The growing attention in water supply system security urges the design of new tools in order to control water system 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, 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 algorithm (Min-Hashing) in order to deal with time series analysis instead of simple discrete event 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. Given a table of real values, to perform such task, we introduce a new similarity measure among columns. We experimentally show its good behaviour with respect to classical correlation. Moreover by making use of randomization Min-Hashing [2] is applied to compute compressed signature matrix. The key point is that ”continuous„ similarity of the original matrix is mapped into ”discrete„ similarity of its signature[2].The proposed algorithm has been experimentally analyzed by using historical data acquired from remote sensing of a real water supply network

Time Series Data Mining: Techniques For Anomalies Detection In Water Supply Network Analysis

GIUGNO, ROSALBA;
2006-01-01

Abstract

The growing attention in water supply system security urges the design of new tools in order to control water system 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, 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 algorithm (Min-Hashing) in order to deal with time series analysis instead of simple discrete event 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. Given a table of real values, to perform such task, we introduce a new similarity measure among columns. We experimentally show its good behaviour with respect to classical correlation. Moreover by making use of randomization Min-Hashing [2] is applied to compute compressed signature matrix. The key point is that ”continuous„ similarity of the original matrix is mapped into ”discrete„ similarity of its signature[2].The proposed algorithm has been experimentally analyzed by using historical data acquired from remote sensing of a real water supply network
2006
Data Mining, temporal series
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/940477
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact