The Experts below are selected from a list of 7962 Experts worldwide ranked by ideXlab platform
Colleen Mccue - One of the best experts on this subject based on the ideXlab platform.
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Predictive Analysis
Encyclopedia of U.S. Intelligence, 2015Co-Authors: Colleen MccueAbstract:Predictive analytics are used in almost every segment of our lives to improve decision making and insight. Advanced analytics increasingly are used in the public safety and national security domain to enable information-based approaches to prevention, thwarting, mitigation, and response. Rather than being confined to a specific algorithm, technology, or technique, though, Predictive analytics is best described as a process that includes the identification of a specific question or challenges, data access and preprocessing, modeling, evaluation, and the creation of usable output. The unique requirements and constraints in the operational public safety and national security environment require additional consideration and insight, particularly regarding data access, and the need for operationally relevant and actionable analytic products. This entry includes an overview of Predictive analytics process best practices as applied to the public safety and national security domain, including use cases and reference to comprehensive sources on specific topics for those interested in additional research on the topic.
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Chapter 4 – Process Models for Data Mining and Predictive Analysis
Data Mining and Predictive Analysis, 2015Co-Authors: Colleen MccueAbstract:Data mining and Predictive analytics can best be understood as a process, rather than specific technology, tool, or tradecraft. Chapter 4 includes an overview of four complementary approaches to Analysis: the Central Intelligence Agency (CIA) Intelligence Process, the CRoss Industry Standard Process for Data Mining (CRISP-DM), SEMMA, and the Actionable Mining and Predictive Analysis process developed specifically for the operational public safety and security environment. The Actionable Mining and Predictive Analysis process addresses unique requirements and constraints associated with the applied setting, including data access and availability, public safety-specific evaluation, and the requirement for operationally relevant and actionable output. Data privacy and security also are addressed.
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Data Mining and Predictive Analysis
Data Mining and Predictive Analysis, 2007Co-Authors: Colleen MccueAbstract:It is now possible to predict the future when it comes to crime. in Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. in this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity. Knowledge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis. The book is a starting point for those thinking about using data mining in a law enforcement setting. It provides terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence Analysis, which law enforcement and intelligence professionals can tailor to their own unique situation and responsibilities. * Serves as a valuable reference tool for both the student and the law enforcement professional * Contains practical information used in real-life law enforcement situations * Approach is very user-friendly, conveying sophisticated analyses in practical terms. © 2007 Elsevier Inc. All rights reserved.
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Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis
2006Co-Authors: Colleen MccueAbstract:Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining in homeland security, security Analysis, and operational law enforcement settings. This revised text highlights new and emerging technology, discusses the importance of analytic context for ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of Predictive analytics in intelligence and security Analysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment. * Discusses new and emerging technologies and techniques, including up-to-date information on Predictive policing, a key capability in law enforcement and security* Demonstrates the importance of analytic context beyond software* Covers new models for effective delivery of advanced analytics to the operational environment, which have increased access to even the most powerful capabilities* Includes terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence Analysis
Kristin Glass - One of the best experts on this subject based on the ideXlab platform.
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Predictive Analysis for social processes I: Multi-scale hybrid system modeling
2009 IEEE Control Applications (CCA) & Intelligent Control (ISIC), 2009Co-Authors: Richard Colbaugh, Kristin GlassAbstract:This two-part paper presents a new approach to Predictive Analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal Analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the ldquoappealrdquo of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process Predictive Analysis.
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CCA/ISIC - Predictive Analysis for social processes II: Predictability and warning Analysis
2009 IEEE International Conference on Control Applications, 2009Co-Authors: Richard Colbaugh, Kristin GlassAbstract:This two-part paper presents a new approach to Predictive Analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces a multi-scale, stochastic hybrid system modeling framework for these systems. In Part II of the paper we develop a systems theory-based, computationally tractable approach to Predictive Analysis for these systems. Among other capabilities, this analytic methodology enables assessment of process predictability, identification of measurables which have Predictive power, discovery of reliable early indicators for events of interest, and robust, scalable prediction. The potential of the proposed approach is illustrated through case studies involving online markets, social movements, and protest behavior.
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CCA/ISIC - Predictive Analysis for social processes I: Multi-scale hybrid system modeling
2009 IEEE International Conference on Control Applications, 2009Co-Authors: Richard Colbaugh, Kristin GlassAbstract:This two-part paper presents a new approach to Predictive Analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal Analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the “appeal” of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002–2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process Predictive Analysis.
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AAAI Spring Symposium: Technosocial Predictive Analytics - Predictive Analysis for social processes.
2009Co-Authors: Kristin Glass, Richard Colbaugh, D. EngiAbstract:This paper presents a new approach to Predictive Analysis for social processes. A key element of the proposed methodology is proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the persuasiveness of an argument) and the social dynamics which is its realization (e.g., the way the argument propagates through a segment of society). We show that this interplay can be modeled within a novel multi-scale framework that is sociologically and mathematically sensible, expressive, illuminating, and amenable to formal Analysis. We then develop a scientifically rigorous, computationally tractable approach to Predictive Analysis. Among other capabilities, this analytic approach enables assessment of process predictability, identification of measurables which have Predictive power, discovery of reliable early indicators for events of interest, and scalable, robust prediction. The potential of the proposed approach is illustrated through a case study involving early warning Analysis for mobilization/protest events.
Abhishek Gupta - One of the best experts on this subject based on the ideXlab platform.
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Posterior Predictive Analysis for Evaluating DSGE Models
2020Co-Authors: Jon Faust, Abhishek GuptaAbstract:This paper evaluates the strengths and weaknesses of dynamic stochastic general equilibrium (DSGE) models from the standpoint of their usefulness in doing monetary policy Analysis. The paper isolates features most relevant for monetary policymaking and uses the diagnostic tools of posterior Predictive Analysis to evaluate these features. The paper provides a diagnosis of the observed flaws in the model with regards to these features that helps in identifying the structural flaws in the model. The paper finds that model misspecification causes certain pairs of structural shocks in the model to be correlated in order to fit the observed data.
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Posterior Predictive Analysis for Evaluating DSGE Models
National Bureau of Economic Research, 2012Co-Authors: Jon Faust, Abhishek GuptaAbstract:While dynamic stochastic general equilibrium (DSGE) models for monetary policy Analysis have come a long way, there is considerable difference of opinion over the role these models should play in the policy process. The paper develops three main points about assessing the value of these models. First, we document that DSGE models continue to have aspects of crude approximation and omission. This motivates the need for tools to reveal the strengths and weaknesses of the models--both to direct development efforts and to inform how best to use the current flawed models. Second, posterior Predictive Analysis provides a useful and economical tool for finding and communicating strengths and weaknesses. In particular, we adapt a form of discrepancy Analysis as proposed by Gelman, et al. (1996). Third, we provide a nonstandard defense of posterior Predictive Analysis in the DSGE context against long-standing objections. We use the iconic Smets-Wouters model for illustrative purposes, showing a number of heretofore unrecognized properties that may be important from a policymaking perspective.
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Posterior Predictive Analysis for Evaluating DSGE Models
2010Co-Authors: Jon Faust, Abhishek GuptaAbstract:In this paper, we develop and apply certain tools to evaluate the strengths and weaknesses of dynamic stochastic general equilibrium (DSGE) models. In particular, this paper makes three contributions: One, it argues the need for such tools to evaluate the usefulness of the these models; two, it defines these tools which take the form of prior and particularly posterior Predictive Analysis and provides illustrations; and three, it provides a justification for the use of these tools in the DSGE context in defense against the standard criticisms for the use of these tools.
Aarti Gupta - One of the best experts on this subject based on the ideXlab platform.
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FMCAD - Efficient Predictive Analysis for detecting nondeterminism in multi-threaded programs
2012Co-Authors: Arnab Sinha, Sharad Malik, Aarti GuptaAbstract:Determinism is often a desired property in multithreaded programs. A multi-threaded program is said to be deterministic if for a given input, different thread interleavings result in the same system state in the execution of the program. This, in turn, requires that different interleavings preserve the values read by each read operation. A related, but less strict condition is for the program to be race-free. A deterministic program is race-free but the converse may not be true. There is much work done in the static Analysis of programs to detect races and nondeterminism. However, this can be expensive and may not complete for large programs in reasonable time. In contrast to static Analysis, Predictive Analysis techniques take a given program trace and explore other possible interleavings that may violate a given property — in this case the property of interest is determinism. Predictive Analysis can be sound, but is not complete as it is limited to a specific set of program runs. Nonetheless, it is of interest as it offers greater scalability than static Analysis. This work presents a Predictive Analysis method for detecting nondeterminism in multi-threaded programs. Potential cases of nondeterminism are checked by constructing a causality graph from the thread events and confirming that it is acyclic. On average, the number of graphs analyzed per benchamrk is one per potential case of nondeterminism, thereby ensuring that it is efficient. We demonstrate its application on some benchmark Java and C/C++ programs.
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symbolic Predictive Analysis for concurrent programs
Formal Aspects of Computing, 2011Co-Authors: Chao Wang, Sudipta Kundu, Rhishikesh Limaye, Malay K Ganai, Aarti GuptaAbstract:Predictive Analysis aims at detecting concurrency errors during runtime by monitoring a concrete execution trace of a concurrent program. In recent years, various models based on the happens-before causality relations have been proposed for Predictive Analysis. However, these models often rely on only the observed runtime events and typically do not utilize the program source code. Furthermore, the enumerative algorithms they use for verifying safety properties in the predicted traces often suffer from the interleaving explosion problem. In this paper, we introduce a precise Predictive model based on both the program source code and the observed execution events, and propose a symbolic algorithm to check whether a safety property holds in all feasible permutations of events of the given trace. Rather than explicitly enumerating and checking the interleavings, our method conducts the search using a novel encoding and symbolic reasoning with a satisfiability modulo theory solver. We also propose a technique to bound the number of context switches allowed in the interleavings during the symbolic search, to further improve the scalability of the algorithm.
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FM - Symbolic Predictive Analysis for Concurrent Programs
FM 2009: Formal Methods, 2009Co-Authors: Chao Wang, Sudipta Kundu, Malay K Ganai, Aarti GuptaAbstract:Predictive Analysis aims at detecting concurrency errors during runtime by monitoring a concrete execution trace of a concurrent program. In recent years, various models based on happens-before causality relations have been proposed for Predictive Analysis to improve the interleaving coverage while ensuring the absence of false alarms. However, these models are based on only the observed events, and typically do not utilize source code. Furthermore, the enumerative algorithms they use for verifying safety properties in the predicted execution traces often suffer from the interleaving explosion problem. In this paper, we introduce a new symbolic causal model based on source code and the observed events, and propose a symbolic algorithm to check whether a safety property holds in all feasible permutations of events in the given execution trace. Rather than explicitly enumerating the interleavings, our algorithm conducts the verification using a novel encoding of the causal model and symbolic reasoning with a satisfiability modulo theory (SMT) solver. Our algorithm has a larger interleaving coverage than known causal models in the literature. We also propose a method to symbolically bound the number of context switches allowed in an interleaving, to further improve the scalability of the algorithm.
Richard Colbaugh - One of the best experts on this subject based on the ideXlab platform.
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Predictive Analysis for social processes I: Multi-scale hybrid system modeling
2009 IEEE Control Applications (CCA) & Intelligent Control (ISIC), 2009Co-Authors: Richard Colbaugh, Kristin GlassAbstract:This two-part paper presents a new approach to Predictive Analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal Analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the ldquoappealrdquo of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process Predictive Analysis.
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CCA/ISIC - Predictive Analysis for social processes II: Predictability and warning Analysis
2009 IEEE International Conference on Control Applications, 2009Co-Authors: Richard Colbaugh, Kristin GlassAbstract:This two-part paper presents a new approach to Predictive Analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces a multi-scale, stochastic hybrid system modeling framework for these systems. In Part II of the paper we develop a systems theory-based, computationally tractable approach to Predictive Analysis for these systems. Among other capabilities, this analytic methodology enables assessment of process predictability, identification of measurables which have Predictive power, discovery of reliable early indicators for events of interest, and robust, scalable prediction. The potential of the proposed approach is illustrated through case studies involving online markets, social movements, and protest behavior.
-
CCA/ISIC - Predictive Analysis for social processes I: Multi-scale hybrid system modeling
2009 IEEE International Conference on Control Applications, 2009Co-Authors: Richard Colbaugh, Kristin GlassAbstract:This two-part paper presents a new approach to Predictive Analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal Analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the “appeal” of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002–2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process Predictive Analysis.
-
AAAI Spring Symposium: Technosocial Predictive Analytics - Predictive Analysis for social processes.
2009Co-Authors: Kristin Glass, Richard Colbaugh, D. EngiAbstract:This paper presents a new approach to Predictive Analysis for social processes. A key element of the proposed methodology is proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the persuasiveness of an argument) and the social dynamics which is its realization (e.g., the way the argument propagates through a segment of society). We show that this interplay can be modeled within a novel multi-scale framework that is sociologically and mathematically sensible, expressive, illuminating, and amenable to formal Analysis. We then develop a scientifically rigorous, computationally tractable approach to Predictive Analysis. Among other capabilities, this analytic approach enables assessment of process predictability, identification of measurables which have Predictive power, discovery of reliable early indicators for events of interest, and scalable, robust prediction. The potential of the proposed approach is illustrated through a case study involving early warning Analysis for mobilization/protest events.
