Database and Expert Systems Applications: 25th International by Hendrik Decker, Lenka Lhotská, Sebastian Link, Marcus Spies,

By Hendrik Decker, Lenka Lhotská, Sebastian Link, Marcus Spies, Roland R. Wagner

This quantity set LNCS 8644 and LNCS 8645 constitutes the refereed complaints of the twenty fifth overseas convention on Database and professional platforms purposes, DEXA 2014, held in Munich, Germany, September 1-4, 2014. The 37 revised complete papers awarded including forty six brief papers, and a couple of keynote talks, have been conscientiously reviewed and chosen from 159 submissions. The papers speak about a number subject matters together with: facts caliber; social internet; XML key-phrase seek; skyline queries; graph algorithms; info retrieval; XML; safety; semantic net; class and clustering; queries; social computing; similarity seek; score; info mining; huge information; approximations; privateness; facts trade; info integration; net semantics; repositories; partitioning; and company applications.

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Extra info for Database and Expert Systems Applications: 25th International Conference, DEXA 2014, Munich, Germany, September 1-4, 2014. Proceedings, Part II

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The results show that participating in activities to achieve social sustainability is most important to increase the awareness of sustainability. 5 Case Study III: Crime Mapping via Social Media A crime map is a tool that visualizes crime information based on the geographical location of crimes. In earliest time, the police use a crime map to recognize the inherent geographical component of crimes by sticking pins into maps displayed on walls, where each pin on the map represents a crime incident.

Although we cannot reason that two authors in similar domain correspond to same person, but it is unlikely for same person to publish in very distinct research areas. Information like title, journal/conference name and keyword depict domain characteristics of a given author. We concatenate all these text features to get a virtual documents. By generating virtual document for each individual author, we have a virtual corpus to model domain features. We utilize two classic approaches in text modeling: TF/IDF-weighted vectors and topic vectors inference by Latent Dirichlet Allocation [12].

Each feature vector corresponds to two virtual publications. However, we know that not all papers have complete information. We give each attribute an identity, and encode feature vector using combination of identities of shared attributes from two virtual publications. For example, if publication A has title, year, venue, keywords, publication B has title, year, venue and affiliation, the feature vector of theirs are encode as “124”. Although there are total 31 kinds of encodes theoretically, we only find 20 feature encodes for the practical academic social network.

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