Database Systems for Advanced Applications: 21st by Shamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du,

By Shamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du, X. Sean Wang, Hui Xiong

This quantity set LNCS 9642 and LNCS 9643 constitutes the refereed court cases of the twenty first overseas convention on Database structures for complex purposes, DASFAA 2016, held in Dallas, TX, united states, in April 2016.

The sixty one complete papers awarded have been rigorously reviewed and chosen from a complete of 183 submissions. The papers hide the next issues: crowdsourcing, facts caliber, entity identity, facts mining and computing device studying, suggestion, semantics computing and information base, textual information, social networks, complicated queries, similarity computing, graph databases, and miscellaneous, complex applications.

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Extra info for Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I

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DASFAA 2015. LNCS, vol. 9049, pp. 21–38. Springer, Heidelberg (2015) 34. : A transfer learning based framework of crowdselection on twitter. In: KDD, pp. 1514–1517 (2013) 35. : Expert finding for question answering via graph regularized matrix completion. IEEE Trans. Knowl. Data Eng. 27, 993– 1004 (2015) 36. : Learning from the wisdom of crowds by minimax entropy. In: NIPS, pp. cn Abstract. Effective result inference is an important crowdsourcing topic as workers may return incorrect results. Existing inference methods assign each task to multiple workers and aggregate the results from these workers to infer the final answer.

The selection of the crowd is only based on the reliability of the workers. However, none of the above-mentioned works utilize the power of social influence for processing crowdsourced queries on microblogs. Our work shows that Twitter users send crowdsourced queries to their friends and that people also answer the Twitter queries based on their friendships. By taking the social influence into consideration, we further study how to mine crowdseed for crowdsourced query processing on microblogs. 26 5 W.

729–732 (2013) 32. : Crowd-selection query processing in crowdsourcing databases: a task-driven approach. In: EDBT (2015) 33. : Cold-start expert finding in community question answering via graph regularization. A. ) DASFAA 2015. LNCS, vol. 9049, pp. 21–38. Springer, Heidelberg (2015) 34. : A transfer learning based framework of crowdselection on twitter. In: KDD, pp. 1514–1517 (2013) 35. : Expert finding for question answering via graph regularized matrix completion. IEEE Trans. Knowl. Data Eng.

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