Detection of Significant Sets of Episodes in Event Sequences

Atallah, Mikhail J.; Szpankowski, Wojciech; Gwadera, Robert

doi:10.1109/icdm.2004.10090

Cited by 37 publications

(46 citation statements)

References 13 publications

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“…If I contains two overlapping time intervals, say, [1][2][3] and [1][2][3][4][5][6][7][8][9][10][11][12], then (1-node) episodes like Að1À3; 1À12Þ and Að1À12Þ are indistinguishable based on their occurrences whatever the input event stream. This is not desirable and, hence, the need for the restriction of disjoint time intervals on I.…”

Section: Event-specific I Setsmentioning

confidence: 99%

“…The results obtained are shown in Table 5. Notice that the duration of B in 2 takes values in the interval [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], which is not fully describable by using the new I. This pushes up the rank of 1 and significantly pushes down the rank of 2 .…”

Section: Set I As a Design Choicementioning

confidence: 99%

“…The framework of frequent episodes discovery in event sequences, introduced by Mannila, et al [2], is a popular pattern discovery method that has been found useful in many applications [5], [6], [7], [8]. The data in this framework is a sequence of events (or symbols), where each event is associated with a corresponding time of occurrence, with an implicit assumption that events are essentially instantaneous.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Laxman

Sastry

Unnikrishnan

2007

IEEE Trans. Knowl. Data Eng.

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Abstract-This paper is concerned with the framework of frequent episode discovery in event sequences. A new temporal pattern, called the generalized episode, is defined, which extends this framework by incorporating event duration constraints explicitly into the pattern's definition. This new formalism facilitates extension of the technique of episodes discovery to applications where data appears as a sequence of events that persist for different durations (rather than being instantaneous). We present efficient algorithms for episode discovery in this new framework. Through extensive simulations, we show the expressive power of the new formalism. We also show how the duration constraint possibilities can be used as a design choice to properly focus the episode discovery process. Finally, we briefly discuss some interesting results obtained on data from manufacturing plants of General Motors.

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Section: Event-specific I Setsmentioning

confidence: 99%

Section: Set I As a Design Choicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Laxman

Sastry

Unnikrishnan

2007

IEEE Trans. Knowl. Data Eng.

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“…In a recent work, Gwadera et al [14] investigated the problem of the reliable detection of an abnormal episode in event sequences, where an episode is a particular ordered sequence occurring as a subsequence of a large event stream within a window of size w, but they did not consider the case of detecting more than one episode. This work was extended in [2] to the case of many pattern sequences, including the important special case of all permutations of the same sequence. All these works are different from ours in that they investigate temporal relationships but only at a single time granularity.…”

Section: Data Miningmentioning

confidence: 99%

Processing count queries over event streams at multiple time granularities

ínal

Saygın

Ulusoy

2006

Information Sciences

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Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at

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“…For example, frequent episodes in event sequences (Mannila et al, 1997;Atallah et al, 2004); path traversal patterns in web logs (Chen et al, 1998;Li et al, 2005); periodic patterns in time-stamped databases (Yang et al, 2001;Sheng et al, 2006); and structural relation patterns in sequence databases (Lu, Chen et al, 2008) all take the mining of sequential patterns to novel structured forms.…”

Section: Introductionmentioning

confidence: 99%

Discovering exclusive patterns in frequent sequences

Chen

Lü

Keech

2010

IJDMMM

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This paper presents a new concept for pattern discovery in frequent sequences with potentially interesting applications. Based on data mining, the approach aims to discover exclusive sequential patterns (ESP) by checking the relative exclusion of patterns across data sequences. ESP mining pursues the post-processing of sequential patterns and augments existing work on structural relations patterns mining. A three phase ESP mining method is proposed together with component algorithms, where a running worked example explains the process. Experiments are performed on real-world and synthetic datasets which showcase the results of ESP mining and demonstrate its effectiveness, illuminating the theories developed. An outline case study in workflow modelling gives some insight into future applicability.

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Detection of Significant Sets of Episodes in Event Sequences

Cited by 37 publications

References 13 publications

Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Processing count queries over event streams at multiple time granularities

Discovering exclusive patterns in frequent sequences

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