Intelligent Techniques for Warehousing and Mining Sensor by Alfredo Cuzzocrea

By Alfredo Cuzzocrea

Clever recommendations for Warehousing and Mining Sensor community facts provides basic and theoretical concerns concerning facts administration. protecting a vast variety of issues on warehousing and mining sensor networks, this complex identify presents major strategies to these in database, information warehousing, and information mining examine groups.

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By Alfredo Cuzzocrea

Clever recommendations for Warehousing and Mining Sensor community facts provides basic and theoretical concerns concerning facts administration. protecting a vast variety of issues on warehousing and mining sensor networks, this complex identify presents major strategies to these in database, information warehousing, and information mining examine groups.

Show description

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Extra info for Intelligent Techniques for Warehousing and Mining Sensor Network Data

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E. the reading timestamp; ai , j ,k is the value associated to the dimenp sional attribute Ak of the P-dimensional p model of the stream source si identified by idi, denoted by M s = 〈D( M s ), H( M s ), i i i M( M s )〉, being D( M s ), H( M s ) and i i i M( M s ) the set of dimensions, the set of i hierarchies and the set of measures of M s , i respectively. The definition above adheres to the so-called multidimensional data stream model, which is a fundamental component of the OLAP stream model introduced in the first Section.

It uses the Oracle extensibility framework to implement R-tree indexes for spatio-temporal data. conclusIon A database-centric platform for building sensor data applications offers many advantages. Integrated database analytics enable effective data integration, in-depth data analysis, and real-time online monitoring capabilities. Additionally, the RDBMS framework offers applications inherent security, scalability, and high availability. Current trends in RDBMSs are moving towards providing all key components for delivering comprehensive state-of-the-art analytic applications supporting streaming sensor data.

Clustering data streams: Theory and practice. IEEE Transactions on Knowledge and Data Engineering, 15(3), 515–528. , & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the Seventh ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (pp. 97-106). , & Yalamanchi, A. (2008). Using Oracle Extensibility Framework for Supporting Temporal and Spatio-Temporal Applications. In Proceedings of the fifteenth International Symposium on Temporal Representation and Reasoning (pp.

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