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.

0 Bookmarks
 · 
51 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Query performance is a critical factor in modern business intelligence and data warehouse systems. An increasing number of companies uses detailed analyses for conducting daily business and supporting management decisions. Thus, several techniques have been developed for achieving near realtime response times - techniques which try to alleviate I/O bottlenecks while increasing the throughputs of available processing units, i.e. by keeping relevant data in compressed main-memory data structures and exploiting the read-only characteristics of analytical workloads. However, update processing and skews in data distribution result in degenerations in these densely packed and highly compressed data structures affecting the memory efficiency and query performance negatively. Reorganization tasks can repair these data structures, but -- since these are usually costly operations -- require a well-considered decision which of several possible strategies should be processed and when, in order to reduce system downtimes. In this paper, we address these problems by presenting an approach for online reorganization in main-memory database systems (MMDBS). Based on a discussion of necessary reorganization strategies in IBM Smart Analytics Optimizer, a read optimized parallel MMDBS, we introduce a framework for executing arbitrary reorganization tasks online, i.e. in the background of normal user workloads without disrupting query results or performance.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June 12-16, 2011; 01/2011
  • Source
    [Show abstract] [Hide abstract]
    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
  • Source
    [Show abstract] [Hide abstract]
    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.

Full-text (2 Sources)

View
286 Downloads
Available from
May 21, 2014