A fast algorithm for finding frequent episodes in event streams

Laxman, Srivatsan; Sastry, P. S.; Unnikrishnan, K. P.

doi:10.1145/1281192.1281238

Cited by 105 publications

(111 citation statements)

References 13 publications

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“…Mannila et al considered the mining of serial and parallel episodes separately, each discovered by a distinct algorithm. Laxman and Sastry improved on the episode discovery algorithm of Mannila by employing new frequency calculation and pruning techniques [9]. Experiments suggest that the improvement of Laxman and Sastry yields a 7 times speedup factor on both real and synthetic datasets.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Frequent Episodes in Event Logs

Leemans

Aalst

2015

Lecture Notes in Business Information Processing

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Abstract. Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.

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Section: Related Workmentioning

confidence: 99%

Discovery of Frequent Episodes in Event Logs

Leemans

Aalst

2015

Lecture Notes in Business Information Processing

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“…However, these automated actions can be annoying or detrimental if the inhabitant must undo the action executed by the house or repair damage caused by a faulty decision. To eliminate the possibility of frequent occurrence of this event, the episode discovery becomes a necessary part of Smart Home prediction logic [4], [6].…”

Section: Episode Discoverymentioning

confidence: 99%

Use of Prediction Algorithms in Smart Homes

Dixit¹,

Naik²

2014

IJMLC

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Abstract-'Smart Homes' or 'Intelligent Homes' are capable in making smart or rational decisions and increase home automation. This is done to maximize inhabitant comfort and minimize operation cost. Tracing and predicting the mobility patterns and usages of devices by the inhabitant, sets a step towards the objective. The paper discusses in detail, the role of certain Prediction algorithms to bring about next event recognition. Further, an Episode Discovery helps in finding the frequency of occurrence of these events and targeting the particular events for automation. The effectiveness of the Prediction algorithms used is demonstrated ;making it clear how they prove to be a key component in the efficient implementation of a Smart Home architecture.

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“…Unfortunately, this frequency measure is not monotonically decreasing. How-ever, the issue can be fixed by defining frequency as the maximal number of non-overlapping minimal windows [13,17]. Zhou et al [24] proposed mining closed serial episodes based on the Minepi method.…”

Section: Related Workmentioning

confidence: 99%

“…However, this measure proved to be non-monotonic. In order to satisfy the downward-closed property, we need to consider only the non-overlapping windows [13,17].…”

Section: Using Minimal Windowsmentioning

confidence: 99%

MARBLES: Mining Association Rules Buried in Long Event Sequences

Čule

Tatti

Goethals

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns that describe events that often occur in the vicinity of each other. Episodes can impose restrictions on the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial and parallel episodes, while discovering general episodes is surprisingly understudied. This is particularly true when it comes to discovering association rules between them.In this paper we propose an algorithm that mines association rules between two general episodes. On top of the traditional definitions of frequency and confidence, we introduce two novel confidence measures for the rules. The major challenge in mining these association rules is pattern explosion. To limit the output, we aim to eliminate all redundant rules. We define the class of closed association rules, and show that this class contains all non-redundant output. To make the algorithm efficient, we use further pruning steps along the way. First of all, we generate only free and closed frequent episodes from which we create candidate rules, we speed up the evaluation of the rules, and finally prune the remaining non-closed rules from the output.

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A fast algorithm for finding frequent episodes in event streams

Cited by 105 publications

References 13 publications

Discovery of Frequent Episodes in Event Logs

Discovery of Frequent Episodes in Event Logs

Use of Prediction Algorithms in Smart Homes

MARBLES: Mining Association Rules Buried in Long Event Sequences

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