Dealing With Concept Drifts in Process Mining

Bose, R. P. Jagadeesh Chandra; Aalst, Wil M. P. van der; Žliobaitė, Indrė; Pechenizkiy, Mykola

doi:10.1109/tnnls.2013.2278313

Cited by 151 publications

(107 citation statements)

References 34 publications

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“…5 [van der Aalst 2012;2011], the area of research dealing with the different kinds of analyses of (business) processes by extracting information from event logs recorded by an information system, handling concept drift has been recognized as an important problem [Carmona and Gavaldà ;Bose et al 2013]. …”

Section: Thus In Process Miningmentioning

confidence: 99%

A survey on concept drift adaptation

et al. 2014

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Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for researchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.

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Section: Thus In Process Miningmentioning

confidence: 99%

A survey on concept drift adaptation

et al. 2014

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“…Process mining refers to the analysis of the process log or data admire the process of a business and extracting the specified info [14]. Process mining technology consists of 3 main types-discovery, correspondence, and improvement [15].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Business Processes using Process Mining

Dawood¹

2018

IJCA

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This paper is aimed to investigate the real time Business process Performance and talent to reinforce the performance. An easy framework was developed to indicate the various steps of analysing and enhancing the process performance. Projected during this paper are a BAM style framework for the real-time business performance management associated an implementation of BAM system model to indicate the pertinence of proposed framework. The goals of business activity observance are to produce real time data concerning the standing and results of varied operations, processes, and transactions. The framework consists from three stages and its facilitates the enhancements of business process.

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“…This phenomenon is known as concept drift and can manifest in different ways, visualized in fig. 2.4: (a) Sudden drift: the current process is substituted at one moment by another process (b) Gradual drift: the current process is substituted but both processes exist in parallel for a certain time (c) Recurring drift: different processes appear in intervals, e.g., due to seasonal effects (d) Incremental drift: the current process is substituted in small steps, e.g., due to incremental reengineering Figure 2.4: Types of concept drift, from [18] The authors of [18] propose a method to detect concept drift by computing statistically significant differences in "populations" of cases. Concept drift can have a major impact on the generalizability of process mining results to new data.…”

Section: Concept Driftmentioning

confidence: 99%

“…Among other things, the authors propose breaking time series into fixed length windows. Such approaches have also be applied to event logs, for example by [18]. In [90], different trace clustering techniques are compared, most of which employ abstract trace representations as feature vectors.…”

Section: Trace Profilesmentioning

confidence: 99%

Feature extraction from event logs for predictive monitoring of business processes

Borchert¹

2018

Preprint

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Many organizations record information about their IT-driven processes in some form of event logs, be it for auditing or debugging purposes or simply by accident. Obtaining knowledge from such event logs is the goal of process mining. Process mining techniques can be used to discover process models from event logs or relate existing process models and observed behaviour in various ways. While we usually envision process models to carry some executable semantics, like Petri net systems or BPMN models, we can also think of purely statistical models or a combination of both. Statistical models are particularly suited for predictive tasks such as early classification of a running case or anomaly detection. In the context of business process management, these activities are denoted as predictive monitoring.However, most standard machine learning and data mining algorithms cannot be directly applied to complex event logs. Some of the most widespread algorithms, like Support Vector Machines or decision tree learners, require the data to be mapped into a vector space first. To this end, meaningful features need to be extracted from event logs. Now, it is not obvious what good features in the domain of business processes are: events describe observed behaviour and are typically equipped with information about execution times and quite often also resources and other attributes. Business processes usually show a certain degree of structure in terms of subprocesses and workflow patterns, which is reflected in the logs. Furthermore, processes may allow for a parallel execution of activities, so that the order in which events are recorded can have different interpretations. Treating the traces in an event log simply as symbolic sequences of activities neglects all of these potentially useful perspectives.In this master's thesis, we investigate feature extraction techniques for event log traces. We provide an overview over the field of predictive monitoring and analyze existing trace profiles proposed in the literature. Based on this, we apply the results obtained in recent research on process discovery to find meaningful abstractions over related subsequences of events.We assess different feature sets by evaluating their predictive power in a supervised classification setting as well as a semi-supervised outlier detection setting. For this purpose, we use two datasets from public administration, describing complex processes in EU agricultural subsidy management. Our particular goal in this domain is to predict negative outcomes, for instance, additional work due to legal claims or corrections necessary after initial payment decisions. iv

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Dealing With Concept Drifts in Process Mining

Cited by 151 publications

References 34 publications

A survey on concept drift adaptation

A survey on concept drift adaptation

Performance Analysis of Business Processes using Process Mining

Feature extraction from event logs for predictive monitoring of business processes

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