Cloud-based architectures for geo-located blogosphere dynamics detection

Athena Vakali, Stefanos Antaris, Maria Giatsoglou

Abstract

Social networking data threads emerge rapidly and such crowd-driven big data streams are valuable for detecting trends and opinions. For such analytics, conventional data mining approaches are challenged by both high-dimensionality and scalability concerns. Here, we leverage on the Cloud4Trends framework for collecting and analyzing geo-located microblogging content, partitioned into clusters under cloud-based infrastructures. Different cloud architectures are proposed to offer flexible solutions for geo-located data analytics with emphasis on incremental trend analysis. The proposed architectures are largely based on a set of service modules which facilitate the deployment of the experimentation on cloud infrastructures. Several experimentation remarks are highlighted to showcase the requirements and testing capabilities of different cloud computing settings.

Keywords

social networks and wisdom of the crowd; geo-located blogosphere dynamics; social geo-located data clus-tering; cloud service deployment

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References

Twitter usage statistics, n.d., viewed February 21, 2016,

Antoniades D and Dovrolis C, 2015, Co-evolutionary dynamics in social networks: a case study of Twitter. Computational Social Networks, vol.2(1): 1–21. http://dx.doi.org/10.1186/s40649-015-0023-6.

Vakali A, Giatsoglou M, and Antaris S, 2012, Social networking trends and dynamics detection via a cloud- based framework design. Proceedings of the 21st International Conference on World Wide Web (WWW’ 2012), 1213–1220. http://dx.doi.org/10.1145/2187980.2188263.

Google trends, n.d., viewed February 22, 2016,

Glance N S, Hurst H and Tomokiyo T, 2004, BlogPulse: automated trend discovery for weblogs, in WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, May 2004.

Trendistic homepage, n.d., viewed February 24, 2016,

FAQs about trends on Twitter, n.d., viewed February 27, 2016,

Livenson I and Laure E, 2011, Towards transparent integration of heterogeneous cloud storage platforms. Proceedings of the Fourth International Workshop on Da-ta-intensive Distributed Computing (DIDC '11), 27–34. http://dx.doi.org/10.1145/1996014.1996020.

Giatsoglou M and Vakali A, 2013, Capturing social data evolution using graph clustering. IEEE Internet Computing, vol.17(1): 74-79. http://dx.doi.org/10.1109/MIC.2012.141.

Uchida M, Shibata N and Shirayama S, 2007, Identification and visualization of emerging trend from blogosphere. Proceedings of the International Conference on Weblogs and Social Media (ICWSM), 305–306.

Mathioudakis M and Koudas N, 2010, TwitterMonitor: trend detection over the twitter stream. Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD '10), 1155–1158. http://dx.doi.org/10.1145/1807167.1807306.

Clarg E and Araki K, 2011, Text normalization in social media: progress, problems and applications for a pre- processing system of casual English. Procedia — Social and Behavioral Sciences: Special Issue of Computational Linguistics and Related Fields, vol.27: 2–11.

http://dx.doi.org/10.1016/j.sbspro.2011.10.577.

Reuter T, Cimiano P, Drumond L, et al. 2011, Scalable event-based clustering of social media via record linkage techniques. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.

Storage Network Industry Association (SNIA), n.d., Cloud Data Management Interface (CDMI), viewed February 11, 2016,

Whang J J, Sui X and Dhillon I S, 2012, Scalable and memory-efficient clustering of large-scale social networks. Proceedings of the 12th International Conference on Data Mining, 10–13 December 2012, Brussels, 705– 714. http://dx.doi.org/10.1109/ICDM.2012.148.

Bao J, Zheng Y, Wilkie D, et al. 2015, Recommendations in location-based social networks: a survey. GeoInformatica, vol19.(3): 525–565. http://dx.doi.org/10.1007/s10707-014-0220-8.

Sysomos MAP social research engine, n.d., viewed February 26, 2016,

Radian6 Buddy Media Social Studio, n.d., viewed February 27, 2016,

LuceneTM, n.d., viewed February 25, 2016,

Venus-C (Virtual Multidisciplinary EnviroNments USing Cloud Infrastructures), n.d., viewed February 27, 2016,

Foster I, Grimshaw A, Lane P, et. al. 2007, Open Grid Services Architecture Basic Execution Service Version 1.0. Document GFD-R.108, Open Grid Forum, viewed January 30, 2016,

Anjomshoaa A, Brisard F, Drescher M, et al. 2005, Job Submission Description Language (JSDL) Specification, Version 1.0. Document GFD-R.056, Open Grid Forum, viewed January 31, 2016,

Microsoft Azure, n.d., viewed February 18, 2016,

OpenNebula.org, n.d., viewed February 18, 2016,

EMOTIVE Cloud (Elastic Management Of Tasks In Virtual Environments) homepage, n.d., viewed February 19, 2016,

Cross Project homepage, n.d., viewed February 9, 2016,


DOI: http://dx.doi.org/10.18063/JSC.2016.01.006
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Copyright (c) 2016 Athena Vakali, Stefanos Antaris, Maria Giatsoglou

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Journal of Smart Cities is a peer-reviewed, open-access journal. All journal content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.