Control-Path Oriented Workflow Intelligence Analysis and Mining System

Park, Min-Jae; Kim, Kyoung-Sook

doi:10.1109/iccit.2007.4420383

Cited by 3 publications

(7 citation statements)

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“…Through a business process intelligence suite, we can accomplish a higher level of enhancement, such as the detection and prevention of nonconformances through auditing [15], refining workflow data preparations, and integrating other data mining techniques, in managing the quality of the workflow execution. The work in [13] is much closer to the conceptual contribution described herein, whereas the approach in [14] is much more concrete. In [14], the authors proposed a framework of control-path oriented workflow intelligence and quality improvement to achieve a higher degree of the workflow traceability and discoverability, and devised an efficient control-path analysis approach through the concept of a minimal workflow model.…”

Section: Related Workmentioning

confidence: 65%

“…Almost all recent workflow management systems provide their own logging mechanisms [6] for organizing such workflow logs and warehouses. In terms of collecting, filtering, and discovering activities with workflow logs and warehouses, to date, the related studies have mainly focused on two specialties of workflow discovery activities, namely, workflow process discovery [3,[7][8][9][10][11][12] and workflow knowledge discovery [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The workflow process discovery is directly concerned with redesigning and reengineering the control-flow aspect of the workflow processes by discovering workflow models from event logs.…”

Section: Related Workmentioning

confidence: 99%

“…The workflow knowledge discovery stems from the concepts of business process intelligence [13] and workflow intelligence [14]. In [13], the authors claimed that business process intelligence is a suite of integrated software tools aiming at managing the workflow execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization.…”

Section: Related Workmentioning

confidence: 99%

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An Experimental Analytics on Discovering Work Transference Networks from Workflow Enactment Event Logs

2019

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Work transference network is a type of enterprise social network centered on the interactions among performers participating in the workflow processes. It is thought that the work transference networks hidden in workflow enactment histories are able to denote not only the structure of the enterprise social network among performers but also imply the degrees of relevancy and intensity between them. The purpose of this paper is to devise a framework that can discover and analyze work transference networks from workflow enactment event logs. The framework includes a series of conceptual definitions to formally describe the overall procedure of the network discovery. To support this conceptual framework, we implement a system that provides functionalities for the discovery, analysis and visualization steps. As a sanity check for the framework, we carry out a mining experiment on a dataset of real-life event logs by using the implemented system. The experiment results show that the framework is valid in discovering transference networks correctly and providing primitive knowledge pertaining to the discovered networks. Finally, we expect that the analytics of the work transference network facilitates assessing the workflow fidelity in human resource planning and its observed performance, and eventually enhances the workflow process from the organizational aspect.

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Section: Related Workmentioning

confidence: 65%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Experimental Analytics on Discovering Work Transference Networks from Workflow Enactment Event Logs

2019

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“…Conclusively speaking, the vertical fragmentation-based mining approach is able to rediscover all possible controlpaths (Park & Kim, 2008), each of which is exactly same to each of the horizontal temporal fragment models, and finally which are used to rediscover the original structured workflow process model by the workflow process mining algorithm to be presented in the next section. A vertical workflow fragment model is formally defined through 3-tuple VWF = (υ, P, S) over an activity set A, where  P is a predecessor activity of some external workcase model, which is connected into the current fragment model;  S is a successor activity of some external workcase model, which is connected from the current fragment model;  , where, : A → (α ∈ A) is a single-valued mapping function of an activity to its immediate successor in a vertical temporal fragment, and : A → (α ∈ A) is a single-valued mapping function of an activity to its immediate predecessor in a vertical temporal fragment.…”

Section: Vertical Temporal Fragmentsmentioning

confidence: 99%

“…Such distributed workflow management systems are becoming a catalyst for triggering emergence of the concept of distributed workflow mining that rediscovers several perspectives-control flow, data flow, social, and organizational perspectives-of workflows from the scattered workflow execution event histories (logs) collected at runtime of distributed workflow models fragmented from an original workflow model. In this paper, we particularly focus on mining the behavioral-control flow-perspective (Park & Kim, 2008) of the fragmented workflow models. In general, a workflow model is described by several entities, such as activity, role, actor, invoked applications, and relevant data, and where, steps of a work process are called activities (jobs or transactions) that flow through the system are called workcases (Ellis, 1979) or workflow instances.…”

Section: Introductionmentioning

confidence: 99%

Mining workflow processes from distributed workflow enactment event logs

2012

Knowledge Management &Amp; E-Learning: An International Journal

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Workflow management systems help to execute, monitor and manage work process flow and execution. These systems, as they are executing, keep a record of who does what and when (e.g. log of events). The activity of using computer software to examine these records, and deriving various structural data results is called workflow mining. The workflow mining activity, in general, needs to encompass behavioral (process/control-flow), social, informational (data-flow), and organizational perspectives; as well as other perspectives, because workflow systems are "people systems" that must be designed, deployed, and understood within their social and organizational contexts. This paper particularly focuses on mining the behavioral aspect of workflows from XML-based workflow enactment event logs, which are vertically (semantic-driven distribution) or horizontally (syntactic-driven distribution) distributed over the networked workflow enactment components. That is, this paper proposes distributed workflow mining approaches that are able to rediscover ICN-based structured workflow process models through incrementally amalgamating a series of vertically or horizontally fragmented temporal workcases. And each of the approaches consists of a temporal fragment discovery algorithm, which is able to discover a set of temporal fragment models from the fragmented workflow enactment event logs, and a workflow process mining algorithm which rediscovers a structured workflow process model from the discovered temporal fragment models. Where, the temporal fragment model represents the concrete model of the XML-based distributed workflow fragment events log.

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ρ-Algorithm: A SICN-Oriented Process Mining Framework

Pham

Kim

2021

IEEE Access

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This paper devises an algorithmic process mining framework characterized by the mathematical process model of structured information control nets (SICN) and the concept of mass-driven ρ-function as a decision-making criterion of structural process patterns. In order to prove the functional correctness of the proposed algorithmic framework, this paper also implements all the related algorithms as a process mining system and carries out an operational experiment on a typical synthetic dataset of process enactment event logs prepared and released in the 4TU Centre for Research Data. The core outcome of the algorithmic framework development is the ρ-Algorithm that ought to be a novel approach not only for mining all the primitive process patterns, such as linear (sequential), disjunctive (selective-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns, with perfectly keeping the structural properties of matched pairing and proper nesting, but also for reasonably discovering structured (even unstructured) information control nets from such IEEE XES-formatted datasets of process enactment event logs. The discovery functionality of the ρ-Algorithm is made up of three stepwise algorithms: STEP-1, STEP-2 and STEP-3 algorithms, and these algorithms are formally described as an algorithmic framework supported by the conceptual process mining archiecture with a series of theoretical concepts with the temporal workcase model and the temporal loop-case model. Finally, we validate the functional correctness as well as the discovery perfectness of the proposed algorithmic framework named as ρ-Algorithm by deploying the implemented ρ-Algorithm on a synthetic, non-noise and IEEE XES-formatted dataset of process enactment event logs recorded from the 10,000 work-cases with 113 activities of the Petrinet-oriented process model named as the Large Bank Transaction Process Model.

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Control-Path Oriented Workflow Intelligence Analysis and Mining System

Cited by 3 publications

References 0 publications

An Experimental Analytics on Discovering Work Transference Networks from Workflow Enactment Event Logs

An Experimental Analytics on Discovering Work Transference Networks from Workflow Enactment Event Logs

Mining workflow processes from distributed workflow enactment event logs

ρ-Algorithm: A SICN-Oriented Process Mining Framework

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