Using Semantics for Query Derivability in Data Warehouse Applications

Albrecht, Jens; Hämmer, W.; Lehner, Wolfgang; Schlesinger, Lutz

doi:10.1007/978-3-7908-1834-5_1

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…(P.Article, T.Day, L.Shop). Measures of this kind are called multiplexable and are introduced by [2]. Therefore, we present here only a short overview and refer for a more in detail discussion to the original article.…”

Section: Multiplexable Measures and Aggregation Typesmentioning

confidence: 99%

“…In [2] the conditions are extended for granularities after involving the the multiplex operator. This article extends the considerations regarding to the derivability of composite measures, which is presented in the next paragraph.…”

Section: Derivability Of Simple Aggregate Viewsmentioning

confidence: 99%

“…prices) in general. Accordingly to [2] we restrict ourselves to the aggregation type Type(M) ∈{FLOW, VPU} for a measure M.…”

Section: Definitionmentioning

confidence: 99%

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Query optimization by using derivability in a data warehouse environment

Albrecht

Hümmer

Lehner

et al. 2000

Proceedings of the 3rd ACM International Workshop on Data Warehousing and OLAP

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Materialized summary tables and cached query results are frequently used for the optimization of aggregate queries in a data warehouse. Query rewriting techniques are incorporated into database systems to use those materialized views and thus avoid the access of the possibly huge raw data. A rewriting is only possible if the query is derivable from these views. Several approaches can be found in the literature to check the derivability and find query rewritings. The specific application scenario of a data warehouse with its multidimensional perspective allows the consideration of much more semantic information, e.g. structural dependencies within the dimension hierarchies and different characteristics of measures. The motivation of this article is to use this information to present conditions for derivability in a large number of relevant cases which go beyond previous approaches. MOTIVATIONWe will motivate our case with an illustrative example. Consider the conceptual schema of the data warehouse of some retail store chain as depicted in figure 1. By adopting a multidimensional terminology, there are three dimensions, Product, Location and Time with several category attributes. The arrows define functional dependencies in database terms. Each path from a terminal attribute, e.g. Article, to the Top category in the respective dimension defines a classification hierarchy (figure 1). Parallel paths result in parallel hierar-

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Section: Multiplexable Measures and Aggregation Typesmentioning

confidence: 99%

Section: Derivability Of Simple Aggregate Viewsmentioning

confidence: 99%

See 1 more Smart Citation

Query optimization by using derivability in a data warehouse environment

Albrecht

Hümmer

Lehner

et al. 2000

Proceedings of the 3rd ACM International Workshop on Data Warehousing and OLAP

Self Cite

View full text Add to dashboard Cite

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Section: Introductionmentioning

confidence: 99%

Improve Performance of Data Warehouse by Query Cache

Gour¹,

Sarangdevot²,

Sharma³

et al. 2010

AIP Conference Proceedings

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Abstract-The primary goal of data warehouse is to free the information locked up in the operational database so that decision makers and business analyst can make queries, analysis and planning regardless of the data changes in operational database. As the number of queries is large, therefore, in certain cases there is reasonable probability that same query submitted by the one or multiple users at different times. Each time when query is executed, all the data of warehouse is analyzed to generate the result of that query. In this paper we will study how using query cache improves performance of Data Warehouse and try to find the common problems faced. These kinds of problems are faced by Data Warehouse administrators which are minimizes response time and improves the efficiency of query in data warehouse overall, particularly when data warehouse is updated at regular interval.

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“…The primary goal is creating strategic planning resulting from long tern data analysis. We can create reports, projection, and business model and can forecast by these analysis [16] According to [2] there are many ways for a data warehouse project to fail. The project can be over budget, the schedule may slip, critical functions may not be implemented, the users could be unhappy and the performance may be unacceptable.…”

Section: Introductionmentioning

confidence: 99%

Performance Tunning Mechanisms for Data Warehouse: Query cache

Gour¹,

Sarangdevot²,

Tanwar³

2010

IJCA

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ABSTRACT:Data warehousing encompasses architectures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse suitable for direct querying or analysis. In recent years data warehousing has become a prominent buzzword in the database industry, but attention from the database research community has been limited. At the warehouse, queries can be answered and data analysis can be performed quickly and efficiently since the information is directly available, with model and semantic differences already resolved. The primary goal of data warehouse is to free the information locked up in the operational database so that decision makers and business analyst can make queries, analysis and planning regardless of the data changes in operational database. As the number of queries is large, therefore, in certain cases there is reasonable probability that same query submitted by the one or multiple users at different times. Each time when query is executed, all the data of warehouse is analyzed to generate the result of that query.In this paper we will try to find the common problems faced. These kinds of problems are faced by Data Warehouse administrators which are minimizes response time and improves the efficiency of data warehouse overall, particularly when data warehouse is updated at regular interval. The overall performance of the system and provide good strategies that make superior Data Warehouse.

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Using Semantics for Query Derivability in Data Warehouse Applications

Cited by 3 publications

References 13 publications

Query optimization by using derivability in a data warehouse environment

Query optimization by using derivability in a data warehouse environment

Improve Performance of Data Warehouse by Query Cache

Performance Tunning Mechanisms for Data Warehouse: Query cache

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