Download Big data in complex and social networks by My T. Thai, Weili Wu, Hui Xiong PDF

By My T. Thai, Weili Wu, Hui Xiong

This publication offers contemporary advancements at the theoretical, algorithmic, and alertness facets of huge facts in advanced and Social Networks. The booklet contains 4 components, protecting quite a lot of subject matters. the 1st a part of the e-book specializes in information garage and information processing. It explores how the effective garage of information can essentially help in depth info entry and queries, which allows refined research. It additionally appears to be like at how facts processing and visualization aid to speak info essentially and successfully. the second one a part of the booklet is dedicated to the extraction of crucial details and the prediction of websites. The ebook exhibits how vast information research can be utilized to appreciate the pursuits, place, and seek heritage of clients and supply extra actual predictions of person habit. The latter elements of the e-book conceal the security of privateness and defense, and emergent functions of huge information and social networks. It analyzes tips to version rumor diffusion, determine incorrect information from large facts, and layout intervention suggestions. functions of huge information and social networks in multilayer networks and multiparty platforms also are coated in-depth.

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Spark SQL reuses Hive’s query parser to generate a logical operator plan. With this compatibility support, general Hive queries can run on Spark SQL without any changes to the execution script. Spark SQL has its own rule-based logical operator plan optimizer for matching the physical operators that run on Spark. 8 45 Spark SQL architecture [2]. model, rather than tuning Spark low-level execution to support Hive’s Hadoop implementation. 8. , bucketed tables in Hive are not currently supported in Spark SQL.

1 Pig . . . . . . . . . . . . . . . . . . . . . . . . . 2 Hive . . . . . . . . . . . . . . . . . . . . . . . . 3 Spark SQL/Shark . . . . . . . . . . . . . . . . . Pig, Hive and Spark SQL Comparison . . . . . . . . . . Ad-hoc Queries: Truthy and Twitter Data . . . . . . . . Iterative Scientific Applications . . . . . . . . . . . . . . 1 K-means Clustering and PageRank .

This is due to the fact that a user’s engagement may influence other users depending on the influence strength of the former. According to [35], a fairness constraint should be added in the optimization problem so that “a similar users’ influence distribution becomes assigned to each advertiser”. 9)) Integer Programming (IP) problem. 9) S,I pj pj j=1 ui ∈Sj ui ∈Sj j:ui ∈Sj where aj is an advertisement (corresponding to an advertiser), A is the set of all advertisements, pj the bid of the advertiser j which is considered ho- 24 Big Data in Complex and Social Networks mogeneous over all users, ui is a user of the OSN (node of the network) with maximum number of impressions assigned to all advertisers ( j:ui ∈Sj Ii,j ) equal to Ii and social influence given by g(ui ).

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