Abstract
The analysis of sequential patterns is a prominent research topic. In this paper, we provide a formalization of a graph-based approach, such that a directed weighted graph/network can be extended using a sequential state transformation function, that “interprets” the network in order to model state transition matrices. We exemplify the approach for deriving such interpretations, in order to assess these and according hypotheses in an industrial application context. Specifically, we present and discuss results of applying the proposed approach for topology and anomaly analytics in a large-scale real-world sensor-network.
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References
ANSI/ISA-S51.1-1979 (R1993): Process instrumentation terminology
ISO 14617-6:2002 Graphical Symbols for Diagrams - Part 6: Measurement and Control Functions
Abele, L., Anic, M., Gutmann, T., Folmer, J., Kleinsteuber, M., Vogel-Heuser, B.: Combining knowledge modeling and machine learning for alarm root cause analysis. In: MIM, pp. 1843–1848. International Federation of Automatic Control (2013)
Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description. Data Min. Knowl. Disc. 29(3), 626–688 (2015)
Atzmueller, M.: Data mining on social interaction networks. JDMDH 1 (2014)
Atzmueller, M.: Detecting community patterns capturing exceptional link trails. In: Proceedings of IEEE/ACM ASONAM. IEEE Press, Boston (2016)
Atzmueller, M., Schmidt, A., Kibanov, M.: DASHTrails: an approach for modeling and analysis of distribution-adapted sequential hypotheses and trails. In: Proceedings of WWW 2016 (Companion). IW3C2/ACM (2016)
Becker, M., Mewes, H., Hotho, A., Dimitrov, D., Lemmerich, F., Strohmaier, M.: SparkTrails: a MapReduce implementation of HypTrails for comparing hypotheses about human trails. In: Proceedings of WWW (Companion). ACM, New York (2016)
Buddhakulsomsiri, J., Zakarian, A.: Sequential pattern mining algorithm for automotive warranty data. Comput. Ind. Eng. 57(1), 137–147 (2009)
Cook, R.: Interpreting piping and instrumentation diagrams. Blog-Entry, September 2010. http://www.aiche.org/chenected/2010/09/interpreting-piping-and-instrumentation-diagrams
Folmer, J., Schuricht, F., Vogel-Heuser, B.: Detection of temporal dependencies in alarm time series of industrial plants. In: Proceedings of 19th IFAC World Congress, pp. 24–29 (2014)
Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)
Kass, R.E., Raftery, A.E.: Bayes factors. J. Am. Stat. Assoc. 90(430), 773–795 (1995)
Krackhardt, D.: QAP partialling as a test of spuriousness. Soc. Netw. 9, 171–186 (1987)
Lempel, R., Moran, S.: The stochastic approach for link-structure analysis (SALSA) and the TKC effect. Comput. Netw. 33(1), 387–401 (2000)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: rapid prototyping for complex data mining tasks. In: Proceedings of KDD, pp. 935–940. ACM, New York (2006)
Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community assessment using evidence networks. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds.) MSM/MUSE -2010. LNCS, vol. 6904, pp. 79–98. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23599-3_5
Orman, G.K., Labatut, V., Plantevit, M., Boulicaut, J.F.: A method for characterizing communities in dynamic attributed complex networks. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 481–484 (2014)
Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. WIREs: Comput. Stat. 7(3), 223–247 (2015)
Singer, P., Helic, D., Hotho, A., Strohmaier, M.: HypTrails: a Bayesian approach for comparing hypotheses about human trails. In: Proceedings of WWW. ACM, New York (2015)
Singer, P., Helic, D., Taraghi, B., Strohmaier, M.: Memory and structure in human navigation patterns. PLoS ONE 9(7) (2014)
Strelioff, C.C., Crutchfield, J.P., Hübler, A.W.: Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling. Phys. Rev. E 76(1), 011106 (2007)
Vogel-Heuser, B., Schütz, D., Folmer, J.: Criteria-based alarm flood pattern recognition using historical data from automated production systems (aPS). Mechatronics 31 (2015)
Acknowledgements
This work was funded by the BMBF project FEE under grant number 01IS14006. We wish to thank Leon Urbas (TU Dresden) and Florian Lemmerich (GESIS, Cologne) for helpful discussions, also concerning Florian’s implementation of HypTrails (https://bitbucket.org/florian_lemmerich/hyptrails4j) [20].
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Atzmueller, M., Schmidt, A., Kloepper, B., Arnu, D. (2017). HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_15
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