【学术报告】从过程建模到过程挖掘:使用改进的大数据
     
发布日期: 2018/4/16  作者: 院办   浏览次数: 301   返回

 报告题目:从过程建模到过程挖掘:使用改进的大数据From Process Modelling to Process Mining: using Big Data for Improvement

时间:2018420日,上午9点,地点:光电大楼946

报告人: Sally McClean教授,阿尔斯特大学

邀请人: 魏国亮 教授

 

报告人介绍:Sally McClean received the MA degree in mathematics from Oxford University, and the MSc degree in mathematical statistics and operational research from Cardiff University, followed by a PhD degree on Markov and semi-Markov models at Ulster University. She is currently professor of mathematics with Ulster University. Her main research interests include stochastic modelling and optimization, particularly for healthcare planning, and computer science, specifically databases, sensor technology, and telecommunications. She has been a grant holder on more than S7M worth of funding, mainly from the EPSRC and other United Kingdom government sources. She has published more than 300 papers and was a recipient of the Ulster University’s Senior Distinguished Research Fellowship. She is a fellow of the Royal Statistical Society, fellow of the Operational Research Society, past president of the Irish Statistical Association, and member of the IEEE.

 

报告内容简介:A process a series of tasks or steps taken in order to achieve a particular end, where well known examples are found in health, Internet of Things, transportation, smart grid, business, multi-player games, and fault prediction. Models of processes using tools such as Markov chain or Petri nets typically use a mathematical or symbolic model to provide a simplified representation of a system. Simulation can then use the model to imitate important aspects of the behavior of the system and allow experimentation without having to disturb the real-life set-up.

 

In general, process mining aims to discover, monitor, and improve processes. This may include discovering the tasks within the overall processes, predicting future process trajectories, or identifying anomalous tasks or task sequences. Such process mining activities may build on standard approaches to data mining problems such as classification, clustering, regression, association rule learning, and sequence mining or more recent approaches for Big Data, such as deep learning. However, if, or when, the structure of the process is known, model-based approaches can also be useful for incorporating structural process knowledge into the analysis and simplifying the problem.

 

Process mining thus unifies and builds on process model-driven approaches and classical data mining, using event logs, or other supplementary Big data, typically streamed, heterogeneous and distributed.  It can be considered as a bridge between data mining and process modelling, providing a framework for design, an underpinning for process improvement and a scientific basis for decision making. Correctness/conformance and performance are among the important issues in the development of complex processes and systems, where process models are often used to assess such issues. Correctness can describe qualitative aspects of a system, such as liveness, safety, boundedness and fairness while compliance determines whether the observed process complies with the theoretical one. Performance describes the quantitative, dynamic, and time-dependent behavior of systems, such as response time, system uptime, throughput or quality of experience.

 

We will discuss these concepts and approaches using a number of projects involving use-cases from healthcare, industry, networks, cloud and sensor technologies, computer games and pervasive computing.