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Home / programming language / Online Java Projects / RSkNN: kNN Search on Road Networks by Incorporating Social Influence
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Java Project - Networking, Final year projects.
  • Java Project - Networking, Final year projects.

RSkNN: kNN Search on Road Networks by Incorporating Social Influence

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SKU: PROJ7562 Categories: 2017 Projects, Final Year Projects, Networking Projects, Online Java Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Automatic Attendance Management System Using Face Recognition

Abstract— Although kNN search on a road network Gr, i.e., finding k nearest objects to a query user q on Gr, has been extensively studied, existing works neglected the fact that the q’s social information can play an important role in this kNN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper, we study a new problem: kNN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.< final year projects >

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