A Modeling and Analysis Framework for Knowledge System Based on Meta-Network Approach

Nowadays some online research platforms (e.g., Web of Science or IEEE Xplore) provide bibliographic content and tools to access, analyze, and manage the world\u27s leading journals and conference proceedings in sciences, social science, arts, and humanities. However, when facing increasingly mass literature, it’s very difficult for researchers to effectively and systematically acquire the knowledge structure about a particular topic by using traditional literature reviewing method. Therefore we need explore new knowledge discovery tools for knowledge representation in an effective and efficient way. This paper proposes a knowledge system meta-network model by identifying the concepts representing entities and relationships from bibliometric data, and a methodology framework for meta-network modeling and analysis by using integrated techniques, including text mining, network text analysis, social network analysis, longitudinal network analysis and visualization. Case study using the Web of Science database as data source, explores the knowledge structure and interdisciplinary cooperation mode, as well as hot topics evolution in the field of World Trade Web

A model-driven approach to broaden the detection of software performance antipatterns at runtime

Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that predicate on four views: (i) the static view that captures the software elements (e.g. classes, components) and the static relationships among them; (ii) the dynamic view that represents the interaction (e.g. messages) that occurs between the software entities elements to provide the system functionalities; (iii) the deployment view that describes the hardware elements (e.g. processing nodes) and the mapping of the software entities onto the hardware platform; (iv) the performance view that collects specific performance indices. In this paper we present a lightweight infrastructure that is able to detect performance antipatterns at runtime through monitoring. The proposed approach precalculates such predicates and identifies antipatterns whose static, dynamic and deployment sub-predicates are validated by the current system configuration and brings at runtime the verification of performance sub-predicates. The proposed infrastructure leverages model-driven techniques to generate probes for monitoring the performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043