An intelligent approach to data extraction and task identification for process mining

Li, Jiexun; Wang, Harry Jiannan; Bai, Xue

doi:10.1007/s10796-015-9564-3

Cited by 21 publications

(18 citation statements)

References 18 publications

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“…In honour of Professor Robert Ayres' contributions to research in technological forecasting and social change, we hope that this study can serve as a useful reference point for researchers in management fields to advance big data research, forecasting and applications of BDA. Analytics technique Liao et al (2014) Analytics technique Yang and Lee (2008) Image semantics Video data Zhang et al (2014) Social influence Mobile Mobile sensing Andrews et al 2016Mobile advertising Fong et al (2015) Mobile targeting Mobile Internet usage Lee (2007) Personalization Li and Du (2012) Mobile advertising Li and Wang (2015) Food supply chain Luo et al (2014) Mobile targeting Recommendation Mobile network Chung et al (2015) Personalization Ghose and Han (2011) Mobile Internet usage Market dynamics Mobile app Ghose and Han (2014) Mobile apps demand Xu et al (2014) Online media He 2013bCase-based reasoning Text mining and Web 2.0 tools can be beneficial to CBR systems with a better user experience. Hu et al (2012) Online reviews Manipulation by firms is found in online reviews, particularly in product ratings.…”

Section: Discussion and Concluding Thoughtsmentioning

confidence: 99%

Technology in the 21st century: New challenges and opportunities

Sheng

Amankwah‐Amoah

Wang

2019

Technological Forecasting and Social Change

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Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research.

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Section: Discussion and Concluding Thoughtsmentioning

confidence: 99%

Technology in the 21st century: New challenges and opportunities

Sheng

Amankwah‐Amoah

Wang

2019

Technological Forecasting and Social Change

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“…On the one hand, the challenges related to the semantics can be resolved with the help of experts who are directly involved in product development. On the other hand, the challenges related to the event log extraction from a database can be met by extending different approaches ( [6], [7], [8], [9], [10], [11]). However, there is still not a generic solution which can be applied to any kind of database which has no redo logs or just reflecting the current state of the data.…”

Section: Discussionmentioning

confidence: 99%

A Cross-Organizational Process Mining Framework for Obtaining Insights from Software Products: Accurate Comparison Challenges

Aksu

Schunselaar

Reijers

2016

2016 IEEE 18th Conference on Business Informatics (CBI)

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Abstract-Software vendors offer various software products to large numbers of enterprises to support their organization, in particular Enterprise Resource Planning (ERP) software. Each of these enterprises use the same product for similar goals, albeit with different processes and configurations. Therefore, software vendors want to obtain insights into how the enterprises use the software product, what the differences are in usage between enterprises, and the reasons behind these differences. Crossorganizational process mining is a possible solution to address these needs, as it aims at comparing enterprises based on their usage.In this paper, we present a novel Cross-Organizational Process Mining Framework which takes as input, besides event log, semantics (meaning of terms in an enterprise) and organizational context (characteristics of an enterprise). The framework provides reasoning capabilities to determine what to compare and how. Besides, the framework enables one to create a catalog of metrics by deducing diagnostics from the usage. By using this catalog, the framework can monitor the (positive) effects of changes on processes. An enterprise operating in a similar context might also benefit from the same changes. To accommodate these improvement suggestions, the framework creates an improvement catalog of observed changes. Later, we provide a set of challenges which have to be met in order to obtain the inputs from current products to show the feasibility of the framework. Next to this, we provide preliminary results showing they can be met and illustrate an example application of the framework in cooperation with an ERP software vendor.

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“…In this paper, we assume that process related data is available. In (Li et al 2015), the authors have proposed an intelligent approach for extracting this data. Utilizing all of the redundant records in all steps of extracting common fragments is not efficient.…”

Section: Preprocessingmentioning

confidence: 99%

Mining variable fragments from process event logs

2016

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Many peer-organizations are now using processaware information systems for managing their organizational processes. Most of these peer-organizations have shared processes, which include many commonalities and some degrees of variability. Analyzing and mining the commonalities of these processes can have many benefits from the reusability point of view. In this paper, we propose an approach for extracting common process fragments from a collection of event logs. To this end, we first analyze the process fragment literature from a theoretical point of view, based on which we present a new process fragment definition, called morphological fragments to support composability and flexibility. Then we propose a novel algorithm for extracting such morphological fragments directly from process event logs. This algorithm is capable of eliciting common fragments from a family of processes that may not have been executed within the same application/organization. We also propose supporting algorithms for detecting and categorizing morphological fragments for the purpose of reusability. Our empirical studies show that our approach is able to support reusability and flexibility in process fragment identification.

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An intelligent approach to data extraction and task identification for process mining

Cited by 21 publications

References 18 publications

Technology in the 21st century: New challenges and opportunities

Technology in the 21st century: New challenges and opportunities

A Cross-Organizational Process Mining Framework for Obtaining Insights from Software Products: Accurate Comparison Challenges

Mining variable fragments from process event logs

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