Skip to main content
SpringerLink
Log in

A parallel data generator for efficiently generating “realistic” social streams

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

A social stream refers to the data stream that records a series of social entities and the dynamic interactions between two entities. It can be employed to model the changes of entity states in numerous applications. The social streams, the combination of graph and streaming data, pose great challenge to efficient analytical query processing, and are key to better understanding users’ behavior. Considering of privacy and other related issues, a social stream generator is of great significance. A framework of synthetic social stream generator (SSG) is proposed in this paper. The generated social streams using SSG can be tuned to capture several kinds of fundamental social stream properties, including patterns about users’ behavior and graph patterns. Extensive empirical studies with several real-life social stream data sets show that SSG can produce data that better fit to real data. It is also confirmed that SSG can generate social stream data continuously with stable throughput and memory consumption. Furthermore, we propose a parallel implementation of SSG with the help of asynchronized parallel processing model and delayed update strategy. Our experiments verify that the throughput of the parallel implementation can increase linearly by increasing nodes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhou A, Qian W, Ma H. Social media data analysis for revealing collective behaviors. In: Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining. 2012, 1402

    Google Scholar 

  2. Olston C, Reed B, Srivastava U, Kumar R, Tomkins A. Pig latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 2008, 1099–1110

    Google Scholar 

  3. Thusoo A, Sarma J S, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R. Hive: a warehousing solution over a mapreduce framework. Proceedings of the VLDB Endowment, 2009, 2(2): 1626–1629

    Google Scholar 

  4. Engle C, Lupher A, Xin R, Zaharia M, Franklin M J, Shenker S, Stoic I. Shark: fast data analysis using coarse-grained distributed memory. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. 2012, 689–692

    Google Scholar 

  5. Pujol J M, Erramilli V, Siganos G, Yang X, Laoutaris N, Chhabra P, Rodriguez P. The little engine(s) that could: scaling online social networks. ACM Special Interest Group on Data Communication, 2010, 40(4): 375–386

    Google Scholar 

  6. Silberstein A, Terrace J, Cooper B F, Ramakrishnan R. Feeding frenzy: selectively materializing users’ event feeds. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 2010, 831–842

    Google Scholar 

  7. Erling O, Averbuch A, Larribapey J, Chafi H, Gubichev A, Prat-Pérez A, Pham M, Boncz P A. The LDBC social network benchmark: interactive workload. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015, 619–630

    Google Scholar 

  8. Ma H, Wei J, Qian W, Yu C, Zhou A. On benchmarking online social media analytical queries. In: Proceedings of the 1st International Workshop on Graph Data Management Experiences and Systems. 2013, 10

    Google Scholar 

  9. Pham M, Boncz P A, Erling O. S3G2: a scalable structure-correlated social graph generator. In: Proceedings of Technology Conference on Performance Evaluation and Benchmarking. 2012, 156–172

    Google Scholar 

  10. Chung F R, Lu L. The average distances in random graphs with given expected degrees. the National Academy of Sciences of the United States of America, 2002, 99(25): 15879–15882

    Google Scholar 

  11. Chung F R, Lu L. Connected components in random graphs with given expected degree Sequences. Annals of Combinatorics, 2002, 6(2): 125–145

    MathSciNet  MATH  Google Scholar 

  12. Ma H, Qian W, Xia F, He X, Xu J, Zhou A. Towards modeling popularity of microblogs. Frontiers of Computer Science, 2013, 7(2): 171–184

    MathSciNet  Google Scholar 

  13. Ross S M. Introduction to Probability Models. 10th ed. New York: Academic Press, 2010

    Google Scholar 

  14. Karypis G, Kumar V. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 1998, 20(1): 359–392

    MathSciNet  MATH  Google Scholar 

  15. Broder A Z, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R, Tomkins A, Wiener J L. Graph structure in the Web. In: Proceedings of the 9th International World Wide Web Conferences. 2000, 309–320

    Google Scholar 

  16. Newman M E J. The structure and function of complex networks. Siam Review, 2003, 45(2): 167–256

    MathSciNet  MATH  Google Scholar 

  17. Dorogovtsev S N, Mendes J F. Evolution of networks. Advances in Physics, 2002, 51(4): 1079–1187

    Google Scholar 

  18. Albert R, Barabasi A. Statistical mechanics of complex networks. Reviews of Modern Physics, 2001, 74(1): 47–97

    MathSciNet  MATH  Google Scholar 

  19. Strogatz S H. Exploring complex networks. Nature, 2001, 410(6825): 268–276

    MATH  Google Scholar 

  20. Newman M E J. Networks: an introduction. Astronomische Nachrichten, 2010, 327(8): 741–743

    Google Scholar 

  21. Chakrabarti D, Faloutsos C. Graph mining: laws, generators, and algorithms. ACM Computing Surveys, 2006, 38(1): 2

    Google Scholar 

  22. Newman ME J. Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 2005, 46(5): 323–351

    Google Scholar 

  23. Clauset A, Shalizi C R, Newman M E J. Power-law distributions in empirical data. Siam Review, 2009, 51(4): 661–703

    MathSciNet  MATH  Google Scholar 

  24. Coan J A, Sbarra D A. Social baseline theory: the social regulation of risk and effort. Current Opinion in Psychology, 2015, 1: 87–91

    Google Scholar 

  25. Abello J, Buchsbaum A L, Westbrook J. A functional approach to external graph algorithms. Algorithmica, 2002, 32(3): 437–458

    MathSciNet  MATH  Google Scholar 

  26. Redner S. How popular is your paper? An empirical study of the citation distribution. European Physical Journal B, 1998, 4(2): 131–134

    Google Scholar 

  27. Kwak H, Lee C, Park H, Moon S B. What is Twitter, a social network or a news media? In: Proceedings of the 19th International World Wide Web Conferences. 2010, 591–600

    Google Scholar 

  28. Ebel H, Mielsch L, Bornholdt S. Scale-free topology of e-mail networks. Physical Review E, 2002, 66(3): 035103

    Google Scholar 

  29. Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the 19th International World Wide Web Conferences. 2010, 431–440

    Google Scholar 

  30. Pandurangan G, Raghavan P, Upfal E. Using pagerank to characterize Web structure. In: Proceedings of the 8th International Conference on Computing and Combinatorics. 2002, 330–339

    Google Scholar 

  31. Tauro S L, Palmer C R, Siganos G, Faloutsos M. A simple conceptual model for the Internet topology. In: Proceedings of Global Communications Conference. 2001, 1667–1671

    Google Scholar 

  32. Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the Internet topology. ACM Special Interest Group on Data Communication, 1999, 29(4): 251–262

    MATH  Google Scholar 

  33. Albert R. Diameter of theWorldWideWeb. Nature, 1999, 401(6749): 130–131

    Google Scholar 

  34. Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393(6684): 440–442

    MATH  Google Scholar 

  35. Srisaard S. Mining the Web: discovering knowledge from hypertext data. Online Information Review, 2003, 27(4): 291

    Google Scholar 

  36. Gkantsidis C, Mihail M, Zegura E W. Spectral analysis of Internet topologies. In: Proceedings of the 22nd International Conference on Computer Communications. 2003, 364–374

    Google Scholar 

  37. Tangmunarunkit H, Govindan R, Jamin S, Shenker S, Willinger W. Network topologies, power laws, and hierarchy. ACM Special Interest Group on Data Communication, 2002, 32(1): 76

    Google Scholar 

  38. Casteigts A, Flocchini P, Quattrociocchi W, Santoro N. Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems, 2012, 27(5): 387–408

    Google Scholar 

  39. Santoro N, Quattrociocchi W, Flocchini P, Casteigts A, Amblard F. Time-varying graphs and social network analysis: temporal indicators and metrics. In: Proceedings of the 3rd AISB Social Networks and Multiagement Systems Symposium. 2011, 32–38

    Google Scholar 

  40. Ferreira A. Building a reference combinatorial model for MANETs. IEEE Network, 2004, 18(5): 24–29

    Google Scholar 

  41. Holme P, Saramki J. Temporal networks. Physics Reports, 2012, 519(3): 97–125

    Google Scholar 

  42. Krapivsky P L, Redner S, Leyvraz F. Connectivity of growing random networks. Physical Review Letters, 2000, 85(21): 4629

    Google Scholar 

  43. Quattrociocchi W, Amblard F, Galeota E. Selection in scientific networks. Social Network Analysis & Mining, 2012, 2(3): 229–237

    Google Scholar 

  44. Leskovec J, Kleinberg J M, Faloutsos C. Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th ACM SIGKOD International Conference on Knowledge Discovery and Data Mining. 2005, 177–187

    Google Scholar 

  45. Leskovec J, Kleinberg J M, Faloutsos C. Graph evolution: densification and shrinking diameters. ACM Transactions on Knowledge Discovery From Data, 2007, 1(1): 2

    Google Scholar 

  46. Erdos P, Rényi A. On the evolution of random graphs. Transactions of the American Mathematical Society, 2011, 286(1): 257–274

    MathSciNet  Google Scholar 

  47. Aiello W, Chung F, Lu L. A random graph model for massive graphs. In: Proceedings of the 32nd Annual ACM Symposium on Theory of Computing. 2000, 171–180

    Google Scholar 

  48. Newman M E J, Strogatz S H, Watts D J. Random graphs with arbitrary degree distributions and their applications. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2001, 64(2): 026118

    Google Scholar 

  49. Simon H A. On a class of skew distribution function. Biometrika, 1955, 42(3/4): 425–440

    MathSciNet  MATH  Google Scholar 

  50. Barabasi A, Albert R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509–512

    MathSciNet  MATH  Google Scholar 

  51. Albert R, Barabasi A. Topology of evolving networks: local events and universality. Physical Review Letters, 2000, 85(24): 5234–5237

    Google Scholar 

  52. Kleinberg J M, Kumar R, Raghavan P, Rajagopalan S, Tomkins A. The Web as a graph: measurements, models, and methods. In: Proceedings of International Computing and Combinatorics Conference. 1999, 1–17

    Google Scholar 

  53. Kumar R, Raghavan P, Rajagopalan S. Stochastic models for the Web graph. In: Proceedings of the 41st Annual Symosium on Foundations of Computer Science. 2000, 57–65

    Google Scholar 

  54. Dorogovtsev S N, Mendes J F, Samukhin A N. Structure of growing networks with preferential linking. Physical Review Letters, 2000, 85(21): 4633–4636

    Google Scholar 

  55. Chen Q, Chang H, Govindan R, Jamin S, Shenker S, Willinger W. The origin of power laws in Internet topologies revisited. In: Proceedings of the 51st International Conference on Computer Communications. 2002, 608–617

    Google Scholar 

  56. Bianconi G, Barabási A L. Competition and multiscaling in evolving networks. Physics Letters, 2000, 30(1): 37–43

    Google Scholar 

  57. Barabási A, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T. Evolution of the social network of scientific collaborations. Physica Astatistical Mechanics and Its Applications, 2002, 311(3): 590–614

    MathSciNet  MATH  Google Scholar 

  58. Aiello W, Fan C, Lu L. Random evolution in massive graphs. Foundations of Computer Science, 2001, 510–519

    Google Scholar 

  59. Borgs C, Chayes J, Riordan O. Directed scale-free graphs. In: Proceedings of the 14th Acm-Siam Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics. 2003, 132–139

    Google Scholar 

  60. Waxman B M. Routing of multipoint connections. IEEE Journal on Selected Areas in Communications, 2002, 6(9): 1617–1622

    Google Scholar 

  61. Looz M V, Staudt C L, Meyerhenke H, Prutkin R. Fast generation of complex networks with underlying hyperbolic geometry. 2015, arXiv preprint arXiv:1501.03545

    MATH  Google Scholar 

  62. Chakrabarti D, Zhan Y, Faloutsos C. R-MAT: a recursive model for graph mining. In: Proceedings of the 2004 SIAM International Conference on Data Mining. 2004, 442–446

    Google Scholar 

  63. Leskovec J, Faloutsos C. Scalable modeling of real graphs using kronecker multiplication. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 497–504

    Google Scholar 

  64. Dominguez-Sal D, Urbón-Bayes P, Giménez-Vaó A, Gómez-Villamor S, Martínez-Bazan N, Larriba-Pey J. Survey of graph database performance on the HPC scalable graph analysis benchmark. In: Proceedings of the International Conference on Web Age Information Management. 2010, 37–48

    Google Scholar 

  65. Gleich D F, Owen A B. Moment-based estimation of stochastic kronecker graph parameters. Internet Mathematics, 2012, 8(3): 232–256

    MathSciNet  MATH  Google Scholar 

  66. Miller B A, Bliss N T, Wolfe P J. Subgraph detection using eigenvector L1 norms. In: Proceedings of the 23rd International Conference on Neural Information Processing Systems. 2010, 1633–1641

    Google Scholar 

  67. Miller B A, Stephens L H, Bliss N T. Goodness-of-fit statistics for anomaly detection in Chung-Lu random graphs. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. 2012, 3265–3268

    Google Scholar 

  68. Mir D J, Wright R N. A differentially private estimator for the stochastic kronecker graph model. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops. 2012, 167–176

    Google Scholar 

  69. Leskovec J, Chakrabarti D, Kleinberg J M, Faloutsos C, Ghahramani Z. Kronecker graphs: an approach to modeling networks. Journal of Machine Learning Research, 2010, 11(Feb): 985–1042

    Google Scholar 

  70. Seshadhri C, Pinar A, Kolda T G. An in-depth study of stochastic kronecker graphs. In: Proceedings of the 11th IEEE International Conference on Data Mining. 2011, 587–596

    Google Scholar 

  71. Sala A, Cao L, Wilson C, Zablit R, Zheng H, Zhao B Y. Measurementcalibrated graph models for social network experiments. In: Proceedings of the 19th International Conferences onWorldWideWeb. 2010, 861–870

    Google Scholar 

  72. Akoglu L, Mcglohon M, Faloutsos C. RTM: laws and a recursive generator for weighted time-evolving fraphs. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 701–706

    Google Scholar 

  73. Hakimi S L. On Realizability of a set of integers as degrees of the vertices of a linear graph. I. Journal of he Society for Industrial and Applied Mathematics, 1962, 10(3): 496–506

    MathSciNet  MATH  Google Scholar 

  74. Seshadhri C, Kolda T G, Pinar A. Community structure and scale free collections of Erdös-Rényi graphs. Physical Review E, 2012, 85(5): 056109

    Google Scholar 

  75. Du N, Wang H, Faloutsos C. Analysis of large multi-modal social networks: patterns and a generator. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery. 2010, 393–408

    Google Scholar 

  76. Armstrong T G, Ponnekanti V, Borthakur D, Callaghan M. LinkBench: a database benchmark based on the Facebook social graph. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013, 1185–1196

    Google Scholar 

  77. Kolda T G, Pinar A, Plantenga T D, Seshadhri C. A scalable generative graph model with community structure. SIAM Journal on Scientific Computing, 2014, 36(5): C424–C452

    Google Scholar 

  78. Yoo A, Henderson K. Parallel generation of massive scale-free graphs. Computer Science, 2010, 7: 123–136

    Google Scholar 

  79. Alam M M, Khan M, Marathe M V. Distributed-memory parallel algorithms for generating massive scale-free networks using preferential attachment model. In: Proceedings of the IEEE International Conference on High Performance Computing Data and Analytics. 2013, 1–12

    Google Scholar 

  80. Lo Y C, Lai H, Li C T, Lin S S. Mining and generating large-scaled social networks via MapReduce. Social Network Analysis and Mining, 2013, 3(4): 1449–1469

    Google Scholar 

  81. Hadian A, Nobari S, Minaeibidgoli B, Qu Q. ROLL: fast in-memory generation of gigantic scale-free networks. In: Proceedings of the 2016 International Conference on Management of Data. 2016, 1829–1842

    Google Scholar 

  82. Barabási A. The origin of bursts and heavy tails in human dynamics. Nature, 2005, 435(7039): 207–211

    Google Scholar 

  83. Cho J, Garciamolina H. Estimating frequency of change. ACM Transactions on Internet Technology, 2003, 3(3): 256–290

    Google Scholar 

  84. Cho J, Garciamolina H. Synchronizing a database to improve freshness. International Conference on Management of Data, 2000, 29(2): 117–128

    Google Scholar 

  85. Eubank S, Guclu H, Kumar V S, Marathe M V, Srinivasan A, Toroczkai Z, Wang N. Modelling disease outbreaks in realistic urban social networks. Nature, 2004, 429(6988): 180–184

    Google Scholar 

  86. Brewington B E, Cybenko G. How dynamic is the Web. In: Proceedings of the 9th International World Wide Web Conferences. 2000, 257–276

    Google Scholar 

  87. Oliveira J G, Barabási A. Human dynamics: Darwin and Einstein correspondence patterns. Nature, 2005, 437(7063): 1251

    Google Scholar 

  88. Li N, Zhang N, Zhou T. Empirical analysis on temporal statistics of human correspondence patterns. Complex System & Complexity Science, 2008, 387(25): 6391–6394

    Google Scholar 

  89. Hong W, Han X P, Zhou T, Wang B H. Heavy-tailed statistics in short-message communication. Chinese Physics Letters, 2009, 26(2): 028902

    Google Scholar 

  90. Candia J, González M C, Wang P, Schoenharl T, Madey G, Barabási A. Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical, 2008, 41(22): 224015

    MathSciNet  Google Scholar 

  91. Dezsö Z, Almaas E, Lukács A, Rácz B, Szakadát I, Barabási A L. Dynamics of information access on the Web. Physical Review E, 2006, 73(6): 066132

    Google Scholar 

  92. Vázquez A, Oliveira J G, Dezsö Z, Goh K, Kondor I, Barabási A. Modeling bursts and heavy tails in human dynamics. Physical Review E, 2005, 73(2): 036127

    Google Scholar 

  93. Gabrielli A, Caldarelli G. Invasion percolation and critical transient in the Barabási Model of human dynamics. Physical Review Letters, 2007, 98(20): 208704

    Google Scholar 

  94. Han X P, Zhou T, Wang B H. Modeling human dynamics with adaptive interest. New Journal of Physics, 2008, 10(7): 073010

    Google Scholar 

  95. Goncalves B, Ramasco J J. Human dynamics revealed through Web analytics. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2008, 78(2): 026123

    Google Scholar 

  96. Malmgren R D, Stouffer D B, Campanharo A S L O, Amaral L A N. On universality in human correspondence activity. Science, 2009, 325(5948): 1696–1700

    Google Scholar 

  97. Sia K C, Cho J, Hino K, Chi Y, Zhu S, Tseng B L. Monitoring RSS feeds based on user browsing pattern. In: Proceedings of International Conference on Weblogs and Social Media. 2007

    Google Scholar 

  98. Sia K C, Cho J, Cho H K. Efficient monitoring algorithm for fast news alerts. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(7): 950–961

    Google Scholar 

  99. Gruhl D, Guha R V, Liben-Nowell D, Tomkins A. Information diffusion through blogspace. In: Proceedings of the 13th International World Wide Web Conferences. 2004, 491–501

    Google Scholar 

  100. Bollen J, Mao H, Pepe A. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of the International Conference on Weblogs and Social Media. 2011, 450–453

    Google Scholar 

  101. Malmgren R D, Stouffer D B, Motter A E, Amaral L A N. A Poissonian explanation for heavy tails in e-mail communication. the National Academy of Sciences of the United States of America, 2008, 105(47): 18153–18158

    Google Scholar 

  102. Vazquez A. Impact of memory on human dynamics. Physica Astatistical Mechanics and its Applications, 2007, 373: 747–752

    Google Scholar 

  103. Stewart W J. Probability, Markov Chains, Queues, and Simulation: the Mathematical Basis of Performance Modeling. Princeton: Princeton Univers Press, 2009

    Google Scholar 

  104. Pennock D M, Flake G W, Lawrence S, Glover E J, Giles C L. Winners don’t take all: characterizing the competition for links on the Web. the National Academy of Sciences of the United States of America, 2002, 99(8): 5207–5211

    Google Scholar 

  105. Bi Z, Faloutsos C, Korn F. The “DGX” distribution for mining massive, skewed data. In: Proceedings of the 7th ACM SIGKOD International Conference on Knowledge Discovery and Data Mining. 2001, 17–26

    Google Scholar 

  106. Welch M J, Schonfeld U, He D, Cho J. Topical semantics of twitter links. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 327–336

    Google Scholar 

  107. Galuba W, Aberer K, Chakraborty D, Despotovic Z, Kellerer W. Outtweeting the twitterers — predicting information cascades in microblogs. In: Proceedings of the 3rd Conference on Online Social Networks. 2010, 3–11

    Google Scholar 

  108. Asur S, Huberman B A. Predicting the future with social media. In: Proceedings of the 2010 IEEE International Conference on Web Intelligence and Intelligent Agent Technology. 2010, 492–499

    Google Scholar 

  109. Martin T, Ball B, Karrer B, Newman M E J. Coauthorship and citation in scientific publishing. Computer Science, 2013, arXiv preprint arXiv:1304.0473

    Google Scholar 

  110. Xie J, Zhang C, Wu M. Modeling microblogging communication based on human dynamics. In: Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery. 2011, 2290–2294

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by the Research Project of Shanghai Polytechnic University (EGD18XQD07), and the Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University (XXKZD1604).

Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

    Chengcheng Yu

  2. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Fan Xia, Weining Qian & Aoying Zhou

Authors
  1. Chengcheng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fan Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weining Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aoying Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weining Qian.

Additional information

Chengcheng Yu is currently a lecturer at College of Computer and Knowledge Engineering, Shanghai Polytechnic University. She received the PhD in Institute of Software Engineer from East China Normal University, China in 2017. Her research interests include social stream data generator and social media data analytics.

Fan Xia received the BS and PhD in Institute of Software Engineer from East China Normal University and is currently a post-doctor at there. His research interests include querying social stream data in social network system and building concept knowledge graph based on textbooks.

Weining Qian is currently a professor at School of Data Science and Engineering, East China Normal University. He received his PhD in computer science from Fudan University, China in 2004. His research interests include database systems for Internet applications, benchmarking big data management systems and social media data analytics.

Aoying Zhou is a professor on computer science at East China Normal University, China where he is heading the School of Data Science and Engineering. He got his master and bachelor degree in computer science from Sichuan University, China in 1988 and 1985 respectively, and won his PhD degree from Fudan University, China in 1993. He is now acting as the vice-director of ACM SIGMOD China and Technology Committee on Database of China Computer Federation. He is serving as a member of the editorial boards of some prestigious academic journals, such as VLDB Journal, and WWW Journal. His research interests include Web data management, data management for data-intensive computing and in-memory data analytics.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, C., Xia, F., Qian, W. et al. A parallel data generator for efficiently generating “realistic” social streams. Front. Comput. Sci. 13, 1072–1101 (2019). https://doi.org/10.1007/s11704-018-8022-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-018-8022-z

Keywords

Search

Navigation