The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Qunxiong Zhu - One of the best experts on this subject based on the ideXlab platform.
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A New Method for Monitoring Management in Chemical Process
2009 First International Conference on Information Science and Engineering, 2009Co-Authors: Qunxiong ZhuAbstract:In the chemical Process, safety guarantee has become the hot issue with common concern. However, in Current Running-Process, monitoring points are massive and the relation among them is complex, which lead to the trouble in guaranteeing the production safety. A new method based on matter-element analysis is put forward to guide the programming of monitoring points, which is composed of matter-element expression, both-interval dependent function establishment, improved weight certainty. Through the actual application in purified terephthalic acid (PTA) solvent system of a chemical plant, cases study and comparison show that the proposed method is significantly better than traditional methods. It is more objective and effective, and provides a more detailed monitoring strategy under the premise of safety production, so that exploits a new way to monitoring management in chemical Process.
Qianlan Liu - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Business Process Outcome based on Historical Log
Proceedings of the 10th International Conference on Computer Modeling and Simulation - ICCMS 2018, 2018Co-Authors: Qianlan LiuAbstract:With the development of data mining and machine learning, we can get much useful information from historical data. For a business Process system, it maintains large amount of Process execution data, especially records of events corresponding to the execution of activities, which can also be called event log. Predictive business Process monitoring methods exploit logs of completed cases of a Process in order to make predictions and recommendation about Current Running cases. This paper proposes an improved approach for Process outcome prediction and next activity recommendation. It estimates the accuracy that a given goal will be fulfilled upon completion of a Current Running Process case through three different methods. Each method includes both clustering phase and classification phase. However, different levels of historical data (business level and control flow level) in event log are used, and the size of data and number of features also differs. We show our improved approach to deal with historical log, encode each feature vector, train predictive model and how to use trained models for predicting the outcome of Current case and recommending the next event. Finally, through a series of experiment, we compare three different method and existing approach.
Oi Olivia Oanea - One of the best experts on this subject based on the ideXlab platform.
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Verification of soundness and other properties of business Processes
2007Co-Authors: Oi Olivia OaneaAbstract:In this thesis we focus on improving Current modeling and verification techniques for complex business Processes. The objective of the thesis is to consider several aspects of real-life business Processes and give specific solutions to cope with their complexity. In particular, we address verification of a proper termination property for workflows, called generalized soundness. We give a new decision procedure for generalized soundness that improves the original decision procedure. The new decision procedure reports on the decidability status of generalized soundness and returns a counterexample in case the workflow net is not generalized sound. We report on experimental results obtained with the prototype implementation we made and describe how to verify large workflows compositionally, using reduction rules. Next, we concentrate on modeling and verification of adaptive workflows — workflows that are able to change their structure at runtime, for instance when some exceptional events occur. In order to model the exception handling properly and allow structural changes of the system in a modular way, we introduce a new class of nets, called adaptive workflow nets. Adaptive workflow nets are a special type of Nets in Nets and they allow for creation, deletion and transformation of net tokens at runtime and for two types of synchronizations: synchronization on proper termination and synchronization on exception. We define some behavioral properties of adaptive workflow nets: soundness and circumspectness and employ an abstraction to reduce the verification of these properties to the verification of behavioral properties of a finite state abstraction. Further, we study how formal methods can help in understanding and designing business Processes. We investigate this for the extended event-driven Process chains (eEPCs), a popular industrial business Process language used in the ARIS Toolset. Several semantics have been proposed for EPCs. However, most of them concentrated solely on the control flow. We argue that other aspects of business Processes must also be taken into account in order to analyze eEPCs and propose a semantics that takes data and time information from eEPCs into account. Moreover, we provide a translation of eEPCs to Timed Colored Petri nets in order to facilitate verification of eEPCs. Finally, we discuss modeling issues for business Processes whose behavior may depend on the previous behavior of the Process, history which is recorded by workflow management systems as a log. To increase the precision of models with respect to modeling choices depending on the Process history, we introduce history-dependent guards. The obtained business Processes are called historydependent Processes.We introduce a logic, called LogLogics for the specification of guards based on a log of a Current Running Process and give an evaluation algorithm for such guards. Moreover, we show how these guards can be used in practice and define LogLogics patterns for properties that occur most commonly in practice.
Ehab Ezat - One of the best experts on this subject based on the ideXlab platform.
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A Resource Recommendation Approach based on Co-Working History
International Journal of Advanced Computer Science and Applications, 2018Co-Authors: Nada Mohammed Abdulhameed, Iman M. A. Helal, Ahmed Awad, Ehab EzatAbstract:Recommending the right resource to execute the next activity of a Running Process instance is of utmost importance for the overall performance of the business Process, as well as the resource and for the whole organization. Several approaches have recommended a resource based on the task requirements and the resource capabilities. Moreover, the Process execution history and the logs have been used to better recommend a resource based on different human-resource recommender criteria like frequency and speed of execution, etc. These approaches considered the recommendation based on the individual’s execution history of the task that will be allocated to the resource. In this paper, a novel approach based on the co-working history of resources has been proposed. This approach considers the resources that had executed the previous tasks in the Current Running Process instances. Then, it recommends a resource that has the best harmony with the rest of the resources.
Nada Mohammed Abdulhameed - One of the best experts on this subject based on the ideXlab platform.
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A Resource Recommendation Approach based on Co-Working History
International Journal of Advanced Computer Science and Applications, 2018Co-Authors: Nada Mohammed Abdulhameed, Iman M. A. Helal, Ahmed Awad, Ehab EzatAbstract:Recommending the right resource to execute the next activity of a Running Process instance is of utmost importance for the overall performance of the business Process, as well as the resource and for the whole organization. Several approaches have recommended a resource based on the task requirements and the resource capabilities. Moreover, the Process execution history and the logs have been used to better recommend a resource based on different human-resource recommender criteria like frequency and speed of execution, etc. These approaches considered the recommendation based on the individual’s execution history of the task that will be allocated to the resource. In this paper, a novel approach based on the co-working history of resources has been proposed. This approach considers the resources that had executed the previous tasks in the Current Running Process instances. Then, it recommends a resource that has the best harmony with the rest of the resources.
