Abstract
An analysis of a huge amount of information is feasible only if information systems are used. First, information needs to be accumulated and stored in a persistent structure enabling effective data access and management. The main aspects of nowadays data processing are: storing data in (mostly relational) databases, improving data processing efficiency by parallel analysis [1], distributed processing (necessary for institution consisting of autonomous, geographically distributed departments), query languages (SQL) remain a fundamental way to access data in databases, data analysis often includes data mining (building data models describing data characteristics or predicting some features) [2].
Regarding the above mentioned circumstances authors propose an enhancement of SQL for data mining of a distributed data structure. Basic assumption is a complete, horizontal data fragmentation and an explicit model format. Building global data model consists of two stages. In the first one, local models are built in a parallel manner. Second one consists of combining these models into a global data picture. Detailed description of combining methods regarding global classification models authors presented in [3].
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Gorawski, M., Pluciennik, E. (2008). Distributed Data Mining by Means of SQL Enhancement. In: Meersman, R., Tari, Z., Herrero, P. (eds) On the Move to Meaningful Internet Systems: OTM 2008 Workshops. OTM 2008. Lecture Notes in Computer Science, vol 5333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88875-8_16
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DOI: https://doi.org/10.1007/978-3-540-88875-8_16
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