ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)
Jimi Carmen Sanchez (Creator)
East Carolina University (ECU )
Web Site:

Abstract: Decision Support System (DSS) are at the core of business intelligence systems. Implementation costs for enterprise level Database Management System (DBMS) and DSS average $10,461 for installation costs. This does not include costs associated with database migrations or testing, which can double the cost, nor does this quoted price include the cost of yearly licensing or support agreements. Depending on the software vendor, there may be additional costs associated with using an application cluster, logical and virtual partitioning, data guards, and even costs per processor core. It is easy to see how the cost of operating a database server can grow expensive rapidly. Information Technology (IT) decision makers and software architects need the ability to choose a DBMS to suit their application's needs. To choose the correct DBMS solution a comprehensive and adaptive benchmark is needed. This benchmark must be capable of predicting how the performance of a given system will scale, as well as offer an estimation of cost. A problematic benchmark that is unable to accurately predict these values is worthless and leads to costly software decision mistakes. To continue to be successful and remain competitive in a given industry it is important for organizations to know their customers, target and acquire new markets, and look to future trends. This is where database business intelligence and decision support systems become useful. DSS allow users to data mine critical information about their work-flows, sales history and trends and have the data readily available so that they may make informed decisions and plan future growth. Business intelligence tools and decision support systems provide executive officers and members of management, the tools needed to create complex ad-hoc queries and mine important data. Presently, IT decision makers and software engineers use the TPC-H decision sup- port system benchmark as a guide to determining the optimal hardware and database vendor configurations to utilize for their decision support system. The TPC-H benchmark is a popular decision support system benchmark. In recent years, however, TPC-H has become heavily criticized for its many problems. The issues outlined within this thesis can lead IT decision makers to purchase and implement improper hardware and software solutions. This thesis examines the criticisms and issues of the TPC-H benchmark. Utilizing Amazon Web Services cloud computing power, we evaluate the Star Schema Benchmark (SSB), as an alternative to TPC-H. We successfully identify and demonstrate several previously undefined problems in the TPC-H benchmark. Our results conclude that the SSB not only resolves the issues inherent in TPC-H, and should serve as a replacement for TPC-H.

Additional Information

Language: English
Date: 2016
star schema, benchmark, ssb, tpc-h
Purchasing; Database management; Decision support systems--Computer programs

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TitleLocation & LinkType of Relationship
INVESTIGATING THE STAR SCHEMA BENCHMARK AS A REPLACEMENT FOR THE TPC-H DECISION SUPPORT SYSTEM described resource references, cites, or otherwise points to the related resource.