TIME SERIES DATA MINING: TECHNIQUES FOR ANOMALIES DETECTION IN WATER SUPPLY NETWORK ANALYSIS

摘要

The growing attention in water supply systems security urges the design of new tools in order to control water systems vulnerability. The water system security depends, among other factors, on the capability of recognizing, as soon as possible, anomalous states of plants whenever they occur. In order to improve this capability a tool, based on Data Mining techniques, designed to detect faults during the remote sensing activity of complex water supply networks, is proposed. This software is based on previous work [1] in which A-Priori and Episode Mining techniques were applied to recognize faults and malfunctions of water plants. In this paper we present an extension of these ideas based on low-support/high-correlation data mining algorithms (Min-Hashing) in order to deal with time series analysis instead of simple discrete events analysis. The algorithm, which is applicable to larger size databases, allows the analysis of smooth processes that are not represented by discrete events giving the possibility of recognizing causal relations among time variant processes. In order to perform such task, given a table of real values, a new similarity measure among columns is proposed. The good behaviour of such similarity, with respect to classical correlation, is experimentally demonstrated. Moreover by making use of randomization, Min-Hashing [2] is applied to compute compressed signature matrices. The key point is that "continuous" similarities of the original matrix columns are mapped into "discrete" similarities of the corresponding signature columns [2]. The proposed algorithm has been experimentally analyzed by using historical data acquired from remote sensing of a real water supply network.