International Business Machines Corporation

Yao-Ching Stephen Chen

Fen-Ling Lin

Guogen Zhang

Yun Yu Wang

Curt Lee Cotner

Jerry Mukai

Xiaohong Fu

Maryela Evelin Weihrauch

Claire Willey McFeely

Yumi Kimura Tsuji

Irene Ching-Hua Liu

Thomas Abel Beavin

Mengchu Cai

Walid Rjaibi

Tammie Dang

Brian Thinh-Vinh Tran

Lee-Chin Hsu Liu

Allan Benjamin Lebovitz

Li-Mey P Lee

Fung Lee

Demai Ni

Ding-Wei Chieh

You-Chin Gene Fuh

James Willis Pickel

Gerald George Kiernan

Jay A Yothers

Timothy Jon Vincent

Hong Sang Tie

Li Jun Xia

Ning Wang

Christopher John Crone

Qinghua Zou

Margaret Zagelow

Margaret Ann Bernal

Yoichi Tsuji

Hsiuying Yen Cheng

Ying Zeng

Binghua Zhen

Liyan Zhou

Hsuiying Yen Cheng

Baoqiu Cui

Joe Cheng-yu Huang

Irene C Liu

Ronald Morton Smith, Sr

Mir Hamid Pirahesh

Paul Miller Bird

Roberta Jo Cochrane

James Zu-Chia Teng

Julie Ann Watts

Ruiping Li

Tuong Chanh Truong

Jyh-Herng Chow

Michael F Cowlishaw

Kalpana Shyam

Nelson Mendonca Mattos

Karelle Lynne Cornwell

Regina J Liu

Manogari Nogi Simanjuntak

Chunfeng Pei

Jason Alexander Cu

Dengfeng Gao

Gregory R Stager

Georgia Carroll Fuller

Yue Huang

Gene Y Fuh

David J Kuang

Scott Ian Logan

Allen William Luniewski

Hui-An Lee

Xun Li

Robert Thomas Miller

Joyce A Taylor

Jeffrey D Richey

George Gerald Kiernan

Jung-Hsin Hu

Glenn M Yuki

Frank Chan

Eric L Alton

Marion B Farber

Xiaoyu Yang

Harley W Boughton

Huong T Tran

Yuping Ding

Baoqui Cui

Yumi Kimara Tsuji

Joe Cheung-Yu c/oIBM Uk Ltd Ip Law Huang

In supporting temporal logical transactions, a database management system (DBMS) determines that a temporal logical transaction time (T) is set for a temporal logical transaction. The DBMS receives a change request for a current row in a current table. A history row for a history table corresponding to the current table is created. The values in the history row are set to the values in the current row, where a begin time in the history row has same value as a begin time in the current row, and an end time in the history row is set to T. When the begin time equals the end time in the history row, the DBMS does not store the history row in the history table. The values in the current row are changed according to the change request, and the begin time in the current row is set to T.

Temporal logical transactions

In an enforcement of temporal referential integrity in a database system, the database system receives a change request for one or more rows in a target table in the database system. The system determines that the target table has temporal referential constraints with a second table. The system compares a non-period child key value in child table row(s) with a non-period parent key value in parent table row(s) and compares a child business time period key value in the child table row(s) with a parent business time period key value in the parent table row(s). When the non-period child key value matches the non-period parent key value and when the child business time period key value is within the parent business time period key value, the system determines that the change request satisfies the temporal referential constraints. Otherwise, the system determines that the change request violates the temporal referential constraints.

Enforcing temporal referential integrity

A method, apparatus, and article of manufacture for optimizing a query in a computer system. Grouping operations are optimized during execution of the query in the computer system by: (1) translating the grouping operations into a plurality of levels, wherein each of the levels is comprised of one or more grouping sets with the same number of grouping expressions; (2) deriving the grouping sets on a level-by-level basis, wherein the grouping sets in a base level are obtained from the database and the grouping sets in a next one of the levels are derived by selecting as an input a smallest one of the grouping sets in a previous one of the levels with which it has a derivation relationship; and (3) combining the derived grouping sets into an output for the query.

Dynamic selection of optimal grouping sequence at runtime for grouping sets, rollup and cube operations in SQL query processing

An approach for using multiple plans in a database management system to access current data and history data is provided. The approach provides a first access plan for a query, stores the first plan as a first section in an application package. The first access plan is capable of accessing the current data. The approach determines whether a bind option for the query is detected. In response to determining that the bind option is detected, the approach provides a second access plan for the query and stores the second access plan as a second section in the application package. The second access plan is capable of accessing the current data and the history data.

Method for using multiple plans to achieve temporal and archive transparency performance

According to one embodiment of the present invention, a system processes a database query, and comprises a computer system including at least one processor. The system identifies one or more expressions within the database query utilizing a database object with value masking. Masking requirements are determined for each identified expression and the database object utilized by that identified expression is replicated to provide masked and actual versions of that database object in response to the masking requirements for that expression including masked values and actual values of that database object. The value masking of the database object is applied to the identified expressions within the database query based on the determined masking requirements to produce search results with masked values for the database query. Embodiments of the present invention further include a method and computer program product for processing a database query in substantially the same manner described above.

Query transformation for masking data within database objects

A method and system for providing a scalable storage scheme for native hierarchically structured data of relational tables, includes a base table with indicator columns with information pertaining to hierarchically structured data of a document, data tables for storing the hierarchically structured data corresponding to the indicator columns, and node identifier indexes corresponding to the data tables for mapping between the indicator columns and the hierarchically structured data in the data tables. In an embodiment, actual data for each hierarchically structured data (such as XML) column is stored in a separate data table, and each data table has a separate node identifier index. The node identifier index is searched with a key containing the document identifier and a logical node identifier is used, and a record identifier of a record in the data table containing the node assigned the logical node identifier is retrieved.

Scalable storage schemes for native XML column data of relational tables

A storage of nodes of hierarchically structured data uses logical node identifiers to reference the nodes stored within and across record data structures. A node identifier index is used to map each logical node identifier to a record identifier for the record that contains the node. When a sub-tree is stored in a separate record, a proxy node is used to represent the sub-tree in the parent record. The mapping in the node identifier index reflects the storage of the sub-tree nodes in the separate record. Since the references between the records are through logical node identifiers, there is no limitation to the moving of records across pages, as long as the indices are updated or rebuilt to maintain synchronization with the resulting data pages. This approach is highly scalable and has a much smaller storage consumption than approaches that use explicit references between nodes.

Packing nodes into records to store XML XQuery data model and other hierarchically structured data

Provided are techniques for creating one or more fine-grained access control rules that are associated with a base table. A materialized query table is created from the base table without applying the one or more fine-grained access control rules associated with the base table when obtaining data from the base table. A fine-grained access control protection indicator is turned on for the materialized query table. In response to receiving a direct access request to the materialized query table in a query referencing the materialized query table, access is provided to the data in the materialized query table by applying one or more fine-grained access control rules associated directly with the materialized query table to the data in the materialized query table before returning the data.

Managing materialized query tables (MQTS) over fine-grained access control (FGAC) protected tables

A method and system for controlling access to data stored in a table of a database are provided. The method includes marking the table of the database as being protected with fine-grained access control (FGAC), creating a system authorization class for the table of the database, the system authorization class having a default row authorization that prevents access to all rows in the table, the system authorization class being unmodifiable, creating a user authorization class for the table of the database, the user authorization class having a default row authorization that prevents access to all rows in the table, the user authorization class being modifiable, and associating the system authorization class and the user authorization class with the table of the database.

Method and system for using fine-grained access control (FGAC) to control access to data in a database

A system and computer readable medium for executing a query to access data stored in a database, wherein the query includes a plurality of DISTINCT keys, is disclosed. The system and computer readable medium includes a capture module for identifying each of the plurality of DISTINCT keys in the query and a sort module coupled to the capture module for determining if more than one sort is needed to execute the query, performing a first DISTINCT operation on a first DISTINCT key of the plurality of DISTINCT keys, storing data fetched from the first DISTINCT operation in a master workfile only if more than one sort process is needed to execute the query, and utilizing the master workfile to perform subsequent DISTINCT operations on the other of the plurality of DISTINCT keys.

System for executing a query having multiple distinct key columns

An XML schema is compiled into an annotated automaton encoding, which includes a parsing table for structural information and annotation for type information. The representation is extended to include a mapping from schema types to states in a parsing table. To validate a fragment against a schema type, it is necessary simply to determine the state corresponding to the schema type, and start the validation process from that state. When the process returns to the state, fragment validation has reached successful completion. This approach is more efficient than a general tree representation. Only the data representation of the schema information is handled, making it much easier than manipulating validation parser code generated by a parser generator. In addition, only one representation is needed for schema information for both document and fragment validation. This approach also provides a basis for incremental validation after update.

Efficient XML schema validation of XML fragments using annotated automaton encoding

Techniques are provided that determine that a query includes at least one subquery that may be correlated or decorrelated. An internal representation of the query that represents the subquery in both correlated and decorrelated form is generated, wherein the internal representation includes at least one virtual table.

Cost-based subquery correlation and decorrelation

A method for generating an execution plan for updating and retrieving data from a database in a single process includes: receiving a statement by a server to update a database with a first set of data and to retrieve a second set of data from the database; building a first execution plan to update the database with the first set of data; building a second execution plan to retrieve the second set of data from the database; and building a single execution plan including a combination of the first and second execution plans. The single execution plan allows for the updating and retrieval of data with a single crossing of an interface between a client and the database. By performing both functions in this manner, the efficiency of performing such tasks on the database is significantly increased.

Method of returning data during insert statement processing

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Saratoga, CA, United States of America

Yao-Ching Stephen Chen