3,114 research outputs found
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
On-line analytical processing
On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft
Analyzing Large Collections of Electronic Text Using OLAP
Computer-assisted reading and analysis of text has various applications in
the humanities and social sciences. The increasing size of many electronic text
archives has the advantage of a more complete analysis but the disadvantage of
taking longer to obtain results. On-Line Analytical Processing is a method used
to store and quickly analyze multidimensional data. By storing text analysis
information in an OLAP system, a user can obtain solutions to inquiries in a
matter of seconds as opposed to minutes, hours, or even days. This analysis is
user-driven allowing various users the freedom to pursue their own direction of
research
XWeB: the XML Warehouse Benchmark
With the emergence of XML as a standard for representing business data, new
decision support applications are being developed. These XML data warehouses
aim at supporting On-Line Analytical Processing (OLAP) operations that
manipulate irregular XML data. To ensure feasibility of these new tools,
important performance issues must be addressed. Performance is customarily
assessed with the help of benchmarks. However, decision support benchmarks do
not currently support XML features. In this paper, we introduce the XML
Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from
the relational decision support benchmark TPC-H. It is mainly composed of a
test data warehouse that is based on a unified reference model for XML
warehouses and that features XML-specific structures, and its associate XQuery
decision support workload. XWeB's usage is illustrated by experiments on
several XML database management systems
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