Abstract: A method of preconditioning time-series data for improved efficiency. The method includes repeatedly recognizing new time series entries for each of a plurality of tracked entities. For each of the plurality of tracked entities, a new time-series data entry for the tracked entity is promoted responsive to the new time-series data entry including a changed value in a normalized field. However, a previous time-series data entry for the tracked entity is maintained responsive to the new time-series data entry including an unchanged value in the normalized field. The method further incudes generating a synthetic time-series data entry for every tracked entity without a time-series data entry in a fixed time period.

Abstract: A computing system includes a database server configured to receive data including a plurality of elements each including a sensor identifier and time element, each time element including a timestamp or time period, and create a time series of the data indexed by the sensor identifiers into buckets spaced at even time intervals. The database server is configured to receive a query including at least one of the sensor identifiers and at least one query time element. The database server is configured to locate target data in the buckets by the at least one of the sensor identifiers and the at least one query time element and output the target data.

Abstract: A computing system includes a database server configured to receive data including a plurality of elements each including a sensor identifier and time element, each time element including a timestamp or time period, and create a time series of the data indexed by the sensor identifiers into buckets spaced at even time intervals. The database server is configured to receive a query including at least one of the sensor identifiers and at least one query time element. The database server is configured to locate target data in the buckets by the at least one of the sensor identifiers and the at least one query time element and output the target data.

Abstract: A system disclosed includes a temporal data warehouse and a platform independent temporal data warehouse load application operable to run on the system. The load application uses timestamp data from incoming data in conjunction with a relational algebra of set operators to identify and sequence net changes between the incoming data and data previously stored within the data warehouse. The load application loads the identified and sequenced net changes into the data warehouse with relatively little intrusion into normal operation of the data warehouse. Optimizations, including but not limited to, distinct partitioning of the workload into parallel streams are selectable via metadata.

Abstract: A system is described that includes a data warehouse and a platform independent data warehouse load application operable to run on the system. The load application includes a sequencing unit configured to utilize timestamp data from incoming data to be stored in the data warehouse and a relational algebra of set operators to identify and sequence net changes between the incoming data and data previously stored within the data warehouse. The load application is configured to non-intrusively load the identified and sequenced net changes into the data warehouse.

Abstract: A system disclosed includes a temporal data warehouse and a platform independent temporal data warehouse load application operable to run on the system. The load application uses timestamp data from incoming data in conjunction with a relational algebra of set operators to identify and sequence net changes between the incoming data and data previously stored within the data warehouse. The load application loads the identified and sequenced net changes into the data warehouse with relatively little intrusion into normal operation of the data warehouse. Optimizations, including but not limited to, distinct partitioning of the workload into parallel streams are selectable via metadata.

Abstract: A system is described that includes a data warehouse and a platform independent data warehouse load application operable to run on the system. The load application includes a sequencing unit configured to utilize timestamp data from incoming data to be stored in the data warehouse and a relational algebra of set operators to identify and sequence net changes between the incoming data and data previously stored within the data warehouse. The load application is configured to non-intrusively load the identified and sequenced net changes into the data warehouse.