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

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 court cases of the twenty first foreign convention on Database structures for complex purposes, DASFAA 2016, held in Dallas, TX, united states, in April 2016.

The sixty one complete papers provided have been conscientiously reviewed and chosen from a complete of 183 submissions. The papers disguise the next subject matters: crowdsourcing, facts caliber, entity id, information mining and computing device studying, suggestion, semantics computing and data base, textual facts, social networks, advanced queries, similarity computing, graph databases, and miscellaneous, complex applications.

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

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We evaluate our methods on two different datasets, Twitter and Gowalla. The results show that our model can improve the estimation accuracy by 5 %–20 % compared with state-of-the-art friend-based models. 1 Introduction A tremendous amount of information is being shared every day on social media platforms such as Facebook, Twitter, or Google+. For example, more than 241 million active Twitter users have published 300 million tweets worldwide, and this number continues to increase at a rate of 5,700 per second [17].

6. A clearer illustration of Examples 3 and 4 in Fig. 5 (Color figure online) 26 4 Y. Yan et al. The Heterogeneous Bass Model The Bass model [8] is one of the most widely applied models in management science. The model was proposed to predict the sales of a new product when it is lunched on the market. The model is different from traditional machine learning method in that it does not need large numbers of training set. Given the first several days or months of sales of a new product, we can easily predict the performance of the product later via only two parameters.

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. We propose models that use them for location estimation.

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