Decision Making On Operational Data: A Remote Approach To Distributed Data Monitoring
Free (open access)
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.
database applications, distributed data, online analysis processing, data mining, SQL.