scholarly journals Data Warehouse Benchmarking with DWEB

2011 ◽  
pp. 302-323
Author(s):  
Jérôme Darmont
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Data Warehouse ◽  
Database Management ◽  
Ad Hoc ◽  
Synthetic Data ◽  
Transaction Processing ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing

Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with software benchmarks that help, for example test architectural choices, compare different technologies, or tune a system. In the particular context of data warehousing and On-Line Analytical Processing (OLAP), although the Transaction Processing Performance Council (TPC) aims at issuing standard decision-support benchmarks, few benchmarks do actually exist. We present in this chapter the Data Warehouse Engineering Benchmark (DWEB), which allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill various data warehouse design needs. However, two levels of parameterization keep it relatively easy to tune. We also expand on our previous work on DWEB by presenting its new Extract, Transform, and Load (ETL) feature, as well as its new execution protocol. A Java implementation of DWEB is freely available online, which can be interfaced with most existing relational DMBSs. To the best of our knowledge, DWEB is the only easily available, up-to-date benchmark for data warehouses.

scholarly journals DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

2014 ◽  
pp. 294-299
Author(s):  
MOHAMMED SHAFEEQ AHMED
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Storage ◽  
Data Warehousing ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Data-driven decision support systems, such as data warehouses can serve the requirement of extraction of information from more than one subject area. Data warehouses standardize the data across the organization so as to have a single view of information. Data warehouses (DW) can provide the information required by the decision makers. The data warehouse supports an on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Both are essential elements of decision support, which has increasingly become a focus of the database industry. This paper provides a detailed picture of Data warehousing (DW), exploring the features of it, applications and the architecture of DW over Data Mining, Online Analytical Processing (OLAP), On-line Transaction Processing (OLTP) technologies.

Data Warehouse Maintenance, Evolution and Versioning

2010 ◽  
pp. 171-188 ◽  
Author(s):  
Johann Eder ◽  
Karl Wiggisser
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Building Blocks ◽  
Data Warehouses ◽  
Master Data ◽  
Analytical Processing ◽  
The Common ◽  
Instance Analysis ◽  
Transactional Data ◽  
Common Understanding

Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.

scholarly journals Data Warehouse Schemas using Multidimensional Data Model for Retail

2017 ◽  
Vol 2 (1) ◽  
pp. 84
Author(s):  
Kheri Arionadi Shobirin ◽  
Adi Panca Saputra Iskandar ◽  
Ida Bagus Alit Swamardika
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Data Warehouse ◽  
Data Model ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Complex Query ◽  
On Line ◽  
Analytical Processing ◽  
Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

Storage Strategies in Data Warehouses

2011 ◽  
pp. 1054-1058
Author(s):  
Xinjian Lu
Keyword(s):  
Data Warehouse ◽  
Data Aggregation ◽  
Historical Data ◽  
Response Times ◽  
Data Warehouses ◽  
Individual Values ◽  
Processing Data ◽  
On Line ◽  
Analytical Processing ◽  
Require Data

A data warehouse stores and manages historical data for on-line analytical processing, rather than for on-line transactional processing. Data warehouses with sizes ranging from gigabytes to terabytes are common, and they are much larger than operational databases. Data warehouse users tend to be more interested in identifying business trends rather than individual values. Queries for identifying business trends are called analytical queries. These queries invariably require data aggregation, usually according to many different groupings. Analytical queries are thus much more complex than transactional ones. The complexity of analytical queries combined with the immense size of data can easily result in unacceptably long response times. Effective approaches to improving query performance are crucial to a proper physical design of data warehouses.

Data Warehouse Maintenance, Evolution and Versioning

2011 ◽  
pp. 566-583
Author(s):  
Johann Eder ◽  
Karl Wiggisser
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Building Blocks ◽  
Data Warehouses ◽  
Master Data ◽  
Analytical Processing ◽  
The Common ◽  
Instance Analysis ◽  
Transactional Data ◽  
Common Understanding

Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.

Materialized View Selection for Data Warehouse Design

2011 ◽  
pp. 1182-1187 ◽  
Author(s):  
Dimitri Theodoratos ◽  
Wugang Xu ◽  
Alkis Simitsis
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Information Sources ◽  
Distributed Databases ◽  
Materialized Views ◽  
Materialized View ◽  
Other Information ◽  
Materialized View Selection ◽  
On Line ◽  
Analytical Processing

A Data Warehouse (DW) is a repository of information retrieved from multiple, possibly heterogeneous, autonomous, distributed databases and other information sources for the purpose of complex querying, analysis and decision support. Data in the DW are selectively collected from the sources, processed in order to resolve inconsistencies, and integrated in advance (at design time) before data loading. DW data are usually organized multidimensionally to support On-Line Analytical Processing (OLAP). A DW can be abstractly seen as a set of materialized views defined over the source relations. During the initial design of a DW, the DW designer faces the problem of deciding which views to materialize in the DW. This problem has been addressed in the literature for different classes of queries and views and with different design goals.

scholarly journals Data-Centric Benchmarking

2019 ◽  
pp. 342-353
Author(s):  
Jérôme Darmont
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Decision Support ◽  
Data Management ◽  
Present State ◽  
Transaction Processing ◽  
Real System ◽  
Future Trends ◽  
On Line ◽  
Line Analysis

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at on-line transaction processing (OLTP), decision-support benchmarks aimed at on-line analysis processing (OLAP), and big data benchmarks. Issues, tradeoffs, and future trends in data-centric benchmarking are also discussed.

Deductive Data Warehouses

2019 ◽  
pp. 58-82
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Logic Programming ◽  
Data Warehouse ◽  
Programming Language ◽  
Small Data ◽  
Data Warehouses ◽  
Logic Programming Language ◽  
Front End ◽  
On Line ◽  
Analytical Processing ◽  
Olap Analysis

This chapter presents the concept of “deductive data warehouses.” Deductive data warehouses rely on deductive databases but use a data warehouse in the background instead of a database. The authors show how Datalog, as a logic programming language, can be used to perform on-line analytical processing (OLAP) analysis on data. For that purpose, a small data warehouse has been implemented. Furthermore, they propose and briefly discuss “Datalog by example” as a visual front-end tool for posing Datalog queries to deductive data warehouses.

scholarly journals Query Performance Optimization in XML Data Warehouses

2010 ◽  
pp. 232-253
Author(s):  
Hadj Mahboubi ◽  
Jérôme Darmont
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Performance Optimization ◽  
Database Management ◽  
Optimization Techniques ◽  
Materialized Views ◽  
Complex Data ◽  
Data Warehouses ◽  
Xml Data ◽  
Xml Database

XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this chapter, the authors present two such techniques. First, they propose an XML join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. Second, the authors present a strategy for selecting XML materialized views by clustering the query workload. To validate these proposals, the authors measure the response time of a set of decision-support XQueries over an XML data warehouse, with and without using their optimization techniques. The authors’ experimental results demonstrate their efficiency, even when queries are complex and data are voluminous.

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