Abstract
In recent years, the concept of human-in-the-loop has been utilized to support environment sensing. Along with IoT (Internet of Things) and wearable computing technologies, connecting people and devices to the internet provides a significant advantage for real-time emergency management. For development of context-aware applications, it is important to utilize higher-level semantic information, such as human activity, social emotions, and human behaviors for event monitoring. Therefore, human users may become part of sensor networks by using mobile devices and social media to report local information around them. In this work, we mainly focus on the use of social messages spreading by human users to model the real-world events, in order to incorporate human sensors into event contexts for situational awareness. First, our algorithm computes the energy of each collected event messages, and then encapsulates ranked temporal, spatial and topical keywords into a structured node, which could reinforce the alert collected from physical nodes. The experimental results show that the proposed approach is able to extract essential entities of events for incorporating human sensors into event contexts for event prevention and risk management.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: 29th International Conference on Very Large Data Bases, vol. 29, pp. 81–92. VLDB Endowment Press, Berlin (2003)
Chi, Y., Song, X., Zhou, D., Hino, K., Tseng, B.L.: Evolutionary spectral clustering by incorporating temporal smoothness. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 153–162. ACM Press, San Jose (2007)
Chou, C.H., Zahedi, F.M., Zhao, H.: Ontology for developing web sites for natural disaster management: methodology and implementation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 41, 50–62 (2011)
Feng, C., Martin, E., Weining, Q., Aoying, Z.: Density-based clustering over an evolving data stream with noise, Victoria, British Columbia, Canada (2006)
Gong, L., Zeng, J., Zhang, S.: Text stream clustering algorithm based on adaptive feature selection. Expert Systems with Applications 38, 1393–1399 (2011)
Heverin, T., Zach, L.: Microblogging for Crisis Communication: Examination of Twitter Use in Response to a 2009 Violent Crisis in Seattle-Tacoma, Washington Area. In: Seventh International ISCRAM Conference, Seattle, Washington (2010)
Chen, H.L., Chen, M.S., Lin, S.C.: A Framework for Clustering Concept-Drifting Categorical Data. IEEE Trans. on Knowl. and Data 21, 652–665 (2009)
Jurisica, I., Mylopoulos, J., Yu, E.: Ontologies for knowledge management: an information systems perspective. Knowledge and Information Systems 6, 380–401 (2004)
Kaneiwa, K., Iwazume, M., Fukuda, K.: An upper ontology for event classifications and relations. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 394–403. Springer, Heidelberg (2007)
Kleinberg, J.: Bursty and hierarchical structure in streams. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, vol. 7, pp. 91–101. ACM (2002)
Lee, C.H.: Mining spatio-temporal information on microblogging streams using a density-based online clustering method. Expert Systems with Applications 39, 9623–9641 (2012)
Lee, C.-H., Wu, C.-H., Chien, T.-F.: BursT: A Dynamic Term Weighting Scheme for Mining Microblogging Messages. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 548–557. Springer, Heidelberg (2011)
Lee, C.H., Wu, C.H., Yang, H.C., Wen, W.S.: Computing Event Relatedness Based on a Novel Evaluation of Social-Media Streams. In: Park, J.J., Leung, V.C.M., Wang, C.L., Shon, T. (eds.) Future Information Technology, Application, and Service. LNEE, vol. 164, pp. 697–707. Springer, Heidelberg (2012)
Lee, C.H., Wu, C.H., Yang, H.C., Wen, W.S., Chiang, C.Y.: Exploiting Online Social Data in Ontology Learning for Event Tracking and Emergency Response. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1167–1174. IEEE Press, Niagara Falls (2013)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: 19th International Conference on World Wide Web, pp. 851–860. ACM Press, Raleigh (2010)
Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: 3rd AAAI International Conference on Weblogs and Social Media, ICWSM, pp. 311–314. AAAI Press, San Jose (2009)
Shaw, R., Troncy, R., Hardman, L.: LODE: Linking open descriptions of events. In: Gómez-Pérez, A., Yu, Y., Ding, Y. (eds.) ASWC 2009. LNCS, vol. 5926, pp. 153–167. Springer, Heidelberg (2009)
Wang, S., Yan, J., Xu, K., Liu, Y., Liu, L., Wang, H.: A conceptual modeling approach to quality management in the context of diary supply chain. In: 2nd International Conference on Information Science and Engineering (ICISE), pp. 13–16. IEEE Press, Hangzhou (2010)
Wang, W., Zhao, D.: Ontology-Based Event Modeling for Semantic Understanding of Chinese News Story. In: Zhou, M., Zhou, G., Zhao, D., Liu, Q., Zou, L. (eds.) NLPCC 2012. CCIS, vol. 333, pp. 58–68. Springer, Heidelberg (2012)
Ye, K., Wang, S., Yan, J., Wang, H., Miao, B.: Ontologies for crisis contagion management in financial institutions. Journal of Information Science 35, 548–562 (2009)
Ye, K., Yan, J., Wang, S., Wang, H., Miao, B.: Knowledge level modeling for systemic risk management in financial institutions. Expert Systems with Applications 38, 3528–3538 (2011)
Zhao, Q., Mitra, P., Chen, B.: Temporal and information flow based event detection from social text streams. In: The National Conference on Artificial Intelligence, pp. 1501–1506. AAAI Press, MIT Press, Menlo Park, Cambridge (2007)
Zhu, Y., Shasha, D.: StatStream: statistical monitoring of thousands of data streams in real time. In: 28th International Conference on Very Large Data Bases, pp. 358–369. VLDB Endowment Press, Hong Kong (2002)
Tsai, P.H., Lin, Y.C., Ou, Y.Z., Chu, E.T.H., Liu, J.W.S.: A Framework for Fusion of Human Sensor and Physical Sensor Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2168–2216 (2013)
Wang, D., Le, H., Kaplan, L., Abdelzaher, T.: On Truth Discovery in Social Sensing: A Maximum Likelihood Estimation Approach. In: 11th International Conference on Information Processing in Sensor Networks, pp. 233–244. ACM Press, New York (2012)
Schmidt, A., Beigl, M., Gellersen, H.W.: There is more to context than location. Computers & Graphics 23, 893–901 (1999)
Chu, E.T., Chen, Y.L., Lin, J.Y., Liu, W.S.: Crowdsourcing support system for disaster surveillance and response. In: 15th International Symposium on Wireless Personal Multimedia Communications, pp. 21–25. IEEE Press, Taipei (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, CH., Wu, CH., Lin, SJ. (2014). Incorporating Human Sensors into Event Contexts for Emergency Management. In: Wang, L.SL., June, J.J., Lee, CH., Okuhara, K., Yang, HC. (eds) Multidisciplinary Social Networks Research. MISNC 2014. Communications in Computer and Information Science, vol 473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45071-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-662-45071-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45070-3
Online ISBN: 978-3-662-45071-0
eBook Packages: Computer ScienceComputer Science (R0)