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  • On the formation of degree and cluster-degree correlations in scale-free networks

    The cluster-degree of a vertex is the number of connections among the neighbors of this vertex. In this paper we study the cluster-degree of the generalized Barabasi Albert model (GBA model) whose exponent of degree distribution ranges from 2 to infinity. We present the mean-field rate equation for clustering and obtain analytically the degree-dependence of the cluster-degree. We study the distribution of the cluster-degree, which is size dependent but the tail is kept invariant for different degree exponents in the GBA model. In addition, for the degree dependence of the clustering coefficient, very different behaviors arise for different cases of the GBA model. The physical sense of the invariance property of cluster-degree is explained and more general cases are discussed. All the above theoretical results are verified by simulation. (c) 2005 Elsevier B.V. All rights reserved.
  • The Road to a Nationwide Electronic Health Record System: Data Interoperability and Regulatory Landscape

    Huang, Jiawei (Scholarship @ Claremont, 2019-01-01)
    This paper seeks to break down how a large scale Electronic Health Records system could improve quality of care and reduce monetary waste in the healthcare system. The paper further explores issues regarding regulations to data exchange and data interoperability. Due to the massive size of healthcare data, the exponential increase in the speed of data generation through innovative technologies, and the complexity of healthcare data types, the widespread of a large-scale EHR system has hit barriers. Much of the data available is unstructured or contained within a singular healthcare provider’s systems. To fully utilize all the data available, methods for making data interoperable and regulations for data exchange to protect and support patients must be made. Through angles addressing data exchange and interoperability, we seek to break down the constraints and issues that EHR systems still face and gain an understanding of the regulatory landscape.
  • Assured Cloud Computing: The Odessa Monitoring System

    ILLINOIS UNIV AT URBANA-CHAMPAIGN; Campbell, Roy (2011-07-11)
    Assuring the Cloud - Summer Workshop, Griffiss Institute, July 11th 2011, Rome, NY
  • Secret Consumer Scores and Segmentations: Separating Consumer 'Haves' from 'Have-Nots'

    Schmitz, Amy J. (University of Missouri School of Law Scholarship Repository, 2014-01-01)
    “Big Data” is big business. Data brokers profit by tracking consumers’ information and behavior both on- and offline and using this collected data to assign consumers evaluative scores and classify consumers into segments. Companies then use these consumer scores and segmentations for marketing and to determine what deals, offers, and remedies they provide to different individuals. These valuations and classifications are based on not only consumers’ financial histories and relevant interests, but also their race, gender, ZIP Code, social status, education, familial ties, and a wide range of additional data. Nonetheless, consumers are largely unaware of these scores and segmentations, and generally have no way to challenge their veracity because they usually fall outside the purview of the Fair Credit Reporting Act (FCRA). Moreover, companies’ use of these data devices may foster discrimination and augment preexisting power imbalances among consumers by funneling the best deals and remedies to the wealthiest and most sophisticated consumers. Use of these scores and segmentations increases the growing gap between powerful “haves” and vulnerable “have-nots.” This Article sheds light on these data devices and aims to spark adoption of data privacy regulations that protect all consumers regardless of their educational, economic, ethnic, or social status.

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