International Business Machines Corporation

International Business Machines Coproartion

Curt Lee Cotner

James Willis Pickel

Paul Miller Bird

Manoj Kumar Agarwal

Amitava Kundu

Timothy Jon Vincent

Walid Rjaibi

Tammie Dang

Yao-Ching Stephen Chen

Claire Willey McFeely

Margaret Ann Dong

Roger Lee Miller

Brian Keith Howell

Rajesh Sambandhan

Prasan Roy

William Robert Bireley

Margaret Zagelow

Namik Hrle

Gerald George Kiernan

Jay A Yothers

Wendy Lynne Koontz

Georgia Carroll Fuller

Donna N Dillenberger

You-Chin Gene Fuh

Maryela Evelin Weihrauch

Jeffrey William Josten

David B Emmes

Jeffrey David Aman

Melvin R Zimowski

John A McPherson, Jr

Peter K Shum

Claus Kempfert

Richard A Crus

Yumi Kimura Tsuji

Gayathiri R Chandran

Adrian B Lobo

Benjamin R Little

Robert Thomas Miller

Thomas J Toomire

Irene C Liu

Liyan Zhou

Michelle D Lam

Frankie K Sun

Robert J Heaps

Hui-An Lee

Baoqiu Cui

Joe Cheng-yu Huang

Balakrishna Raghavendra Iyer

Charles Daniel Wolfson

Robert William Lyle

Stephen Andrew Brodsky

Thomas Abel Beavin

James Z Teng

James Alan Ruddy

Howard Justin Glaser

Julie Ann Watts

Patrick Dantressangle

Paul Arnold Ostler

Laurence Edward England

Mary Ann Roth

Anshul Dawra

Holger Karn

Howard M Hess

Frederick Thomas Sharp

Patrick Dooling Bossman

David Joseph Wisneski

Bryan Frederick Smith

Nelson Mendonca Mattos

Brent P Gross

Swaminathan Gounder

Regina J Liu

Randy Mitchell Nakagawa

Manogari Nogi Simanjuntak

Charles H Lin

Margaret Ann Bernal

Irene Ching-Hua Liu

Chunfeng Pei

Robert Alan Begg

Delmar Eugene Blevins

Dengfeng Gao

Shu-Huar Joseph Yeh

Gregory R Stager

Catherine E Drummond

Fung Lee

Adarsh R Pannu

Jerome Quan Wong

Daya Vivek

Stephen Yao Ching Chen

Gerald Herman Roth

Harry Otto Radke

Kun Peng Ren

David J Kuang

Christopher M Farrar

Ding-Wei Chieh

Koichi Yamaguchi

Thomas Eng

Susan Malaika

Xun Li

Roy Lorenzo Smith

Eric L Alton

In a particular embodiment, a method includes storing, at a staging table of a database, a data update generated based on a transaction performed with respect to a data table that is associated with one or more indexes. Each index of the one or more indexes is maintained externally to the database. The method further includes maintaining the data update at the staging table at least until index update data is received at the database. The index update data represents a modification, based on the data update, to a particular index of the one or more the indexes.

Maintaining index data in a database

Access control methods provide multilevel and mandatory access control for a database management system. The access control techniques provide access control at the row level in a relational database table. The database table contains a security label column within which is recorded a security label that is defined within a hierarchical security scheme. A user's security label is encoded with security information concerning the user. When a user requests access to a row, a security mechanism compares the user's security information with the security information in the row. If the user's security dominates the row's security, the user is given access to the row.

Row-level security in a relational database management system

Database operation records are sequentially read from two or more log files. If the transaction identifier is new and the record is not an end-of-transaction record, an open transactions list entry is created. If the transaction identifier is new and the record is an end-of-transaction record, a transaction type list entry is created or updated. If the transaction identifier is not new and is not an end-of-transaction record, an open transactions list entry is updated. If the transaction identifier is not new and the record is an end-of-transaction record, a transaction type list entry is created or updated. When all log file records are read, analytical comparison between the information associated with two or more of the log files in data fields in the transaction type list entries is performed.

Comparing database performance without benchmark workloads

Identifying matching transactions. First and second log files contain operation records of transactions in a transaction workload, each file recording a respective execution of the transaction workload, the method comprising. A first record location in the first file and an associated window of a defined number of sequential second record locations in the second file are advanced one record location at a time. Whether each operation record of a complete transaction at a first record location has a matching operation record at one of the record locations in the associated window of second record locations is determined. If so, the complete transaction in the first file and the transaction that includes the matching operation records in the second file are identified as matching transactions.

Matching transactions in multi-level records

Methods for processing a query in a multi-tenancy environment utilizing an electronic computing device are presented, the methods including: causing the electronic computing device to receive the query to a database, where the query includes a number of objects; causing the electronic computing device to determine whether any of the number of objects correspond with the set of tenant database objects; if any of the number of objects correspond with the set of tenant database objects, causing the electronic computing device to process the number of objects from the tenant production table; and if any of the number of objects correspond with the set of instance database objects, causing the electronic computing device to process the number of objects from the instance production table.

Multi-tenancy in database namespace

A predefined number of matches is identified between records in a first file and records in a second file. For the matches, determine the span of the actual range of record positions in the second file relative to the positions of the operation records in the first file within which all matches were found. If the actual span is smaller than the span of a current defined range of record positions by at least a first threshold value, decrease the span of the current defined range. If the actual span is within a second threshold value of the span of the current defined range, increase the span of the current defined range. If an amount above a third threshold value of operation records in the first file are not matched to operation records in the second file, increasing the span of the current defined range.

Match window size for matching multi-level transactions between log files

Access to a data element stored within a database object is controlled. A request is received from a user to perform an operation in relation to the database object, the operation including retrieval of information from the data element of the database object. Prior to retrieving information from the data element, a determination is made whether at least a portion of the information from the data element is subject to masking in accordance with an access policy. In response to determining that information from the data element is subject to masking, the request is modified to require that information from the data element be retrieved in a masked condition.

Masking sensitive data of table columns retrieved from a database

A log file contains operation records, each operation record is of a certain type, and each operation record is associated with a transaction. A plurality of operation records is read from the log file into a record store. Records of the plurality of operation records of each operation record type are sampled at a predefined sampling rate. Operation records in the plurality of operations records are identified that are associated with completed transactions of which the sampled operation records are associated. The identified operation records are then extracted from the record store into a data store.

Sampling transactions from multi-level log file records

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 for identifying matching transactions between two log files where each transaction includes one or more statements. Each log file record records the execution of a statement and includes a transaction identifier. Each record in turn in one log file is compared to an advancing window of records in the other log file. A first table contains associations of statements to transactions and transactions to statements for records in the window. If a match is found between a record in the one file and a record in the window, information associating partial transactions in the one file to potential transactions of the records in the window is added to a second table. If an end-of-transaction record is read from the one file, a best match is found between the ended transaction and the potential transactions based on information in the first and second tables.

Techniques are provided for tuning work. In response to identifying a trigger for a work, automatic tuning is invoked. The trigger indicates that a performance goal for the work has been missed. The work is analyzed. A tuning operation is selected for the work based on a tuning history. The selected tuning operation is implemented for the work.

Tuning of work to meet performance goal

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Gilroy, CA, United States of America

Curt Lee Cotner