Abstract: A method of identifying causal relationships between time series may include accessing a hierarchy of nodes in a data structure, where each node in the plurality of nodes may include a time series of data. The method may also include identifying a subset of nodes in the plurality of nodes for which causal relationships may exist in the corresponding time series. The method may additionally include generating a model for each of the subset of nodes, where the model may receive the subset of nodes and generate coefficients indicating how strongly each of the subset of nodes causally affects other nodes in the subset of nodes. The method may further include generating a ranked output of nodes that causally affect a first node in the subset of nodes based on an output of the corresponding model.

Abstract: A business intelligence (BI) server maintains a plurality of metadata objects to support the extract, transform and load (ETL) processes. These metadata objects includes a transparent view object, which takes a joined set of source tables and represents a data shape of the joined set of source tables using a transformation, and a ETL mapping association object that maps the transformation contained in the transparent view object to a target table. The BI server can then orchestrate the movement of data from source systems into the target data warehouses in a source and target system agnostic way.

Abstract: In accordance with an embodiment, described herein is a system and method for providing support for extensibility and customization in an analytic applications environment. An extract, transform, load (ETL) or other data pipeline or process provided by the analytic applications environment, can operate in accordance with an analytic applications schema and/or a customer schema associated with a customer (tenant), to receive data from the customer's enterprise software application or data environment, for loading into a data warehouse instance. A semantic layer enables the use of custom semantic extensions to extend a semantic model, and provide custom content at a presentation layer. Extension wizards or development environments can guide users in using the custom semantic extensions to extend or customize the semantic model, through a definition of branches and steps, followed by promotion of the extended or customized semantic model to a production environment.

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: Systems and method for SaaS/PaaS resource usage and allocation in an analytic applications environment. An exemplary method can provide an analytic applications environment, a control plane comprising a server, the control plane further comprises a provisioning component and a console interface, a data warehouse, and a monitoring agent. The method can provision an instance of the data warehouse in the context of a tenant, the provisioned instance of the data warehouse having an initial size. Upon the provisioning the instance of the data warehouse, the method can add adding an entry to a metrics repository of the monitoring agent, the added entry indicative of the initial size of the provisioned instance of the data warehouse, the added entry being tagged, the tag being indicative of the tenant. The method can monitor, by the monitoring agent, an amount of data stored at the provisioned instance of the data warehouse.

Abstract: A computer-implemented method of executing a user query includes presenting a user interface to allow a user to enter a query, receiving a user-entered textual request through the interface, launching a search service to rewrite the textual request into a search query, sending the search query to a presentation server, receiving an answer to the query, and returning the answer to the user as a graphical representation. A computer-implemented method includes receiving a crawl request from a user, launching a crawl manager to monitor the crawl request and track statistics related to the crawl, starting a crawl task based upon the crawl request, indexing a business intelligence presentation server to create a data index, and storing the data index.

Abstract: Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

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: Described herein are systems and techniques for identifying at-risk opportunities and generating a recommendation that can be used by the representatives to help salvage the opportunities. Historical information as well as machine learning algorithms are used to identify the failing opportunities by classifying new and currently in-pursuit opportunities using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk. Distances between opportunities are estimated based on local neighborhoods determined by relevant variables influencing those opportunities in the local neighborhood. The shortest distance between at risk opportunities and winning opportunities can be identified and utilized to generate the recommendation based on the relevant variables for the shortest path. In some embodiments, an ordered list of actions or changes to actions needed for a successful disposition of the opportunity may be generated and provided to the representative.

Abstract: Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

Abstract: An administration tool can be used to provide data flexibility in a business intelligence (BI) server that is associated with a data warehouse. The administration tool can display one or more data objects that are adapted to be imported from an application framework into a physical model maintained on the BI server. The administration tool can further map the one or more data objects in the physical model into a logical model, also maintained on the BI server, which corresponds to at least one target table in the data warehouse. Additionally, the administration tool can publish an extension input to an extender associated with the data warehouse based on the logical model, wherein the extender operates to generate one or more metadata extensions based on the extension input.

Abstract: Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

Abstract: Gaps in proficiencies may be identified within an enterprise. Understanding gaps in the existing workforce may help inform training, hiring, and firing decisions to ensure successful completion of the upcoming projects and deadlines. Using a multi-level model for each proficiency that accounts for enterprise needs as well as hiring, retraining, and the like, a relationship between proficiencies, projects, and employees over time may be generated as a multi-dimensional temporal model. The temporal model may be simulated to forecast gaps in proficiencies of the employed workforce. Recommendations regarding retraining, hiring, and termination can be made to help users remedy the deficiencies. Additionally, the proficiencies most valuable to the enterprise may be determined using a catalog of proficiencies to cluster the proficiencies into proficiency clusters for each job or job category and the proficiencies scored.

Abstract: Predictive analysis techniques are described herein as applied to business variables. In some embodiments, a dynamic dependency model may be generated using a time-series data from a first time period. The model may define relationships between business variables during the first time period. A prediction of values of a variable (e.g., a business variable such as sales, revenue, attrition, or the like) can be generated based on the dynamic dependency model. The prediction of values may be for a second time period after the first time period. The actual values of the variable over the second time period can be obtained and compared to the predicted values to generate a statistical deviation. The statistical deviation may exceed a threshold and, a notification of the statistical deviation may be transmitted to a user device. The notification may alert the user that the variable is likely to miss the targeted/predicted value.

Abstract: Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

Abstract: An input extension specification can be generated, via an administration tool on a business intelligence (BI) server, for an extender associated with a data warehouse in order to extend the data warehouse. The administration tool can request for data flow information of the data warehouse from the extender. The data flow information indicates the mapping relationship between one or more source data objects and one or more target tables in the data warehouse. The administration tool further allows a user to select said one or more target tables in the data warehouse to be extended based on the data flow information, and builds the input extension specification for the extender.

Abstract: A computer-implemented method of executing a user query includes presenting a user interface to allow a user to enter a query, receiving a user-entered textual request through the interface, launching a search service to rewrite the textual request into a search query, sending the search query to a presentation server, receiving an answer to the query, and returning the answer to the user as a graphical representation. A computer-implemented method includes receiving a crawl request from a user, launching a crawl manager to monitor the crawl request and track statistics related to the crawl, starting a crawl task based upon the crawl request, indexing a business intelligence presentation server to create a data index, and storing the data index.

Abstract: A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.

Abstract: An extender associated with a data warehouse can support metadata extension for the data warehouse. The extender can create an ETL extension plan for extending the data warehouse. The ETL extension plan includes one or more ETL plan elements that indicate extensions on ETL metadata objects. The one or more ETL plan elements within the ETL extension plan can be ordered, and ETL metadata extension can be performed based on the ETL extension plan.

Abstract: An input extension specification can be generated, via an administration tool on a business intelligence (BI) server, for an extender associated with a data warehouse in order to extend the data warehouse. The administration tool can request for data flow information of the data warehouse from the extender. The data flow information indicates the mapping relationship between one or more source data objects and one or more target tables in the data warehouse. The administration tool further allows a user to select said one or more target tables in the data warehouse to be extended based on the data flow information, and builds the input extension specification for the extender.