Abstract: A data store system may include a storage device configured to store a plurality of data store tables. The data store may further include a plurality of processing units. At least one processing unit from the plurality of processing units may receive an analytic function call. The at least one processing unit may further identify, in the analytic function call, at least one column of a data store table on which to execute an analytic function in the analytic function call and may further identify, in the analytic function call, an identifier column of the data store table. Each row of the at least one column may be associated with a common row value of the identifier column. The at least one processing unit may further identify, in the analytic function call, at least one index column of the data store table. Each value in each at the least one index column may identify an index value on which to index each value of the at least one column with respect to each value of the identifier column.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for generating, storing, and using non-unique secondary indexes of semi-structured data in database tables. One method embodiment includes creating and storing a non-unique secondary index (NUSI) for a database table based on column data stored in rows of the database table. The NUSI in some such embodiments identifying an offset to specific data included in the column data of at least some rows of the database table where the specific data is located. Other embodiments include building a NUSI index that includes values embedded therein.

Abstract: Improved techniques for performing Spatial Joins multi-processing computing systems and environments are disclosed. One or more intersection of bounds (or limits) of data sets is determined as a join bounding space. The join bounding space is in a space (Global space or Global universe) where a spatial join between (or for) the data can be performed. The determined join bounding space can be partitioned into sub-partitions of the join bounding space. The sub-partitions of the join bounding space can assigned respectively to multiple processing unit for processing in parallel in. In addition, distribution cost information associated with the cost of distribution of the datasets (and/or their components) to the processing units of a multi-processing system can be provided and/or used to effectively distribute and/or redistribute processing of the Spatial Join between the processing units of a multi-processing system.

Abstract: A Data Manipulation Language (DML) syntax is extended for identifying a group-by-time-based operation based on a user-defined time series. The underlying database processing is extended for identifying the time-based operation and generating instructions for processing a query having the time-based operation against the database and providing time-context in query results for the query.

Abstract: Metadata can be provided to multiple processing units of a database system by using local storages respectively provided for the processing units, such that a local storage is accessible only to its respective processing unit. As a result, processing units can access metadata when needed (e.g., when needed to process a database request at runtime) without having to access a source external to the database system. In addition, metadata (e.g., an XML object, XML schema, XSLT stylesheets, XQuery modules) can be provided using a database request or command, for example, by using a register statement.

Abstract: Metadata can be provided to multiple processing units of a database system by using local storages respectively provided for the processing units, such that a local storage is accessible only to its respective processing unit. As a result, processing units can access metadata when needed (e.g., when needed to process a database request at runtime) without having to access a source external to the database system. In addition, metadata (e.g., an XML object, XML schema, XSLT stylesheets, XQuery modules) can be provided using a database request or command, for example, by using a register statement.

Abstract: A system and method for storing time series data in a distributed relational database.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for generating, storing, and using non-unique secondary indexes of semi-structured data in database tables. One method embodiment includes creating and storing a non-unique secondary index (NUSI) for a database table based on column data stored in rows of the database table. The NUSI in some such embodiments identifying an offset to specific data included in the column data of at least some rows of the database table where the specific data is located. Other embodiments include building a NUSI index that includes values embedded therein.

Abstract: Techniques for precedence based storage are presented. Storage for a database is organized into storage pools; collections of pools form storage classes. The storage pools within a particular class are organized in a precedence-based order so that when storage for the database is needed, the storage pools are used in the defined order of precedence. Additionally, each storage pool or storage class can be circumscribed by security limitations, quality of service limitations, and/or backup procedures.

Abstract: Router threads read real-time stream data as that data is received at processing nodes along a processing path for the data. The data is initially parsed into workloads. Each router thread maintains real-time analytics for the workloads and reports deviations in the analytics to a workload coordinator. The workload coordinator dynamically and in real time provides updated data distribution maps based on the reported deviations, and the router threads use the data distribution maps to determine a next processing unit to forward the workloads, where the next processing unit includes an instance of a next processing node in the processing path for the workload. The next processing node performs additional processing on the workloads along the processing path.

Abstract: Router threads read real-time stream data as that data is received at processing nodes along a processing path for the data. The data is initially parsed into workloads. Each router thread maintains real-time analytics for the workloads and reports deviations in the analytics to a workload coordinator. The workload coordinator dynamically and in real time provides updated data distribution maps based on the reported deviations, and the router threads use the data distribution maps to determine a next processing unit to forward the workloads, where the next processing unit includes an instance of a next processing node in the processing path for the workload. The next processing node performs additional processing on the workloads along the processing path.

Abstract: Streaming data is populated to an in-memory data table and a continuous query is executed against an in-memory data table using a database interface to perform analytical operations on the populated in-memory data table. Results from the analytical operations performed are streamed to consuming applications.

Abstract: Techniques for organizing single or multi-column temporal data into R-tree spatial indexes are provided. Temporal data for single or multiple column data, within a database system, is converted into one or more line segments. The resulting line segments are transformed into a minimum bounding rectangle (MBR). Finally, the MBR is inserted into an R-tree spatial index.

Abstract: Techniques for mapping a virtual R-Tree to an extensible-hash based file system for databases are provided. Spatial data is identified within an existing file system, which stores data for a database. Rows of the spatial data are organized into collections; each collection represents a virtual block. The virtual blocks are used to form an R-Tree spatial index that overlays an existing index for the database on the existing file system. Each row within its particular virtual block includes a pointer to its native storage location within the existing file system.

Abstract: There is provided a method, a system and a machine readable medium to optimize storage allocation in a database management system. The method comprises receiving a processing step at a step processing module of an access module processor from a dispatcher module. The method further comprises determining whether a fast access storage flag is set in the processing step, the fast access storage flag indicating use of an intermediate file in fast access storage to store one or more redistributed data rows of a table of a database that is distributed across one or more storage devices of the database management system; Yet further the method comprises selectively allocating a free fast access storage data block to the intermediate file from a fast access storage pool based on the determination that the fast access storage flag is set. Lastly, the method comprises writing a redistributed data row from the one or more redistributed data rows to the allocated fast access storage data block.

Abstract: Metadata can be provided to multiple processing units of a database system by using local storages respectively provided for the processing units, such that a local storage is accessible only to its respective processing unit. As a result, processing units can access metadata when needed (e.g., when needed to process a database request at runtime) without having to access a source external to the database system. In addition, metadata (e.g., an XML object, XML schema, XSLT stylesheets, XQuery modules) can be provided using a database request or command, for example, by using a register statement.

Abstract: Techniques for collecting statistics of column data or R-Tree indexes are provided. A distributed database system includes a plurality of processing nodes controlling portions of spatial data. The nodes are instructed to create minimum bounding rectangles (MBR's) for their spatial data or R-Trees. The individual MBR's are merged and reformatted into a grid of equally sized cells. Each processing node is provided a copy of the grid to update based on statistics of each processing node's spatial data for a target table. The updated grids are then merged into a single grid and used by an optimizer to evaluate queries before the queries are executed.

Abstract: Techniques for collecting statistics of column data or R-Tree indexes are provided. A distributed database system includes a plurality of processing nodes controlling portions of spatial data. The nodes are instructed to create minimum bounding rectangles (MBR's) for their spatial data or R-Trees. The individual MBR's are merged and reformatted into a grid of equally sized cells. Each processing node is provided a copy of the grid to update based on statistics of each processing node's spatial data for a target table. The updated grids are then merged into a single grid and used by an optimizer to evaluate queries before the queries are executed.

Abstract: Techniques for mapping a virtual R-Tree to an extensible-hash based file system for databases are provided. Spatial data is identified within an existing file system, which stores data for a database. Rows of the spatial data are organized into collections; each collection represents a virtual block. The virtual blocks are used to form an R-Tree spatial index that overlays an existing index for the database on the existing file system. Each row within its particular virtual block includes a pointer to its native storage location within the existing file system.

Abstract: Techniques for organizing single or multi-column temporal data into R-tree spatial indexes are provided. Temporal data for single or multiple column data, within a database system, is converted into one or more line segments. The resulting line segments are transformed into a minimum bounding rectangle (MBR). Finally, the MBR is inserted into an R-tree spatial index.