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Chen, Liangzhe

Publications and source records attributed to Chen, Liangzhe.

Data Mining Critical Infrastructure Systems: Models and Tools

Critical infrastructures (CIs) such as power, water, transportation, and telecommunication are highly complex interacting systems that are vital to national security, economy and public life. They play an important role in several core urban computing challenges. Advances in computing resources and techniques have led to enormous progress in developing intelligent frameworks for analyzing these large heterogeneous systems. In this article, we survey state-of-the-art and foundational work in this upcoming area from a data mining perspective. We discuss basic concepts of CIs, their properties, impacts on them due to natural or human-caused disturbances and different computational methodologies used for modeling and understanding their behavior. We also discuss recent work that specifically deals with two critical sectors of CIs, namely power and transportation systems. Lastly, we also describe several existing tools and methods that are used to facilitate decision making for domain operators, enable efficient and faster disaster response for federal and state agencies and help improve the security and resiliency of these CIs.

97 MATHEMATICS AND COMPUTING↗

HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks

Critical Infrastructure Systems such as transportation, water and power grid systems are vital to our national security, economy, and public safety. Recent events, like the 2012 hurricane Sandy, show how the interdependencies among different CI networks lead to catastrophic failures among the whole system. Hence, analyzing these CI networks, and modeling failure cascades on them becomes a very important problem. However, traditional models either do not take multiple CIs or the dynamics of the system into account, or model it simplistically. In this paper, we study this problem using a heterogeneous network viewpoint. We first construct heterogeneous CI networks with multiple components using national-level datasets. Then we study novel failure maximization problems on these networks, to compute critical nodes in such systems. We then provide HotSpots, a scalable and effective algorithm for these problems, based on careful transformations. Finally, we conduct extensive experiments on real CIS data from multiple US states, and show that our method HotSpots outperforms non-trivial baselines, gives meaningful results and that our approach gives immediate benefits in providing situational-awareness during large-scale failures.

Chen, Liangzhe↗

URBAN-NET-TOOLKIT

Strengthening and maintaining secure, functioning, and resilient Critical Infrastructure Systems (CISs) is one of our nation's primary goals, and it requires proactive and coo0rdinated efforts among federal, state, local, public and private owners and operators of CISs. Recent worldwide extreme events have shown how interdependencies across CISs (electric grid, telecommunications, transportation, water networks, etc.) can drastically increase system vulnerability. To understand these complex interdependencies, ORNL has instantiated Urban-NET, a novel decision support system for CIS operators who need to identify vulnerabilities for minimizing the cascading impacts - saving utilities, the federal and state government, and private sector companies potentially billions by avoiding a complete CI failure and/or shutdown. The developed system includes a stack of software tools for preprocessing, modeling, interconnecting multiple CIS datasets as an integrated heterogeneous graph (i.e. network), identifying vulnerabilities through topological analysis, and predicting consequences based on simulations along with visualization.

Chinthavali, Supriya↗