International Business Machines Corporation

Mathias Trumpp

Knut Stolze

Andreas Brodt

Oliver Schiller

Marc Schwind

James L Finnie

Torsten Steinbach

Gregor Moehler

The present disclosure relates to a method for processing data blocks of a data table. The data blocks comprise an attribute representing multidimensional spatial data objects, where the attribute has multiple values representing respective dimensions of the multidimensional spatial data object. The method comprises creating for each data block of the data blocks, a respective attribute metadata. The attribute metadata comprises attribute value information defining a multidimensional minimum bounding rectangle that covers attribute values of the attribute comprised in the data block. A query is processed on the multidimensional spatial data objects associated with a query area using the multidimensional minimum bounding rectangle to check whether a data block of the data blocks contains attribute values of the attribute in the query area.

Efficient spatial queries in large data tables

The present disclosure relates to a method for processing a data table. The data table is divided into a plurality of data blocks, and a multidimensional data structure for representing the plurality of data blocks is generated. For each data block of at least part of the plurality of data blocks, a respective data element is added into the multidimensional data structure. The data element has multiple values indicative of a first attribute of the data block. The multidimensional data structure is stored for selectively processing a portion of the data blocks using the multidimensional data structure.

Processing large data tables

The present disclosure provides a computer implemented method and system for processing queries. The first data table comprises a set of data blocks. Each of the set of data blocks may be assigned respective attribute value information. A query involving a query condition on at least a first attribute of the first data table may be received. And a subset of the set of data blocks to be accessed may be selected based on the query condition and using the attribute value information. Furthermore, a guaranteed bound may be determined for a statistical metric on the first attribute based on at least one of the number of data blocks of the subset of data blocks and the attribute value information of the subset of data blocks. The guaranteed bound for the statistical metric may be used when determining a query execution plan for the received query.

Method for query execution planning

A method includes storing data entities in data storage blocks, a logical structure of the storage of the data entities in the data storage blocks is a database including the data entities stored in tables, receiving a request message including an instruction to execute operations using data of the data entities being logically stored in one or more rows of the data entities in the table and physically stored in the data storage blocks of a processing set, determining that the data entity to be used for execution of the operations is stored across the data storage blocks, generating a processing subset in response to the determining that the data entity is stored across the data storage blocks, and executing the operations using a portion of the data stored in the processing subset.

Method and system for handling binary large objects

Determining an extremum value of an attribute of a first data table. The first data table includes a set of data blocks. Assigning each data block of the set of data blocks an attribute value information indicative of a range of values of the attribute. Assigning the attribute value information results in an extremum set of indicative maximum and minimum values of the attribute. Determining a reference value as the infimum or supremum of the extremum set. If the extremum is a minimum, selecting a subset of the set of data blocks, each having a respective minimum value of the attribute smaller than the reference value. If the extremum is a maximum, selecting a subset of the set of data blocks, each having a respective maximum value of the attribute higher than the reference value. Processing at least part of the subset of data blocks for identifying the extremum value.

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Ulm, Germany

Mathias Trumpp