Access methods for multiversion data

Lomet, David; Salzberg, Betty

doi:10.1145/66926.66956

Cited by 116 publications

(56 citation statements)

References 7 publications

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“…The first focus of this paper is the integrated indexing of historical and current records, by both key and by time, into Immortal DB using the Time Split B+tree, or TSB-tree [28]. Given the performance results reported in [29], we expected performance to be quite good.…”

Section: Time Split B+treementioning

confidence: 99%

“…Because versions have interval time extents, versions that span the time chosen for the split must be present in both of the resulting pages. Readers are referred to [28] for details on these operations. We focus more on performance tradeoffs and handling uncommitted data in this subsection.…”

Section: Data Page Splittingmentioning

confidence: 99%

“…This representation shares the data page characteristic that unversioned read access to a TSB-tree does not require that the reader know that the data is versioned. It differs from the scheme presented in [28], where pointers to all index terms are present in the slot array.…”

Section: Index Page Version Supportmentioning

confidence: 99%

“…We have extended our Immortal DB prototype [27] to use the TSB-tree [28] as an integrated index for accessing both current and historical versions of data. This enables it to provide logarithmic (in the number of versions) access to all versions of record.…”

Section: Introduction 1overviewmentioning

confidence: 99%

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Transaction time indexing with version compression

et al. 2008

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Immortal DB is a transaction time database system designed to enable high performance for temporal applications. It is built into a commercial database engine, Microsoft SQL Server. This paper describes how we integrated a temporal indexing technique, the TSB-tree, into Immortal DB to serve as the core access method. The TSB-tree provides high performance access and update for both current and historical data. A main challenge was integrating TSB-tree functionality while preserving original B+tree functionality, including concurrency control and recovery. We discuss the overall architecture, including our unique treatment of index terms, and practical issues such as uncommitted data and log management. Performance is a primary concern. To increase performance, versions are locally delta compressed, exploiting the commonality between adjacent versions of the same record. This technique is also applied to index terms in index pages. There is a tradeoff between query performance and storage space. We discuss optimizing performance regarding this tradeoff throughout the paper. The result of our efforts is a high-performance transaction time database system built into an RDBMS engine, which has not been achieved before. We include a thorough experimental study and analysis that confirms the very good performance that it achieves.

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Section: Time Split B+treementioning

confidence: 99%

Section: Data Page Splittingmentioning

confidence: 99%

Section: Index Page Version Supportmentioning

confidence: 99%

Section: Introduction 1overviewmentioning

confidence: 99%

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Transaction time indexing with version compression

et al. 2008

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“…We have built our Immortal DB transaction time database system [12,13,14] into the kernel of a commercial database management system, SQL Server using the TSB-tree [15,16] to index both current and previous database states. Our objective is to ensure transaction time performance close to that of an unversioned current time database.…”

Section: Introductionmentioning

confidence: 99%

Improving Transaction-Time DBMS Performance and Functionality

Lomet

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-Immortal DB is a transaction time database system that is built into a commercial database system rather than being layered on top. This enables it to have performance that is very close to the performance of an unversioned current time database system. Achieving such competitive performance is essential for wide acceptance of this temporal functionality. In this paper we describe further performance improvements in two critical dimensions.First Immortal DB range search performance is improved for current time data via improved current version storage utilization, making this performance essentially the same as unversioned performance. Second, Immortal DB update performance is increased by further reducing the cost for the timestamping of versions. Finally, we show how a simple modification, integrated into the timestamping mechanism, can provide a foundation for auditing database activity. Our algorithms have been incorporated into a commercial database engine and experiments using this database engine demonstrate the effectiveness of our approach.

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Video and Multimedia Indexing

Aref

McIver

Al-Khatib

et al. 2002

Encyclopedia of Software Engineering

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With the continuous evolution of telecommunication and computing technologies, more and more repositories of digital video data are being developed to support a wide range of applications in digital libraries, telemedicine, distance learning, tourism, entertainment, etc. With the rapid proliferation of the Web, these applications are rapidly emerging. Content‐based retrieval of video data has been the subject of extensive research since 1990. Because of the huge volume of data, it becomes crucial to develop indexing techniques that will carry out the process of content‐based retrieval more efficiently. The problem of video indexing is to create and maintain index structures and algorithms that support the efficient execution of queries about the contents of video presentations. Such queries may ask about features of objects or regions contained within a video, or relationships between objects or regions contained within a video. Additionally, queries may concern these features or relationships in relation to time. The spatial and temporal aspects of video indexing taken separately are nontrivial problems. Each type of indexing has been studied widely, and many research problems remain. Indexing of video, however, must be differentiated from spatial, temporal, and spatiotemporal indexing techniques in that information to be indexed may include not only spatiotemporal information, but possibly highly dimensional feature data such as texture, textual closed captioning information, shape, color histograms, and object trajectories or animation operations. A video indexing technique must, therefore, support efficient searches for objects and images on the basis of the three major facets of a video: its spatial, temporal, and feature values. A simple multimedia object model is presented to provide a common reference point for discussing multimedia indexing in the remainder of this chapter. The model is contained in the following definitions discussed.

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Access methods for multiversion data

Cited by 116 publications

References 7 publications

Transaction time indexing with version compression

Transaction time indexing with version compression

Improving Transaction-Time DBMS Performance and Functionality

Video and Multimedia Indexing

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