Teradata Us, Inc.

Ncr Corporation

Michael Leon Reed

John Douglas Frazier

Kevin Dean Virgil

Like Gao

Congnan Luo

Yu Long

Judy Wu

Elizabeth J Brealey

Diane M Wewerka

Carol R Thomas

Kevin K Gold

Bhashyam Ramesh

Gregory Howard Milby

Todd A Walter

Heng Yuan

Angelica Garcia-Gutierrez

Andreas Marek

Guofang Li

Cameron Lewis

John A Greene

A data store system may include a storage device that may store a plurality of data. The data store system may also include a processor in communication with the storage device. The processor may receive a request that references at least one input array having an element type, a size, and a dimension. The processor may further generate an array based on the request. The array may have an element type, a size, and a dimension and at least one of the element type, the size, and the dimension of the array may be different than the element type, the size, and the dimension of the input array. The processor may further generate, using the array, an output in response to the request. A method and computer-readable medium may also be implemented.

Dynamic array type in a data store system

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.

Providing metadata to database systems and environments with multiple processing units or modules

Timing data associated with a database or database system can be stored in a reduced or compressed form which can be decompressed back to a full or original form. In doing so, timing data can be compressed by using a subset of a full set of possible values (e.g., a determined range which is more likely to occur) instead of using a full set of possible values. Timing data can also be compressed by eliminating redundant, insignificant duplicate and/or common values, for example, between one or more components (e.g., start and end times of a period of time) of the timing data.

Compression of timing data of database systems and environments

A system, method, and computer-readable medium for the calculation of execution time estimates of user defined functions/user defined methods are provided. The execution of a UDF or UDM is timed several times at the time of the UDF/UDM creation, and an average execution time of the UDF/UDM is obtained. The resulting average execution time is then stored in a data dictionary where the optimizer may consult this value to factor it into the cost of execution of a query.

System, method, and computer-readable medium for costing user-defined functions and methods in a database management system

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

A prediction regarding one or more functions can be made for a database environment. In particular, a predication can be made with respect to values stored in at least one column of at least one table in a database, based on the evaluation of one or more functions for a subset of possible column values (i.e., resultant values derived from the evaluation of a subset of possible column values) without the need to calculate the function(s) for all of the actual entries in the column of the table(s). In effect, a functional predicate can be transformed (or translated) to a predicate that is dependent on the column values instead of the evaluation of one or more functions for the column values.

Making predictions regarding evaluation of functions for a database environment

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.

Techniques for constructing and using run-time JAVA archives (JAR) for JAVA Stored Procedures (JSPS)

A spatial feature is considered for storage in a database system running on a computer. The spatial feature includes a polygon, P. The polygon includes an outer ring, OR, and the area within the outer ring. The polygon includes an interior ring contained within the polygon. The computer removes the interior ring from the polygon. The computer determines a line along which to split the polygon without regard for the location of the inner ring within the polygon. The computer splits the polygon into two polygons, SPand SP, along the line. The computer applies the interior ring to the two split polygons by invoking a point set difference between the interior ring and the two split polygons. The computer stores the split polygons in the database system on the computer.

Splitting polygons with interior rings

In a database system, compression user-defined data types (UDTs) are defined to enable compression of data. Each compression UDT is associated with one or more UDT methods to compress data according to one or more respective compression algorithms. Also, one or more data structures are associated with data stored according to each compression UDT to store values pertaining to the compression of the data.

Compressing data stored in a database system

A continuous dynamic mobile query system including a mobile device is disclosed. The mobile device includes a mobile device memory, a query stored in the mobile device memory, a transmitter for continuously transmitting the query, a receiver for receiving a response, a mobile device output device for displaying the response. The system also includes a location source device for providing the location of the mobile device and a network adapted to receive the query and the location of the mobile device. The system also includes a database management system adapted to receive the query and the location of the mobile device from the network. The database management system is further adapted to execute the query taking into account the location of the mobile device to produce a response and provide the response to the network. The network is further adapted to receive the response from the database management system and transmit the response to the mobile device.

Growing community of inventors

San Diego, CA, United States of America

Michael Leon Reed