Transactions on Large-Scale Data- and Knowledge-Centered by Roberto De Virgilio, Franco Milicchio (auth.), Abdelkader

By Roberto De Virgilio, Franco Milicchio (auth.), Abdelkader Hameurlain, Josef Küng, Roland Wagner (eds.)

The LNCS magazine Transactions on Large-Scale information- and Knowledge-Centered platforms specializes in information administration, wisdom discovery, and data processing, that are center and scorching issues in desktop technological know-how. because the Nineteen Nineties, the net has turn into the most motive force in the back of software improvement in all domain names. a rise within the call for for source sharing throughout diversified websites attached via networks has ended in an evolution of information- and knowledge-management platforms from centralized structures to decentralized platforms permitting large-scale dispensed functions supplying excessive scalability. present decentralized platforms nonetheless concentrate on information and data as their major source. Feasibility of those platforms is predicated primarily on P2P (peer-to-peer) recommendations and the help of agent platforms with scaling and decentralized regulate. Synergy among grids, P2P structures, and agent applied sciences is the most important to information- and knowledge-centered structures in large-scale environments. This, the 7th factor of Transactions on Large-Scale information- and Knowledge-Centered structures, comprises 5 revised chosen general papers at the following themes: info administration, facts streams, service-oriented computing, summary algebraic frameworks, RDF and ontologies, and conceptual version frameworks.

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Q1 in the query set: this is normal behavior, since databases present indexes that enhance selection queries, and thus we perform on par. With path oriented queries, 24 R. De Virgilio and F. Milicchio t t 203. 7006. 100 1000 100 10 10 5 105 1 106 5 106 1 107 d 5 105 1 106 (a) Q1 5 106 1 107 d (b) Q2 t t 2703. 8032. 1000 1000 100 100 5 105 1 106 5 106 1 107 d 5 105 1 106 (c) Q3 5 106 1 107 d (d) Q4 t t 8123. 1000 1000 100 100 5 105 1 106 5 106 1 107 d 10 5 105 (e) Q5 t t 5034. 5041. 1000 1000 100 100 5 105 1 106 1 106 5 106 1 107 d (f) Q6 5 106 1 107 d 5 105 1 106 (g) Q7 5 106 1 107 d (h) Q8 t t 7064.

Conf. on Management of Data, SIGMOD, pp. 291–302 (2008) 8. : Streaming pattern discovery in multiple time-series. In: Proc. of the 31st Int. Conf. on Very Large Data Bases, VLDB, pp. 697–708 (2005) 9. : RFID data processing with a data stream query language. In: Proc. of Int. Conf. on Data Engineering, ICDE, pp. 1184–1193 (2007) 10. : Adaptive cleaning for RFID data streams. In: Proc. of the 32nd Int. Conf. on Very Large Data Bases, VLDB, pp. 163–174 (2006) 11. : A pipelined framework for online cleaning of sensor data streams.

3896, pp. 588–607. Springer, Heidelberg (2006) 30 R. De Virgilio and F. Milicchio 13. : Linear Algebra. Prentice Hall (1971) 14. : Manifolds, Tensor Analysis, and Applications. Springer (1988) 15. : Introduction to Tensor Calculus and Continuum Mechanics. Trafford Publishing (2001) 16. : The extended grassmann algebra of R3 . In: Clifford (Geometric) Algebras with Applications to Physics, Mathematics, and Engineering. Birkh¨ auser, Boston (1996) 17. : RFID data management: Challenges and opportunities.

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