Teradata Us, Inc.

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Michael Leon Reed

Yu Long

Congnan Luo

Judy Wu

Bhashyam Ramesh

John Douglas Frazier

John A Greene

A database system may include a memory device configured to store a database dictionary and a processor in communication with the storage device. The processor may be configured to receive a temporal user-defined function (UDF) and to store, in the database dictionary, the temporal UDF and a period of time over which to apply the temporal UDF. A method may include receiving a temporal UDF and include determining a period of time over which to apply the temporal UDF. The method may further include storing the temporal UDF and the period of time over which to apply the temporal UDF. A computer-readable medium may include instructions to receive a temporal UDF and instructions to store the temporal UDF. The computer-readable medium may further include instructions to determine a period of applicability of the temporal UDF and instructions to store the period of applicability in a storage device.

Temporal user-defined functions

Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Database partition management

Apparatus, systems, and methods may operate to receive requests to execute a plurality of compression and/or decompression mechanisms on one or more database objects; to execute each of the compression and/or decompression mechanisms, on a sampled basis, on the database objects; to determine comparative performance characteristics associated with each of the compression and/or decompression mechanisms; and to record at least some of the performance characteristics and/or derivative characteristics derived from the performance characteristics in a performance summary table. The table may be published to a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Database compression analyzer

A temporal target table stored on a computer is scanned using the computer. The temporal target table has a ValidTime dimension and a value dimension. A qualifying-row having a value dimension having a value equal to a value of a value dimension in a row-to-be-inserted is found during the scanning. The row-to-be-inserted has a ValidTime dimension and the value dimension. The qualifying-row is processed. The row-to-be-inserted is inserted in the temporal target table. A ValidTime dimension of the inserted row-to-be-inserted is set to be as the union of the ValidTime dimension of the row-to-be-inserted and the ValidTime dimension of the qualifying-row.

Processing insert with normalize statements

Apparatus, systems, and methods may operate to receive a set of ordered user-selected compression rules as a compression rule set comprising at least one compression threshold condition, to create or transform a database object with rows to be selectively compressed according to the compression rules in the compression rule set (providing a transformed object), and to publish at least a portion of the transformed object to one of a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Database compression

Techniques for constructing and using run-time JAVA Archive (JAR) files for JAVA Stored Procedures (JSPs) are provided. JSP methods referenced by an application via a plurality of different JAR files are extracted and packed into a single packed JAR file. When the application tries to execute a JSP, the attempt to process the JSP methods is redirected to a new JSP that calls the single packed JAR file.

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San Diego, CA, United States of America

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