Masters Theses

Abstract

"In today's society, social networks are a popular way to connect with friends and family and share what's going on in your life. With the Internet connecting us all closer than ever before, it is increasingly common to use social networks to meet new friends online that share similar interests instead of only connecting with those you already know. For the problem of attempting to connect people with similar interests, this paper proposes the foundation for a Geo-social network that aims to extract the semantic meaning from users' location history and use this information to find the similarity between users. Once the similarity scores are obtained, the results are examined to extract the groups of similar users for the Geo-social network. Computing similarity for a large number of users and then grouping based on the results is a computationally intensive task, but fortunately Apache Spark can be leveraged to execute the comparison and clustering of users in parallel across multiple computers, increasing the computation speed when compared to a centralized version and working quickly enough to suggest friends in real time for a given user"--Abstract, page iii.

Advisor(s)

Lin, Dan

Committee Member(s)

Jiang, Wei
Fu, Yanjie

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2018

Pagination

vii, 28 pages

Note about bibliography

Includes bibliographical references (page 27).

Rights

© 2018 Tyler Clark Percy, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11393

Electronic OCLC #

1051222684

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