Big Data: 29th British National Conference on Databases, by Dan Suciu (auth.), Georg Gottlob, Giovanni Grasso, Dan

By Dan Suciu (auth.), Georg Gottlob, Giovanni Grasso, Dan Olteanu, Christian Schallhart (eds.)

This e-book constitutes the completely refereed post-conference complaints of the twenty ninth British nationwide convention on Databases, BNCOD 2013, held in Oxford, united kingdom, in July 2013. The 20 revised complete papers, offered including 3 keynote talks, tutorials, and one panel consultation, have been conscientiously reviewed and chosen from forty two submissions. unique concentration of the convention has been "Big info" and so the papers conceal a variety of issues reminiscent of question and replace processing; relational garage; benchmarking; XML question processing; great information; spatial facts and indexing; facts extraction and social networks.

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5823, pp. 634–649. Springer, Heidelberg (2009) 25. : Query languages for graph databases. ACM SIGMOD Record 41(1), 50–60 (2012) 26. : GRAIL: Scalable reachability index for large graphs. PVLDB 3(1-2), 276–284 (2010) 27. : Efficient subgraph search over large uncertain graphs. PVLDB 4(11) (2011) 28. : A distributed graph engine for web scale RDF data. PVLDB 6(4) (2013) 29. : Mining frequent subgraph patterns from uncertain graph data. TKDE 22(9), 1203–1218 (2010) Adapting to Node Failure in Sensor Network Query Processing Alan B.

Recall that a Steiner tree is a minimum spanning tree (and hence likely to be energy-efficient) that necessarily includes a given set of nodes. , the base station). Step 5 in Fig. 2 decides which fragment instances to place for execution in which node. This partitions the PAF into fragment instances and assigns the latter to RT nodes with a view to conserving energy by reducing the number of tuples that need to be transmitted. The resulting DAF for the example query is shown in Fig. 3(b). Dashed boxes define fragment boundaries; the list in curly brackets at the bottom-right corner (below the fragment identifier) denotes how many instances of that fragment there are and in which nodes they run.

Se/~ viggo/wwwcompendium/ 7. : MapReduce: simplified data processing on large clusters. Commun. ACM 51(1) (2008) 8. : Optimal aggregation algorithms for middleware. JCSS 66(4), 614–656 (2003) 28 W. Fan 9. : Foundations of Data Quality Management. Morgan & Claypool Publishers (2012) 10. : Making queries tractable on big data with preprocessing. In: PVLDB (2013) 11. : Adding regular expressions to graph reachability and pattern queries. In: ICDE (2011) 12. : Graph pattern matching: From intractability to polynomial time.

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