Conference Paper

Performance Evaluation of a Temporal Database Management System.

DOI: 10.1145/16856.16864 Conference: Proceedings of the 1986 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 28-30, 1986.
Source: DBLP

ABSTRACT A prototype of a temporal database management system was built by extending Ingres. It supports the temporal query language TQuel, a superset of Quel, handling four types of database static, rollback, historical and temporal. A benchmark set of queries was run to study the performance of the prototype on the four types of databases. We analyze the results of the benchmark, and identify major factors that have the greatest impact on the performance of the system. We also discuss several mechanisms to address the performance bottlenecks we encountered.

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Available from: Richard Snodgrass, Jul 21, 2015
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    • "This technique applies to temporal database applications that are modeled by TTHR, proposed in Halawani and Alromema (2010). This model is proposed based on the data models which are discussed in (Gregersen and Jensen, 1998; Ahn and Snodgrass, 1986). Our proposed data model is based on tuple time stamping with two relations, one relation is for the current snapshot data and the other one is the auxiliary relation that holds the temporal aspects of whole time-varying attributes, the proposed temporal data model achieves saving in memory usage range from 70-90% over the temporal data model discussed in (Novikov and Gorshkova, 2008), where a framework for temporal database implementation is discussed. "
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    ABSTRACT: Problem statement: In the field of database technology, Recoding time related data are referred to as temporal databases. Conventional relational databases are queried using the underlying constructs of SQL which are translated into Relational Algebra (RA). Relational Algebra (RA) is the writing of logical expressions that uses a set of relational operators to perform operations on specific relation(s) and returns results as relation. RA operations are not applied on querying temporal database, because temporal database holds a sequence of snapshot relations. Approach: This study focuses on the retrieval of temporal database, by extending RA to TRA. TRA is involved to help querying temporal database that is modeled using Tuple Timestamp Historical Relation (TTHR) Model. Results: A technique for querying temporal database modeled by TTHR is the topic of this study. We examine the current issues and problems in temporal database and propose data retrieval optimization technique for temporal database. Conclusion: We proposed a data retrieval optimization technique for querying temporal database; this technique applies for the temporal database applications that are modeled by TTHR.
    Journal of Computer Science 01/2012; 8:243-250.
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    • "Research in temporal databases has been pursued for almost twenty years (e.g., see [2] [9] [12] [14] [18] [20] [29] [31] [34]). However, the migration of this research into commercial databases is limited. "
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    ABSTRACT: Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. Immortal DB builds transaction time database support into a database engine, not in middleware. It supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the "clock times" of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to transaction updates after commit. Versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing, which is essential for high performance historical queries. Experiments show that Immortal DB introduces little overhead for accessing recent database states while providing access to past states.
    Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, 3-8 April 2006, Atlanta, GA, USA; 01/2006
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    • "These facts have been adopted by researchers: in [3], nine temporal database system implementations are reported 1 , out of which only two (HDBMS and TDBMS) have not been based on a snapshot DBMS (notably, however, HDBMS uses BTrieve/Objectrieve for data storage and retrieval). The remaining seven implementations (GCH-OSQL [5], Calanda [15], Chronolog [2], TempCase [19], TempIS [1], TimeDB [4], and VT-SQL [14]) have been based on snapshot DBMSs. However, diverse design approaches have been followed in these implementations. "
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    ABSTRACT: In the past years, a number of implementations of temporal DBMSs has been reported. Most of these implementations share a common feature, which is that they have been built as an extension to a snapshot DBMS. In this paper, we present three alternative design approaches that can be used for extending a snapshot DBMS to support temporal data, and evaluate the suitability of each approach, with respect to a number of design objectives.
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