Abstract: In accordance with an embodiment, an analytic applications environment enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud environment; and supports the development of computer-executable software analytic applications. A data pipeline or process, such as, for example, an extract, transform, load process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema.

Abstract: In accordance with an embodiment, an analytic applications environment enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud environment; and supports the development of computer-executable software analytic applications. A data pipeline or process, such as, for example, an extract, transform, load process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema.

Abstract: Described herein are techniques for identifying highly relevant content for a user to view in the form of KPI cards and providing the relevant view to the user automatically or by suggestion. The KPIs of highest practical and statistical significance are provided when the user accesses the user interface. In some embodiments, when the user is viewing a KPI, other relevant KPIs may be provided for the user to view as suggestions. Further, in some embodiments, the user may be provided with the KPIs of significance based on anomaly detection, and the explanation for the anomaly as well as suggestions for remedying any issues may be provided to the user. The highly informational content can be surfaced through the use of the advanced machine learning algorithms described herein.

Abstract: In accordance with an embodiment, described herein is a system and method for extending a semantic model, for use with an analytic applications, analytics cloud, or other type of business intelligence or data analytics environment. A semantic model extension process introspects a customer's data, for example as stored in a data warehouse instance, and evaluates metadata associated therewith to determine custom facts, custom dimensions, and/or other types of data source model extensions. A payload or indication of such extensions is used to extend or customize a semantic model that enables surfacing of business intelligence or data analytics at a presentation layer. In accordance with an embodiment, the system can include an administrative console application and user interface that allows a user to view and validate a customer's data as loaded from their source environment into a data warehouse instance for use with other types of data analytics environments.

Abstract: A method for automatically determining one or more cross customer metrics from a cross customer store for cross customer analytics is provided. The method includes the steps of: (i) automatically standardizing dimensions associated with the one or more customers to create one or more cross customer metrics; (ii) automatically determining incremental data from one or more customer specific stores by tracking (a) a time stamp of a last pull time (T) for customer data stored in the one or more customer specific stores, and (b) a number of items involved (I); (iii) automatically updating the one or more cross customer metrics in the cross customer store with the incremental data; and (iv) automatically comparing the one or more cross customer metrics of a customer with one or more peer groups created at run time based on customer selected values for one or more parameters.

Abstract: In accordance with an embodiment, an analytic applications environment enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud environment; and supports the development of computer-executable software analytic applications. A data pipeline or process, such as, for example, an extract, transform, load process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema.

Abstract: Described herein are techniques for identifying highly relevant content for a user to view in the form of KPI cards and providing the relevant view to the user automatically or by suggestion. The KPIs of highest practical and statistical significance are provided when the user accesses the user interface. In some embodiments, when the user is viewing a KPI, other relevant KPIs may be provided for the user to view as suggestions. Further, in some embodiments, the user may be provided with the KPIs of significance based on anomaly detection, and the explanation for the anomaly as well as suggestions for remedying any issues may be provided to the user. The highly informational content can be surfaced through the use of the advanced machine learning algorithms described herein.

Abstract: A method for automatically determining one or more cross customer metrics from a cross customer store for cross customer analytics is provided. The method includes the steps of: (i) automatically standardizing dimensions associated with the one or more customers to create one or more cross customer metrics; (ii) automatically determining incremental data from one or more customer specific stores by tracking (a) a time stamp of a last pull time (T) for customer data stored in the one or more customer specific stores, and (b) a number of items involved (I); (iii) automatically updating the one or more cross customer metrics in the cross customer store with the incremental data; and (iv) automatically comparing the one or more cross customer metrics of a customer with one or more peer groups created at run time based on customer selected values for one or more parameters.

Abstract: Approaches for preserving customizations made to a data management system. Metadata that identifies a mapping between data fields of one or more data source schemas to columns of one or more data tier schemas is maintained. Each of the one or more data tier schemas stores data retrieved from the one or more data source schemas. Values from the metadata are read to dynamically generate software processes. The software processes may update one or more existing ETL processes to reflect an updated state of the one or more data tier schemas and may generate any new ETL processes required to retrieve data stored in the one or more data source schemas and load the retrieved data into the one or more data tier schemas. Customizations made to portions the data management system are preserved in an automated fashion when new versions of system components are deployed.

Abstract: An automated system for defining a star schema for a data source. The system based on automatically gathered information from the data source such as entities and columns, entity column types and lengths, entity keys, relationships between and within entities, measures, workflow and correlated attributes, specialized entities, an update frequency of entities and columns, and grouping of entity and column updates associated with the source database automatically determines facts, dimensions, dimension hierarchies, measures, workflow specific measures (if data source has workflows) and workflow correlated attribute specific measures (if data source has temporal, priority, ownership and progress tracking attributes) to come up with a star schema for the data source.

Abstract: An automated system for validating and refining a template warehouse star schema for a customized data source.

Abstract: An automated system for validating and refining a template warehouse star schema for a customized data source.

Abstract: An automated system for defining a star schema for a data source. The system based on automatically gathered information from the data source such as entities and columns, entity column types and lengths, entity keys, relationships between and within entities, measures, workflow and correlated attributes, specialized entities, an update frequency of entities and columns, and grouping of entity and column updates associated with the source database automatically determines facts, dimensions, dimension hierarchies, measures, workflow specific measures (if data source has workflows) and workflow correlated attribute specific measures (if data source has temporal, priority, ownership and progress tracking attributes) to come up with a star schema for the data source.