Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Special Session on Industry 4.0

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BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems

In this report, we present work towards a framework for modeling and checking behavior of spatially distributed component systems. Design goals of our framework are the ability to model spatial behavior in a component oriented, simple and intuitive way, the possibility to automatically analyse and verify systems and integration possibilities with other modeling and verification tools. We present examples and the verification steps necessary to prove properties such as range coverage or the absence of collisions between components and technical details

Formalizing Cyber--Physical System Model Transformation via Abstract Interpretation

Model transformation tools assist system designers by reducing the labor--intensive task of creating and updating models of various aspects of systems, ensuring that modeling assumptions remain consistent across every model of a system, and identifying constraints on system design imposed by these modeling assumptions. We have proposed a model transformation approach based on abstract interpretation, a static program analysis technique. Abstract interpretation allows us to define transformations that are provably correct and specific. This work develops the foundations of this approach to model transformation. We define model transformation in terms of abstract interpretation and prove the soundness of our approach. Furthermore, we develop formalisms useful for encoding model properties. This work provides a methodology for relating models of different aspects of a system and for applying modeling techniques from one system domain, such as smart power grids, to other domains, such as water distribution networks.Comment: 8 pages, 4 figures; to appear in HASE 2019 proceeding

Verification of information flow security in cyber-physical systems

With a growing number of real-world applications that are dependent on computation, securing the information space has become a challenge. The security of information in such applications is often jeopardized by software and hardware failures, intervention of human subjects such as attackers, incorrect design specification and implementation, other social and natural causes. Since these applications are very diverse, often cutting across disciplines a generic approach to detect and mitigate these issues is missing. This dissertation addresses the fundamental problem of verifying information security in a class of real world applications of computation, the Cyber-physical systems (CPSs). One of the motivations for this work is the lack of a unified theory to specify and verify the complex interactions among various cyber and physical processes within a CPS. Security of a system is fundamentally characterized by the way information flows within the system. Information flow within a CPS is dependent on the physical response of the system and associated cyber control. While formal techniques of verifying cyber security exist, they are not directly applicable to CPSs due to their inherent complexity and diversity. This Ph.D. research primarily focuses on developing a uniform framework using formal tools of process algebras to verify security properties in CPSs. The merits in adopting such an approach for CPS analyses are three fold- i) the physical and continuous aspects and the complex CPS interactions can be modeled in a unified way, and ii) the problem of verifying security properties can be reduced to the problem of establishing suitable equivalences among the processes, and iii) adversarial behavior and security properties can be developed using the features like compositionality and process equivalence offered by the process algebras --Abstract, page iii

Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks