Abstract: In one embodiment, a method includes determining which objects from a substantial dataset are expected to lead to the largest increase in model quality by applying a samples-selection algorithm using computational capability comprising a processor and/or a memory (e.g., of a processing system and/or a graphics processing unit). The aspect quantifies an informativeness score of data elements in the substantial dataset to determine how likely and/or by what degree data elements will lead to model improvement. The method then automatically determines which data elements of the substantial dataset are in need of human annotation based on a prioritization order derived from the informativeness score and chooses a selected data based on the automatically determining which elements of the substantial dataset are in need of human annotation based on the prioritization order derived from the informativeness score. The method then matches the selected data to an expert.

Abstract: Disclosed is a method and system to automatically generate attributes based on semantic categorization of large datasets in artificial intelligence models and/or applications. In one embodiment, a method of automatic representation of data includes organizing a substantial dataset in a manner through which it can serve as an input to a machine learning model and/or an artificial-intelligence application. The machine learning model is focused on classification, prediction, pattern search, trend search, data cluster search, data mining, and/or knowledge discovery. The method includes automatically creating a set of attributes for the machine learning model and/or the artificial-intelligence application that is usable on the substantial dataset. In addition, the method includes efficiently generating a data representation for the machine learning model and/or the artificial-intelligence application that is usable on the substantial dataset.

Abstract: At least one system and method herein are for camera handling in live environments. This includes determining a musical score to be performed in the live environment, analyzing a live version of the musical score with the recorded version of the musical score in a live environment, where the analysis is to determine a time-based difference of at least one frame in the live version against the recorded version, and where the time-based difference is to be used, along with at least one predominant effect of the recorded version or the live version, to enable camera selection or camera adjustments of one or more cameras in the live environment.

Abstract: Embodiments may provide methods and systems for intelligent capture and fast transformation of granulated data summaries. An engine may be used to transform input data summaries into result sets representing query outcomes. The data summaries contain enough knowledge about the original data to accurately perform operations on the summaries without needing to access the original data. In an embodiment, the contents of data summaries are accessible via an SQL approximate engine which retrieves summaries stored on disk and utilizes them for its operations. Alternatively, the contents of data summaries are accessible via virtual tables which give users direct access to the summary contents and allow for the creation and implementation of algorithms to work with the data summaries independently from the SQL approximate engine.

Abstract: Embodiments may provide methods and systems for intelligent capture and fast transformation of granulated data summaries. An engine may be used to transform input data summaries into result sets representing query outcomes. The data summaries contain enough knowledge about the original data to accurately perform operations on the summaries without needing to access the original data. In an embodiment, the contents of data summaries are accessible via an SQL approximate engine which retrieves summaries stored on disk and utilizes them for its operations. Alternatively, the contents of data summaries are accessible via virtual tables which give users direct access to the summary contents and allow for the creation and implementation of algorithms to work with the data summaries independently from the SQL approximate engine.

Abstract: A method of resolving data queries in a data processing system. The method comprises receiving in the data processing system a data query, where the data processing system stores a plurality of information units describing pluralities of data elements, a first information unit having a retrieval subunit that includes information for retrieving all unique data elements in a first plurality of data elements and a summary subunit including summarized information about data elements in the first plurality of data elements. The method further includes deriving, via the data processing system, a result of the data query, wherein the result of the data query comprises a plurality of new data elements. The data processing system uses summary subunits of information units to select a set of information units describing data elements that are sufficient to resolve the data query.

Abstract: In a method for storing data in a relational database system using a processor, a collection of values is assigned to a structure dictionary, each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection. and the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, automatic detection of frequencies of values occurring in data, analysis of a history of queries, and predetermined information about structures relevant to data content that is stored in the system. For each structure, forming a structure match list from ordinal numbers of values matching the structure, and a structure sub-collection from values matching the structure, using the processor.

Abstract: A method comprises grouping, via a data processing system, a plurality of data elements in a same data type into a plurality of data units. The method further comprises gathering, via the data processing system, information about data elements in a first data unit into a first information unit. The first information unit comprising a representation of information that is less than all unique information in the first data unit. The method also comprises storing, via the data processing system, the first information unit on a computer readable storage device. The method additionally comprises identifying, via the data processing system, based at least in part on the first information unit, whether the data elements in the first data unit are required to be retrieved to resolve a first data query received by the data processing system and to resolve the first data query.

Abstract: A system and method of processing a data query in a data processing system is provided. The data in the data processing system includes a plurality of individual data elements. The data elements are grouped and stored in at least one data unit. The information about the at least one data unit is gathered and stored in at least one information unit. The method comprises receiving the data query to be executed; using the information in the at least one information unit to optimize and execute the query; resolving the data query; and returning results of the data query for use by the data processing system.

Abstract: A system and method of processing a data query in a data processing system is provided. The data in the data processing system includes a plurality of individual data elements. The data elements are grouped and stored in at least one data unit. The information about the at least one data unit is gathered and stored in at least one information unit. The method comprises receiving the data query to be executed; using the information in the at least one information unit to optimize and execute the query; resolving the data query; and returning results of the data query for use by the data processing system.

Abstract: A method for applying adaptive data compression in a relational database system using a filter cascade having at least one compression filter stage in the filter cascade. The method comprises applying a data filter associated with the compression filter stage to the data input to produce reconstruction information and filtered data, then compressing the reconstruction information to be included in a filter stream. The filtered data is provided as a compression filter stage output. The method may comprise evaluating whether the compression filter stage provides improved compression compared to the data input. The filter stage output may be used as the input of a subsequent compression filter stage.

Abstract: A method for applying adaptive data compression in a relational database system using a filter cascade having at least one compression filter stage in the filter cascade. The method comprises applying a data filter associated with the compression filter stage to the data input to produce reconstruction information and filtered data, then compressing the reconstruction information to be included in a filter stream. The filtered data is provided as a compression filter stage output. The method may comprise evaluating whether the compression filter stage provides improved compression compared to the data input. The filter stage output may be used as the input of a subsequent compression filter stage.

Abstract: In a method for managing metadata in a relational database system using a processor, the metadata is created in a form of rough values corresponding to collections of values, wherein each rough value represents summarized information about values, the values are elements of the corresponding collection of values, and each rough value is substantially smaller than the corresponding collection of values. A collection of values is assigned to a structure dictionary, wherein each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection, and wherein the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, and predetermined information about structures relevant to data content that is stored in the system. A match granule is formed, and for each structure in the structure dictionary, a structure granule is formed.

Abstract: In a method for storing data in a relational database system using a processor, a collection of values is assigned to a structure dictionary, wherein each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection, and wherein the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, and predetermined information about structures relevant to data content that is stored in the system. For each structure in the structure dictionary, a structure match list is formed from ordinal numbers of values matching the structure, and a structure sub-collection from values matching the structure, using the processor.

Abstract: A relational database having a plurality of records is organized by using a processing arrangement to perform a clustering operation on the records so as to create a number of clusters. At least one of the clusters is characterized by a selected metadata parameter. The clustering operation is performed to optimize a calculated value of a selected precision factor for the selected metadata parameter. The selected metadata parameter is selected to optimize execution of a database query and the value of the selected precision factor is related to efficiency of execution of the database query.

Abstract: In a method for storing data in a relational database system using a processor, a collection of values is assigned to a structure dictionary, wherein each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection, and wherein the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, and predetermined information about structures relevant to data content that is stored in the system. For each structure in the structure dictionary, a structure match list is formed from ordinal numbers of values matching the structure, and a structure sub-collection from values matching the structure, using the processor.

Abstract: In a method for storing data in a relational database system using a processor, a collection of values is assigned to a structure dictionary, wherein each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection, and wherein the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, and predetermined information about structures relevant to data content that is stored in the system. For each structure in the structure dictionary, a structure match list is formed from ordinal numbers of values matching the structure, and a structure sub-collection from values matching the structure, using the processor.

Abstract: A relational database having a plurality of records is organized by using a processing arrangement to perform a clustering operation on the records so as to create a number of clusters. At least one of the clusters is characterized by a selected metadata parameter. The clustering operation is performed to optimize a calculated value of a selected precision factor for the selected metadata parameter. The selected metadata parameter is selected to optimize execution of a database query and the value of the selected precision factor is related to efficiency of execution of the database query.

Abstract: A system and method of processing a data query in a data processing system is provided. The data in the data processing system includes a plurality of individual data elements. The data elements are grouped and stored in at least one data unit. The information about the at least one data unit is gathered and stored in at least one information unit. The method comprises receiving the data query to be executed; using the information in the at least one information unit to optimize and execute the query; resolving the data query; and returning results of the data query for use by the data processing system.

Abstract: A method for applying adaptive data compression in a relational database system using a filter cascade having at least one compression filter stage in the filter cascade. The method comprises applying a data filter associated with the compression filter stage to the data input to produce reconstruction information and filtered data, then compressing the reconstruction information to be included in a filter stream. The filtered data is provided as a compression filter stage output. The method may comprise evaluating whether the compression filter stage provides improved compression compared to the data input. The filter stage output may be used as the input of a subsequent compression filter stage.