Approach for Discovering and Handling Crisis in a Service-Oriented Environment

In an emergency situation failure to respond in a timely manner poses a significant threat. Data needed for timely response comes from various sources and sensors. These individual data streams when viewed in isolation may appear irrelevant, however, when analyzed collectively may identify potential threats. An effective and timely response also requires collaboration and information sharing among various government agencies at all levels. This collaboration information sharing among agencies can be achieved using service-oriented architecture, where agencies provide access to their information resources and applications using Web services. Each of these agencies has its own rules/policies for providing their services. It is therefore, important to verify the correctness of the emergency response processes with respect to the rules/policies of the collaborating agencies involved in the execution of such processes. In this paper we present an approach which addresses the above challenges. Specifically, the proposed approach: a) employs multi stream data mining for identification of potential threats and disambiguation of alarms; b) provides a methodology for the discovery and selection of relevant Web services; c) employs a timed automata based verification methodology for determining the correctness of emergency response processes with respect to the rules of the collaborating agencies. We provide an overview of the initial implementation of the proposed approach.

[1]  Gerd Wagner,et al.  Rule-based agents for the semantic web , 2003, Electron. Commer. Res. Appl..

[2]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[3]  Johannes Gehrke,et al.  Mining data streams under block evolution , 2002, SKDD.

[4]  Basit Shafiq,et al.  Secure Information Sharing in a Virtual Multi-Agency Team Environment , 2007, Electron. Notes Theor. Comput. Sci..

[5]  D. Kleinbaum,et al.  Student solutions manual for Kleinbaum, Kupper, Muller and Nizam's Applied regression analysis and other multivariable methods , 1998 .

[6]  Christof Bornhövd,et al.  Web Service Discovery: Adding Semantics through Service Request Expansion and Latent Semantic Indexing , 2007, IEEE International Conference on Services Computing (SCC 2007).

[7]  Xiang Fu,et al.  Design for verification for asynchronously communicating Web services , 2005, WWW '05.

[8]  Hui Xiong,et al.  Mining strong affinity association patterns in data sets with skewed support distribution , 2003, Third IEEE International Conference on Data Mining.

[9]  Vijayalakshmi Atluri,et al.  Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets , 2004, SAC '04.

[10]  Athanasios K. Tsakalidis,et al.  Web Service Discovery Mechanisms: Looking for a Needle in a Haystack? , 2004 .

[11]  Vijayalakshmi Atluri,et al.  Semantics-based threat structure mining , 2006, DG.O.

[12]  Aloysius K. Mok,et al.  A Graph-Theoretic Approach for Timing Analysis and its Implementation , 1987, IEEE Transactions on Computers.

[13]  Tao Lin,et al.  DM-AMS: employing data mining techniques for alert management , 2005, DG.O.

[14]  Víctor A. Braberman,et al.  A scenario-matching approach to the description and model checking of real-time properties , 2005, IEEE Transactions on Software Engineering.

[15]  Diego Calvanese,et al.  Automatic Composition of E-services That Export Their Behavior , 2003, ICSOC.

[16]  Todd M. Austin Design for Verification? , 2001, IEEE Des. Test Comput..

[17]  Jimeng Sun,et al.  Streaming Pattern Discovery in Multiple Time-Series , 2005, VLDB.

[18]  Vijayalakshmi Atluri,et al.  Collusion Set Detection Through Outlier Discovery , 2005, ISI.

[19]  Xiang Fu,et al.  Conversation protocols: a formalism for specification and verification of reactive electronic services , 2003, Theor. Comput. Sci..

[20]  Christos Faloutsos,et al.  Online data mining for co-evolving time sequences , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[21]  Susan T. Dumais,et al.  LSI meets TREC: A Status Report , 1992, TREC.

[22]  Diego Garbervetsky,et al.  Improving the Verification of Timed Systems Using Influence Information , 2002, TACAS.

[23]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[24]  Abraham Kandel,et al.  Knowledge discovery in time series databases , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[25]  M. Diaz,et al.  Modeling and Verification of Time Dependent Systems Using Time Petri Nets , 1991, IEEE Trans. Software Eng..

[26]  D. Kleinbaum,et al.  Applied Regression Analysis and Other Multivariate Methods , 1978 .