Abstract: In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with an embodiment, described herein is a system and method for supporting queries having sub-select constructs in a multidimensional database computing environment. The system enables a sub-select construct to be provided as part of an input query, for example using a Multidimensional Expressions (MDX), or other type of query. The inner sub-select, specified by the input query, is not executed by the system before the main query, but is used to restrict the data space for execution of the main select. The approach to processing the sub-select enables support for security-sensitive or other types of aggregation use cases.

Abstract: In accordance with an embodiment, described herein is a system and method for use of a dynamic flow process (referred to herein in some examples as Query Processing Dynamic Flow (QPDF)) in a multidimensional database computing environment. The dynamic flow process enables hybrid use of, for example, aggregate storage option, block storage option, or other types of storage containers, and provides a common flow to process a received input query in bottom-up mode. The approach can be used to reduce the size of the cube, which provides for efficient calculation of dynamic members.

Abstract: In accordance with an embodiment, described herein is a system and method for supporting queries having sub-select constructs in a multidimensional database computing environment. The system enables a sub-select construct to be provided as part of an input query, for example using a Multidimensional Expressions (MDX), or other type of query. The inner sub-select, specified by the input query, is not executed by the system before the main query, but is used to restrict the data space for execution of the main select. The approach to processing the sub-select enables support for security-sensitive or other types of aggregation use cases.

Abstract: In accordance with an embodiment, described herein is a system and method for use of a dynamic flow process (referred to herein in some examples as Query Processing Dynamic Flow (QPDF)) in a multidimensional database computing environment. The dynamic flow process enables hybrid use of, for example, aggregate storage option, block storage option, or other types of storage containers, and provides a common flow to process a received input query in bottom-up mode. The approach can be used to reduce the size of the cube, which provides for efficient calculation of dynamic members.

Abstract: In accordance with an embodiment, described herein are systems and methods for real time data aggregation in a virtual cube in a multidimensional database, in accordance with an embodiment. A method can provide, at a computer that includes one or more microprocessors, a multidimensional database server executing on the computer, wherein the multidimensional database server supports at least one multidimensional cube, a data source associated with the multidimensional cube, and at least one source of real-time data accessible by the data source. The method can query, by the multidimensional cube, the data source, the query comprising an identification of the at least one source of real-time data. The method can provide, by the data source, on-demand access to the multidimensional cube to the at least one source of real-time data. The method can use targeted data from the at least one source of real-time data within the multidimensional cube without being stored therein.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more segments within a data set, associated with a target attribute value, based on, for example, the use of a classification and regression tree and a combination of different driving factors, or same driving factors with different values. Information describing segments associated with the data set can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with an embodiment, described herein is a system and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment. A cube schema inference component can successively perform column-wise splits to calculate information gain ratios between each pair of a plurality of columns in the tabular data. A cross correlation matrix can be constructed from the information gain ratios between each pair of columns. The system can determine relationships among the plurality of columns based on the cross correlation matrix; create a hierarchy directed graph to represent the relationships; and further transform the hierarchy directed graph into a cube schema that can be used to create a cube for loading the tabular data, or to map the tabular data into an existing cube in the multidimensional database environment.

Abstract: In accordance with an embodiment, the system supports bottom-up aggregation in a multidimensional database computing environment. A dynamic flow, coupled with a data retrieval layer or data fetching component, which in some environments can incorporate a kernel-based data structure, referred to herein as an odometer retriever, or odometer, that manages pointers to data blocks, contains control information, or otherwise operates as an array of arrays of pointers to stored members, enables bottom-up aggregation of cube data which, for example with pure aggregating queries, provides considerable run time improvement.

Abstract: In accordance with an embodiment, described herein is a system and method for supporting queries having sub-select constructs in a multidimensional database computing environment. The system enables a sub-select construct to be provided as part of an input query, for example using a Multidimensional Expressions (MDX), or other type of query. The inner sub-select, specified by the input query, is not executed by the system before the main query, but is used to restrict the data space for execution of the main select. The approach to processing the sub-select enables support for security-sensitive or other types of aggregation use cases.

Abstract: In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

Abstract: In accordance with an embodiment, described herein is a system and method for providing sandboxing support in a multidimensional database environment. A plurality of sandboxes and a baseline data can be provided in a multidimensional database server, with each sandbox created for a particular “what-if” analysis, and configured to store one or more changes to the baseline data. When a request is received for a report on a particular “what-if” analysis from a client/user, the multidimensional database server can split the request into a first query and the second query, with the first query directed to a sandbox associated with the client/user, and the second query directed to the baseline data. The multidimensional database server can merge results from the first query and the second query, and perform aggregations and calculations on the merged data, before sending the merged data to a client.

Abstract: In accordance with an embodiment, the system supports automatic slice merge in a multidimensional database computing environment. In a multidimensional database that uses an aggregate storage option container for data storage, the system can create a plurality of slices to support data load transactions, or modifications to the data in response to requests from clients. When the system receives a data update request, for example to update a record within the cube, the system can allow the data to be updated, by writing the updated data to another slice. Subsequently, the system can determine to merge two or more of the slices having modified data, to reduce the overall size of the stored data footprint, and to improve system performance.

Abstract: In accordance with an embodiment, described herein is a system and method for use of a dynamic flow process (referred to herein in some examples as Query Processing Dynamic Flow (QPDF)) in a multidimensional database computing environment. The dynamic flow process enables hybrid use of, for example, aggregate storage option, block storage option, or other types of storage containers, and provides a common flow to process a received input query in bottom-up mode. The approach can be used to reduce the size of the cube, which provides for efficient calculation of dynamic members.

Abstract: A method of effectively representing and processing data sets with time series is disclosed. The method may comprise representing time series as a virtual part of data in a data store layer of a user system, thereby allowing processing of time-series related queries in said data store layer of said user system.

Abstract: The current invention is a method and a system for analyzing templates of queries, with different levels of precision. When the system of the current invention takes a decision to arrange data, these templates are used, and a single data arrangement object (HRBlob) is designed which may serve a single template or several templates and not just a single query. This approach has an important impact on data arrangement performance and on queries performance, since this approach may help many more queries to be handled using less data arrangement objects.

Abstract: The current invention is a method and a system for analyzing templates of queries, with different levels of precision. When the system of the current invention takes a decision to arrange data, these templates are used, and a single data arrangement object (HRBlob) is designed which may serve a single template or several templates and not just a single query. This approach has an important impact on data arrangement performance and on queries performance, since this approach may help many more queries to be handled using less data arrangement objects.