By Dan Suciu (auth.), Georg Gottlob, Giovanni Grasso, Dan Olteanu, Christian Schallhart (eds.)
This ebook 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, provided including 3 keynote talks, tutorials, and one panel consultation, have been conscientiously reviewed and chosen from forty two submissions. detailed concentration of the convention has been "Big info" and so the papers disguise quite a lot of subject matters corresponding to question and replace processing; relational garage; benchmarking; XML question processing; colossal facts; spatial info and indexing; information extraction and social networks.
Read Online or Download Big Data: 29th British National Conference on Databases, BNCOD 2013, Oxford, UK, July 8-10, 2013. Proceedings PDF
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Extra resources for Big Data: 29th British National Conference on Databases, BNCOD 2013, Oxford, UK, July 8-10, 2013. Proceedings
In: WWW (2004) 32. : On the computational complexity of dynamic graph problems. TCS 158(1-2) (1996) 33. : Social matching: A framework and research agenda. ACM Trans. -Hum. Interact. 12(3) (2005) 34. : Approximate distance oracles. JACM 52(1), 1–24 (2005) 35. : An algorithm for subgraph isomorphism. JACM 23(1), 31–42 (1976) 36. : Approximation Algorithms. uk 1 Overview Large volumes of graph-structured data are becoming increasingly prevalent in areas such as – social and professional network analysis – recommendation services, such as product advertisement, news and media alerts, learning resource recommendation, itinerary recommendation – scientiﬁc computing: life and health sciences, physical sciences – crime investigation and intelligence gathering – telecoms network management, for dependency analysis, root cause analysis, location-based service provision – linked open data – geospatial data – business process management: logistics, ﬁnance chains, fraud detection, risk analysis, asset management – organization management Graph-structured data diﬀers from other “big” data in its greater focus on the relationships between entities, regarding these relationships as important as the entities themselves, and allowing the possibility of modelling the attributes of relationships just as for entities, specifying constraints on relationships, and undertaking querying, analysis and reasoning over relationships.
A. W. , TinyDB makes no attempt to partition the plan into fragments), and, as a result, ignoring a failed node is a sound strategy. Thus, whilst TinyDB is not, strictly speaking, adaptive, it is, to a certain degree, resilient to some forms of node failure. However, tuple transmission is not globally scheduled (as it is in SNEE), so there is no way of estimating how many tuples might be lost as a result of failed nodes. The SmartCIS project  builds upon TinyDB with a particular goal (among others) of supporting fault-tolerant routing trees via multi-path transmissions.
28(3), 480–503 (1996) 24. : A model of computation for MapReduce. In: SODA (2010) 25. : Parallel evaluation of conjunctive queries. In: PODS (2011) 26. : Data integration: A theoretical perspective. In: PODS (2002) 27. : Capturing topology in graph pattern matching. PVLDB 5(4) (2011) 28. : Communication and Concurrency. Prentice Hall (1989) 29. : What do people ask their social networks, and why? A survey study of status message Q&A behavior. In: CHI (2010) 30. : Understanding the structure of a drug traﬃcking organization: a conversational analysis.
Big Data: 29th British National Conference on Databases, BNCOD 2013, Oxford, UK, July 8-10, 2013. Proceedings by Dan Suciu (auth.), Georg Gottlob, Giovanni Grasso, Dan Olteanu, Christian Schallhart (eds.)