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, Sean X. Wang, Hui Xiong

This quantity set LNCS 9642 and LNCS 9643 constitutes the refereed complaints of the twenty first foreign convention on Database platforms for complex functions, 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 disguise the subsequent subject matters: crowdsourcing, facts caliber, entity identity, information mining and desktop studying, advice, semantics computing and information base, textual info, social networks, advanced queries, similarity computing, graph databases, and miscellaneous, complicated applications.

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Predicting the future with social media. In: IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499 (2010) 6. : Retweet behavior prediction using hierarchical dirichlet process. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 403–409 (2015) 7. : Seismic: a selfexciting point process model for predicting tweet popularity. In: ACM SIGKDD International Conference on Knowledge Discovery andData Mining (SIGKDD), pp. 1513–1522 (2015) 8.

2, we still take it into consideration here. It should be noted that not all of these features contribute to the final result of our experiments. Those nonsignificant features will be ruled out by PCA (Principal Component Analysis). ⎡ ⎤ log num Of Followings ⎢ log num Of Followers ⎥ ⎥ (4) x=⎢ ⎣ log num Of Tweets ⎦ log num Of Favorites ⎤ log num Of Creating Time ⎦ y = ⎣ log num Of URLs log num Of Characters ⎡ (5) In order to find Y (t) we must solve the non-linear differential equation: q dY = pm + (q − p)Y (t) − [Y (t)]2 + αx + βy dt m (6) For simplicity, let V = pm + αx + βy.

Soc. 99(3), 614–639 (1993) 31. : Predicting the popularity of online content. Commun. edu Abstract. A tremendous amount of information is being shared every day on social media sites such as Facebook, Twitter or Google+. Current methods in location estimation using social relationships consider social friendship as a simple binary relationship. However, social closeness between users and structure of friends have strong implications on geographic distances. In this paper, we introduce new measures to evaluate the social closeness between users and structure of friends.

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