Recently, the article "Quantitative Verification for Monitoring Event-Streaming Systems" written by Associate Professor Liu Li, from the research group of the IOT& Motion Big Data, School of Big Data and Software, Chongqing University, was accepted by IEEE Transactions on Software Engineering (TSE), a top academic journal in the field of software engineering (CCF A class, JCR zone 1). Co-authors of this article include Dr. Su Guoxin and Professor Minjie Zhang from Wollongong University, Australia, and Professor David S. Rosenblum (ACM/IEEE Fellow) from National University of Singapore.

High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed service systems and applications. Compared with the traditional message queuing middleware, a streaming platform enables the implementation of event-streaming systems (ESS) which include not only complex queues but also applications that transform and react to the streams of data. By analysing the centralised data streams, one can evaluate the Quality-of-Service for other systems and components that produce or consume the streams. This paper considers the exploitation of probabilistic model checking as a performance monitoring technique for ESS systems. Probabilistic model checking is a mature, powerful verification technique with successful application in performance analysis. However, an ESS system may contain quantitative parameters that are determined by event streams observed in a certain period of time. In this paper, the authors present a novel theoretical framework called QV4M (meaning “quantitative verification for monitoring”) for ESS system monitoring based on two recent methods of probabilistic model checking. Our framework QV4M assumes the parameters in a probabilistic system model as random variables and infers the statistical confidence of a probabilistic model checking output. Two case studies as an empirical evaluation of QV4M are presented.

The periodical IEEE Transactions on Software Engineering (TSE) is the top periodical in the field of software engineering, the A-class periodical recommended by china computer federation (CCF), which encourages Chinese scholars to break through, and the first periodical of JCR. This article is the first TSE article with Chongqing University as the corresponding unit which will play a positive role in promoting the construction of software engineering and data science disciplines in our university.

The IOT& Motion Big Data Lab(http://bdl.cqu.edu.cn), established by Associate Professor Liu Li, was established in May 2018, and has been engaged in the research of big data analysis technology and its application. At present, the laboratory has undertaken more than 10 projects such as the National Fund Committee Project, the national "New Generation Artificial Intelligence Major Project" sub-project, and Chongqing Artificial Intelligence Major Special Project; More than 100 papers have been published, including more than 40 SCI papers, 6 SSCI papers, 2 ESI highly cited papers and more than 50 CCF list papers; Apply for more than 10 patents; Won one second prize of provincial and ministerial level scientific and technological progress award. At the same time, since 2018, the laboratory has received more than 60 visits from relevant research institutes and schools in Chongqing(with a total of more than 2,000 people) and held more than 10 times of science popularization.