to claim 15, wherein at least one of the generated plurality of data visualizations includes a data visualization type selected based on a set of statistics calculated for a column represented by at least one of the generated plurality of data visualizations.

17. A program for causing a computer to execute the method according to any one of claims 9 to 16.

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