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Analysis and visualization of meteorological emergencies

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Abstract

The capability to sample and store meteorological information across a wide area allows to analyze the historical evolution of data and to extract events that are potentially bound to emergency and critical events. In this contribution we detect events when a station shows values that are sensibly different from the neighbor stations. We check the co-occurrence of these events with emergency reported in web news. Results are encouraging and show how the statistical analysis can allow to forecast emergencies and to reduce the impact of critical situations.

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  1. http://www.sir.toscana.it/.

References

  • Breunig MM, Kriegel H-P, Ng RT, Sander J (1999) Optics-of: identifying local outliers. In: Principles of data mining and knowledge discovery. Springer, Berlin, pp 262–270

  • Budalakoti S, Srivastava A, Akella R, Turkov E (2006) Anomaly detection in large sets of high-dimensional symbol sequences. NASA Ames Research Center, Tech. Rep. NASA TM-2006-214553

  • Cabrera JB, Lewis L, Mehra RK (2001) Detection and classification of intrusions and faults using sequences of system calls. Acm sigmod Rec 30(4):25–34

    Article  Google Scholar 

  • Chandola V, Mithal V, Kumar V (2008) Comparative evaluation of anomaly detection techniques for sequence data. In: 2008 Eighth IEEE international conference on data mining, IEEE. IEEE, pp 743–748

  • Cipolla E, Vella F (2014) Identification of spatio-temporal outliers through minimum spanning tree. In: Tenth international conference on signal-image technology and internet-based systems, SITIS 2014, Marrakech, Morocco, November 23–27, 2014, pp 248–255

  • Dasgupta D, Nino F (2000) A comparison of negative and positive selection algorithms in novel pattern detection. In: Systems, man, and cybernetics, 2000 IEEE international conference on, vol 1, IEEE. IEEE, pp 125–130

  • Dean RB, Dixon WJ (1951) Simplified statistics for small numbers of observations. doi:10.1021/ac60052a025

  • Dobkin D, Friedman S, Supowit K (1990) Delaunay graphs are almost as good as complete graphs. Discrete Comput Geom 5(1):399–407. doi:10.1007/BF02187801

    Article  MathSciNet  MATH  Google Scholar 

  • Endler D (1998) Intrusion detection. Applying machine learning to Solaris audit data. In: Computer Security Applications Conference, 1998. Proceedings 14th Annual, IEEE. IEEE, pp 268–279

  • Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection. In: Applications of data mining in computer security. Springer, Berlin, pp 77–101

  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol 96, pp 226–231

  • Evangelista PF, Bonnisone P, Embrechts MJ, Szymanski BK (2005) Fuzzy roc curves for the 1 class svm: application to intrusion detection. In: Proceedings international joint conference on neural networks, IJCNN, Citeseer

  • Florez-Larrahondo G, Bridges SM, Vaughn R (2005) Efficient modeling of discrete events for anomaly detection using hidden markov models. In: Information security. Springer, Berlin, pp 506–514

  • Galarus DE, Angryk RA, Sheppard JW (2012) Automated weather sensor quality control. In: FLAIRS Conference, pp 388–393

  • Gao B, Ma HY, Yang YH (2002) Hmms (hidden markov models) based on anomaly intrusion detection method. In: Machine learning and cybernetics, 2002. Proceedings 2002 International Conference on, vol 1, IEEE. IEEE, pp 381–385

  • Ghosh AK, Schwartzbard A, Schatz M (1999) Learning program behavior profiles for intrusion detection. In: Workshop on intrusion detection and network monitoring, vol 51462

  • Ghosh AK, Wanken J, Charron F (1998) Detecting anomalous and unknown intrusions against programs. In: Computer security applications conference, 1998. Proceedings 14th Annual, IEEE. IEEE, pp 259–267

  • González FA, Dasgupta D (2003) Anomaly detection using real-valued negative selection. Genet Program Evolvable Mach 4(4):383–403

    Article  Google Scholar 

  • Gould BA (1855) On Peirce’s criterion for the rejection of doubtful observations, with tables for facilitating its application. Astron J 4:81–87. doi:10.1086/100480

    Article  Google Scholar 

  • Gupta M, Gao J, Aggarwal C, Han J (2014) Outlier detection for temporal data:a survey. Trans Knowl Data Eng 25(1):1–20

    Google Scholar 

  • Izakian H (2013) Anomaly detection in time series data using a fuzzy c-means clustering. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, pp 1513–1518

  • Kang D-K, Fuller D, Honavar V (2005) Learning classifiers for misuse detection using a bag of system calls representation. In: Intelligence and security informatics. Springer, Berlin, pp 511–516

  • Kowalski AM, Martín MT, Plastino A, Judge G (2012) On extracting probability distribution information from time series. Entropy 14(10):1829–1841

    Article  MATH  Google Scholar 

  • Kruskal JB (1956) On the shortest spanning subtree of a graph and the traveling salesman problem. In: Proceedings of the american mathematical society, vol 7

  • Lee W, Stolfo SJ (1998) Data mining approaches for intrusion detection. In: Usenix Security

  • Li X, Han J, Kim S, Gonzalez H (2007) ROAM: rule-and motif-based anomaly detection in massive moving object data sets. In: SDM, vol 7, SIAM. SIAM, pp 273–284

  • Lin J, Ye D, Chen C, Gao M (2008) Minimum spanning tree based spatial outlier mining and its applications. In: Rough sets and knowledge technology. Springer, Berlin, pp 508–515

  • Liu W, Zheng Y, Chawla S, Yuan J, Xing X (2011) Discovering spatio-temporal causal interactions in traffic data streams. In: Proc. of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York. ISBN 978-1-4503-0813-7. doi:10.1145/2020408.2020571

  • Marceau C (2001) Characterizing the behavior of a program using multiple-length n-grams. In: Proceedings of the 2000 workshop on new security paradigms, ACM. ACM, pp 101–110

  • Nairac A, Townsend N, Carr R, King S, Cowley P, Tarassenko L (1999) A system for the analysis of jet engine vibration data. Integr Comput Aided Eng 6(1):53–66

    Google Scholar 

  • Ng RT, Han J (2002) Clarans: a method for clustering objects for spatial data mining. Knowl Data Eng IEEE Trans 14(5):1003–1016

    Article  Google Scholar 

  • Pan X, Tan J, Kavulya S, Gandhi R, Narasimhan P (2010) Ganesha: blackbox diagnosis of mapreduce systems. ACM SIGMETRICS Perform Eval Rev 37(3):8–13

    Article  Google Scholar 

  • Rorabacher DB (1991) Statistical treatment for rejection of deviant values: critical values of dixon Q parameter and related subrange ratios at the 95 percent confidence level. Anal Chem 63(2):139–146

    Article  Google Scholar 

  • Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423623656

    Article  MathSciNet  MATH  Google Scholar 

  • Shekhar S, Lu C-T, Zhang P (2003) A unified approach to spatial outliers detection. GeoInformatica 7:139–166

    Article  Google Scholar 

  • Tobler W (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr 46(2):234–240

    Article  Google Scholar 

  • Vella F, Infantino I, Gaglio S, Vetrano G (2012) Image segmentation through a hierarchy of minimum spanning trees. In: Intern. Conf. on signal image technology and internet based systems, Naples, pp 381–388. doi:10.1109/SITIS.2012.62

  • Yao Y, Sharma A, Golubchik L, Govindan R (2010) Online anomaly detection for sensor systems: a simple and efficient approach. Perform Eval 67(11):1059–1075

    Article  Google Scholar 

  • Zhao J, Lu C-T, Kou Y (2003) Detecting region outliers in meteorological data. In: GIS, ACM, New York, pp 49–55

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Authors and Affiliations

  1. Institute for High Performance Computing and Networking, National Research Council of Italy, Viale della Scienze ed. 11, Rome, Italy

    Emanuele Cipolla, Umberto Maniscalco, Riccardo Rizzo, Dario Stabile & Filippo Vella

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  1. Emanuele Cipolla
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  2. Umberto Maniscalco
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  3. Riccardo Rizzo
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  4. Dario Stabile
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  5. Filippo Vella
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Correspondence to Filippo Vella.

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Cipolla, E., Maniscalco, U., Rizzo, R. et al. Analysis and visualization of meteorological emergencies. J Ambient Intell Human Comput 8, 57–68 (2017). https://doi.org/10.1007/s12652-016-0351-x

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