This book introduces the concept of Event Mining for building explanatory models from analyses of correlated data. Such a model may be used as the basis for predictions and corrective actions. The idea is to create, via an iterative process, a model that explains causal relationships in the form of structural and temporal patterns in the data. The first phase is the data-driven process of hypothesis formation, requiring the analysis of large amounts of data to find strong candidate hypotheses. The second phase is hypothesis testing, wherein a domain expert’s knowledge and judgment is used to test and modify the candidate hypotheses.
The book is intended as a primer on Event Mining for data-enthusiasts and information professionals interested in employing these event-based data analysis techniques in diverse applications. The reader is introduced to frameworks for temporal knowledge representation and reasoning, as well as temporal data mining and pattern discovery. Also discussed are the design principles of event mining systems. The approach is reified by the presentation of an event mining system called EventMiner, a computational framework for building explanatory models. The book contains case studies of using EventMiner in asthma risk management and an architecture for the objective self. The text can be used by researchers interested in harnessing the value of heterogeneous big data for designing explanatory event-based models in diverse application areas such as healthcare, biological data analytics, predictive maintenance of systems, computer networks, and business intelligence.
- R. Agarwal and R. Srikant. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference. 487–499. Google Scholar
Digital Library
- R. Agrawal and R. Srikant. 1995. Mining sequential patterns. In Data Engineering, 1995. Proceedings of the Eleventh International Conference on. 3–14. IEEE. DOI: .Google Scholar
Digital Library
- R. Agrawal, C. Faloutsos, and A. Swami. 1993a. Efficient Similarity Search in Sequence Databases. Springer. DOI: .Google Scholar
Digital Library
- R. Agrawal, T. Imieliński, and A. Swami. 1993b. Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22, 2, 207–216. DOI: .Google Scholar
Digital Library
- W. Aigner and S. Miksch. 2006. CareVis: Integrated visualization of computerized protocols and temporal patient data. Artif. Intell. Med. 37, 3, 203–218. DOI: .Google Scholar
Digital Library
- J. F. Allen. 1981. An interval-based representation of temporal knowledge. In IJCAI. 81, 221–226. Google Scholar
Digital Library
- S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. 1990. Basic local alignment search tool. J. Mol. Biol. 215, 3, 403–410. DOI: .Google Scholar
Cross Ref
- R. V. Ammon, C. Emmersberger, F. Springer, and C. Wolff. 2008. Event-driven business process management and its practical application taking the example of DHL.Google Scholar
- K. Amphawan, P. Lenca, and A. Surarerks. 2009. Mining top-k periodic-frequent pattern from transactional databases without support threshold. In B. Papasratorn, W. Chutimaskul, K. Porkaew, and V. Vanijja (Eds.), Advances in Information Technology: Third International Conference, IAIT 2009, Bangkok, Thailand, December 1–5, 2009. Proceedings. 18–29. Springer, Berlin. ISBN 978-3-642-10392-6. DOI: .Google Scholar
Cross Ref
- G. Androulidakis, V. Chatzigiannakis, and S. Papavassiliou. 2009. Network anomaly detection and classification via opportunistic sampling. IEEE Netw. 23, 1, 6–12. Google Scholar
Digital Library
- W. G. Aref, M. G. Elfeky, and A. K. Elmagarmid. 2004. Incremental, online, and merge mining of partial periodic patterns in time-series databases. IEEE Trans. Knowl. Data Eng. 16, 3, 332–342. DOI: .Google Scholar
Digital Library
- J. Ayres, J. Flannick, J. Gehrke, and T. Yiu. 2002. Sequential pattern mining using a bitmap representation. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 429–435. ACM. DOI: .Google Scholar
Digital Library
- B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom. 2002. Models and issues in data stream systems. In Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. 1–16. ACM. DOI: .Google Scholar
Digital Library
- W. D. Bae, S. Alkobaisi, S. Narayanappa, and C. C. Liu. 2012. A mobile data analysis framework for environmental health decision support. In Information Technology: New Generations (ITNG), 2012 Ninth International Conference on. 155–161. IEEE. DOI: .Google Scholar
Digital Library
- T. L. Bailey and C. Elkan. 1994. Fitting a mixture model by expectation maximization to discover motifs in bipolymers. Proceedings of International Conference on Intelligent Systems for Molecular Biology. 28–36.Google Scholar
- M. Baptista, S. Sankararaman, I. P. de Medeiros, C. Nascimento Jr, H. Prendinger, and E. M. Henriques. 2018. Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Comput. Ind. Eng. 115, 41–53. DOI: .Google Scholar
Cross Ref
- E. Ben-Chetrit, C. Chen-Shuali, E. Zimran, G. Munter, and G. Nesher. 2012. A simplified scoring tool for prediction of readmission in elderly patients hospitalized in internal medicine departments. Isr. Med. Assoc. J. 14, 12, 752–756.Google Scholar
- G. Birkhoff. 1948. Lattice Theory. Vol. 25. American Mathematical Society, New York.Google Scholar
- C. Boccuti and G. Casillas. 2017. Aiming for fewer hospital U-turns: The Medicare Hospital Readmission Reduction Program. https://www.kff.org/medicare/issue-brief/aiming-for-fewer-hospital-u-turns-the-medicare-hospital-readmission-reduction-program/.Google Scholar
- A. Borrie, G. K. Jonsson, and M. S. Magnusson. 2002. Temporal pattern analysis and its applicability in sport: An explanation and exemplar data. J. Sports Sci. 20, 10, 845–852. DOI: .Google Scholar
Cross Ref
- I. Bowman, S. H. Joshi, and J. Van Horn. 2012. Visual systems for interactive exploration and mining of large-scale neuroimaging data archives. Front. Neuroinform. 6, 11, 1143–1150. DOI: .Google Scholar
Cross Ref
- S. Boytcheva, G. Angelova, D. Tcharaktchiev, and Z. Angelov. 2019. Big data analytics in healthcare – Pattern mining of temporal clinical events. J. Syst. Sci. Syst. Eng. 28. DOI: .Google Scholar
Cross Ref
- L. Breiman. 2001. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statist. Sci. 16, 3, 199–231. DOI: .Google Scholar
Cross Ref
- S. Cakmak, R. E. Dales, and F. Coates. 2012. Does air pollution increase the effect of aeroallergens on hospitalization for asthma? J. Allergy Clin. Immunol. 129, 1, 228–231. DOI: .Google Scholar
Cross Ref
- V. D. Calhoun and T. Adali. 2009. Feature-based fusion of medical imaging data. IEEE Trans. Inf. Technol. Biomed. 13, 5, 711–720. DOI: .Google Scholar
Digital Library
- C. S. Calude and G. Longo. 2017. The deluge of spurious correlations in big data. Found. Sci. 22, 3, 595–612. DOI: .Google Scholar
Cross Ref
- H. Cao, D. W. Cheung, and N. Mamoulis. 2004. Discovering partial periodic patterns in discrete data sequences. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. 653–658. Springer, Berlin, Heidelberg. DOI: .Google Scholar
Cross Ref
- G. Casas-Garriga. 2003. Discovering Unbounded Episodes in Sequential Data. Springer, Berlin, Heidelberg. DOI: .Google Scholar
Cross Ref
- R. Casati and A. Varzi. 2015. Events. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, 2015.Google Scholar
- U. Cetintemel. 2003. The Aurora and Medusa projects. Data Eng. 51, 3.Google Scholar
- Z. Chaochen, C. A. R. Hoare, and A. P. Ravn. 1991. A calculus of durations. Inf. Process. Lett. 40, 5, 269–276. DOI: .Google Scholar
Cross Ref
- S.-S. Chen, T. C.-K. Huang, and Z.-M. Lin. 2011. New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports. J. Syst. Softw. 84, 10, 1638–1651. DOI: .Google Scholar
Digital Library
- E. M. Clarke and E. A. Emerson. 1981. Design and synthesis of synchronization skeletons using branching time temporal logic. In Workshop on Logic of Programs. 52–71. Springer. DOI: .Google Scholar
Digital Library
- P. R. Cohen. 2001. Fluent learning: Elucidating the structure of episodes. In Advances in Intelligent Data Analysis. 268–277. Springer, Berlin, Heidelberg. DOI: .Google Scholar
Digital Library
- J. Cohen. 2004. Bioinformatics – An introduction for computer scientists. ACM Comput. Surv. 36, 2, 122–158. DOI: .Google Scholar
Digital Library
- E. Coward and F. Drablos. 1998. Detecting periodic patterns in biological sequences. Bioinformatics 14, 6, 498–507.Google Scholar
Cross Ref
- J. A. Cruz and D. S. Wishart. 2006. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2, 59–77. DOI: .Google Scholar
Cross Ref
- A. A. Cruz, J. Bousquet, and N. Khaltaev. 2007. Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach. World Health Organization, Geneva.Google Scholar
- G. D’Amato, G. Liccardi, M. D’amato, and M. Cazzola. 2002. Outdoor air pollution, climatic changes and allergic bronchial asthma. Eur. Respir. J. 20, 3, 763–776. DOI: .Google Scholar
Cross Ref
- B. V. Dasarathy. 1994. Decision Fusion. Vol. 1994. IEEE Computer Society Press, Los Alamitos, CA. Google Scholar
Digital Library
- T. Desautels, R. Das, J. Calvert, M. Trivedi, C. Summers, D. J. Wales, and A. Ercole. 2017. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: A cross-sectional machine learning approach. BMJ Open. 7, 9, e017199. DOI: .Google Scholar
Cross Ref
- S. Duncan and N. T. Collier. 2002. C-quence: A tool for analyzing qualitative sequential data. Behav. Res. Methods 34, 1, 108–116. DOI: .Google Scholar
Cross Ref
- A. Endert, P. Fiaux, and C. North. 2012. Semantic interaction for visual text analytics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 473–482. ACM. DOI: .Google Scholar
Digital Library
- A. Ericsson and M. Berndtsson. 2007. Rex, the rule and event explorer. In Proceedings of the 2007 Inaugural International Conference on Distributed Event-Based Systems. 71–74. ACM. DOI: .Google Scholar
Digital Library
- B. Evelson and N. Norman. 2008. Topic overview: Business intelligence. Forrester Res. 61.Google Scholar
- W. J. Ewens and G. R. Grant. 2006. Statistical Methods in Bioinformatics: An Introduction. Springer Science & Business Media. DOI: .Google Scholar
Cross Ref
- J. A. Fails, A. Karlson, L. Shahamat, and B. Shneiderman. 2006. A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. In Visual Analytics Science and Technology, 2006 IEEE Symposium On. 167–174. IEEE. DOI: .Google Scholar
Cross Ref
- Z. Feldman, F. Fournier, R. Franklin, and A. Metzger. 2013. Proactive event processing in action: A case study on the proactive management of transport processes (industry article). In Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems. 97–106. ACM. DOI: .Google Scholar
Digital Library
- O. Firschein. 1983. Syntactic pattern recognition and applications. Proc. IEEE 71, 10, 1231.Google Scholar
Cross Ref
- C. Freksa. 1992. Temporal reasoning based on semi-intervals. Artif. Intell. 54, 1, 199–227. DOI: .Google Scholar
Digital Library
- N. Friedman, K. Murphy, and S. Russell. 1998. Learning the structure of dynamic probabilistic networks. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. 139–147. Morgan Kaufmann Publishers Inc. Google Scholar
Digital Library
- M. C. Frith, N. F. Saunders, B. Kobe, and T. L. Bailey. 2008. Discovering sequence motifs with arbitrary insertions and deletions. PLoS Comput. Biol. 4, 5, e1000071. DOI: .Google Scholar
Cross Ref
- I. Galan, A. Tobias, J. Banegas, and E. Aranguez. 2003. Short-term effects of air pollution on daily asthma emergency room admissions. Eur. Respir. J. 22, 5, 802–808. DOI: .Google Scholar
Cross Ref
- B. Ganter. 2010. Two basic algorithms in concept analysis. In L. Kwuida, B. Sertkaya (Eds.), Formal Concept Analysis. ICFCA 2010. Lecture Notes in Computer Science, vol. 5986. Springer, Berlin, Heidelberg. DOI: .Google Scholar
Digital Library
- B. Ganter and R. Wille. 2012. Formal Concept Analysis: Mathematical Foundations. Springer Science & Business Media. DOI: .Google Scholar
Cross Ref
- B. Ganter, G. Stumme, and R. Wille. 2003. Formal Concept Analysis: Methods, and Applications in Computer Science. TU Dresden. Google Scholar
Digital Library
- M. N. Garofalakis, R. Rastogi, and K. Shim. 1999. Spirit: Sequential pattern mining with regular expression constraints. In VLDB. Vol. 99. 7–10. Google Scholar
Digital Library
- S. Gatziu and K. R. Dittrich. 1994. Events in an active object-oriented database system. In Rules in Database Systems. 23–39. Springer. DOI: .Google Scholar
Cross Ref
- N. H. Gehani, H. V. Jagadish, and O. Shmueli. 1992. Composite event specification in active databases: Model & implementation. In VLDB. Vol. 92, 327–338. Citeseer. Google Scholar
Digital Library
- J. Gemmell, G. Bell, R. Lueder, S. Drucker, and C. Wong. 2002. MyLifeBits: Fulfilling the Memex vision. In Proceedings of the Tenth ACM International Conference on Multimedia. 235–238. ACM. DOI: .Google Scholar
Digital Library
- D. Girardi, J. Küng, R. Kleiser, M. Sonnberger, D. Csillag, J. Trenkler, and A. Holzinger. 2016. Inter-active knowledge discovery with the doctor-in-the-loop: A practical example of cerebral aneurysms research. Brain Inform. 3, 3, 133–143. DOI: .Google Scholar
Cross Ref
- E. Goldstein. 2016. Sensation and Perception. Cengage Learning, 10th edition (February 2, 2016).Google Scholar
- D. Gotz and K. Wongsuphasawat. 2012. Interactive intervention analysis. In AMIA Annual Symposium Proceedings. Vol. 2012, 274. American Medical Informatics Association.Google Scholar
- D. Gotz, F. Wang, and A. Perer. 2014. A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. J. Biomed. Inform. 48, 148–159. DOI: .Google Scholar
Digital Library
- K. Gouda and M. J. Zaki. 2001. Efficiently mining maximal frequent itemsets. In Proceedings 2001 IEEE International Conference on Data Mining. San Jose, CA, USA, 163–170. DOI: .Google Scholar
Digital Library
- D. Gunning. 2017a. Explainable Artificial Intelligence (XAI). https://www.darpa.mil/program/explainable-artificial-intelligence.Google Scholar
- D. Gunning and D. Aha. 2019. DARPA’s explainable artificial intelligence (XAI) program. AI Magazine 40, 2, 44–58. DOI: .Google Scholar
Digital Library
- C. Gupta and A. Farahat. 2020. Tutorial on deep learning for industrial AI: Challenges, new methods and best practices. KDD. https://sites.google.com/view/dl-for-industrial-ai.Google Scholar
- C. Gurrin, A. F. Smeaton, and A. R. Doherty. 2014. Lifelogging: Personal big data. Found. Trends Inf. Ret. 8, 1, 1–125. DOI: .Google Scholar
Digital Library
- J. Y. Halpern and Y. Shoham. 1991. A propositional modal logic of time intervals. J. ACM 38, 4, 935–962. DOI: .Google Scholar
Digital Library
- J. Han, W. Gong, and Y. Yin. 1998. Mining segment-wise periodic patterns in time-related databases. In KDD. 214–218. Google Scholar
Digital Library
- J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu. 2000a. FreeSpan: Frequent pattern-projected sequential pattern mining. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 355–359. ACM. Google Scholar
Digital Library
- J. Han, J. Pei, and Y. Yin. 2000b. Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29, 2, 1–12. DOI: .Google Scholar
Digital Library
- J. Han, J. Wang, Y. Lu, and P. Tzvetkov. 2002. Mining top-k frequent closed patterns without minimum support. In Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on. 211–218. IEEE. DOI: .Google Scholar
Digital Library
- J. Han, J. Pei, and M. Kamber. 2011. Data Mining: Concepts and Techniques. Elsevier. DOI: .Google Scholar
Cross Ref
- J. Hipp, U. Güntzer, and G. Nakhaeizadeh. 2000. Algorithms for association rule mining – A general survey and comparison. ACM Sigkdd Explor. Newsletter 2, 1, 58–64. DOI: .Google Scholar
Digital Library
- M. Hirao, S. Inenaga, A. Shinohara, M. Takeda, and S. Arikawa. 2001. A practical algorithm to find the best episode patterns. In Discovery Science. 435–440. Springer. DOI: .Google Scholar
Digital Library
- T. K. Ho, J. J. Hull, and S. N. Srihari. 1994. Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16, 1, 66–75. DOI: .Google Scholar
Digital Library
- W. Hou, Q. Pan, Q. Peng, and M. He. 2017. A new method to analyze protein sequence similarity using dynamic time warping. Genomics 109, 2, 123–130. DOI: .Google Scholar
Cross Ref
- V. M. Hudson, P. A. Schrodt, and R. Whitmer. 2005. A new kind of social science? Moving ahead with reverse Wolfram models applied to event data. In Proceedings of the 46th Annual International Studies Association Convention.Google Scholar
- M. Jamei, A. Nisnevich, E. Wetchler, S. Sudat, and E. Liu. 2017. Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PLoS One 12, 7, e0181173. DOI: .Google Scholar
Cross Ref
- D. H. Jeong, C. Ziemkiewicz, B. D. Fisher, W. Ribarsky, and R. Chang. 2009. iPCA: An interactive system for PCA-based visual analytics. Comput. Graph. Forum 28, 767–774. DOI: .Google Scholar
Digital Library
- Y. Kaku, K. Kuramoto, S. Kobashi, and Y. Hata. 2012. Asthmatic attacks prediction considering weather factors based on fuzzy-AR model. In Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on. 1–4. IEEE. DOI: .Google Scholar
Cross Ref
- E. J. Keogh and M. J. Pazzani. 1998. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In KDD. Vol. 98. 239–243. Google Scholar
Digital Library
- E. Keogh and S. Kasetty. 2003. On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Min. Knowl. Discov. 7, 4, 349–371. DOI: .Google Scholar
Digital Library
- E. Keogh and J. Lin. 2005. Clustering of time-series subsequences is meaningless: Implications for previous and future research. Knowl. Inf. syst. 8, 2, 154–177. DOI: .Google Scholar
Digital Library
- M. M. H. Khan, H. K. Le, H. Ahmadi, T. F. Abdelzaher, and J. Han. 2008. Dustminer: Troubleshooting interactive complexity bugs in sensor networks. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. 99–112. ACM. DOI: .Google Scholar
Digital Library
- N.-K. Kim, K. Tharakaraman, and J. L. Spouge. 2006. Adding sequence context to a Markov background model improves the identification of regulatory elements. Bioinformatics 22, 23, 2870–2875. DOI: .Google Scholar
Cross Ref
- J. Kittler, M. Hatef, R. P. Duin, and J. Matas. 1998. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 3, 226–239. Google Scholar
Digital Library
- J. Kleinberg. 2003. Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7, 4, 373–397. DOI: .Google Scholar
Digital Library
- J. Kölling, D. Langenkämper, S. Abouna, M. Khan, and T. W. Nattkemper. 2012. WHIDE – A web tool for visual data mining colocation patterns in multivariate bioimages. Bioinformatics 28, 8, 1143–1150. DOI: .Google Scholar
Cross Ref
- C. Kruegel, D. Mutz, W. Robertson, and F. Valeur. 2003. Bayesian event classification for intrusion detection. In Null. 14. IEEE. DOI: .Google Scholar
Digital Library
- L. I. Kuncheva. 2004. Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons. DOI: .Google Scholar
Digital Library
- L. I. Kuncheva, J. C. Bezdek, and R. P. Duin. 2001. Decision templates for multiple classifier fusion: An experimental comparison. Pattern Recognit. 34, 2, 299–314.Google Scholar
Cross Ref
- S. O. Kuznetsov and S. A. Obiedkov. 2002. Comparing performance of algorithms for generating concept lattices. J. Expl. Theor. Artif. Intell. 14, 2-3, 189–216. DOI: .Google Scholar
Cross Ref
- L. Lam and C. Y. Suen. 1995. Optimal combinations of pattern classifiers. Pattern Recognit. Lett. 16, 9, 945–954. DOI: .Google Scholar
Digital Library
- M. Last, A. Kandel, and H. Bunke. 2004. Data Mining in Time Series Databases. Vol. 57. World Scientific. DOI: .Google Scholar
Digital Library
- S. Laxman, P. Sastry, and K. Unnikrishnan. 2005. Discovering frequent episodes and learning hidden Markov models: A formal connection. Knowl. Data Eng. IEEE Trans. 17, 11, 1505–1517. DOI: .Google Scholar
Digital Library
- V. Leat. 2007. Introduction to business intelligence. IBM Software Group, Raspoloživo na. http://www07.ibm.com/sg/events/blueprint/pdf/day1/IntroductiontoBusinessIntelligence.pdf [Pristupljeno 07.11.2016].Google Scholar
- C. C. Lee, S. C. Sheridan, and S. Lin. 2012. Relating weather types to asthma-related hospital admissions in New York State. EcoHealth 9, 4, 427–439. DOI: .Google Scholar
Cross Ref
- N. Lesh, M. J. Zaki, and M. Ogihara. 1999. Mining features for sequence classification. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 342–346. ACM. DOI: .Google Scholar
Digital Library
- J. Y. Lettvin, H. R. Maturana, W. S. McCulloch, and W. H. Pitts. 1959. What the frog’s eye tells the frog’s brain. Proc. IRE 47, 11, 1940–1951. DOI: .Google Scholar
Cross Ref
- C.-S. Li, P. S. Yu, and V. Castelli. 1996. Hierarchyscan: A hierarchical similarity search algorithm for databases of long sequences. In Data Engineering, 1996. Proceedings of the Twelfth International Conference on. 546–553. IEEE. Google Scholar
Digital Library
- M.-Y. Lin and S.-Y. Lee. 2003. Improving the efficiency of interactive sequential pattern mining by incremental pattern discovery. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on. 8. IEEE. DOI: .Google Scholar
Digital Library
- W. Lin, M. A. Orgun, and G. J. Williams. 2002. An overview of temporal data mining. In AusDM. 83–90.Google Scholar
- J. Lin, E. Keogh, L. Wei, and S. Lonardi. 2007. Experiencing SAX: A novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 2, 107–144. DOI: .Google Scholar
Digital Library
- N. Littlestone and M. K. Warmuth. 1994. The weighted majority algorithm. Inf. Comput. 108, 2, 212–261. DOI: .Google Scholar
Digital Library
- X. Liu, D. L. Brutlag, and J. S. Liu. 2000. BioProspector: Discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. In Biocomputing 2001. 127–138. World Scientific. DOI: .Google Scholar
Cross Ref
- X. S. Liu, D. L. Brutlag, and J. S. Liu. 2002. An algorithm for finding protein–DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat. Biotechnol. 20, 8, 835–839. DOI: .Google Scholar
Cross Ref
- E. Lorenz. 1972. Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas? na.Google Scholar
- J.-G. Lou, Q. Fu, Y. Wang, and J. Li. 2010. Mining dependency in distributed systems through unstructured logs analysis. ACM SIGOPS Operating Syst. Rev. 44, 1, 91–96. DOI: .Google Scholar
Digital Library
- D. C. Luckham and B. Frasca. 1998. Complex event processing in distributed systems. Computer Systems Laboratory Technical Report CSL-TR-98-754. Stanford University, Stanford. 28.Google Scholar
- S. M. Lundberg and S.-I. Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 4765–4774. Google Scholar
Digital Library
- S. Ma and J. L. Hellerstein. 2001. Mining partially periodic event patterns with unknown periods. In Data Engineering, 2001. Proceedings. 17th International Conference on. 205–214. IEEE. DOI: .Google Scholar
Digital Library
- M. S. Magnusson. 2000. Discovering hidden time patterns in behavior: T-patterns and their detection. Behav. Res. Methods Instrum. Comput. 32, 1, 93–110. DOI: .Google Scholar
Cross Ref
- A. A. Makanju, A. N. Zincir-Heywood, and E. E. Milios. 2009. Clustering event logs using iterative partitioning. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1255–1264. ACM. DOI: .Google Scholar
Digital Library
- U. G. Mangai, S. Samanta, S. Das, and P. R. Chowdhury. 2010. A survey of decision fusion and feature fusion strategies for pattern classification. IETE Tech. Rev. 27, 4, 293–307.Google Scholar
Cross Ref
- H. Mannila, H. Toivonen, and A. I. Verkamo. 1997. Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1, 3, 259–289. DOI: .Google Scholar
Digital Library
- F. Masseglia, P. Poncelet, and M. Teisseire. Mar. 2009. Efficient mining of sequential patterns with time constraints: Reducing the combinations. Expert Syst. Appl. 36, 2, 2677–2690. ISSN 0957-4174. DOI: .Google Scholar
Digital Library
- Y. Mizunuma, S. Yamamoto, Y. Yamaguchi, A. Ikeuchi, T. Satoh, and S. Shimada. 2014. Twitter bursts: Analysis of their occurrences and classifications. In ICDS 2014, The Eighth International Conference on Digital Society. 182–187.Google Scholar
- F. Moerchen. 2006. Algorithms for time series knowledge mining. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 668–673. ACM. DOI: .Google Scholar
Digital Library
- F. Moerchen and D. Fradkin. 2010. Robust mining of time intervals with semi-interval partial order patterns. In SDM. 315–326. DOI: .Google Scholar
Cross Ref
- C. S. Möller-Levet, F. Klawonn, K.-H. Cho, and O. Wolkenhauer. 2003. Fuzzy clustering of short time-series and unevenly distributed sampling points. In International Symposium on Intelligent Data Analysis. 330–340. Springer.Google Scholar
- C. Mooney and J. F. Roddick. 2004. Mining relationships between interacting episodes. In SDM. 1–10. SIAM. DOI: .Google Scholar
Cross Ref
- T. Morzy, M. Wojciechowski, and M. Zakrzewicz. 2002. Efficient constraint-based sequential pattern mining using dataset filtering techniques. In Databases and Information Systems II. 297–309. Springer.Google Scholar
- B. C. Moszkowski. 1983. Reasoning about Digital Circuits. Technical report, DTIC Document.Google Scholar
- M. Müller. 2007. Dynamic time warping. Information Retrieval for Music and Motion. 69–84. DOI: .Google Scholar
Cross Ref
- S. B. Needleman and C. D. Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 3, 443–453. DOI: .Google Scholar
Cross Ref
- L. Nourine and O. Raynaud. 1999. A fast algorithm for building lattices. Inf. Process. Lett. 71, 5, 199–204. DOI: .Google Scholar
Digital Library
- D. Oberle. 2006. Semantic Management of Middleware, Vol. 1. Springer Science & Business Media. DOI: .Google Scholar
Digital Library
- H. Oh and R. Jain. 2017. From multimedia logs to personal chronicles. In Proceedings of the 25th ACM International Conference on Multimedia. ACM, 881–889. DOI: .Google Scholar
Digital Library
- H. Oh and R. Jain. 2019. Detecting events of daily living using multimodal data. arXiv preprint arXiv:1905.09402.Google Scholar
- G. D. Oosthuizen. 1992. The Use of a Lattice in Knowledge Processing. Ph.D. Dissertation, University of Strathclyde. Google Scholar
Digital Library
- Ozden, S. Ramaswamy, and A. Silberschatz. 1998. Cyclic association rules. In Data Engineering, 1998. Proceedings., 14th International Conference on. IEEE, 412–421. DOI: .Google Scholar
Digital Library
- Pastrello, E. Pasini, M. Kotlyar, D. Otasek, S. Wong, W. Sangrar, S. Rahmati, and I. Jurisica. 2014. Integration, visualization and analysis of human interactome. Biochem. Biophys. Res. Commun. 445, 4, 757–773. DOI: .Google Scholar
Cross Ref
- Patnaik, P. Butler, N. Ramakrishnan, L. Parida, B. J. Keller, and D. A. Hanauer. 2011. Experiences with mining temporal event sequences from electronic medical records: Initial successes and some challenges. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 360–368. DOI: .Google Scholar
Digital Library
- D. Patnaik, S. Laxman, B. Chandramouli, and N. Ramakrishnan. 2012. Efficient episode mining of dynamic event streams. In Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 605–614. DOI: .Google Scholar
Digital Library
- J. Pearl. 2000. Causality: Models, Reasoning and Inference, Vol. 29. Springer. Google Scholar
Digital Library
- J. Pearl. 2019. The seven tools of causal inference, with reflections on machine learning. Commun. ACM 62, 3, 54–60. DOI: .Google Scholar
Digital Library
- J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu. 2000. Mining access patterns efficiently from web logs. In Knowledge Discovery and Data Mining. Current Issues and New Applications. Springer, 396–407. DOI: .Google Scholar
Digital Library
- J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In ICCCN. IEEE, 215. DOI: .Google Scholar
Digital Library
- J. Pei, J. Han, and W. Wang. 2002. Mining sequential patterns with constraints in large databases. In Proceedings of the Eleventh International Conference on Information and Knowledge Management. ACM, 18–25. DOI: .Google Scholar
Digital Library
- J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. 2004. Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Trans. Knowl. Data. Eng. 16, 10, 1424–1440. DOI: .Google Scholar
Digital Library
- G. R. Peterson. 2003. Demarcation and the scientistic fallacy. Zygon® 38, 4, 751–761. DOI: .Google Scholar
Cross Ref
- C. Plaisant, R. Mushlin, A. Snyder, J. Li, D. Heller, and B. Shneiderman. 1998. LifeLines: Using visualization to enhance navigation and analysis of patient records. In Proceedings of the AMIA Symposium. American Medical Informatics Association, 76.Google Scholar
- A. Pnueli. 1977. The temporal logic of programs. In 18th Annual Symposium on Foundations of Computer Science (sfcs 1977). IEEE, 46–57. DOI: .Google Scholar
Digital Library
- Z. Qiu, C. Gurrin, and A. F. Smeaton. 2016. Evaluating access mechanisms for multimodal representations of lifelogs. In MultiMedia Modeling. Springer, 574–585. DOI: .Google Scholar
Digital Library
- A. Quinton. 1979. Objects and events. Mind 88, 350, 197–214.Google Scholar
- M. Ramoni, P. Sebastiani, and P. Cohen. 2000. Multivariate clustering by dynamics. In AAAI/IAAI. 633–638. Google Scholar
Digital Library
- M. Ramoni, P. Sebastiani, and P. Cohen. 2002. Bayesian clustering by dynamics. Mach. Learn. 47, 1, 91–121. DOI: .Google Scholar
Digital Library
- M. T. Ribeiro, S. Singh, and C. Guestrin. 2018. Anchors: High-precision model-agnostic explanations. In Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
- S. Rose. 2013. Mortality risk score prediction in an elderly population using machine learning. Am. J. Epidemiol. 177, 5, 443–452. DOI: .Google Scholar
Cross Ref
- O. P. Rud. 2009. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy, Vol. 18. John Wiley & Sons.Google Scholar
- A. Russell and B. Brunekreef. 2009. A focus on particulate matter and health. Environ. Sci. Technol. 43, 13, 4620–4625. DOI: .Google Scholar
Cross Ref
- H. Sayyadi and L. Raschid. 2013. A graph analytical approach for topic detection. ACM Transactions on Internet Technology (TOIT) 13, 2, 4. DOI: .Google Scholar
Digital Library
- A. Scherp and V. Mezaris. 2014. Survey on modeling and indexing events in multimedia. Multimed. Tools Appl. 70, 1, 7–23. Google Scholar
Digital Library
- K. Shameer, K. W. Johnson, A. Yahi, R. Miotto, L. Li, D. Ricks, J. Jebakaran, P. Kovatch, P. P. Sengupta, S. Gelijns, A. Moskovitz, B. Darrow, D. L. David, A. Kasarskis, N. P. Tatonetti, S. Pinney, and J. T. Dudley. 2017. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: A case-study using Mount Sinai heart failure cohort. In Pacific Symposium on Biocomputing 2017. World Scientific, 276–287. DOI: .Google Scholar
Cross Ref
- I. Shams, S. Ajorlou, and K. Yang. 2015. A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Manag. Sci. 18, 1, 19–34.Google Scholar
Cross Ref
- P. Shan Kam and W.-C. A. Fu. 2000. Discovering Temporal Patterns for Interval-Based Events. Springer. DOI: .Google Scholar
Digital Library
- X.-S. Si, W. Wang, C.-H. Hu, and D.-H. Zhou. 2011. Remaining useful life estimation – a review on the statistical data driven approaches. Eur. J. Oper. Res. 213, 1, 1–14.Google Scholar
Cross Ref
- V. K. Singh and R. Jain. 2016. Situation Recognition Using EventShop. Springer. DOI: .Google Scholar
Digital Library
- R. Sipos, D. Fradkin, F. Moerchen, and Z. Wang. 2014. Log-based predictive maintenance. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1867–1876. DOI: .Google Scholar
Digital Library
- H. Skutkova, M. Vitek, P. Babula, R. Kizek, and I. Provaznik. 2013. Classification of genomic signals using dynamic time warping. BMC Bioinform. 14, 10, S1. DOI: .Google Scholar
Cross Ref
- T. F. Smith and M. S. Waterman. 1981. Identification of common molecular subsequences. J. Mol. Biol. 147, 1, 195–197. DOI: .Google Scholar
Cross Ref
- P. Smyth. 1997. Clustering sequences with hidden Markov models. In Advances in Neural Information Processing Systems. 648–654. Google Scholar
Digital Library
- R. Srikant and R. Agrawal. 1996. Mining sequential patterns: Generalizations and performance improvements. Advances in Database Technology—EDBT’96. 1–17. DOI: .Google Scholar
Digital Library
- G. K. Tam, V. Kothari, and M. Chen. 2017. An analysis of machine- and human-analytics in classification. IEEE Trans. Vis. Comput. Graph. 23, 1, 71–80. DOI: .Google Scholar
Digital Library
- S. K. Tanbeer, C. F. Ahmed, B.-S. Jeong, and Y.-K. Lee. 2009a. Discovering periodic-frequent patterns in transactional databases. In T. Theeramunkong, B. Kijsirikul, N. Cercone, and T.-B. Ho (Eds.), Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27–30, 2009 Proceedings. Springer Berlin Heidelberg, Berlin, 242–253. ISBN: 978-3-642-01307-2. DOI: .Google Scholar
Digital Library
- S. K. Tanbeer, C. F. Ahmed, B.-S. Jeong, and Y.-K. Lee. 2009b. Discovering periodic-frequent patterns in transactional databases. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 242–253. DOI: .Google Scholar
Digital Library
- L. Tang and T. Li. 2010. LogTree: A framework for generating system events from raw textual logs. In 2010 IEEE International Conference on Data Mining. IEEE, 491–500. DOI: .Google Scholar
Digital Library
- L. Tang, T. Li, and C.-S. Perng. 2011. LogSig: Generating system events from raw textual logs. In Proceedings of the 20th ACM international Conference on Information and Knowledge Management. ACM, 785–794. DOI: .Google Scholar
Digital Library
- Z. Troníček. 2001. Episode matching. In Combinatorial Pattern Matching. Springer, 143–146. DOI: .Google Scholar
Digital Library
- R. Vaculin and K. Sycara. 2007. Specifying and monitoring composite events for semantic web services. In Fifth European Conference on Web Services (ECOWS’07), Halle, Germany. IEEE, 87–96. .Google Scholar
Digital Library
- P. Valtchev, R. Missaoui, and P. Lebrun. 2002. A partition-based approach towards constructing Galois (concept) lattices. Discrete Math. 256, 3, 801–829. DOI: .Google Scholar
Digital Library
- M. Vlachos, G. Kollios, and D. Gunopulos. 2002. Discovering similar multidimensional trajectories. In Data Engineering, 2002. Proceedings. 18th International Conference on. IEEE, 673–684. DOI: .Google Scholar
Digital Library
- I. A. Wadman, H. Osada, G. G. Grütz, A. D. Agulnick, H. Westphal, A. Forster, and T. H. Rabbitts. 1997. The LIM-only protein Lmo2 is a bridging molecule assembling an erythroid, DNA-binding complex which includes the TAL1, E47, GATA-1 and Ldb1/NLI proteins. EMBO J. 16, 11, 3145–3157. DOI: .Google Scholar
Cross Ref
- B. C. Wengerter, K. Y. Pei, D. Asuzu, and K. A. Davis. 2018. Rothman index variability predicts clinical deterioration and rapid response activation. Am. J. Surg. 215, 1, 37–41. DOI: .Google Scholar
Cross Ref
- U. Westermann and R. Jain. 2006. Events in multimedia electronic chronicles (E-chronicles). International Journal on Semantic Web and Information Systems (IJSWIS) 2, 2, 1–23. DOI: .Google Scholar
Cross Ref
- U. Westermann and R. Jain. 2007. Toward a common event model for multimedia applications. IEEE MultiMedia 14, 1. DOI: .Google Scholar
Digital Library
- K. Wongsuphasawat and B. Shneiderman. 2009. Finding comparable temporal categorical records: A similarity measure with an interactive visualization. In Visual Analytics Science and Technology, 2009. VAST 2009. IEEE Symposium on. IEEE, 27–34. DOI: .Google Scholar
Cross Ref
- K. Wongsuphasawat and D. Gotz. 2011. Outflow: Visualizing patient flow by symptoms and outcome. In IEEE VisWeek Workshop on Visual Analytics in Healthcare, Providence, Rhode Island, USA. American Medical Informatics Association, 25–28.Google Scholar
- K. Wongsuphasawat and D. Gotz. 2012. Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. Visualization and Computer Graphics, IEEE Transactions on 18, 12, 2659–2668. DOI: .Google Scholar
Digital Library
- S.-Y. Wu and Y.-L. Chen. 2007. Mining nonambiguous temporal patterns for interval-based events. IEEE Trans. Knowl. Data Eng. 19, 6, 742–758. DOI: .Google Scholar
Digital Library
- J. Wu, J. Roy, and W. F. Stewart. 2010. Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Med. Care, S106–S113. DOI: .Google Scholar
Cross Ref
- L. Xie, H. Sundaram, and M. Campbell. 2008. Event mining in multimedia streams. Proc. IEEE 96, 4, 623–647. DOI: .Google Scholar
Cross Ref
- Y. Xiong and D.-Y. Yeung. 2002. Mixtures of ARMA models for model-based time series clustering. In Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on. IEEE, 717–720. DOI: .Google Scholar
Digital Library
- H. Yan, L. Breslau, Z. Ge, D. Massey, D. Pei, and J. Yates. 2012. G-RCA: A generic root cause analysis platform for service quality management in large IP networks. IEEE/ACM Transactions on Networking (TON) 20, 6, 1734–1747. Google Scholar
Digital Library
- H. Yao, H. J. Hamilton, and C. J. Butz. 2004. A foundational approach to mining itemset utilities from databases. In Proceedings of the 2004 SIAM International Conference on Data Mining. SIAM, 482–486. DOI: .Google Scholar
Cross Ref
- M. J. Zaki. 2000. Scalable algorithms for association mining. IEEE Trans. Knowl. Data. Eng. 12, 3, 372–390. DOI: .Google Scholar
Digital Library
- G. Zweig. 1998. Speech Recognition with Dynamic BAYESIAN Networks. Ph.D. Dissertation. University of California, Berkeley. Google Scholar
Digital Library
Cited By
-
Muñoz O, Monroy R, Cañete-Sifuentes L and Ramirez-Marquez J (2024). Automated Discovery of Successful Strategies in Association Football, Applied Sciences, 10.3390/app14041403, 14:4, (1403)
-
Podell J, Pergakis M, Yang S, Felix R, Parikh G, Chen H, Chen L, Miller C, Hu P and Badjatia N (2022). Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications, Neurocritical Care, 10.1007/s12028-022-01491-6, 37:S2, (206-219), Online publication date: 1-Aug-2022.
-
Jain R Lifeblood of Health is Data, IEEE MultiMedia, 10.1109/MMUL.2022.3151996, 29:1, (128-135)
Index Terms
- Event Mining for Explanatory Modeling
Recommendations
A tree structure for event-based sequence mining
The incorporation of temporal semantics into traditional data mining techniques has led to the development of a new field called temporal data mining. This is especially necessary for extracting useful knowledge from dynamic domains, which by nature are ...
Event modeling and mining: a long journey toward explainable events
AbstractRecently, 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 ...