Discovering compressing serial episodes from event sequences

Ibrahim, Aidarus M.; Sastry, Shivakumar; Sastry, P. S.

doi:10.1007/s10115-015-0854-3

Cited by 17 publications

(28 citation statements)

References 33 publications

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“…One of the defects in frequent pattern mining is that there are abundant redundant patterns in the very large number of output patterns [22]. As a result, how to effectively reduce redundancy of the output becomes an essential problem of current research [23,13,11,18,16,10,6,8]. Frequent episode mining [14] (FEM for short), as one of the sub-topics of frequent pattern mining, which aims at discovering frequently appeared ordered sets of events from a single symbol (event) sequence, is facing the similar problem as well.…”

Section: Introductionmentioning

confidence: 99%

Free-Rider Episode Screening via Dual Partition Model

Liu

Huang

et al. 2018

Database Systems for Advanced Applications

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One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.

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Section: Introductionmentioning

confidence: 99%

Free-Rider Episode Screening via Dual Partition Model

Liu

Huang

et al. 2018

Database Systems for Advanced Applications

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“…We compare different chunks of two sequences, which are generated from the same topology (we used 3I-3O topology), but different sets of active paths. (Refer [2] for more details on topologies and paths). Each sequence is chopped into 4 non-overlapping chunks.…”

Section: Simulationsmentioning

confidence: 98%

Pattern set kernel

Ibrahim

Sastry

2015

Proceedings of the Second ACM IKDD Conference on Data Sciences

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“…However, traditional mining approaches often generate a huge number of patterns that confuse users a lot. To solve the well-known problem of pattern explosion [9], we resort to the minimum description length (MDL) principle [10], which has been used in several previous works [9,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…GoKrimp [9] and SQS [12] punish gaps by allocating higher cost when encoding patterns with large gaps, which do not consider the regularity of interarrival times at all. CSC [11] considers patterns restricted to fixed intervals and does not allow any event type appearing more than once, which strongly limits the expressiveness of a pattern. e generated patterns of previous methods either mix together and result in high redundancy [9,12] or are too simple to represent the true behaviors [11].…”

Section: Introductionmentioning

confidence: 99%

Log Pattern Mining for Distributed System Maintenance

Jia

Wang

et al. 2020

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Due to the complexity of the network structure, log analysis is usually necessary for the maintenance of network-based distributed systems since logs record rich information about the system behaviors. In recent years, numerous works have been proposed for log analysis; however, they ignore temporal relationships between logs. In this paper, we target on the problem of mining informative patterns from temporal log data. We propose an approach to discover sequential patterns from event sequences with temporal regularities. Discovered patterns are useful for engineers to understand the behaviors of a network-based distributed system. To solve the well-known problem of pattern explosion, we resort to the minimum description length (MDL) principle and take a step forward in summarizing the temporal relationships between adjacent events of a pattern. Experiments on real log datasets prove the efficiency and effectiveness of our method.

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Discovering compressing serial episodes from event sequences

Cited by 17 publications

References 33 publications

Free-Rider Episode Screening via Dual Partition Model

Free-Rider Episode Screening via Dual Partition Model

Pattern set kernel

Log Pattern Mining for Distributed System Maintenance

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