Decision Making On Operational Data: A Remote
Approach To Distributed Data Monitoring
Author(s)
V. Benson
Abstract
Information gathering and assimilation is normally performed by data mining
tools and Online analytic processing (OLAP) operating on historic data stored in
a data warehouse. Data mining and OLAP queries are very complex, access a
significant fraction of a database and require significant time and resources to be
executed. Therefore, it has been impossible to draw the data analysis benefits in
operational data environments. When it comes to analysis of operational
(dynamic) data, running complex queries on frequently changing data is next to
impossible. The complexity of active data integration increases dramatically in
distributed applications which are very common in automated or e-commerce
applications.
We suggest a remote data analysis approach to find hidden patterns and
relationships in distributed operational data, which does not adversely affect
routine transaction processing. Distributed data integration on frequently updated
data has been performed by analysing SQL commands coming to the distributed
databases and aggregating data centrally to produce a real-time view of fast
changing data. This approach has been successfully evaluated on data sources for
over 30 data sources for hotel properties. This paper presents the performance
results of the method, and its comparative study of the state-of-the art data
integration techniques. The remote approach to data integration and analysis has
been built into a scalable data monitoring system. It demonstrates the ease of
application and performance results of operational data integration.
Keywords: database applications, distributed data, online analysis processing,
data mining, SQL.
Keywords
database applications, distributed data, online analysis processing,
data mining, SQL.
Related Book
Other papers in this volume
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