Abstract: Large-scale advanced database models, systems, and methods provide a responsive, scalable data storage solution which is ripe for use in data warehousing and analytics environments. These advanced database models, systems, and methods provide for dramatically increased performance in accessing data as well as increased storage capabilities in the data set.

Abstract: Large-scale advanced database models, systems, and methods provide a responsive, scalable data storage solution which is ripe for use in data warehousing and analytics environments. These advanced database models, systems, and methods provide for dramatically increased performance in accessing data as well as increased storage capabilities in the data set.

Abstract: Large-scale advanced database models, systems, and methods provide a responsive, scalable data storage solution which is ripe for use in data warehousing and analytics environments. These advanced database models, systems, and methods provide for dramatically increased performance in accessing data as well as increased storage capabilities in the data set.

Abstract: A computing system includes memory storing executable instructions and one or more processors operatively connected to the memory. The one or more processors execute the executable instructions to effectuate a method. The method may include (i) analyzing raw data obtained from a plurality of different data sources in order to identify one or more data structures of the raw data and to tag data identifying at least one of the plurality of different data sources; and (ii) generating a plurality of Universal Data Model (UDM) constructs. Each UDM construct may be based at least in part on the identified data structure(s) of the raw data. Each UDM construct may exclude the tagged data identifying at least one of the plurality of different data sources. Each UDM construct may organize the raw data into a particular arrangement of rows and columns.

Abstract: Large-scale advanced database models, systems, and methods provide a responsive, scalable data storage solution which is ripe for use in data warehousing and analytics environments. These advanced database models, systems, and methods provide for dramatically increased performance in accessing data as well as increased storage capabilities in the data set.

Abstract: A computing system includes memory comprising executable instructions and one or more processors operatively connected to the memory and configured to execute the executable instructions in order to effectuate a method. The method may include (i) analyzing raw data obtained from a plurality of different data sources in order to identify one or more data structures of the raw data and to tag data identifying at least one of the plurality of different data sources; and (ii) generating a plurality of Universal Data Model (UDM) constructs. Each UDM construct may be based at least in part on the identified one or more data structures of the raw data and each UDM construct may exclude the tagged data identifying at least one of the plurality of different data sources. Each UDM construct may organize the raw data into a particular arrangement of at least one row and at least one column.

Abstract: Large-scale advanced database models, systems, and methods provide a responsive, scalable data storage solution which is ripe for use in data warehousing and analytics environments. These advanced database models, systems, and methods provide for dramatically increased performance in accessing data as well as increased storage capabilities in the data set.

Abstract: A non-transitory computer-readable medium includes executable instructions that when executed by a processor cause the processor to effectuate a method. The method may include obtaining raw data from a plurality of different data sources; analyzing the raw data to identify one or more data structures of the raw data and to tag data identifying at least one of the plurality of different data sources; generating a plurality of Universal Data Model (UDM) constructs, each UDM construct being based at least in part on the one or more data structures of the raw data and each UDM construct excluding the source-identifying data.

Abstract: A non-transitory computer-readable medium is disclosed comprising executable instructions that when executed by a processor cause the processor to effectuate a method comprising the steps of: obtaining raw data from a plurality of different data sources; analyzing the raw data to identify one or more data structures of the raw data and to tag data identifying at least one of the plurality of different data sources; generating a plurality of Universal Data Model (UDM) constructs, each UDM being based at least in part on the one or more data structures of the raw data and each UDM excluding the source-identifying data; redacting the source-identifying data from the raw data to generate anonymous raw data; and generating anonymous UDM transformed data by integrating the anonymous raw data by extracting, transforming, and loading the anonymous raw data such that the anonymous raw data conforms to the UDM construct.

Abstract: Business intelligence, decision support and knowledge management network systems and methods provide efficient, robust, and business-friendly services for the rapid analysis of massive amounts of business, electronic, and other disparate data into actionable intelligence. An advantageous element of the multi-party knowledge network allows the aggregation of common data formats in order to analyze a combined dataset consisting of information from multiple parties, thereby providing additional business intelligence than with a single set of data alone.

Abstract: Business intelligence, decision support and knowledge management network systems and methods provide efficient, robust, and business-friendly services for the rapid analysis of massive amounts of business, electronic, and other disparate data into actionable intelligence. An advantageous element of the multi-party knowledge network allows the aggregation of common data formats in order to analyze a combined dataset consisting of information from multiple parties, thereby providing additional business intelligence than with a single set of data alone.

Abstract: Certain embodiments of this disclosure include methods and devices for adjusting a graphical user interface. According to one embodiment, a method is provided. The method may include obtaining external stimulus data including data associated with at least one user interface element of a plurality of user interface elements making up a graphical user interface (GUI); analyzing the external stimulus data to detect a presence of a GUI presentation adjustment condition; in response to detecting the presence of the GUI presentation adjustment condition, generating GUI presentation adjustment condition data including data describing the GUI presentation adjustment condition; and adjusting presentation of at least one of the plurality of user interface elements based on the GUI presentation adjustment condition data.

Abstract: Business intelligence, decision support and knowledge management network systems and methods provide efficient, robust, and business-friendly services for the rapid analysis of massive amounts of business, electronic, and other disparate data into actionable intelligence. An advantageous element of the multi-party knowledge network allows the aggregation of common data formats in order to analyze a combined dataset consisting of information from multiple parties, thereby providing additional business intelligence than with a single set of data alone.

Abstract: Business intelligence, decision support and knowledge management network systems and methods provide efficient, robust, and business-friendly services for the rapid analysis of massive amounts of business, electronic, and other disparate data into actionable intelligence. An advantageous element of the multi-party knowledge network allows the aggregation of common data formats in order to analyze a combined dataset consisting of information from multiple parties, thereby providing additional business intelligence than with a single set of data alone.