I did my Bachelor’s Degree (2007-2011) in Biomedical Engineering at the Northeastern University, China. During that time, I’ve participated in several projects. From June, 2010 to June,2011, I was the leader of a team in the Fourth National Undergraduate Innovative Experiment. My team did some experiment about “The Monte Carlo Simulation and Experiment of Photo Delivery in PET Detector”. From March 2009 to January 2010, I’ve participated in the Student Research Training Science and technology innovation and incubation plat form in Sino-Dutch Biomedical and Information Engineering College. Our title is “A remote medical auscultation consultation system design and implementation”. We focused on using networking technology to enable the remote medical treatment with auscultation.
Then I start with my Ph.D. course directly in September, 2011. In the past year, I followed some courses and did some work relating to clinical decision support system and clinical pathway analysis toolkit, such as displaying the clinical processes and analysis the clinical processes.
From November of 2011, I take part in the Brain Bridge Project “A toolkit for clinical pathway analysis”, which is a collaboration project between TU/e, Zhejiang University and Philips Research. This project aims to analysis clinical pathways and to optimize the pathways finally and compare pathways in both Chinese and Dutch hospitals.
Memetic Algorithms for Ontology Alignment
Born primarily as means to model knowledge, ontologies have successfully been exploited to enable knowledge exchange among people, organizations and software agents. However, because of strong subjectivity of ontology modeling, a matching process is necessary in order to lead ontologies into mutual agreement and obtain the relative alignment, i.e., the set of correspondences among them. The topic of this presentation is to propose the application of an emergent class of evolutionary
algorithms, named Memetic Algorithms, to perform an automatic matching process. As shown in the performed experiments, the memetic approaches result suitable for solving ontology alignment problem.
On Wednesday, Nov. 28th, Jana Samalikova Kapustova will defend her Ph.D. thesis entitled Process mining application in software process assessment.
The thesis text is already available from the TU/e library.
Wil van der Aalst
Process Mining: Making Sense of Processes Hidden in Big Event Data
The two most prominent process mining tasks are process discovery (i.e., learning a process model from an event log) and conformance checking (i.e., diagnosing and quantifying differences between observed and modeled behavior). The increasing availability of event data makes these tasks highly relevant for process analysis and improvement. Therefore, process mining is considered to be one of the key technologies for Business Process Management (BPM). In recent years, we have applied process mining in over 100 organizations. However, as event logs and process models grow, process mining becomes more challenging. Therefore, we propose a fully generic approach to decompose process mining problems into smaller problems that can be analyzed more efficiently. As shown, process discovery and conformance checking can be done per process fragment and the results can be aggregated. This has advantages in terms of efficiency and diagnostics. Moreover, in his talk prof. Van der Aalst will also show additional challenges and solutions approaches related to “Big Event Data”. For example, approaches for concept drift analysis and on-the-fly process mining will be sketched.
Computational Intelligence Approaches to Ontology Alignment
Achieving semantic interoperability is an essential task for all distributed and open knowledge based systems. Currently, the technology recognized for fulfilling this complex task is represented by ontologies. However, the power of ontological representation is reduced by the semantic heterogeneity problem which affects two ontologies when they are characterized by terminological and conceptual discrepancies. The most solid solution to overcome this problem is to perform an ontology alignment process capable of leading two heterogeneous ontologies into a mutual agreement by detecting a set of correspondences between them. Performing this task is an essential step to allow the exchange of information between people, organizations and web applications using ontologies for representing their view of the world. In this presentation, we consider several computational intelligence approaches to ontology alignment. In particular, the use of memetic algorithms, evolutionary approaches and fuzzy set methods are discussed for tackling different aspects of the problem.
After this talk a drink will take place in the PVOC.