Towards Comprehensive Support for Privacy Preservation Cross-Organization Business Process Mining

Liu, Cong; Duan, Hua; Zeng, Qingtian; Zhou, MengChu; Lu, Faming; Cheng, Jiujun

doi:10.1109/tsc.2016.2617331

Cited by 88 publications

(49 citation statements)

References 55 publications

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“…Process mining is an extension of data mining in process management, and its applications include process discovery, conformance checking, and bottleneck analysis [15][16][17][18][19][20][21], etc. Among them, process discovery gets the process model based on multiple sequences in the event log.…”

Section: Video Learning Behavior Process Model Miningmentioning

confidence: 99%

“…if (vi, vj not belong to V) then: : add vi, vj to V//record videos that meet the conditions : end if : add (vi , vj) to E//record the order relationship between videos : end if : end for : output (V, E) Algorithm 2 first counts the number of directly following relationships (lines 7-9) betwee ery two adjacent videos vi and vi+1 in the learning sequence. Next, it calculates the dependenc tween every two videos vi and vj (lines [11][12][13][14][15][16]. Then, if the number of directly followin ationships and the dependence of vi and vj are greater than Tf and Td, vi and vj are added into th eo set of the VLBP model and the sequence relationship of vi and vj is added into the relationshi t of the VLBP model (lines 17-22).…”

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confidence: 99%

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MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

Feng

Liu

2020

Sustainability

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In the teaching based on MOOC (Massive Open Online Courses) and flipped classroom, a teacher needs to understand the difficulty and importance of MOOC videos in real time for students at different knowledge levels. In this way, a teacher can be more focused on the different difficulties and key points contained in the videos for students in a flipped classroom. Thus, the personalized teaching can be implemented. We propose an approach of MOOC video personalized classification based on cluster analysis and process mining to help a teacher understand the difficulty and importance of MOOC videos for students at different knowledge levels. Specifically, students are first clustered based on their knowledge levels through question answering data. Then, we propose the process model of a group of students which reflects the overall video watching behavior of these students. Next, we propose to use the process mining technique to mine the process model of each student cluster by the video watching data of the involved students. Finally, we propose an approach to measure the difficulty and importance of a video based on a process model. With this approach, MOOC videos can be classified for students at different knowledge levels according to difficulty and importance. Therefore, a teacher can carry out a flipped classroom more efficiently. Experiments on a real data set show that the difficulty and importance of videos obtained by the proposed approach can reflect students’ subjective evaluation of the videos.

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Section: Video Learning Behavior Process Model Miningmentioning

confidence: 99%

mentioning

confidence: 99%

MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

Feng

Liu

2020

Sustainability

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“…Already in 2012, the Process Mining Manifesto [23] stated that event logs of the highest quality should address privacy and security considerations adequately and the development of privacypreserving process mining methods would be required for cross-organizational mining [23]. Liu et al consider the privacy of internal event logs and process models in a crossorganizational process discovery setting in [24]. A few very valuable guidelines for the privacy-aware application of process mining, from a practical viewpoint and mainly in a consulting context, are given by Rozinat et al in [25].…”

Section: Privacy In Process Miningmentioning

confidence: 99%

Privacy Challenges for Process Mining in Human-Centered Industrial Environments

Mannhardt

Petersen

Oliveira

2018

2018 14th International Conference on Intelligent Environments (IE)

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Operators in industrial manufacturing environments are under pressure to cope with increasing flexibility and complexity of work. The automation of manufacturing requires operators to adopt new techniques and shifts the focus from lowcomplexity repetitive tasks to dealing with the execution of highcomplexity tasks in cooperation with machines. The emergence of wearable technologies makes it possible to equip operators with miniaturized sensors that may be used to determine the physical and mental stress experienced by operators. Process mining technologies are suited to analyze such sensor data in the context of the manufacturing process with the ultimate goal of improving the operator's well-being through reorganization of work and the work place. However, the storage and processing of such highly personalized data comes with many privacy challenges. Whereas there are many potential benefits, such as improve the work environment, there are also many justified reasons for operators to oppose the processing of their data. Apart from employee concerns, data protection regulations, such as EU GDPR (Europe's General Data Protection Regulation), imposes many compliance challenges for the design of a process mining systems dealing with personal data. We contribute an analysis of the privacy challenges of using process mining on data recorded from sensorized operators in human-centered industrial environments. Guided by privacy research and the regulation imposed by the GDPR, we describe guidelines for privacy in process mining systems.

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“…In this section, we mainly discuss related work on process mining, software process mining, and software usability analysis. Process mining deals with dis-covering, monitoring, and improving real business processes by extracting knowledge from event logs that are available in modern information systems [1,3,4]. There are three major types of process mining: (i) process discovery, (ii) conformance checking, and (iii) enhancement.…”

Section: Related Workmentioning

confidence: 99%

User behavior discovery from low‐level software execution log

Liu

Wang

Gao

et al. 2018

IEEJ Transactions Elec Engng

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During the execution of software, its execution log can be recorded. This provides valuable information on software runtime analysis, based on which software developers and vendors can find useful insights into how users behave and enable software usability improvements. However, the recorded software execution log usually is in such a fine granularity (usually in the method‐call level) that it cannot reflect real user behavior explicitly. To this end, we propose a supervised learning approach to detect high‐level user operation behavior from low‐level software execution log. More specifically, we first construct a behavioral pattern (represented by Petri nets) for each user operation. Then, we apply an alignment‐based matching approach which takes a set of behavior patterns and a software execution log as input to generate an abstract user operation log. Next, existing process discovery approaches are used to discover user behavior model from the obtained user operation log. The proposed approaches are supported by tools implemented in the open‐source process mining toolkit ProM. By experimental analysis, we demonstrate that the proposed approach facilitates the discovery of insightful user behavior with high accuracy from the software execution log. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Towards Comprehensive Support for Privacy Preservation Cross-Organization Business Process Mining

Cited by 88 publications

References 55 publications

MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

Privacy Challenges for Process Mining in Human-Centered Industrial Environments

User behavior discovery from low‐level software execution log

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