Abstract: A computer system receives an input corresponding to one or more data pipeline metrics and generates a first plurality of data windows from a first portion of a plurality of streaming data, the first plurality of data windows corresponding to a first data pipeline metric. The computer system generates a first plurality of data aggregations corresponding to the first plurality of data windows, wherein generating the first plurality of data aggregations includes aggregating a data associated with each of the first plurality of windows based on the first data pipeline metric. The computer system accesses a database to determine a first set of reference data aggregations that corresponds to a first data aggregation of the first plurality of data aggregations and determines whether the first data aggregation corresponds to an anomaly based on utilizing a model to compare the first set of reference data aggregations to the first data aggregation.

Abstract: A computer system receives an input corresponding to one or more data pipeline metrics and generates a first plurality of data windows from a first portion of a plurality of streaming data, the first plurality of data windows corresponding to a first data pipeline metric. The computer system generates a first plurality of data aggregations corresponding to the first plurality of data windows, wherein generating the first plurality of data aggregations includes aggregating a data associated with each of the first plurality of windows based on the first data pipeline metric. The computer system accesses a database to determine a first set of reference data aggregations that corresponds to a first data aggregation of the first plurality of data aggregations and determines whether the first data aggregation corresponds to an anomaly based on utilizing a model to compare the first set of reference data aggregations to the first data aggregation.

Abstract: Improved computer data query processing by a) identifying a cut point value (CPV) for a data set, where an attribute value of each data set record is normalized between first and second normal bounds, where the CPV is any value between the normal bounds that is associated with a maximum data skipping performance value (DSP) calculated by applying a DSP function based on the normalized values and the CPV, and where the DSP function relates to a selected data query distribution profile, b) partitioning the data set into multiple partitions, one partition including a data record subset whose corresponding normalized attribute values are greater than the CPV, and another partition including a data record subset whose corresponding normalized attribute values are less than the CPV, and c) storing the partitions on physical data storage media for access by a computer configured to perform data query processing.

Abstract: Improved computer data query processing by a) identifying a cut point value (CPV) for a data set, where an attribute value of each data set record is normalized between first and second normal bounds, where the CPV is any value between the normal bounds that is associated with a maximum data skipping performance value (DSP) calculated by applying a DSP function based on the normalized values and the CPV, and where the DSP function relates to a selected data query distribution profile, b) partitioning the data set into multiple partitions, one partition including a data record subset whose corresponding normalized attribute values are greater than the CPV, and another partition including a data record subset whose corresponding normalized attribute values are less than the CPV, and c) storing the partitions on physical data storage media for access by a computer configured to perform data query processing.