More Web Proxy on the site http://driver.im/

research-article

Social big data

Authors:

Gema Bello-Orgaz,

David CamachoAuthors Info & Claims

Information Fusion, Volume 28, Issue C

Pages 45 - 59

https://doi.org/10.1016/j.inffus.2015.08.005

Published: 01 March 2016 Publication History

Abstract

The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.

References

[1]

IBM, Big Data and Analytics, 2015. URL http://www-01.ibm.com/software/data/bigdata/what-is-big-data.html

[2]

Infographic, The Data Explosion in 2014 Minute by Minute, 2015. URL http://aci.info/2014/07/12/the-data-explosion-in-2014-minute-by-minute-infographic

[3]

X. Wu, X. Zhu, G.-Q. Wu, W. Ding, Data mining with big data, IEEE Trans. Knowl. Data Eng., 26 (2014) 97-107.

Digital Library

[4]

A. Cuzzocrea, I.-Y. Song, K.C. Davis, Analytics over large-scale multidimensional data: the big data revolution!, ACM, 2011.

[5]

D. Laney, 3D Data Management: Controlling Data Volume, Velocity, and Variety, 2001.

[6]

M.A. Beyer, D. Laney, The Importance of 'Big Data': A Definition, Gartner, Stamford, CT (2012).

[7]

I.A.T. Hashema, I. Yaqooba, N.B. Anuara, S. Mokhtara, A. Gania, S.U. Khanb, The rise of big data on cloud computing: review and open research issues, Inf. Syst., 47 (2015) 98-115.

Digital Library

[8]

R.L. Grossman, Y. Gu, J. Mambretti, M. Sabala, A. Szalay, K. White, An overview of the open science data cloud, ACM, New York, NY, USA, 2010.

[9]

N. Khan, I. Yaqoob, I.A.T. Hashem, Z. Inayat, W.K.M. Ali, M. Alam, M. Shiraz, A. Gani, Big data: survey, technologies, opportunities, and challenges, The Sci. World J., 2014 (2014) 1-18.

[10]

N. Couldry, Polity, 2012.

[11]

T. Correa, A.W. Hinsley, H.G. De Zuniga, Who interacts on the web?: the intersection of users' personality and social media use, Comput. Hum. Behav., 26 (2010) 247-253.

Digital Library

[12]

A.M. Kaplan, M. Haenlein, Users of the world, unite! the challenges and opportunities of social media, Bus. Horizons, 53 (2010) 59-68.

[13]

P.A. Tess, The role of social media in higher education classes (real and virtual)-a literature review, Comput. Hum. Behav., 29 (2013) A60-A68.

[14]

M. Salathé, D.Q. Vu, S. Khandelwal, D.R. Hunter, The dynamics of health behavior sentiments on a large online social network, EPJ Data Sci., 2 (2013) 1-12.

[15]

E. Cambria, D. Rajagopal, D. Olsher, D. Das, Big social data analysis, Big Data Comput., 13 (2013) 401-414.

[16]

L. Manovich, Trending: the promises and the challenges of big social data, Debates Digit. Hum. (2011) 460-475.

[17]

S. Kaisler, F. Armour, J.A. Espinosa, W. Money, Big data: Issues and challenges moving forward, IEEE, 2013.

[18]

H. Chen, R.H. Chiang, V.C. Storey, Business intelligence and analytics: from big data to big impact, MIS Q., 36 (2012) 1165-1188.

Digital Library

[19]

T. White, O'Reilly Media, 2009.

[20]

M. Zaharia, M. Chowdhury, M.J. Franklin, S. Shenker, I. Stoica, Spark: Cluster computing with working sets, USENIX Association, Berkeley, CA, USA, 2010. http://dl.acm.org/citation.cfm?id=1863103.1863113

[21]

S. Owen, R. Anil, T. Dunning, E. Friedman, Manning Publications, 2011.

[22]

X. Meng, J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman, D. Liu, J. Freeman, D. Tsai, M. Amde, S. Owen, et¿al., MLlib: machine learning in apache spark, 2015, pp. 1-7, arXiv:1505.06807.

[23]

T. Kraska, A. Talwalkar, J.C. Duchi, R. Griffith, M.J. Franklin, M.I. Jordan, Mlbase: a distributed machine-learning system, 2013. http://www.cidrdb.org/cidr2013/Papers/CIDR13

[24]

E.R. Sparks, A. Talwalkar, V. Smith, J. Kottalam, X. Pan, J.E. Gonzalez, M.J. Franklin, M.I. Jordan, T. Kraska, MLI: an API for distributed machine learning, 2013.

[25]

J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, USENIX Association, 2004.

[26]

J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM, 51 (2008) 107-113.

Digital Library

[27]

K. Shim, Mapreduce algorithms for big data analysis, Proc. VLDB Endow., 5 (2012) 2016-2017.

Digital Library

[28]

M. Zaharia, A. Konwinski, A.D. Joseph, R. Katz, I. Stoica, Improving mapreduce performance in heterogeneous environments, USENIX Association, Berkeley, CA, USA, 2008. http://dl.acm.org/citation.cfm?id=1855741.1855744

[29]

R.S. Xin, J. Rosen, M. Zaharia, M.J. Franklin, S. Shenker, I. Stoica, Shark: Sql and rich analytics at scale, ACM, New York, NY, USA, 2013.

[30]

A. Mostosi, Useful stuff, 2015. http://blog.andreamostosi.name/big-data/

[31]

A. Mostosi, The big-data ecosystem table, 2015. URL http://bigdata.andreamostosi.name/

[32]

C. Emerick, B. Carper, C. Grand, O'Really, 2011.

[33]

M. Burrows, The chubby lock service for loosely-coupled distributed systems, USENIX Association, Berkeley, CA, USA, 2006. http://dl.acm.org/citation.cfm?id=1298455.1298487

[34]

A. Alexandrov, R. Bergmann, S. Ewen, J.-C. Freytag, F. Hueske, A. Heise, O. Kao, M. Leich, U. Leser, V. Markl, F. Naumann, M. Peters, A. Rheinländer, M.J. Sax, S. Schelter, M. Höger, K. Tzoumas, D. Warneke, The stratosphere platform for big data analytics, VLDB J., 23 (2014) 939-964.

Digital Library

[35]

S. Ghemawat, H. Gobioff, S.-T. Leung, The google file system, ACM, New York, NY, USA, 2003.

Digital Library

[36]

F. Chang, J. Dean, S. Ghemawat, W.C. Hsieh, D.A. Wallach, M. Burrows, T. Chandra, A. Fikes, R.E. Gruber, Bigtable: a distributed storage system for structured data, USENIX Association, Berkeley, CA, USA, 2006. http://dl.acm.org/citation.cfm?id=1267308.1267323

[37]

G. Malewicz, M.H. Austern, A.J. Bik, J.C. Dehnert, I. Horn, N. Leiser, G. Czajkowski, Pregel: a system for large-scale graph processing, ACM, New York, NY, USA, 2010.

Digital Library

[38]

K. Chodorow, O'Reilly Media, Inc., 2013.

[39]

M.J. Crawley, Wiley Publishing, 2007.

[40]

S. Bennett, Twitter now seeing 400 million tweets per day, increased mobile ad revenue, says ceo, 2012. URL http://www.adweek.com/socialtimes/twitter-400-million-tweets

[41]

L. Ott, M. Longnecker, R.L. Ott, Duxbury Pacific Grove, CA, 2001.

[42]

B. Elser, A. Montresor, An evaluation study of bigdata frameworks for graph processing, IEEE, 2013.

[43]

L.G. Valiant, A bridging model for parallel computation, Commun. ACM, 33 (1990) 103-111.

Digital Library

[44]

S. Seo, E.J. Yoon, J. Kim, S. Jin, J.-S. Kim, S. Maeng, Hama: an efficient matrix computation with the mapreduce framework, IEEE, 2010.

[45]

A. Clauset, Finding local community structure in networks, Phys. Rev. E, 72 (2005) 026132.

[46]

F. Santo, Community detection in graphs, Phys. Rep., 486 (2010) 75-174.

[47]

R. Kannan, S. Vempala, A. Veta, On clusterings-good, bad and spectral, IEEE Computer Society, Washington, DC, USA, 2000.

[48]

I.M. Bomze, M. Budinich, P.M. Pardalos, M. Pelillo, The maximum clique problem, Kluwer Academic Publishers, 1999.

[49]

M. Girvan, M.E.J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci., 99 (2002) 7821-7826.

[50]

M.E.J. Newman, Fast algorithm for detecting community structure in networks, Phys. Rev. E, 69 (2004) 066133+.

[51]

A. Clauset, M.E. Newman, C. Moore, Finding community structure in very large networks, Phys. Rev. E, 70 (2004) 066111.

[52]

M.E. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci., 103 (2006) 8577-8582.

[53]

T. Richardson, P.J. Mucha, M.A. Porter, Spectral tri partitioning of networks, Phys. Rev. E, 80 (2009) 036111.

[54]

G. Wang, Y. Shen, M. Ouyang, A vector partitioning approach to detecting community structure in complex networks, Comput. Math. Appl., 55 (2008) 2746-2752.

Digital Library

[55]

H. Zhou, R. Lipowsky, Network brownian motion: a new method to measure vertex-vertex proximity and to identify communities and subcommunities, Springer, 2004.

[56]

Y. Dong, Y. Zhuang, K. Chen, X. Tai, A hierarchical clustering algorithm based on fuzzy graph connectedness, Fuzzy Sets Syst., 157 (2006) 1760-1774.

[57]

G. Bello-Orgaz, H.D. Menéndez, D. Camacho, Adaptive k-means algorithm for overlapped graph clustering, Int. J. Neural Syst., 22 (2012) 1250018.

[58]

J. Xie, S. Kelley, B.K. Szymanski, Overlapping community detection in networks: the state-of-the-art and comparative study, ACM Comput. Surv. (CSUR), 45 (2013) 43.

[59]

O. Zamir, O. Etzioni, Web document clustering: a feasibility demonstration, ACM, New York, NY, USA, 1998.

Digital Library

[60]

, Prentice-Hall, 1992.

[61]

C.D. Manning, P. Raghavan, H. Schütze, Cambridge University Press, New York, NY, USA, 2008.

[62]

X. Hu, H. Liu, Text analytics in social media, Springer, 2012.

[63]

S. Wold, K. Esbensen, P. Geladi, Principal component analysis, Chemom. Intell. Lab. Syst., 2 (1987) 37-52.

[64]

S.C. Deerwester, S.T. Dumais, T.K. Landauer, G.W. Furnas, R.A. Harshman, Indexing by latent semantic analysis, JAsIs, 41 (1990) 391-407.

[65]

D.M. Blei, A.Y. Ng, M.I. Jordan, Latent Dirichlet allocation, J. Mach. Learn. Res., 3 (2003) 993-1022.

Digital Library

[66]

L. Yao, D. Mimno, A. McCallum, Efficient methods for topic model inference on streaming document collections, ACM, 2009.

[67]

A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: a review, ACM Comput. Surv., 31 (1999) 264-323.

Digital Library

[68]

B. Larsen, C. Aone, Fast and effective text mining using linear-time document clustering, ACM, New York, NY, USA, 1999.

[69]

Y. Zhao, G. Karypis, Evaluation of hierarchical clustering algorithms for document datasets, ACM, New York, NY, USA, 2002.

[70]

Y. Zhao, G. Karypis, Empirical and theoretical comparisons of selected criterion functions for document clustering, Mach. Learn., 55 (2004) 311-331.

Digital Library

[71]

F. Sebastiani, Machine learning in automated text categorization, ACM Comput. Surv. (CSUR), 34 (2002) 1-47.

[72]

B. Pang, L. Lee, S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, Association for Computational Linguistics, 2002.

[73]

C.C. Aggarwal, Springer Science & Business Media, 2007.

[74]

S. Zhong, Efficient online spherical k-means clustering, IEEE, 2005.

[75]

W. Chen, C. Wang, Y. Wang, Scalable influence maximization for prevalent viral marketing in large-scale social networks, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25-28, 2010, ACM, Washington, DC, USA, 2010, pp. 1029-1038.

Digital Library

[76]

D.T. Nguyen, J.J. Jung, Real-time event detection on social data stream, Mobile Netw. Appl., 20 (2015) 475-486.

Digital Library

[77]

A. Guille, H. Hacid, C. Favre, D.A. Zighed, Information diffusion in online social networks: a survey, SIGMOND Rec., 42 (2013) 17-28.

Digital Library

[78]

M. Gomez-Rodriguez, J. Leskovec, A. Krause, Inferring networks of diffusion and influence, ACM Trans. Knowl. Discov. Data, 5 (2012) 21.

Digital Library

[79]

E. Sadikov, M. Medina, J. Leskovec, H. Garcia-Molina, Correcting for missing data in information cascades, in: Proceedings of the 4th International Conference on Web Search and Web Data Mining (WSDM 2011), Hong Kong, China, February 9-12, 2011, ACM, 2011, pp. 55-64.

[80]

E. Anshelevich, A. Hate, M. Magdon-Ismail, Seeding influential nodes in non-submodular models of information diffusion, Auton. Agents Multi-Agent Syst., 29 (2015) 131-159.

Digital Library

[81]

C. Jiang, Y. Chen, K.R. Liu, Graphical evolutionary game for information diffusion over social networks, IEEE J. Sel. Top. Signal Process., 8 (2014) 524-536.

[82]

C. Jiang, Y. Chen, K.R. Liu, Evolutionary dynamics of information diffusion over social networks, IEEE Trans. Signal Process., 62 (2014) 4573-4586.

[83]

T.-c. Fu, A review on time series data mining, Eng. Appl. Artif. Intell., 24 (2011) 164-181.

Digital Library

[84]

J. Lin, E. Keogh, S. Lonardi, B. Chiu, A symbolic representation of time series, with implications for streaming algorithms, ACM, 2003.

Digital Library

[85]

M. Cataldi, L.D. Caro, C. Schifanella, Emerging topic detection on twitter based on temporal and social terms evaluation, ACM, New York, NY, USA, 2010.

[86]

D.T. Nguyen, J.J. Jung, Privacy-preserving discovery of topic-based events from social sensor signals: an experimental study on twitter, Sci. World J., 2014 (2014) 1-5.

[87]

J.J. Jung, Integrating social networks for context fusion in mobile service platforms, J. Univers. Comput. Sci., 16 (2010) 2099-2110.

[88]

H.H. Hoang, T.N.-P. Cung, D.K. Truong, D. Hwang, J.J. Jung, Semantic information integration with linked data mashups approaches, Int. J. Distrib. Sens. Networks, 2014 (2014) 1-12.

[89]

N.H. Long, J.J. Jung, Privacy-aware framework for matching online social identities in multiple social networking services, Cybern. Syst., 46 (2015) 69-83.

Digital Library

[90]

S. Caton, C. Haas, K. Chard, K. Bubendorfer, O.F. Rana, A social compute cloud: allocating and sharing infrastructure resources via social networks, IEEE Trans. Serv. Comput., 7 (2014) 359-372.

[91]

T.H. Davenport, J.G. Harris, Harvard Business Press, 2007.

[92]

C. Maurer, R. Wiegmann, Springer, 2011.

[93]

C. Trattner, F. Kappe, Social stream marketing on Facebook: a case study, Int. J. Soc. Humanist. Comput., 2 (2013) 86-103.

[94]

B.J. Jansen, M. Zhang, K. Sobel, A. Chowdury, Twitter power: tweets as electronic word of mouth, J. Am. Soc. Inf. Sci. Tech., 60 (2009) 2169-2188.

Digital Library

[95]

S. Asur, B. Huberman, Predicting the future with social media, IEEE, 2010.

Digital Library

[96]

H. Ma, H. Yang, M.R. Lyu, I. King, Mining social networks using heat diffusion processes for marketing candidates selection, ACM, 2008.

[97]

R. Wortley, L. Mazerolle, Willan, 2013.

[98]

O. Knutsson, E. Sneiders, A. Alfalahi, Opportunities for improving egovernment: using language technology in workflow management, ACM, New York, NY, USA, 2012.

[99]

C.-H. Ku, G. Leroy, A decision support system: automated crime report analysis and classification for e-government, Gov. Inf. Q., 31 (2014) 534-544.

[100]

P. Phillips, I. Lee, Mining co-distribution patterns for large crime datasets, Expert Syst. Appl., 39 (2012) 11556-11563.

Digital Library

[101]

S. Chainey, L. Tompson, S. Uhlig, The utility of hotspot mapping for predicting spatial patterns of crime, Secur. J., 21 (2008) 4-28.

[102]

M.S. Gerber, Predicting crime using twitter and Kernel density estimation, Decis. Support Syst., 61 (2014) 115-125.

[103]

E. Kirkos, C. Spathis, Y. Manolopoulos, Data mining techniques for the detection of fraudulent financial statements, Expert Syst. Appl., 32 (2007) 995-1003.

Digital Library

[104]

J.T. Quah, M. Sriganesh, Real-time credit card fraud detection using computational intelligence, Expert Syst. Appl., 35 (2008) 1721-1732.

[105]

S.-H. Li, D.C. Yen, W.-H. Lu, C. Wang, Identifying the signs of fraudulent accounts using data mining techniques, Comput. Hum. Behav., 28 (2012) 1002-1013.

[106]

C. Paquet, D. Coulombier, R. Kaiser, M. Ciotti, Epidemic intelligence: a new framework for strengthening disease surveillance in europe., Euro surveillance: bulletin europeen sur les maladies transmissibles European communicable disease bulletin, 11 (2005) 212-214.

[107]

A.M. Cohen, W.R. Hersh, A survey of current work in biomedical text mining, Brief. Bioinform., 6 (2005) 57-71.

[108]

V. Lampos, N. Cristianini, Nowcasting events from the social web with statistical learning, ACM Trans. Intell. Syst. Technol. (TIST), 3 (2012) 72.

[109]

N. Collier, R.M. Goodwin, J. McCrae, S. Doan, A. Kawazoe, M. Conway, A. Kawtrakul, K. Takeuchi, D. Dien, An ontology-driven system for detecting global health events, Association for Computational Linguistics, 2010.

[110]

A. Culotta, Towards detecting influenza epidemics by analyzing twitter messages, ACM, 2010.

[111]

E. Aramaki, S. Maskawa, M. Morita, Twitter catches the flu: detecting influenza epidemics using twitter, Association for Computational Linguistics, 2011.

[112]

T. Bodnar, M. Salathé, Validating models for disease detection using twitter, International World Wide Web Conferences Steering Committee, 2013.

[113]

M. Fisichella, A. Stewart, A. Cuzzocrea, K. Denecke, Detecting health events on the social web to enable epidemic intelligence, Springer, 2011.

[114]

D.M. Hartley, N.P. Nelson, R. Walters, R. Arthur, R. Yangarber, L. Madoff, J. Linge, A. Mawudeku, N. Collier, J.S. Brownstein, The landscape of international event-based biosurveillance., Emerg. Health Threat., 3 (2010).

[115]

E. Mykhalovskiy, L. Weir, The global public health intelligence network and early warning outbreak detection, Can. J. Public Health, 97 (2006) 42-44.

[116]

N. Collier, S. Doan, A. Kawazoe, R.M. Goodwin, M. Conway, Y. Tateno, Q.-H. Ngo, D. Dien, A. Kawtrakul, K. Takeuchi, Biocaster: detecting public health rumors with a web-based text mining system, Bioinformatics, 24 (2008) 2940-2941.

Digital Library

[117]

J.S. Brownstein, C.C. Freifeld, B.Y. Reis, K.D. Mandl, Surveillance sans frontieres: internet-based emerging infectious disease intelligence and the healthmap project, PLoS Med., 5 (2008) e151.

[118]

M. Keller, M. Blench, H. Tolentino, C.C. Freifeld, K.D. Mandl, A. Mawudeku, G. Eysenbach, J.S. Brownstein, Use of unstructured event-based reports for global infectious disease surveillance, Emerg. Infect. Dis., 15 (2009) 689.

[119]

A. Lyon, M. Nunn, G. Grossel, M. Burgman, Comparison of web-based biosecurity intelligence systems: biocaster, epispider and healthmap, Transbound. Emerg. Dis., 59 (2012) 223-232.

[120]

D. Keim, H. Qu, K.-L. Ma, Big-data visualization, IEEE Comput. Gr. Appl., 33 (2013) 20-21.

[121]

X.P. Kotval, M.J. Burns, Visualization of entities within social media: toward understanding users' needs, Bell Labs Tech. J., 17 (2013) 77-101.

[122]

A. Miroshnikov, E.M. Conlon, Parallelmcmccombine: an r package for bayesian methods for big data and analytics, PLOS One, 9 (2014).

[123]

D.F. Swayne, D.T. Lang, A. Buja, D. Cook, GGobi: evolving from XGobi into an extensible framework for interactive data visualization, Comput. Stat. Data Anal., 43 (2003) 423-444.

Digital Library

[124]

P. Ashok, D. Tesar, A visualization framework for real time decision making in a multi-input multi-output system, IEEE Syst. J., 2 (2008) 129-145.

[125]

C. Gurrin, A.F. Smeaton, A.R. Doherty, Now Publishers, 2014.

[126]

M. Blum, A. Pentland, G. Troster, Insense: interest-based life logging, Multimed. IEEE, 13 (2006) 40-48.

Digital Library

[127]

F.B. Viegas, M. Wattenberg, F. van Ham, J. Kriss, M. McKeon, Manyeyes: a site for visualization at internet scale, IEEE Trans. Vis. Comput. Gr., 13 (2007) 1121-1128.

Digital Library

[128]

D. Hwang, J.E. Jung, S. Park, H.T. Nguyen, Social data visualization system for understanding diffusion patterns on twitter: a case study on korean enterprises, Comput. Inform., 33 (2014) 591-608.

[129]

L. Sweeney, K-anonymity: a model for protecting privacy, Int. J. Uncertain. Fuzziness Knowledge-based Syst., 10 (2002) 557-570.

Digital Library

[130]

C. Dwork, Differential privacy: a survey of results, in: Lecture Notes in Computer Science, 4978, Springer, 2008, pp. 1-19.

Digital Library

[131]

S. Landau, Educating engineers: teaching privacy in a world of open doors, IEEE Secur. Priv., 12 (2014) 66-70.

[132]

A. Fiat, Online Algorithms: The State of the Art, in: Lecture Notes in Computer Science, 1442, 1998.

[133]

K. Crammer, Y. Singer, Ultraconservative online algorithms for multiclass problems, J. Mach. Learn. Res., 3 (2003) 951-991.

Digital Library

[134]

M. Charikar, L. O'Callaghan, R. Panigrahy, Better streaming algorithms for clustering problems, ACM, 2003.

[135]

J. Cheng, Y. Ke, W. Ng, A survey on algorithms for mining frequent itemsets over data streams, Knowl. Inf. Syst., 16 (2008) 1-27.

Digital Library

[136]

H.D. Menéndez, D.F. Barrero, D. Camacho, A multi-objective genetic graph-based clustering algorithm with memory optimization, IEEE, 2013.

[137]

W. Zhao, H. Ma, Q. He, Parallel k-means clustering based on mapreduce, Springer, 2009.

[138]

C. Chu, S.K. Kim, Y.-A. Lin, Y. Yu, G. Bradski, A.Y. Ng, K. Olukotun, Map-reduce for machine learning on multicore, Adv. Neural Inf. Process. Syst., 19 (2007) 281.

[139]

W.-Y. Chen, Y. Song, H. Bai, C.-J. Lin, E.Y. Chang, Parallel spectral clustering in distributed systems, IEEE Trans. Pattern Anal. Mach. Intell., 33 (2011) 568-586.

Digital Library

[140]

H.D. Menendez, D.F. Barrero, D. Camacho, A co-evolutionary multi-objective approach for a k-adaptive graph-based clustering algorithm, IEEE, 2014.

[141]

H.D. Menendez, D. Camacho, Gany: a genetic spectral-based clustering algorithm for large data analysis, IEEE, 2015.

[142]

A. Ng, M. Jordan, Y. Weiss, On Spectral Clustering: Analysis and an algorithm, in: Advances in Neural Information Processing Systems, MIT Press, 2001, pp. 849-856. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100

Digital Library

[143]

F. Bach, M. Jordan, Learning spectral clustering, with application to speech separation, J. Mach. Learn. Res., 7 (2006) 1963-2001.

Digital Library

[144]

R. Kumar, M. Wolenetz, B. Agarwalla, J. Shin, P. Hutto, A. Paul, U. Ramachandran, Dfuse: a framework for distributed data fusion, ACM, 2003.

[145]

D. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, G. Melançon, Springer, 2008.

[146]

B. Cui, A.K. Tung, C. Zhang, Z. Zhao, Multiple feature fusion for social media applications, ACM, 2010.

[147]

E. Bakshy, I. Rosenn, C. Marlow, L. Adamic, The role of social networks in information diffusion, ACM, 2012.

Digital Library

[148]

H. Becker, M. Naaman, L. Gravano, Event identification in social media., 2009.

[149]

H. Becker, M. Naaman, L. Gravano, Learning similarity metrics for event identification in social media, ACM, 2010.

[150]

P.C. Wong, J. Thomas, Visual analytics, IEEE Comput. Gr. Appl. (2004) 20-21.

[151]

G. Andrienko, N. Andrienko, S. Wrobel, Visual analytics tools for analysis of movement data, ACM SIGKDD Explor. Newsl., 9 (2007) 38-46.

Digital Library

[152]

G. Andrienko, N. Andrienko, U. Demsar, D. Dransch, J. Dykes, S.I. Fabrikant, M. Jern, M.-J. Kraak, H. Schumann, C. Tominski, Space, time and visual analytics, Int. J. Geogr. Inf. Sci., 24 (2010) 1577-1600.

Digital Library

Cited By

Singh SMuhuri SMishra SSrivastava DShakya HKumar N(2024)Social Network Analysis: A Survey on Process, Tools, and ApplicationACM Computing Surveys10.1145/364847056:8(1-39)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3648470
Alsoubai APark JQadir SStringhini GRazi AWisniewski P(2024)Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media PlatformsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642315(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642315
Gao YMiao HChen JSong BHu XWang W(2024)Explosive Cyber Security Threats During COVID-19 Pandemic and a Novel Tree-Based Broad Learning System to OvercomeIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316018225:1(786-795)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TITS.2022.3160182
Show More Cited By

Social big data
1. Information systems
  1. Information systems applications

Recommendations

Big Social Network Mining for "Following" Patterns
C3S2E '15: Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering

Many social networking sites such as Facebook and Twitter have been used for sharing knowledge and information among social entities. Social entities in these social networks are often linked by some interdependency such as friendship or "following" ...
Big data

We use structuralism and functionalism paradigms to analyze the origins of big data applications.Current trends and sources of big data.Processing technologies, methods and analysis techniques for big data are compared in detail.We analyze major ...
Big data mining of social networks for friend recommendation
ASONAM '16: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

In the current era of big data, high volumes of valuable data can be easily collected and generated. Social networks are examples of generating sources of these big data. Users in these social networks are often linked by some interdependency such as ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Fusion

Information Fusion Volume 28, Issue C

March 2016

99 pages

ISSN:1566-2535

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2016

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

96
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Singh SMuhuri SMishra SSrivastava DShakya HKumar N(2024)Social Network Analysis: A Survey on Process, Tools, and ApplicationACM Computing Surveys10.1145/364847056:8(1-39)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3648470
Alsoubai APark JQadir SStringhini GRazi AWisniewski P(2024)Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media PlatformsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642315(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642315
Gao YMiao HChen JSong BHu XWang W(2024)Explosive Cyber Security Threats During COVID-19 Pandemic and a Novel Tree-Based Broad Learning System to OvercomeIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316018225:1(786-795)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TITS.2022.3160182
Cuzzocrea ABaldo AFadda E(2024)A bayesian-neural-networks framework for scaling posterior distributions over different-curation datasetsJournal of Intelligent Information Systems10.1007/s10844-023-00837-662:4(951-969)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10844-023-00837-6
Gao JSarwar Z(2024)How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?Information Technology and Management10.1007/s10799-022-00380-w25:3(283-304)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10799-022-00380-w
Li BXu ZWang X(2024)Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09789-728:17-18(9371-9386)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00500-024-09789-7
Li CGao JPan QZhou ZYang YZhou S(2023)Design and Development of a Big Data Platform for Disease Burden Based on the Spark EngineComputational Intelligence and Neuroscience10.1155/2023/89630532023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8963053
Dasari SKaluri R(2023)Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and ChallengesWireless Communications & Mobile Computing10.1155/2023/39763022023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/3976302
Schäfer KChoi J(2023)Transparency in MessengersProceedings of the 34th ACM Conference on Hypertext and Social Media10.1145/3603163.3609034(1-3)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1145/3603163.3609034
Tang JNine WWang Y(2023)Preschoolers' Mathematics Game Preferences and Learning Performance through Designing a Degree of Freedom for a Tablet GameEducation and Information Technologies10.1007/s10639-023-11865-828:12(16311-16331)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10639-023-11865-8
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents