Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, USA (1999) 33 -- 42  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [26, 27, 30, 34, 36, 38, 43, 45]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 13, 28, 44] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

V. Guralnik, J. Srivastava, "Event detection from time series data," Intl. Conf. Knowledge Discovery and Data Mining, 1999.

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [23, 24, 27, 31, 33, 35, 41]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 12, 25, 40] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

V. Guralnik, J. Srivastava, "Event detection from time series data," Intl. Conf. Knowledge Discovery and Data Mining, 1999.

....refers to the automatic identification of unforeseen or abnormal phenomena embedded in a large amount of normal data. 4, 8, 17] It can be applied in both time sensitive and time insensitive scenarios. This paper targets the time sensitive case of detecting novelty in temporal sequences [2, 4, 5, 8], which has many immediate applications. For example, in a safety critical environment, it is very helpful to have an automatic supervising system, which can screen the time series generated by monitoring sensors, and report any abnormal observations. Another promising application is to help ....

....Furthermore, the ability to conduct online novelty detection is especially desirable for temporal sequences . However, few of the existing algorithms explicitly address this issue. The most relevant result is an incremental algorithm designed to detect normal events, instead of novel events. [5] In this paper, we propose a direction to detect novelty in temporal sequences. As with other detection algorithms, it is impossible for our proposed direction to succeed in all scenarios. However, it can at least provide an alternative and complementary solution to some problems in which other ....

Guralnik, Valery, Jaideep Srivastava, Event Detection from Time Series Data. In Proceedings of the International Conference Knowledge Discovery and Data Mining, San Diego, California, 1999.

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [24, 25, 28, 32, 34, 36, 41, 43]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 12, 26, 42] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

V. Guralnik, J. Srivastava, "Event detection from time series data," Intl. Conf. Knowledge Discovery and Data Mining, 1999.

.... Another class of data which are closely related to strings are time series or sequential data in which sequences of events are stored in strings [11] A number of approaches for traditional problems such as clustering, indexing and subpattern identification have also been developed for this domain [3, 9, 10, 13]. An important data mining problem is that of classification. The classification problem has been widely studied in the data mining, artificial intelligence and machine learnPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee ....

V. Guralnik, J. Srivastava. Event detection from time series data. KDD Conference, 1999.

....to post on servers or to redistribute to lists, requires prior specific permission and or a fee. SIGKDD 02 Edmonton, Alberta, Canda Copyright 2002 ACM 1 58113 567 X 02 0007 . 5.00. become one of the issues receiving scant attention in data mining, vhich is recognized as event change detection [5] and closely related to activity monitoring [4] Let us illustrate these issues by using netsyork access log analysis as an example. Suppose that you have a data stream of network access logs, each of which is specified by numerical variables including access time, duration, etc. We may first ....

....the number of change points and decide the stationary model to be used for fit ting betveen successive change points (see e.g. 1] 6] 7] 8] However, the locally stationary assumption should be eliminated since the statistical regularity may be changing over time in real applications. In [5] the issue of change point detection was addressed without making any assumption that the data source is locally stationary. Instead, a piecewise segmented function tvas used in [5] to fit the time dependent data tvhere the change points are defined as the points between successive segments. In ....

[Article contains additional citation context not shown here]

V. Guralnik and J. Srivastava, Event detection from time series data, in Proc. KDD-, pp:33 42, 1999.

.... queries [30] multiresolution queries [39, 48] dynamic time warping [42, 46] autocorrelation queries [57] and relevance feedback [30] Support concurrent mining of text and time series [37] Support novel clustering and classification algorithms [30] Support change point detection [20, 23]. Surprisingly, in spite of the ubiquity of this representation, with the exception of [52] there has been little attempt to understand and compare the algorithms that produce it. Although appearing under different names and with slightly different implementation details, most time series ....

Guralnik, V. & Srivastava, J. (1999). Event detection from time series data. In proceedings of the 5tn ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining. San Diego, CA, Aug 15-18. pp 33-42.

....may lead to a loss of important information, and it does not work well for erratic series [Ikeda et al. 1999] Chan and his colleagues applied Haar wavelet transforms to time series and showed several advantages of this technique over Fourier transforms [Chan and Fu, 1999; Chanet al... 2003] Guralnik and Srivastava [1999] considered the problem of detecting a change in the trend of a data stream, and developed a technique for finding change points in a series. Last et al. 2001] proposed a general framework for knowledge discovery in time series, which included representation of a series by its key features, ....

Valery Guralnik and Jaideep Srivastava. Event detection from time series data. In Proceedings of the Fifth/CM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 33-42, 1999.

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [23, 24, 27, 31, 33, 35, 41]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 12, 25, 40] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

V. Guralnik, J. Srivastava, "Event detection from time series data," Intl. Conf. Knowledge Discovery and Data Mining, 1999.

....and Srivastava 1997; Viveros, Wright, Elo Dean and Duri 1997; Madria, Bhowmick, Ng and Lim 1999) which attempts to derive a broad understanding of sequences of user activity on the internet. Examples of the latter includes time series analysis and signal processing (Weigend and Gershenfeld 1993; Guralnik and Srivastava 1999; Han, Dong and Yin 1999) The rules resulting from investigations in both of these areas may or may not be the result of behavioural or structural conditions but significantly it is the rule 1 itself, rather that the underlying reasons behind the rule, which is generally the focus of interest. ....

GURALNIK, V. and SRIVASTAVA, J. (1999): Event Detection from Time Series Data. Proc. Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 33-42, CHAUDHURI, S. and MADIGAN, D. (eds). ACM Press.

....time necessary for a prediction to be useful. Change points represent an important type of events in time series. These are time points, where there is a change in the parameters of the underlying data model or even in the model itself. The problem of identifying the change points is studied in [6]. The change point detection algorithm of [6] is based on the recursive binary partitioning of the time segment by using likelihood criteria. Linear regression is used as the underlying model for each segment. An alternative approach to finding the optimal number of linear segments in time series ....

....Change points represent an important type of events in time series. These are time points, where there is a change in the parameters of the underlying data model or even in the model itself. The problem of identifying the change points is studied in [6] The change point detection algorithm of [6] is based on the recursive binary partitioning of the time segment by using likelihood criteria. Linear regression is used as the underlying model for each segment. An alternative approach to finding the optimal number of linear segments in time series was developed by Keogh [9] The segmentation ....

[Article contains additional citation context not shown here]

V. Guralnik and J. Srivastava, "Event detection from time series data," in Proc. Fifth ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, 1999, pp. 33--42.

....the time. By using this approach, Web marketers can predict future visit patterns which will be helpful in placing advertisements aimed at certain user groups. Other types 78 of temporal analysis that can be performed on sequential patterns includes trend analysis [21] or change point detection [54]. Trend analysis can be used to detect changes in the usage patterns of a site over time, and change point detection identi es when speci c changes take place. For example: Page views for the Donkey Kong Video Game have been decreasing over the last two quarters. The Donkey Kong Video Game ....

Valery Guralnik and Jaideep Srivastava. Event detection from time series data. In 5th International Conference on Knowledge Discovery and Data Mining, San Diego, CA, 1999.

No context found.

Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, USA (1999) 33 -- 42

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Guralnik, V. and J. Srivastava: 1999, `Event detection from time series data'. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 33--42.

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V. Guralnik and J. Srivastava. Event detection from time series data. In Proc. Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pages 33--42, 1999.

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Valery Guralnik and Jaideep Srivastava. Event detection from time series data. In Proceedings of the Fifth acm sigmod International Conference on Knowledge Discovery and Data Mining, pages 33-42, 1999.

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Guralnik, V. and J. Srivastava: 1999, `Event detection from time series data'. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 33--42.

No context found.

V. Guralnik and J. Srivastava. Event detection from time series data. In Proc. Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pages 33--42, 1999.

No context found.

Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, USA (1999) 33 -- 42

No context found.

V. Guralnik, J. Srivastava, "Event detection from time series data," Intl. Conf. Knowledge Discovery and Data Mining, 1999.

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Guralnik, V., Srivastava, J.: Event detection from time series data. Proc. of KDD (1999) 33--42

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V. Guralnik and J. Srivastava. Event detection from time series data. In Knowledge Discovery and Data Mining, pages 33--42, 1999.

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Guralnik, V., Srivastava, J.: Event detection from time series data. In: KDD-99, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, AAAI Press (1999)

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Guralnik. V., Srivastava. J., Event Detection from Time Series Data. Proc. Fifth ACM SIGKDD, 1999.

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V. Guralnik and J. Srivastava. Event detection from time series data. Proc. ACM SIGKDD, 33-42, 1999.

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