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 complaints of the twenty first overseas convention on Database structures for complex functions, DASFAA 2016, held in Dallas, TX, united states, in April 2016.

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

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

Example text

To make a fair comparison with the baseline algorithms, we penalize the users with high costs in both Degree-based and Centrality-based algorithms. e. deg(ui )) and then selects the users according to that order. e. deg(ui )/c(ui )). Similarly, we build a new Centralitybased algorithm for comparison. Figure 4(a), (b) and (c) show that the cost of the crowdsourced query increases when reducing the error rate threshold. However, our algorithm also 30 W. Chen et al. 10000 10000 1000 1000 1000 100 Greedy Degree Centrality CELF LDAG 10 1 -1 10 10 -2 -3 10 10 -4 10 COST 10000 COST COST outperforms four other baseline algorithms in terms of the query cost.

Table 1. Statistics of datasets Dataset No. of nodes Epin NetHEPT Twitter 75888 15233 11555 No. of edges 508837 58891 500000 Baseline Algorithms. We evaluate the effectiveness and robustness of the proposed algorithms using the three real social graphs aforementioned. We also propose four baseline algorithms such as Degree-based algorithm, Centrality-based algorithm, the CELF++ [11] Algorithm and the LDAG [5] The Degree-based and Centrality-based algorithms rank all the vertices based on vertices degree and centrality first.

On the other hand, if we replicate too many queries, we may have to suffer high cost. – Incentive Mechanisms. Crowdsourcing workers may be reluctant to process any queries until they know they will receive a reward. Thus, we aim to design an incentive mechanism that is able to reduce the workforce cost and at the same time, meet a given specified accuracy threshold. – Query Sharing. We utilize the word of mouth effect to model the worker behavior of query sharing on microblogs. Intuitively, microblog users are more willing to answer a crowdsourced query under a social influence and to send messages to an interested group.

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