Data Mining Practical Machine Learning Tools and Techniques

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Kaizen: The key to Japan‚s competitive success

Business Process Management (BPM) research resulted in a plethora of methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. This survey aims to structure these results and provide an overview of the state-of-the-art in BPM. In BPM the concept of a process model is fundamental. Process models may be used to configure information systems, but may also be used to analyze, understand, and improve the processes they describe. Hence, the introduction of BPM technology has both managerial and technical ramifications and may enable significant productivity improvements, cost savings, and flow-time reductions. The practical relevance of BPM and rapid developments over the last decade justify a comprehensive survey.

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Business Process Management: A Comprehensive Survey

Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history using the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

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Replaying history on process models for conformance checking and performance analysis

A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

Process discovery is the learning task that entails the construction of process models from event logs of information systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for process discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of process executions which makes the application of process mining most interesting. However, simply applying existing process discovery techniques will often yield highly incomprehensible process models because of their inaccuracy and complexity. With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge discovery process. In this paper, we propose a novel trace clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown that our technique significantly outperforms currently available trace clustering techniques.

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Active Trace Clustering for Improved Process Discovery

Within process mining research, one of the most important fields of study is process discovery, which can be defined as the extraction of control-flow models from audit trails or information system event logs. The evaluation of discovered process models is an essential but difficult task for any process discovery analysis. With this paper, we propose a novel approach for evaluating discovered process models based on artificially generated negative events. This approach allows for the definition of a behavioral F-measure for discovered process models, which is the main contribution of this paper.

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A robust F-measure for evaluating discovered process models

Purpose




The purpose of this study is to measure the interaction between media sentiment and the Bitcoin price. Because some researchers argued that the Bitcoin value is also determined by perception of users and investors, this paper examines how.




Design/methodology/approach




The database of relative news articles as well as blog posts has been collected for the purpose of this research. Hence, each article has been given a sentiment score depending on the negative and positive words used in the article.




Findings




This paper has identified that interaction between media sentiment and the Bitcoin price exists, and that there is a tendency for investors to overreact on news in a short period of time.




Originality/value




While sentiment analysis of Twitter posts as a predictor of the Bitcoin price has been conducted in the past, this research does not have any analog because psycho-semantic dictionaries have not been applied earlier in the Bitcoin research.

Jochen De Weerdt

Papers

A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

Active Trace Clustering for Improved Process Discovery

A robust F-measure for evaluating discovered process models

Using sentiment analysis to predict interday Bitcoin price movements

Process Mining for the multi-faceted analysis of business processes-A case study in a financial services organization