Abstract
The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and expressions, which points to the importance of understanding and discovering the knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an effective and elegant way. In this paper we present an event cube (EC) model which is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. Specifically, based on the essential event elements of 5W1H (i.e., When, Where, Who, What, Why, and How), the EC model is developed to organize the discovered events from multiple dimensions, to operate on the events at various levels of granularity, so as to facilitate analyzing and mining hidden/inherent relationships among the events effectively. Case studies are provided to illustrate the usages and show the benefits of EC facilities in on-line analytical processing of events and their relationships.
Similar content being viewed by others
References
Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM (1998)
Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 28–36. ACM (1998)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)
Zhou, X., Chen, L.: Event detection over twitter social media streams. VLDB J. 23(3), 381–400 (2014)
Xie, W., Zhu, F., Jiang, J., Lim, E.P., Wang, K.: TopicSketch: real-time bursty topic detection from Twitter. IEEE Trans. Knowl. Data Eng. 28(8), 2216–2229 (2016)
Chen, L., Roy, A.: Event detection from Flickr data through wavelet-based spatial analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 523–532. ACM, November 2009
Reuter, T., Papadopoulos, S., Petkos, G., Mezaris, V., Kompatsiaris, Y., Cimiano, P., Geva, S.: Social event detection at MediaEval 2013: challenges, datasets, and evaluation. In: Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, 18–19 October 2013 (2013)
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. (TOIT) 17(2), 19 (2017)
Reuter, T., Cimiano, P.: Event-based classification of social media streams. In: Proceedings of ICMR. Article No. 22 (2012)
Huang, D., Hu S., Cai Y., Min, H.: Discovering event evolution graphs based on news articles relationships. In: Proceedings of ICEBE, pp. 246–251 (2014)
Petkos, G., Papadopoulos, S., Schinas, E., Kompatsiaris, Y.: Graph-based multimodal clustering for social event detection in large collections of images. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014. LNCS, vol. 8325, pp. 146–158. Springer, Cham (2014). doi:10.1007/978-3-319-04114-8_13
Kaneko, T., Yanai, K.: Event photo mining from twitter using keyword bursts and image clustering. Neurocomputing 172, 143–158 (2016)
Yang, Z., Li, Q., Liu, W., Ma, Y.: Learning manifold representation from multimodal data for event detection in Flickr-like social media. In: Gao, H., Kim, J., Sakurai, Y. (eds.) DASFAA 2016. LNCS, vol. 9645, pp. 160–167. Springer, Cham (2016). doi:10.1007/978-3-319-32055-7_14
Mei, Q., Liu, C., Su, H., Zhai, C.: A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th International Conference on World Wide Web, pp. 533–542. ACM (2006)
Yang, C.C., Shi, X., Wei, C.P.: Discovering event evolution graphs from news corpora. IEEE Trans. Syst. Man Cybernetics-Part A: Syst. Hum. 39(4), 850–863 (2009)
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, pp. 446–453. ACM (2004)
Feng, A., Allan, J.: Finding and linking incidents in news. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 821–830. ACM (2007)
Feng, A., Allan, J.: Incident threading for news passages. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1307–1316. ACM (2009)
Deng, L., Ding, Z., Xu, B., Zhou, B., Jia, Y., Zou, P.: Exploring event evolution patterns at the atomic level. In: 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 40–47. IEEE (2011)
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.) DASFAA 2013. LNCS, vol. 7826, pp. 179–193. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37450-0_13
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Disc. 1(1), 29–53 (1997)
Sinnott, R.W.: Virtues of the Haversine (1984)
Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. Inf. Syst. 25(5), 345–366 (2000)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, vol. 14, pp. 1188–1196 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993). ACM
Acknowledgement
The authors are thankful to the useful comments and suggestions made by our partners Prof. Lei Chen (HKUST), Prof. Ho-fung Leung (CUHK), Dr. Hong-Va Leong (PolyU), and members of our research group. This work has been supported by a Strategic Research Grant from City University of Hong Kong (project no. 7004420) and a General Research Fund by the Hong Kong Research Grant Council (project no. CityU 11211417).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, Q., Ma, Y., Yang, Z. (2017). Event Cube – A Conceptual Framework for Event Modeling and Analysis. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-68783-4_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68782-7
Online ISBN: 978-3-319-68783-4
eBook Packages: Computer ScienceComputer Science (R0)