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LEADER 03042cam a2200505Ii 4500

001 on1001281756;

003 OCoLC;

005 20171031024953.0;

006 m o d ;

007 cr unu||||||||;

008 170816s2017 ncua obm 000 0 eng d;

035 a| (Sirsi) o1001281756;

035 a| (OCoLC)1001281756;

049 a| EREE;

090 a| TK5103.2;

100 1 a| Yasrobi, Seyedfaraz,e| author.?| UNAUTHORIZED;

245 10 a| User behavior analysis using smartphones /c| by Seyedfaraz Yasrobi.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2017.;

300 a| 45 pages :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 803.6 KB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2017.;

500 a| Presented to the faculty of the Department of Computer Science.;

500 a| Advisor: Nasseh Tabrizi;

500 a| Title from PDF t.p. (viewed October 30. 2017);

520 3 a| Users' activities produce an enormous amount of data when using popular devices such as smartphones. These data can be used to develop behavioral models in several areas including fraud detection, finance, recommendation systems, and marketing. However, enabling fast analysis of such a large volume of data using traditional data analytics may not be applicable. In-memory analytics is a new technology for faster querying and processing of data stored in computer's memory (RAM) rather than disk storage. This research reports on the feasibility of user behavior analytics based on their activities in applications with a large number of users using in-memory processing. We present a new instantaneous behavioral model to examine users' activities and actions rather than results of their activities in order to analyze and predict their behaviors. For the purpose of this research, we designed a software to simulate user activity data such as users' swipes and taps, and studied the performance and scalability of this architecture for a large number of the users.;

504 a| Includes bibliographical references.;

650 0 a| Smartphonesx| Data processing.=| ^A781523;

650 0 a| Information storage and retrieval systems.=| ^A14867;

650 0 a| Big data.=| ^A1157100;

653 a| User behavior;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/6330;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4733348;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4733348-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 8/16/2017x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4733348-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 8/16/2017x| ETDz| HERESOURCE;

User behavior analysis using smartphones / by Seyedfaraz Yasrobi.

Author/creator	Yasrobi, Seyedfaraz author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2017.
Description	45 pages : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Smartphones--Data processing. Information storage and retrieval systems. Big data.

Summary	Users' activities produce an enormous amount of data when using popular devices such as smartphones. These data can be used to develop behavioral models in several areas including fraud detection, finance, recommendation systems, and marketing. However, enabling fast analysis of such a large volume of data using traditional data analytics may not be applicable. In-memory analytics is a new technology for faster querying and processing of data stored in computer's memory (RAM) rather than disk storage. This research reports on the feasibility of user behavior analytics based on their activities in applications with a large number of users using in-memory processing. We present a new instantaneous behavioral model to examine users' activities and actions rather than results of their activities in order to analyze and predict their behaviors. For the purpose of this research, we designed a software to simulate user activity data such as users' swipes and taps, and studied the performance and scalability of this architecture for a large number of the users.
General note	Presented to the faculty of the Department of Computer Science.
General note	Advisor: Nasseh Tabrizi
General note	Title from PDF t.p. (viewed October 30. 2017)
Dissertation note	M.S. East Carolina University 2017.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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User behavior analysis using smartphones / by Seyedfaraz Yasrobi.

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