Discovering Social Networks from Event Logs

Aalst, Wil M. P. van der; Reijers, Hajo A.; Song, Minseok

doi:10.1007/s10606-005-9005-9

Cited by 360 publications

(173 citation statements)

References 30 publications

(35 reference statements)

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“…New issues in control flow perspective has been recently addressed by [29] that proposes genetic algorithms to tackle log noise problem or non-trivial constructs using a global search technique, and by [30] that uses Region based synthesis methods and compares their efficiency and usefulness. While the organizational perspective, [31] discovers information related to the social network in a process. And in the case perspective, [32] deals with the performance characteristics and business rules based on the case-related information about a Process.…”

Section: Discussionmentioning

confidence: 99%

Log-based mining techniques applied to Web service composition reengineering

Gaaloul

Bäına

Godart

2008

SOCA

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There is no doubt that SOA and BPM will continue to evolve dependently for the next ten years. Preparing common research infrastructures will require most important efforts of web service community researchers. Web services composition is becoming more and more chaotic, involving numerous interacting ad-hoc services through huge business processes. Analysing and Reengineering of complex Web Service Compositions will enable them to be well understood, controlled, and redesigned. Our contribution to this problem is a patterns mining algorithm which is based on a statistical technique to discover patterns from execution log. Our approach is characterised by a "local" patterns discovery that allows to cover partial results through a dynamic programming algorithm. Those local discovered patterns are then composed iteratively until discovering the Composite Web Service. The analysis of the disparities between the discovered model and the initial ad-hoc CS model (delta-analysis) enables initial design gaps to be detected and thus the Web service composition to be re-engineered.

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Section: Discussionmentioning

confidence: 99%

Log-based mining techniques applied to Web service composition reengineering

Gaaloul

Bäına

Godart

2008

SOCA

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“…Palo is employed for its OLAP capabilities. The ProCube plugin creates sublogs per cell on-the-fly and visualizes process models discovered using the fuzzy [34] and heuristics [60] miner, social networks derived using ProM's social network miner [10], and dotted charts [55] In the remainder, we do not focus on specific process mining techniques or a specific implementation of the process cube notion. Instead, we conceptualize the essential ideas.…”

Section: Process Cubesmentioning

confidence: 99%

Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Aalst

2013

Lecture Notes in Business Information Processing

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Abstract. Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.

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“…Afterwards, several techniques (like process mining algorithms [13][14][15] and business process intelligence [12]) are applied on process trace data to extract knowledge. Application usage mining [2] techniques analyze user's behavior with applications and attempt to improve the interaction between applications, humans and their working.…”

Section: Framework For Improvementmentioning

confidence: 99%

Business Process Improvement Framework and Representational Support

Lodhi

Köppen

Saake

2012

Advances in Intelligent Systems and Computing

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Abstract. Business process management and improvement are vital for enterprises in competitive environments. Understanding of a process is a pre-requisite and important step for improvement. Interaction between humans, computers, and business objects provide excellent opportunities for knowledge extraction. However, the specification of a framework is required for business process improvement, which extends from data collection, analytical methods, storage, and representation of knowledge. The process models conceived for information system development are not sufficient for post execution analysis and improvement. In this paper, we specify such a framework briefly and focus on providing representational support for business process improvement. The main objective is to improve the overall improvement process by providing enriched graphical process models. Furthermore, we use a case study to explain the proposed usage and extensions of an existing modeling language for business process improvement.

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Discovering Social Networks from Event Logs

Cited by 360 publications

References 30 publications

Log-based mining techniques applied to Web service composition reengineering

Log-based mining techniques applied to Web service composition reengineering

Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Business Process Improvement Framework and Representational Support

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