An entropy-based uncertainty measure of process models

In managing business processes, the process uncertainty and variability are significant factors causing difficulties in prediction and decision making, which evokes and augments the importance and need of process measures for systematic analysis. We propose an entropy-based process measure to quantify the uncertainty of business process models. The proposed measure enables capturing the dynamic behavior of processes, in contrast to previous work which focused on providing measures for the static aspect of process models.     

An entropy-based algorithm to solve the facility layout design problem

This paper presents a new design approach used in order to solve the facility layout problem. The layout problem is viewed from the general perspective as a problem of the arrangement of elements within a system. The main attributes and relationships among the elements of the system are analyzed.     

An objective, entropy-based approach to evaluating migration issues in computer IS

Recent breakthroughs in computing technology have created a set of perplexing new problems for information systems (IS) professionals. These revolve around decisions to be made about replacing current systems with newer technology, upgrading existing systems, and migrating to other platforms or environments. Many decision makers must rely on subjective assessments, such as their instincts or the recommendation of vendors rather than on an objective analysis of their information needs and how they can be met by various system alternatives. A model to quantify these issues, providing an objective measure for comparing system alternatives, including migration, would be valuable. Such a model is demonstrated here; it uses the Shannon-Weaver entropy model in conjunction with quality measures to quantify actual and potential system effectiveness.     

An entropy-based algorithm for data elimination in time-driven software instrumentation

While monitoring, instrumented long running parallel applications generate huge amount of instrumentation data. Processing and storing this data incurs overhead, and perturbs the execution. A technique that eliminates unnecessary instrumentation data and lowers the intrusion without loosing any performance information is valuable for tool developers. This paper presents a new algorithm for software instrumentation to measure the amount of information content of instrumentation data to be collected. The algorithm is based on entropy concept introduced in information theory, and it makes selective data collection for a time-driven software monitoring system possible.     

Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition

In this study we concentrate on qualitative topological analysis of the local behavior of the space of natural images. To this end, we use a space of 3 by 3 high-contrast patches ℳ. We develop a theoretical model for the high-density 2-dimensional submanifold of ℳ showing that it has the topology of the Klein bottle. Using our topological software package PLEX we experimentally verify our theoretical conclusions. We use polynomial representation to give coordinatization to various subspaces of ℳ. We find the best-fitting embedding of the Klein bottle into the ambient space of ℳ. Our results are currently being used in developing a compression algorithm based on a Klein bottle dictionary.     

IA*: An adjacency-based representation for non-manifold simplicial shapes in arbitrary dimensions

We propose a compact, dimension-independent data structure for manifold, non-manifold and non-regular simplicial complexes, that we call the Generalized Indexed Data Structure with Adjacencies (IA^?data structure). It encodes only top simplices, i.e. the ones that are not on the boundary of any other simplex, plus a suitable subset of the adjacency relations. We describe the IA^? data structure in arbitrary dimensions, and compare the storage requirements of its 2D and 3D instances with both dimension-specific and dimension-independent representations. We show that the IA^? data structure is more cost effective than other dimension-independent representations and is even slightly more compact than the existing dimension-specific ones. We present efficient algorithms for navigating a simplicial complex described as an IA^? data structure. This shows that the IA^? data structure allows retrieving all topological relations of a given simplex by considering only its local neighborhood and thus it is a more efficient alternative to incidence-based representations when information does not need to be encoded for boundary simplices.     

Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families

Decision tree (DT) induction is among the more popular of the data mining techniques. An important component of DT induction algorithms is the splitting method, with the most commonly used method being based on the Conditional Entropy (CE) family. However, it is well known that there is no single splitting method that will give the best performance for all problem instances. In this paper we explore the relative performance of the Conditional Entropy family and another family that is based on the Class-Attribute Mutual Information (CAMI) measure. Our results suggest that while some datasets are insensitive to the choice of splitting methods, other datasets are very sensitive to the choice of splitting methods. For example, some of the CAMI family methods may be more appropriate than the popular Gain Ratio (GR) method for datasets which have nominal predictor attributes, and are competitive with the GR method for those datasets where all predictor attributes are numeric. Given that it is never known beforehand which splitting method will lead to the best DT for a given dataset, and given the relatively good performance of the CAMI methods, it seems appropriate to suggest that splitting methods from the CAMI family should be included in data mining toolsets. Kweku-Mauta Osei-Bryson is Professor of Information Systems at Virginia Commonwealth University, where he also served as the Coordinator of the Ph.D. program in Information Systems during 2001–2003. Previously he was Professor of Information Systems and Decision Analysis in the School of Business at Howard University, Washington, DC, U.S.A. He has also worked as an Information Systems practitioner in both industry and government. He holds a Ph.D. in Applied Mathematics (Management Science & Information Systems) from the University of Maryland at College Park, a M.S. in Systems Engineering from Howard University, and a B.Sc. in Natural Sciences from the University of the West Indies at Mona. He currently does research in various areas including: Data Mining, Expert Systems, Decision Support Systems, Group Support Systems, Information Systems Outsourcing, Multi-Criteria Decision Analysis. His papers have been published in various journals including: Information & Management, Information Systems Journal, Information Systems Frontiers, Business Process Management Journal, International Journal of Intelligent Systems, IEEE Transactions on Knowledge & Data Engineering, Data & Knowledge Engineering, Information & Software Technology, Decision Support Systems, Information Processing and Management, Computers & Operations Research, European Journal of Operational Research, Journal of the Operational Research Society, Journal of the Association for Information Systems, Journal of Multi-Criteria Decision Analysis, Applications of Management Science. Currently he serves an Associate Editor of the INFORMS Journal on Computing, and is a member of the Editorial Board of the Computers & Operations Research journal. Kendall E. Giles received the BS degree in Electrical Engineering from Virginia Tech in 1991, the MS degree in Electrical Engineering from Purdue University in 1993, the MS degree in Information Systems from Virginia Commonwealth University in 2002, and the MS degree in Computer Science from Johns Hopkins University in 2004. Currently he is a PhD student (ABD) in Computer Science at Johns Hopkins, and is a Research Assistant in the Applied Mathematics and Statistics department. He has over 15 years of work experience in industry, government, and academic institutions. His research interests can be partially summarized by the following keywords: network security, mathematical modeling, pattern classification, and high dimensional data analysis.     

Collaboration,connectedness, and community: An examination of the factors influencing student persistence in virtual communities

Institutions of higher education are being called upon to provide a more robust pathway to a college degree and improve upon the advanced workforce for the needs of the 21st century. While active collaborative learning environments have been encouraged in higher education to improve student engagement, there is a gap in the literature when it comes to connecting the two research areas of collaborative learning and student intention to persist. This research fills this gap by creating and conducting research to examine a model that measures the factors that significantly influence a student's persistence in a virtual collaborative learning environment. The model examines how collaborative learning, campus connectedness, sense of community, organizational commitment, and turnover intention influence student persistence. The model was tested using a sample of students who participated in a virtual learning community (VLC) and the results suggest that all but one of the factors were found to significantly influence student persistence, with the final factor dependent on the number of hours of system usage. We discuss the implications of the research and the model for team-based theory and organizational practice in education and teamwork.     

An iterative algorithm for homology computation on simplicial shapes

We propose a new iterative algorithm for computing the homology of arbitrary shapes discretized through simplicial complexes. We demonstrate how the simplicial homology of a shape can be effectively expressed in terms of the homology of its sub-components. The proposed algorithm retrieves the complete homological information of an input shape including the Betti numbers, the torsion coefficients and the representative homology generators.To the best of our knowledge, this is the first algorithm based on the constructive Mayer–Vietoris sequence, which relates the homology of a topological space to the homologies of its sub-spaces, i.e. the sub-components of the input shape and their intersections. We demonstrate the validity of our approach through a specific shape decomposition, based only on topological properties, which minimizes the size of the intersections between the sub-components and increases the efficiency of the algorithm.