Cleaning structured event logs: A graph repair approach

Abstract

Abstract-Event data are often dirty owing to various recording conventions or simply system errors. These errors may cause many serious damages to real applications, such as inaccurate provenance answers, poor profiling results or concealing interesting patterns from event data. Cleaning dirty event data is strongly demanded. While existing event data cleaning techniques view event logs as sequences, structural information do exist among events. We argue that such structural information enhances not only the accuracy of repairing inconsistent events but also the computation efficiency. It is notable that both the structure and the names (labeling) of events could be inconsistent. In real applications, while unsound structure is not repaired automatically (which needs manual effort from business actors to handle the structure error), it is highly desirable to repair the inconsistent event names introduced by recording mistakes. In this paper, we propose a graph repair approach for 1) detecting unsound structure, and 2) repairing inconsistent event name.

Mining Uncertain Event Data in Process Mining

“…In these cases, and if this behavior is infrequent enough to allow the event log to remain meaningful, the most common way for existing process mining techniques to deal with missing data is by filtering out the affected traces and performing discovery and conformance checking on the resulting filtered event log. While filtering out missing values is straightforward, various methodologies of event log filtering have been proposed in the past to solve the problem of incorrect event attributes: the filtering can take place thanks to a reference model, which can be given as process specification [12], or from information discovered from the frequent and well-formed traces of the same event log; for example extracting an automaton from the frequent traces [7], computing conditional probabilities of frequent sequences of activities [9], or discovering a probabilistic automaton [13]. In the latter cases, the noise is identified as infrequent behavior.…”

Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles

“…When queue Q is empty, it indicates that we cannot find candidate paths, and thus return an empty query answer set ∅ (lines [19][20]. Otherwise, we will prepare for expanding the length of candidate paths in Q (lines [21][22][23][24][25][26][27][28] Candidate Path Refinement: After the index traversal and path expansion, we can obtain a number of candidate paths in set S cand . Then, we can refine these paths by checking constraints of keywords, weather, and traveling times, and return actual PRAO answers (line 41).…”

Filtering Spurious Events from Event Streams of Business Processes

“…With respect to noise filtering in context of event logs, three approaches are described in literature [9,12,21]. The approach proposed by Wang et al [21] relies on a reference process model to repair a log whose events are affected by labels that do not match the expected behaviour of the reference model. The approach proposed by Conforti et al [9] removes events that cannot be reproduced by an automaton constructed using frequent process behaviour recorded in the log.…”