Software Technology Lab
Computing Science @ Simon Fraser University, B.C., Canada


  • Mohammad Ali Tayebi, Ph.D.
    • Narek Nalbandyan, Ph.D. Candidate
      • Hamed Yaghoubi Shahir, Ph.D. Candidate
        • Zahra Zohrevand, Ph.D. Candidate
          • Amir Yaghoubi Shahir, M.Sc. Candidate



            Social Network Analysis of Crime DataFrom co-offending behaviour to criminal networks to organized crime

            Crime prevention and reduction is the major concern of law enforcement agencies in order to protect the lives and properties of citizens. Over the past two decades, law enforcement and intelligence agencies have realized the importance of co-offending network analysis for crime investigations. In this research we aim to develop efficient social network analysis methods for crime reduction and prevention. Our concentration is on covering different problems in the crime world, where social network analysis can contribute toward reducing crime rates through criminal network destabilization, suspect investigation and organized crime group detection. The main outcome of the set of proposed solutions for the mentioned problems is providing important insights into co-offending behavior, and the ways in which criminal networks shape, and the factors that motivate organized crime to syndicate.

            Distributed Situation AwarenessA formal semantic framework

            The Distributed Situation Analysis (DSA) project presents a novel distributed formal semantic framework for engineering and prototyping of Situation Analysis (SA) models using the Abstract State Machine (ASM) method for the construction and analysis of ground models and their refinement. The novel aspects of the DSA framework are its completely decentralized organization based on an asynchronous computation model with multiple mobile observers along with a clear separation of generic SA system concepts from application context specific situation analysis methods needed for processing time series to detect and identify objects of interest, analyse their behavior, and detect anomalies. Our goal is to provide a precise and well defined semantic foundation for the meaning of central SA system concepts in abstract operational terms as an ASM ground model, the validity of which can be established by observation and experimentation.

            Maritime Rendezvous Anomaly DetectionDetecting maritime multi-vessel interactions and anomalies

            Strictly speaking, the concept of rendezvous refers to a maneuver between two vessels. For example, two cargo ships meet at an agreed rendezvous location off the normal shipping lanes, staying in close proximity for some time, and then move back towards the shipping lanes. However, rather than strictly interpreting rendezvous in a narrow sense, one may assume a broader meaning that pertains to a range of scenarios with common characteristics that resemble a rendezvous, such as a close encounter of two vessels on a collision course, or a single cargo ship surrounded by several high speed boats. We propose here a novel algorithmic approach generalizing the semantic definition of the concept of rendezvous to any number and type of marine vessels and to any kind of interaction between vessels that operate in relative proximity to each other over some period of time.

            Vignette Generation and AnalysisGenerating test cases for marine safety and security scenarios

            Methodical approaches for analyzing and validating situation analysis methods, decision support models, and information fusion algorithms require realistic vignettes that describe in great detail how a situation unfolds over time depending on initial configurations, dynamic environmental conditions and uncertain operational aspects. Meaningful results from simulation runs require appropriate test cases, the production of which is in itself a complex activity. To simplify this task, we introduce here the conceptual design of a Vignette Generator that has been developed and tested in an industrial research project.


            The CoreASM project focuses on the design of a lean executable ASM (Abstract State Machines) language, in combination with a supporting tool environment for high-level design, experimental validation and formal verification (where appropriate) of abstract system models. For more details, visit


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            Software Technology Lab
            School of Computing Science
            Simon Fraser University
            8888 University Drive, Burnaby, BC
            Canada V5A 1S6


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