Abstract Process Data Warehousing

Casati, Fabio; Castellanos, Malú; Salazar, Norman; Dayal, Umeshwar

doi:10.1109/icde.2007.369018

Cited by 11 publications

(12 citation statements)

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“…The dirty event data lead to wild data provenance answers [28], mislead the aggregation profiling in process data warehousing [9], or obstruct finding interesting process patterns [17]. Indeed, the event data quality is essential in process mining, and known as the first challenge in the Process Mining Manifesto by the IEEE Task Force on Process Mining [30].…”

Section: Introductionmentioning

confidence: 99%

Cleaning structured event logs: A graph repair approach

Wang

Song

Lin

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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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.

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Section: Introductionmentioning

confidence: 99%

Cleaning structured event logs: A graph repair approach

Wang

Song

Lin

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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“…In analogy to the general notion of services, Web services could be seen as a possible counterpart in the digital world (see [CD02] for details). However, Web services are defined only to provide standardized interfaces for enabling application-to-application communication [W3C07c].…”

Section: Definitionsmentioning

confidence: 99%

“…For example, in a pipe-and-filter style, components have input and output streams on which they operate, and streams are directed between components using pipes as connectors. As a more recent approach, Web services [CD02,W3C07c] …”

Section: Architectural Stylesmentioning

confidence: 99%

Product Lines for Digital Information Products

2007

Wirtsch. Inform.

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“…Moreover, two events Check Inventory(C) and Validate(D) in L1 correspond to one composite event Inventory Checking & Validation (4). For simplicity, we use ABCDEF to denote event names in L1, while 123456 are events in L2.…”

Section: Introductionmentioning

confidence: 99%

“…The company has started to integrate these heterogeneous event data into a unified business process data warehouse [3,4], where different types of analyses can be performed, e.g., querying similar complex procedures or discovering interesting event patterns in different subsidiaries (complex event processing, CEP [6]), comparing business processes in different subsidiaries to find common parts for process simplification and reuse [19], or obtaining a more abstract global picture of business processes (workflow views [2]) in the company. Without exploring the correspondence among heterogeneous events, applications such as query and analysis over the event data may not yield any meaningful results.…”

Section: Introductionmentioning

confidence: 99%

Matching heterogeneous event data

Zhu

Song

Lian³

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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Identifying duplicate events are essential to various business process applications such as provenance querying or process mining. Distinct features of heterogeneous events including opaque names, dislocated traces and composite events, prevent existing data integration from techniques performing well. To address these issues, in this paper, we propose an event similarity function by iteratively evaluating similar neighbors. We prove the convergence of iterative similarity computation, and propose several pruning and estimation methods. To efficiently support matching composite events, we devise upper bounds of event similarities. Experiments on real and synthetic datasets demonstrate that the proposed event matching approaches can achieve significantly higher accuracy than the state-of-the-art matching methods.

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Abstract Process Data Warehousing

Cited by 11 publications

References 1 publication

Cleaning structured event logs: A graph repair approach

Cleaning structured event logs: A graph repair approach

Product Lines for Digital Information Products

Matching heterogeneous event data

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