The Experts below are selected from a list of 55224 Experts worldwide ranked by ideXlab platform
Khalid Belhajjame - One of the best experts on this subject based on the ideXlab platform.
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A framework for mining Process models from emails logs
2019Co-Authors: Diana Jlailaty, Daniela Grigori, Khalid BelhajjameAbstract:Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business Processes of companies and institutions. Towards this aim, a few researchers investigated the problem of Extracting Process oriented information from email logs in order to take benefit of the many available Process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining Process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different Process models
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Multi-level clustering for Extracting Process-related information from email logs
2017 11th International Conference on Research Challenges in Information Science (RCIS), 2017Co-Authors: Diana Jlailaty, Daniela Grigori, Khalid BelhajjameAbstract:Emails represent a valuable source of information that can be harvested for understanding undocumented business Processes of institutions. Towards this aim, a few researchers investigated the problem of Extracting Process oriented information from email logs to make benefit of the many available Process mining techniques. In this work, we go further in this direction, by proposing a new method for mining Process models from email logs that leverages unsupervised machine learning techniques. Moreover, our method allows to label emails with activity names, that can be used for activity recognition in new incoming emails. A use case illustrates the usefulness of the proposed solution.
Diana Jlailaty - One of the best experts on this subject based on the ideXlab platform.
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A framework for mining Process models from emails logs
2019Co-Authors: Diana Jlailaty, Daniela Grigori, Khalid BelhajjameAbstract:Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business Processes of companies and institutions. Towards this aim, a few researchers investigated the problem of Extracting Process oriented information from email logs in order to take benefit of the many available Process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining Process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different Process models
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Multi-level clustering for Extracting Process-related information from email logs
2017 11th International Conference on Research Challenges in Information Science (RCIS), 2017Co-Authors: Diana Jlailaty, Daniela Grigori, Khalid BelhajjameAbstract:Emails represent a valuable source of information that can be harvested for understanding undocumented business Processes of institutions. Towards this aim, a few researchers investigated the problem of Extracting Process oriented information from email logs to make benefit of the many available Process mining techniques. In this work, we go further in this direction, by proposing a new method for mining Process models from email logs that leverages unsupervised machine learning techniques. Moreover, our method allows to label emails with activity names, that can be used for activity recognition in new incoming emails. A use case illustrates the usefulness of the proposed solution.
Daniela Grigori - One of the best experts on this subject based on the ideXlab platform.
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A framework for mining Process models from emails logs
2019Co-Authors: Diana Jlailaty, Daniela Grigori, Khalid BelhajjameAbstract:Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business Processes of companies and institutions. Towards this aim, a few researchers investigated the problem of Extracting Process oriented information from email logs in order to take benefit of the many available Process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining Process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different Process models
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Multi-level clustering for Extracting Process-related information from email logs
2017 11th International Conference on Research Challenges in Information Science (RCIS), 2017Co-Authors: Diana Jlailaty, Daniela Grigori, Khalid BelhajjameAbstract:Emails represent a valuable source of information that can be harvested for understanding undocumented business Processes of institutions. Towards this aim, a few researchers investigated the problem of Extracting Process oriented information from email logs to make benefit of the many available Process mining techniques. In this work, we go further in this direction, by proposing a new method for mining Process models from email logs that leverages unsupervised machine learning techniques. Moreover, our method allows to label emails with activity names, that can be used for activity recognition in new incoming emails. A use case illustrates the usefulness of the proposed solution.
Jianguo Jiang - One of the best experts on this subject based on the ideXlab platform.
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ultrasound enhanced and microwave assisted extraction of lipid from dunaliella tertiolecta and fatty acid profile analysis
IEEE Journal of Solid-state Circuits, 2014Co-Authors: Qinfan Zhou, Jianguo JiangAbstract:Microalgal lipid is considered as a potential biodiesel resource due to its advantages compared to other bioresources. The production of biofuel from microalgae includes several stages like microalgae cultivation, biomass harvest, biomass treatment, lipid extraction, and the ultimate biodiesel synthesis. Lipid extraction is closely associated with the productivity and cost of energy production. In the present study, lipid of green algae Dunaliella tertiolecta was extracted by chemical agents with involvement of ultrasound and microwave. The optimization of experimental conditions was carried out by response surface methodology and orthogonal test design. Using the ultrasonic technique, an extraction rate of 45.94% was obtained under the optimum conditions of ultrasonic power 370 W, extraction time 5 min and liquid/solid ratio 125 mL/g. The extraction rate of 57.02% was obtained by the means of microwave assistance under the optimized conditions of extraction time 160 s, microwave power 490 W and liquid/solid ratio 100 mL/g. The comparison of the two results indicated microwave was more effective than ultrasound in Extracting Process. When the two techniques were utilized in combination, the optimized condition was ultrasonic power 320 W, ultrasonic time 4 min, microwave power 280 W, microwave time 120 s and liquid/solid ratio 100 mL/g, and the extraction rate was 49.97%.
Wil M. P. Van Der Aalst - One of the best experts on this subject based on the ideXlab platform.
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mining blockchain Processes Extracting Process mining data from blockchain applications
Business Process Management, 2019Co-Authors: Christopher Klinkmüller, Alexander Ponomarev, Ingo Weber, An Binh Tran, Wil M. P. Van Der AalstAbstract:Blockchain technology has been gaining popularity as a platform for developing decentralized applications and executing cross-organisational Processes. However, Extracting data that allows analysing the Process view from blockchains is surprisingly hard. Therefore, blockchain data are rarely used for Process mining. In this paper, we propose a framework for alleviating that pain. The framework comprises three main parts: a manifest specifying how data is logged, an extractor for retrieving data (structured according to the XES standard), and a generator that produces logging code to support smart contract developers. Among others, we propose a convenient way to encode logging data in a compact form, to achieve relatively low cost and high throughput for on-chain logging. The proposal is evaluated with logs created from generated logging code, as well as with existing blockchain applications that do not make use of the proposed code generator.
