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Protecting the integrity of state estimates that inform the physical state of a power transmission network is vital for the safe operation. Existing methods to protect the critical state estimates in smart-grid against data spoofing attacks assume a static set of critical buses. Instead, we propose a generalized optimal protection scheme based on a prize-collecting Steiner tree formulation that captures...
Subgraph isomorphism is a fundamental graph problem with many applications. Due to its NP-Hard nature, subgraph isomorphism in large dynamic graphs is considered as a challenging problem. In this paper, we present a distributed graph pruning algorithm (D-IDS) for dynamic graphs to enable efficient subgraph isomorphism. D-IDS continuously maintains the maximum dual simulation match in a dynamic graph...
Accurate estimation of complex voltage phase angles at buses in the power-grid is crucial for determining the operational state of the power system. Existing methods for protection of the state estimate of critical buses against data injection attacks focus on design time assuming a static set of critical buses. We formulate a set of optimal protection schemes to enable operational time protection,...
Determining voltage phase angles of buses in a Smart Grid is a critical operation in the power system state estimation process. Invalid state estimate of strategic buses can cause a severe socioeconomic impact. In this paper, we present an optimal protection scheme to protect the voltage phase angle estimation of strategic buses in a Smart Grid against data spoofing attacks. We discuss the limitations...
Graphs are a key form of Big Data, and performing scalable analytics over them is invaluable to many domains. There is an emerging class of inter-connected data which accumulates or varies over time, and on which novel algorithms both over the network structure and across the time-variant attribute values is necessary. We formalize the notion of time-series graphs and propose a Temporally Iterative...
Existing Big Data streams coming from social and other connected sensor networks exhibit intrinsic inter-dependency enabling unique challenges to scalable graph analytics. Data from these graphs is usually collected in different geographically located data servers making it suitable for distributed processing on clouds. While numerous solutions for large scale static graph analysis have been proposed,...
Unprecedented growth of online social networks, communication networks and internet of things have given birth to large volume, fast changing datasets. Data generated from such systems have an inherent graph structure in it. Updates in staggering frequencies (e.g. edges created by message exchanges in online social media) impose a fundamental requirement for real-time processing of unruly yet highly...
Detecting community structures in graphs is a well studied problem in graph data analytics. Unprecedented growth in graph structured data due to the development of the world wide web and social networks in the past decade emphasizes the need for fast graph data analytics techniques. In this paper we present a simple yet efficient approach to detect communities in large scale graphs by modifying the...
Continuous data flows complement scientific work-flows by allowing composition of real time data ingest and analytics pipelines to process data streams from pervasive sensors and "always-on" scientific instruments. Such data flows are mission-critical applications that cannot suffer downtime, need to operate consistently, and are long running, but may need to be updated to fix bugs or add...
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