Skip to main content
SpringerLink
Log in

Event modeling and mining: a long journey toward explainable events

  • Special Issue Paper
  • Published:
The VLDB Journal Aims and scope Submit manuscript

Abstract

Recently, research on event management has redrawn much attention and made great progress. As the core tasks of event management, event modeling and mining are essential for accessing and utilizing events effectively. In this survey, we provide a detailed review of event modeling and event mining. Based on a general definition, different characteristics of events are described, along with the associated challenges. Then, we define four forms of events in order to better classify currently available but somewhat confusing event types; we also compare different event representation and relationship analysis techniques used for different forms of events. Finally, we discuss several pending issues and application-specific challenges which also shed light on future research directions.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. We only briefly introduced several existing event query languages since existing event query languages are mostly designed for complex event processing and hence we focused more on the challenges, as to be examined and discussed in Sect. 6.2.4.

References

  1. Aalst, W.V.D.: Spreadsheets for business process management: using process mining to deal with “events” rather than “numbers”? Bus. Process Manag. J. 24(1), 105–127 (2018)

    Google Scholar 

  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)

    Google Scholar 

  3. Akbar, A., Carrez, F., Moessner, K., Sancho, J., Rico, J.: Context-aware stream processing for distributed IoT applications. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 663–668 (2015)

  4. Akpınar, K., Hua, K.A.: Eql: event query language for the sharing of internet-of-things infrastructure and collaborative applications development. In: Service-Oriented Computing—ICSOC 2016 Workshops, pp. 73–78 (2017)

    Google Scholar 

  5. Amati, G., Angelini, S., Capri, F., Gambosi, G., Rossi, G., Vocca, P.: Modelling the temporal evolution of the retweet graph. Int. J. Comput. Sci. Inf. Syst. 11(2), 19–30 (2016)

    Google Scholar 

  6. Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: Ep-sparql: a unified language for event processing and stream reasoning. In: Proceedings of the 20th International Conference on World Wide Web, WWW ’11, pp. 635–644 (2011)

  7. Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015)

    MathSciNet  Google Scholar 

  8. Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: C-sparql: sparql for continuous querying. In: Proceedings of the 18th International Conference on World Wide Web, WWW ’09, pp. 1061–1062 (2009)

  9. Beck, F., Burch, M., Diehl, S., Weiskopf, D.: A taxonomy and survey of dynamic graph visualization. Comput. Graph. Forum 36(1), 133–159 (2017)

    Google Scholar 

  10. Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 115–148. Springer US, Boston (2011)

    Google Scholar 

  11. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  12. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. TACL 5, 135–146 (2017)

    Google Scholar 

  13. Bok, K., Kim, D., Yoo, J.: Complex event processing for sensor stream data. Sensors 18(9), 3084–3100 (2018)

    Google Scholar 

  14. Bonino, D., De Russis, L.: Complex event processing for city officers: a filter and pipe visual approach. IEEE Internet Things J. 5(2), 775–783 (2018)

    Google Scholar 

  15. Boukerche, A., Martirosyan, A.: An efficient algorithm for preserving events’ temporal relationships in wireless sensor actor networks. In: 32nd IEEE Conference on Local Computer Networks (LCN 2007), pp. 771–780 (2007)

  16. Brenna, L., Demers, A., Gehrke, J., Hong, M., Ossher, J., Panda, B., Riedewald, M., Thatte, M., White, W.: Cayuga: A high-performance event processing engine. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD ’07, pp. 1100–1102 (2007)

  17. Brodie, M.L., Ridjanovic, D.: On the design and specification of database transactions. In: Brodie, M.L., Mylopoulos, J., Schmidt, J.W. (eds.) On Conceptual Modelling: Perspectives from Artificial Intelligence, Databases, and Programming Languages, pp. 277–312. Springer, New York (1984)

    Google Scholar 

  18. Cai, Y., Li, Q., Xie, H., Wang, T., Min, H.: Event relationship analysis for temporal event search. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) Database Systems for Advanced Applications, pp. 179–193. Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  19. Calbimonte, J.P., Corcho, O., Gray, A.J.G.: Enabling ontology-based access to streaming data sources. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) The Semantic Web–ISWC 2010, pp. 96–111. Springer, Berlin, Heidelberg (2010)

    Google Scholar 

  20. Cao, J., Zhu, Z., Shi, L., Liu, B., Ma, Z.: Multi-feature based event recommendation in event-based social network. Int. J. Comput. Intell. Syst. 11, 618–633 (2018)

    Google Scholar 

  21. Chen, C., Terejanu, G.: Sub-event detection on twitter network. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) Artificial Intelligence Applications and Innovations, pp. 50–60. Springer, Cham (2018)

    Google Scholar 

  22. Chen, G., Xu, N., Mao, W.: An encoder-memory-decoder framework for sub-event detection in social media. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18, pp. 1575–1578 (2018)

  23. Chen, K., Lu, M., Tan, G., Wu, J.: Crsm: Crowdsourcing based road surface monitoring. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp. 2151–2158 (2013)

  24. Chen, X., Zhou, X., Sellis, T., Li, X.: Social event detection with retweeting behavior correlation. Expert Syst. Appl. 114, 516–523 (2018)

    Google Scholar 

  25. Cheng, Y., Yuan, Y., Chen, L., Giraud-Carrier, C., Wang, G.: Complex event-participant planning and its incremental variant. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 859–870 (2017)

  26. Choffnes, D.R., Bustamante, F.E., Ge, Z.: Crowdsourcing service-level network event monitoring. In: Proceedings of the ACM SIGCOMM 2010 Conference, SIGCOMM ’10, pp. 387–398 (2010)

  27. Cugola, G., Margara, A.: Tesla: A formally defined event specification language. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, DEBS ’10, pp. 50–61 (2010)

  28. Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44(3), 15:1–15:62 (2012)

    Google Scholar 

  29. Cugola, G., Margara, A.: The complex event processing paradigm. In: Colace, F., De Santo, M., Moscato, V., Picariello, A., Schreiber, F.A., Tanca, L. (eds.) Data Management in Pervasive Systems, pp. 113–133. Springer International Publishing, Cham (2015)

    Google Scholar 

  30. Cui, L., Zhang, X., Zhou, X., Salim, F.: Topical event detection on twitter. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) Databases Theory and Applications, pp. 257–268. Springer, Cham (2016)

    Google Scholar 

  31. Cui, W., Wang, P., Du, Y., Chen, X., Guo, D., Li, J., Zhou, Y.: An algorithm for event detection based on social media data. Neurocomputing 254, 53–58 (2017)

    Google Scholar 

  32. Cyganiak, R., Wood, D., Lanthaler, M.: Rdf 1.1 Concepts and Abstract Syntax. https://www.w3.org/TR/rdf11-concepts/. Accessed 25 Feb 2014

  33. Dai, A.M., Storkey, A.J.: The supervised hierarchical dirichlet process. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 243–255 (2015)

    Google Scholar 

  34. Das Sarma, A., Jain, A., Yu, C.: Dynamic relationship and event discovery. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pp. 207–216 (2011)

  35. Dayarathna, M., Perera, S.: Recent advancements in event processing. ACM Comput. Surv. 51(2), 33:1–33:36 (2018)

    Google Scholar 

  36. Dell’Aglio, D., Dao-Tran, M., Calbimonte, J.P., Le Phuoc, D., Della Valle, E.: A query model to capture event pattern matching in RDF stream processing query languages. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) Knowledge Engineering and Knowledge Management, pp. 145–162. Springer, Cham (2016)

    Google Scholar 

  37. Deng, Q., Cai, G., Zhang, H., Liu, Y., Huang, L., Sun, F.: Enhancing situation awareness of public safety events by visualizing topic evolution using social media. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, pp. 7:1–7:10 (2018)

  38. Dou, W., Wang, X., Ribarsky, W., Zhou, M.: Event detection in social media data. In: Proceedings of the IEEE VisWeek Workshop on Interactive Visual Text Analytics—Task Driven Analytics of Social Media Content, pp. 971–980 (2012)

  39. Ertugrul, A.M., Velioglu, B., Karagoz, P.: Word embedding based event detection on social media. In: de Pisón, F.J.M., Urraca, R., Quintián, H., Corchado, E. (eds.) Hybrid Artificial Intelligent Systems, pp. 3–14. Springer, Cham (2017)

    Google Scholar 

  40. Fan, S., Shi, C., Wang, X.: Abnormal event detection via heterogeneous information network embedding. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18, pp. 1483–1486 (2018)

  41. Flouris, I., Giatrakos, N., Garofalakis, M., Deligiannakis, A.: Issues in complex event processing systems. In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol. 2, pp. 241–246. IEEE Computer Society Washington, DC, USA (2015). https://doi.org/10.1109/Trustcom.2015.590

  42. Gillani, S., Zimmermann, A., Picard, G., Laforest, F.: A query language for semantic complex event processing: syntax, semantics and implementation. Semant. Web 10(1), 53–93 (2019)

    Google Scholar 

  43. Gnouma, M., Ejbali, R., Zaied, M.: Abnormal events’ detection in crowded scenes. Multimed. Tools Appl. 77(19), 24843–24864 (2018)

    Google Scholar 

  44. Green, P.J., Richardson, S.: Modelling heterogeneity with and without the dirichlet process. Scand. J. Stat. 28(2), 355–375 (2001)

    MathSciNet  MATH  Google Scholar 

  45. Gui, H., Liu, J., Tao, F., Jiang, M., Norick, B., Kaplan, L., Han, J.: Embedding learning with events in heterogeneous information networks. IEEE Trans. Knowl. Data Eng. 29(11), 2428–2441 (2017)

    Google Scholar 

  46. Hasan, M., Orgun, M.A., Schwitter, R.: A survey on real-time event detection from the twitter data stream. J. Inf. Sci. 44(4), 443–463 (2018)

    Google Scholar 

  47. Herzberg, N., Meyer, A., Weske, M.: An event processing platform for business process management. In: 2013 17th IEEE International Enterprise Distributed Object Computing Conference, pp. 107–116 (2013)

  48. Horie, S., Kiritoshi, K., Ma, Q.: Abstract-concrete relationship analysis of news events based on a 5w representation model. In: Hartmann, S., Ma, H. (eds.) Database and Expert Systems Applications, pp. 102–117. Springer, Cham (2016)

    Google Scholar 

  49. Huang, L., Lv, S., Zang, L., Su, Y., Han, J., Hu, S.: A fresh look at understanding news events evolution. In: Companion Proceedings of the The Web Conference 2018, WWW ’18, pp. 29–30 (2018)

  50. Huang, Y., Shen, C., Li, T.: Event summarization for sports games using twitter streams. World Wide Web 21(3), 609–627 (2018)

    Google Scholar 

  51. Ishwaran, H., Zarepour, M.: Exact and approximate sum representations for the dirichlet process. Can. J. Stat. 30(2), 269–283 (2002)

    MathSciNet  MATH  Google Scholar 

  52. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427–431 (2017)

  53. Kaleel, S.B., Abhari, A.: Cluster-discovery of twitter messages for event detection and trending. J. Comput. Sci. 6, 47–57 (2015)

    Google Scholar 

  54. Kangwei, L., Jianhua, W., Zhongzhi, H.: Abnormal event detection and localization using level set based on hybrid features. Signal Image Video Process. 12(2), 255–261 (2018)

    Google Scholar 

  55. Kim, T.Y., Kim, J., Lee, J., Lee, J.H.: A tweet summarization method based on a keyword graph. In: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, ICUIMC ’14, pp. 96:1–96:8 (2014)

  56. King, R., McLeod, D.: A unified model and methodology for conceptual database design. In: Brodie, M.L., Mylopoulos, J., Schmidt, J.W. (eds.) On Conceptual Modelling: Perspectives from Artificial Intelligence, Databases, and Programming Languages, pp. 313–331. Springer, New York (1984)

    Google Scholar 

  57. Kojiri, T., Nate, F., Tokutake, K.: Understanding support of causal relationship between events in historical learning. IEICE Trans. Inf. Syst. E101.D(8), 2072–2081 (2018)

    Google Scholar 

  58. Kolchinsky, I., Schuster, A.: Efficient adaptive detection of complex event patterns. PVLDB 11(11), 1346–1359 (2018)

    Google Scholar 

  59. Koren, Y.: The Bellkor Solution to the Netflix Grand Prize. Netflix prize documentation 81, 1–10 (2009)

    Google Scholar 

  60. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning, vol. 32, pp. 1188–1196 (2014)

  61. Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) The Semantic Web–ISWC 2011, pp. 370–388. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  62. Lee, C.H., Yu, P.S., Chen, M.S.: Mining relationship between triggering and consequential events in a short transaction database. In: Proceedings of the 2002 SIAM International Conference on Data Mining, pp. 403–419 (2002)

  63. Lee, H., Abdar, M., Yen, N.Y.: Event-based trend factor analysis based on hashtag correlation and temporal information mining. Appl. Soft Comput. 71, 1204–1215 (2018)

    Google Scholar 

  64. Lee, I.T., Goldwasser, D.: Feel: Featured event embedding learning. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 4840–4847 (2018)

  65. Leetaru, K., Schrodt, P.A.: Gdelt: Global Data on Events, Location and Tone, 1979–2012. http://data.gdeltproject.org/documentation/ISA.2013.GDELT.pdf. Accessed 29 Mar 2013

  66. Li, C., Duan, Y., Wang, H., Zhang, Z., Sun, A., Ma, Z.: Enhancing topic modeling for short texts with auxiliary word embeddings. ACM Trans. Inf. Syst. 36(2), 11:1–11:30 (2017)

    Google Scholar 

  67. Li, Q., Ma, Y., Yang, Z.: Event cube–a conceptual framework for event modeling and analysis. In: Bouguettaya, A., Gao, Y., Klimenko, A., Chen, L., Zhang, X., Dzerzhinskiy, F., Jia, W., Klimenko, S.V., Li, Q. (eds.) Web Information Systems Engineering–WISE 2017, pp. 499–515. Springer, Cham (2017)

    Google Scholar 

  68. Liu, S., Wang, B., Xu, M.: Event recommendation based on graph random walking and history preference reranking. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17, pp. 861–864 (2017)

  69. Liu, X., He, Q., Tian, Y., Lee, W., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pp. 1032–1040 (2012)

  70. Liu, X., Wang, M., Huet, B.: Event analysis in social multimedia: a survey. Front. Comput. Sci. 10(3), 433–446 (2016)

    Google Scholar 

  71. Luo, D., Yang, J., Krstajic, M., Ribarsky, W., Keim, D.: Eventriver: visually exploring text collections with temporal references. IEEE Trans. Vis. Comput. Graph. 18(1), 93–105 (2012)

    Google Scholar 

  72. Ma, J., Petridis, M., Knight, B.: Formulating the temporal causal relationships between events and their results. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXX, pp. 199–210. Springer, Cham (2013)

    Google Scholar 

  73. Macedo, A.Q., Marinho, L.B., Santos, R.L.: Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys ’15, pp. 123–130 (2015)

  74. Magid, Y., Sharon, G., Arcushin, S., Ben-Harrush, I., Rabinovich, E.: Industry experience with the IBM active middleware technology (AMIT) complex event processing engine. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, DEBS ’10, pp. 140–149 (2010)

  75. Mejri, M., Akaichi, J.: A survey of textual event extraction from social networks. In: Proceedings of the First Conference on Language Processing and Knowledge Management, LPKM 2017 (2017)

  76. Meladianos, P., Xypolopoulos, C., Nikolentzos, G., Vazirgiannis, M.: An optimization approach for sub-event detection and summarization in twitter. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) Advances in Information Retrieval, pp. 481–493. Springer, Cham (2018)

    Google Scholar 

  77. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space (2013). arXiv:1301.3781

  78. Modi, A.: Event embeddings for semantic script modeling. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL, pp. 75–83 (2016)

  79. Mondo, G.D., Stell, J.G., Claramunt, C., Thibaud, R.: A graph model for spatio-temporal evolution. J. Univers. Comput. Sci. 16(11), 1452–1477 (2010)

    MathSciNet  MATH  Google Scholar 

  80. Mozafari, B., Zeng, K., D’antoni, L., Zaniolo, C.: High-performance complex event processing over hierarchical data. ACM Trans. Database Syst. 38(4), 21:1–21:39 (2013)

    MathSciNet  MATH  Google Scholar 

  81. Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, CIKM ’04, pp. 446–453 (2004)

  82. Oki, M., Takeuchi, K., Uematsu, Y.: Mobile network failure event detection and forecasting with multiple user activity data sets. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 7786–7792 (2018)

  83. Patil, N., Biswas, P.K.: Global abnormal events detection in crowded scenes using context location and motion-rich spatio-temporal volumes. IET Image Process. 12(4), 596–604 (2018)

    Google Scholar 

  84. Qiao, Z., Zhang, P., Zhou, C., Cao, Y., Guo, L., Zhang, Y.: Event recommendation in event-based social networks. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 3130–3131 (2014)

  85. Ritter, A., Clark, S., Mausam, Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1524–1534 (2011)

  86. Rozsnyai, S., Schiefer, J., Roth, H.: Sari-sql: event query language for event analysis. In: 2009 IEEE Conference on Commerce and Enterprise Computing, pp. 24–32 (2009)

  87. Rozsnyai, S., Vecera, R., Schiefer, J., Schatten, A.: Event cloud—searching for correlated business events. In: The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (CEC-EEE 2007), pp. 409–420 (2007)

  88. Rudrapal, D., Das, A., Bhattacharya, B.: A survey on automatic twitter event summarization. JIPS 14(1), 79–100 (2018)

    Google Scholar 

  89. Segaran, T., Evans, C., Taylor, J.: Programming the Semantic Web. O’Reilly Media, Newton (2009)

    Google Scholar 

  90. She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD ’15, pp. 1629–1643 (2015)

  91. She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)

    Google Scholar 

  92. She, J., Tong, Y., Chen, L., Song, T.: Feedback-aware social event-participant arrangement. In: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD ’17, pp. 851–865 (2017)

  93. Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)

    Google Scholar 

  94. Sousa, D.N.F., Sampaio, J.O.: Intelligent subevent detection based on social network data. In: 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), pp. 820–827 (2017)

  95. Srijith, P., Hepple, M., Bontcheva, K., Preotiuc-Pietro, D.: Sub-story detection in twitter with hierarchical dirichlet processes. Inf. Process. Manag. 53(4), 989–1003 (2017)

    Google Scholar 

  96. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a large ontology from wikipedia and wordnet. J. Web Semant. 6(3), 203–217 (2008)

    Google Scholar 

  97. Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. Newsl. 14(2), 20–28 (2013)

    Google Scholar 

  98. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pp. 1067–1077 (2015)

  99. Thost, V., Holste, J., Özçep, Ö.L.: On implementing temporal query answering in DL-lite (extended abstract). In: Proceedings of the 28th International Workshop on Description Logics, Athens, Greece, 7–10 June 2015 (2015)

  100. Tong, C., Li, J., Zhu, F.: A convolutional neural network based method for event classification in event-driven multi-sensor network. Comput. Electr. Eng. 60, 90–99 (2017)

    Google Scholar 

  101. Valkanas, G., Gunopulos, D.: Event detection from social media data. IEEE Data Eng. Bull. 36(3), 51–58 (2013)

    Google Scholar 

  102. Wang, D., Al-Rubaie, A., Clarke, S.S., Davies, J.: Real-time traffic event detection from social media. ACM Trans. Internet Technol. 18(1), 9:1–9:23 (2017)

    Google Scholar 

  103. Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06, pp. 424–433 (2006)

  104. Wang, Y., Cao, K.: Context-aware complex event processing for event cloud in internet of things. In: 2012 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6 (2012)

  105. Xi, Y., Li, B., Liu, Y.: A semantic aspect-based vector space model to identify the event evolution relationship within topics. JCSE 9(2), 73–82 (2015)

    Google Scholar 

  106. Xing, C., Wang, Y., Liu, J., Huang, Y., Ma, W.Y.: Hashtag-based sub-event discovery using mutually generative lda in twitter. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 2666–2672 (2016)

  107. Xu, Z., Liu, Y., Yen, N., Mei, L., Luo, X., Wei, X., Hu, C.: Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans. Cloud Comput (2016). https://doi.org/10.1109/TCC.2016.2517638

    Article  Google Scholar 

  108. Xu, Z., Zhang, H., Hu, C., Mei, L., Xuan, J., Choo, K.K.R., Sugumaran, V., Zhu, Y.: Building knowledge base of urban emergency events based on crowdsourcing of social media. Concurr. Comput. Pract. Exp. 28(15), 4038–4052 (2016)

    Google Scholar 

  109. Yang, C.C., Shi, X., Wei, C.: Discovering event evolution graphs from news corpora. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(4), 850–863 (2009)

    Google Scholar 

  110. Yang, Z., Li, Q., Lu, Z., Ma, Y., Gong, Z., Liu, W.: Dual structure constrained multimodal feature coding for social event detection from flickr data. ACM Trans. Internet Technol. 17(2), 19:1–19:20 (2017)

    Google Scholar 

  111. Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 4800–4810. Curran Associates Inc., New York (2018)

    Google Scholar 

  112. Zhang, C., Lei, D., Yuan, Q., Zhuang, H., Kaplan, L., Wang, S., Han, J.: Geoburst+: effective and real-time local event detection in geo-tagged tweet streams. ACM Trans. Intell. Syst. Technol. 9(3), 34:1–34:24 (2018)

    Google Scholar 

  113. Zhou, D., Zhang, X., He, Y.: Event extraction from twitter using non-parametric Bayesian mixture model with word embeddings. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 1, pp. 808–817 (2017)

  114. Zhou, X., Chen, L.: Event detection over twitter social media streams. VLDB J. 23(3), 381–400 (2014)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. City University of Hong Kong, Kowloon, Hong Kong

    Xinhong Chen

  2. Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

Authors
  1. Xinhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinhong Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Li, Q. Event modeling and mining: a long journey toward explainable events. The VLDB Journal 29, 459–482 (2020). https://doi.org/10.1007/s00778-019-00545-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00778-019-00545-0

Keywords

Search

Navigation