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LEADER 03098cam 2200517Ii 4500

001 ocn968303100;

003 OCoLC;

005 20170424101533.0;

006 m o d ;

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

008 170113s2016 ncuab obm 000 0 eng d;

035 a| (Sirsi) o968303100;

035 a| (OCoLC)968303100;

049 a| EREE;

090 a| Q180.55.E4;

100 1 a| Yadranjiaghdam, Babak,e| author.?| UNAUTHORIZED;

245 10 a| Developing a real-time data analytics framework for Twitter streaming data /c| by Babak Yadranjiaghdam.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2016.;

300 a| 49 pages :b| illustrations (chiefly color), maps;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 932.2 KB2| rda;

538 a| System requirements: Adobe Reader.;

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

502 b| M.S.c| East Carolina Universityd| 2016.;

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 February 10, 2017).;

520 3 a| Twitter is an online social networking service with more than 300 million users, generating a huge amount of information every day. Twitter's most important characteristic is its ability for users to tweet about events, situations, feelings, opinions, or even something totally new, in real time. Currently there are different workflows offering real-time data analysis for Twitter, presenting general processing over streaming data. This study will attempt to develop an analytical framework with the ability of in-memory processing to extract and analyze structured and unstructured Twitter data. The proposed framework includes data ingestion and stream processing and data visualization components with the Apache Kafka messaging system that is used to perform data ingestion task. Furthermore, Spark makes it possible to perform sophisticated data processing and machine learning algorithms in real time. We have conducted a case study on tweets about the earthquake in Japan and the reactions of people around the world with analysis on the time and origin of the tweets.;

504 a| Includes bibliographical references.;

650 0 a| Qualitative researchx| Computer programs.=| ^A459026;

650 0 a| Social mediax| Data processing.=| ^A770054;

653 a| Real-time;

653 a| Big Data;

653 a| Twitter;

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/10342/6045;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4604960;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604960-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/13/2017x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604960-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/13/2017x| ETDz| HERESOURCE;

Developing a real-time data analytics framework for Twitter streaming data / by Babak Yadranjiaghdam.

Author/creator	Yadranjiaghdam, Babak 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], 2016.
Description	49 pages : illustrations (chiefly color), maps
Supplemental Content	Access via ScholarShip
Subjects	Qualitative research--Computer programs. Social media--Data processing.

Summary	Twitter is an online social networking service with more than 300 million users, generating a huge amount of information every day. Twitter's most important characteristic is its ability for users to tweet about events, situations, feelings, opinions, or even something totally new, in real time. Currently there are different workflows offering real-time data analysis for Twitter, presenting general processing over streaming data. This study will attempt to develop an analytical framework with the ability of in-memory processing to extract and analyze structured and unstructured Twitter data. The proposed framework includes data ingestion and stream processing and data visualization components with the Apache Kafka messaging system that is used to perform data ingestion task. Furthermore, Spark makes it possible to perform sophisticated data processing and machine learning algorithms in real time. We have conducted a case study on tweets about the earthquake in Japan and the reactions of people around the world with analysis on the time and origin of the tweets.
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 February 10, 2017).
Dissertation note	M.S. East Carolina University 2016.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Developing a real-time data analytics framework for Twitter streaming data / by Babak Yadranjiaghdam.

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