Patrick R. Paulson

A multi-layer, hierarchical information management system for the smart grid

This paper presents the modeling approach, methodologies, and initial results of setting up a multi-layer, hierarchical information management system (IMS) for the smart grid. The IMS allows its users to analyze the data collected by multiple control and communication networks to characterize the states of the smart grid. Abnormal, corrupted, or erroneous measurement data and outliers are detected and analyzed to identify whether they are caused by random equipment failures, human error, or tampering. Data collected from different information networks are crosschecked for data integrity based on redundancy, dependency, correlation, or cross-correlations, which reveal the interdependency between data sets. A hierarchically structured reasoning mechanism is used to rank possible causes of an event to enable system operators to proactively respond or provide mitigation recommendations to remove or neutralize the threats. The model satisfactorily identifies the cause of an event and significantly reduces the need to process myriads of data.

The development of a smart distribution grid testbed for integrated information management systems

This paper presents a smart distribution grid testbed to test or compare designs of integrated information management systems (I2MSs). An I2MS extracts and synthesizes information from a wide range of data sources to detect abnormal system behaviors, identify possible causes, assess the system status, and provide grid operators with response suggestions. The objective of the testbed is to provide a modeling environment with sufficient data sources for the I2MS design. The testbed includes five information layers and a physical layer; it generates multi-layer chronological data based on actual measurement playbacks or simulated data sets produced by the physical layer. The testbed models random hardware failures, human errors, extreme weather events, and deliberate tampering attempts to allow users to evaluate the performance of different I2MS designs. Initial results of I2MS performance tests showed that the testbed created a close-to-real-world environment that allowed key performance metrics of the I2MS to be evaluated.

Semantic catalog of things, services, and data to support a wind data management facility

Transparency and data integrity are crucial to any scientific study wanting to garner impact and credibility in the scientific community. The purpose of this paper is to discuss how this can be achieved using what we define as the Semantic Catalog. The catalog exploits community vocabularies as well as linked open data best practices to seamlessly describe and link things, data, and off-the-shelf (OTS) services to support scientific offshore wind energy research for the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Wind and Water Power Program. This is largely made possible by leveraging collaborative advances in the Internet of Things (IoT), Semantic Web, Linked Services, Linked Open Data (LOD), and Resource Description Framework (RDF) vocabulary communities, which provides the foundation for our design. By adapting these linked community best practices, we designed a wind characterization Data Management Facility (DMF) capable of continuous data collection, processing, and preservation of in situ and remote sensing instrument measurements. The design incorporates the aforementioned Semantic Catalog which provides a transparent and ubiquitous interface for its user community to the things, data, and services for which the DMF is composed.

Exploiting Term Relations for Semantic Hierarchy Construction

This paper presents a method to induce semantic taxonomies by applying the lattice theoretical technology of formal concept analysis to relations of predicates extracted from a natural language corpus. Our initial research results are in support of a future overall methodology for the semi-automatic construction of semantic hierarchies from term relations extracted from text. We describe our formal method for hierarchy construction, selection and processing of a test corpus for extracting verb-noun pairs from natural language, measurement and filtering of the resulting verb-noun term matrices for density optimization, and look at the resulting semantic hierarchies produced by FCA.

Provenance Store Evaluation

Requirements for the provenance store and access API are developed. Existing RDF stores and APIs are evaluated against the requirements and performance benchmarks. The team’s conclusion is to use MySQL as a database backend, with a possible move to Oracle in the near-term future. Both Jena and Sesame’s APIs will be supported, but new code will use the Jena API

Knowledge Signatures for Information Integration

Information analysis, scientific discovery, web navigation and many other information related activities rely on the collection, analysis, and integration of unstructured, disparate data. In spite of significant research efforts, automatic methods for information integration are still vastly inferior to human capabilities. Tasks which are simple for humans, such as recognizing the underlying similarities between superficially different objects or sorting out the semantics of ambiguous statements, continue to present significant technical challenges for automated processes.

Agent-centric approach for cybersecurity decision-support with partial observability

Generating automated cyber resilience policies for real-world settings is a challenging research problem that must account for uncertainties in system state over time and dynamics between attackers and defenders. In addition to understanding attacker and defender motives and tools, and identifying “relevant” system and attack data, it is also critical to develop rigorous mathematical formulations representing the defenders decision-support problem under uncertainty. Game-theoretic approaches involving cyber resource allocation optimization with Markov decision processes (MDP) have been previously proposed in the literature. However, as is the case in strategic card games such as poker, research challenges using game-theoretic approaches for practical cyber defense applications include equilibrium solvability, existence, and possible multiplicity. Moreover, mixed uncertainties associated with player payoffs also need to be accounted for within game settings. This paper proposes an agent-centric approach for cybersecurity decision-support with partial system state observability. Multiple partially observable MDP (POMDP) problems are formulated and solved from a cyber defenders perspective, against a fixed attacker type, using synthetic (notional) system and attack parameters estimated from a Monte Carlo based sampling scheme. The agent-centric problem formulation helps address equilibrium related research challenges and represents a step toward automated and dynamic cyber resilience policy generation and implementation.

User-Centric Approach for Benchmark RDF Data Generator in Big Data Performance Analysis

Changes in data generation sources such as social networks, mobile devices, and process automation, along with an increase in the number of instruments generating observational data have pushed beyond the boundaries of current day data analysis systems. New algorithms, specialized hardware systems, and computing paradigms are designed to solve problems exhibited by large datasets, but at the same time there is a dearth of flexible and easy to use tools to assess the effectiveness of these proposed solutions. Benchmarking tools are required to compare the performance and the cost associated with any Big Data system. This research focuses on a user-centric approach of building such tools and proposes a flexible, extensible, and easy to use framework to support performance analysis of Big Data systems. Finally, case studies from two different domains are presented to validate the framework.

HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks

This report outlines techniques for extending benchmark generation products so they support uncertainty quantification by benchmarked systems. We describe how uncertainty quantification requirements can be presented to candidate analytical tools supporting SPARQL. We describe benchmark data sets for evaluating uncertainty quantification, as well as an approach for using our benchmark generator to produce data sets for generating benchmark data sets.

A predictive defense system for the smart grid

This paper presents the modeling approach, methodologies, and initial results of a multi-layer, hierarchical predictive defense model to assist in information management of the smart grid. The predictive defense model allows users to analyze data collected by multiple control and communication networks to characterize the states of the smart grid. Abnormal, corrupted, or erroneous measurement data and outliers are detected and analyzed to identify whether they are caused by random equipment failures, human error, or tampering. Data collected from different information networks are crosschecked for data integrity based on redundancy, dependency, correlation, or cross-correlations, which reveal the interdependency between data sets. A hierarchically structured reasoning mechanism ranks possible causes of an event to facilitate proactive response or inform mitigation recommendations to remove or neutralize the threats. The model satisfactorily identifies the cause of an event and significantly reduces the operator’s processing burden.