Teradata Us, Inc.

Teredata, Us, Inc.

Guilian Wang

Congnan Luo

Heng Yuan

Gary Allen Roberts

Daniel Wong

Olli Pekka Kostamaa

Steven Bart Cohen

John R Catozzi

Relational data for a database table having multiple columns (dimensions) is represented as a histogram. The buckets of the histogram are clustered into clusters, the clusters are fewer in number than the buckets of the histogram. Each cluster is represented in hyper-space as a line segment (vector) with a thickness value. Average frequencies for the line segments (vectors) and their thickness values are computed. The average frequencies are processed by a query optimizer when processing a query predicate of a query to use as an estimation of selectivity or an estimation of a cardinality of a join operation.

Techniques for query processing using high dimension histograms

Statistics for a Relational Database Management System (RDBMS) set of tables are normalized and a grid computed. A particular grid cell having above threshold number of entries from the tables is determined and an R-Tree is dynamically constructed for that grid cell for use when performing a geospatial join operation against the tables.

Parallelizable gridded geospatial join (PGGJ)

A database system may implement compression management of tables in the database system. The compression management may include determination of a pattern of usage of various database tables in the database system. Based on this pattern of usage, the database tables may be selected as candidates for compression or decompression at the appropriate time. In one example, the pattern of usage may be based on the contents of a query log of the database system. The compression management may also include evaluation of various compression strategies to apply to a candidate database table. Each compression strategy may be evaluated to determine if application to a database table or a portion of the database table would be beneficial based on various conditions. The compression management may also include consideration of each available compression strategy to be applied solely or in combination with one another.

Selective data compression in a database system

A system and method for compressing and decompressing multiple types of character data. The system and method employ multiple encoding tables, each designed for encoding a subset of character data, such as numeric data, uppercase letters, lowercase letters, Latin, or UNICODE data, to perform compressions and decompression of character data, and. The compression solution also provides for the creation of new encoding tables on the fly, and accommodates the compression of lengthy character streams using multiple different general compression algorithms, automatically choosing a suitable general compression algorithm for specific input data.

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Guilian Wang