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2014 | 2 | 345--354
Tytuł artykułu

Key Risk Factors for Polish State Fire Service: a Data Mining Competition at Knowledge Pit

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we summarize AAIA'14 Data Mining Competition: Key risk factors for Polish State Fire Service which was held between February 3, 2014 and May 5, 2014 at the Knowledge Pit platform http://challenge.mimuw.edu.pl/. We describe the scope and background of this competition and we explain in details the evaluation procedure. We also briefly overview the results of this analytical challenge, showing the way in which those results can be beneficial to our more general project related to the problem of improving firefighter safety at a fire scene. Finally, we reveal some technical details regarding the architecture and functionalities of the Knowledge Pit competition platform, which we are developing in order to facilitate solving of practical problems that require advanced data analytics.(original abstract)
Słowa kluczowe
Rocznik
Tom
2
Strony
345--354
Opis fizyczny
Twórcy
  • University of Warsaw, Poland
  • Infobright Inc.
  • University of Warsaw, Poland
  • University of Warsaw, Poland
  • University of Warsaw, Poland
Bibliografia
  • Agrawal R., Mannila H., Srikant R., Toivonen H., Verkamo A. I. et al., "Fast discovery of association rules." Advances in knowledge discovery and data mining, vol. 12, no. 1, pp. 307-328, 1996.
  • Bąk K., Krasuski A., and Szczuka M., "Searching for Concepts in Natural Language Part of Fire Service Reports," in Concurrency Specificaton and Programming, 2013.
  • Cole J., Using Moodle, 1st ed. O'Reilly, 2005.
  • Core Team R., R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013. [Online]. Available: http://www.R-project.org/
  • Gilbert B., Nichols D., Aisbett B., Phillips M., Sargeant M. et al., "Fighting with fire: how bushfire suppression can impact on fire fighters' health," Australian family physician, vol. 36, no. 12, p. 994, 2007.
  • Hastie T., Tibshirani R., and Friedman J., The Elements of Statistical Learning, ser. Springer Series in Statistics. New York, NY, USA: Springer New York Inc., 2001.
  • Isaacson P. C., "Building a simple website using open source software (gnu/linux, apache, mysql, and python)," J. Comput. Sci. Coll., vol. 19, no. 1, pp. 286-288, Oct. 2003. [Online]. Available: http://dl.acm.org/citation.cfm?id=948737.948777
  • Janusz A. and Ślęzak D., "Rough set methods for attribute clustering and selection," Applied Artificial Intelligence, vol. 28, no. 3, pp. 220-242, march 2014.
  • Janusz A., Nguyen H. S., Ślęzak D., Stawicki S., and Krasuski A., "JRS'2012 Data Mining Competition: Topical Classification of Biomedical Research Papers," in Proceedings of RSCTC'12, ser. LNAI, J.T. Yao et al., Ed., vol. 7413. Springer, Heidelberg, 2012, pp. 417-426.
  • Johansson H., Decision Making in Fire Risk Management. Dept. of Fire Safety Engineering, Lund University, 2001.
  • Kaufman L., Rousseeuw P., and Corporation E., Finding Groups in Data: an Introduction to Cluster Analysis. Wiley Online Library, 1990, vol. 39.
  • Krasuski A. and Janusz A., "Semantic tagging of heterogeneous data: Labeling fire & rescue incidents with threats," in FedCSIS, 2013, pp. 77-82.
  • Krasuski A., Jankowski A., Skowron A., and Ślęzak D., "From sensory data to decision making: A perspective on supporting a fire commander," Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, vol. 3, pp. 229-236, 2013.
  • Mitchell T. M., Machine Learning, ser. McGraw Hill series in computer science. McGraw-Hill, 1997.
  • Plugge E., Hawkins T., and Membrey P., The Definitive Guide to MongoDB: The NoSQL Database for Cloud and Desktop Computing, 1st ed. Berkely, CA, USA: Apress, 2010.
  • Rosebrock E. and Filson E., Setting Up LAMP: Getting Linux, Apache, MySQL, and PHP Working Together. Alameda, CA, USA: SYBEX Inc., 2004.
  • Ware L., Ware B., Open Source Development with LAMP: Using Linux, Apache, MySQL and PHP. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 2002.
  • Wojnarski M., Janusz A., Nguyen H. S., Bazan J., Luo C., Chen Z., Hu F., Wang G., Guan L., Luo H., Gao J., Shen Y., Nikulin V., Huang T. -H., McLachlan G. J., Bošnjak M., and Gamberger D., "RSCTC'2010 discovery challenge: Mining DNA microarray data for medical diagnosis and treatment," in Proceedings of RSCTC'2010, ser. LNAI, M. S. Szczuka et al., Ed., vol. 6086. Heidelberg: Springer, 2010, pp. 4-19.
  • Wojnarski M., Stawicki S., and Wojnarowski P., "TunedIT.org: System for automated evaluation of algorithms in repeatable experiments," in Proceedings of RSCTC'2010, ser. LNAI, vol. 6086. Springer, 2010, pp. 20-29.
  • Wróblewski J. and Stawicki S., "Sql-based kdd with infobright's rdbms: Attributes, reducts, trees," in RSEISP, ser. LNCS, M. Kryszkiewicz, C. Cornelis, D. Ciucci, J. Medina-Moreno, H. Motoda, and Z. W. Raś, Eds., vol. 8537. Springer, 2014, pp. 28-41.
  • Zaksek M. and Arvai J. L., "Toward improved communication about wildland fire: mental models research to identify information needs for natural resource management," Risk analysis, vol. 24, no. 6, pp. 1503-1514, 2004.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171325209

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