Institution
Worcester Polytechnic Institute
Education•Worcester, Massachusetts, United States•
About: Worcester Polytechnic Institute is a education organization based out in Worcester, Massachusetts, United States. It is known for research contribution in the topics: Computer science & Population. The organization has 6270 authors who have published 12704 publications receiving 332081 citations. The organization is also known as: WPI.
Topics: Computer science, Population, Data envelopment analysis, Nonlinear system, Finite element method
Papers published on a yearly basis
Papers
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TL;DR: The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures, which are applied to analyze the gap between an organization and best practices.
1,542 citations
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14 Mar 2010TL;DR: This paper utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements.
Abstract: Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.
1,408 citations
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TL;DR: A new study reveals businesses are defining data quality with the consumer in mind, and within this larger context of information systems, data is collected from multiple data sources and stored in databases.
Abstract: ATA-QUALITY (DQ) PROBLEMS ARE INCREASINGLY EVIdent, particularly in organizational databases. Indeed, 50% to 80% of computerized criminal records in the U.S. were found to be inaccurate, incomplete, or ambiguous. The social and economic impact of poor-quality data costs billions of dollars. [5-7, 10]. Organizational databases, however, reside in the larger context of information systems (IS). Within this larger context, data is collected from multiple data sources and stored in databases. From this stored data, useful information is generated for organizational decision-making. A new study reveals businesses are defining data quality with the consumer in mind.
1,296 citations
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TL;DR: It is shown that the standard DEA model can be used to improve the performance via increasing the desirable outputs and decreasing the undesirable outputs, and the linearity and convexity of DEA are preserved.
1,254 citations
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01 Oct 2009TL;DR: This paper reviewed the history and current trends in the field of EDM and discussed trends and shifts in the research conducted by this community, and discussed the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries, and the reduction in the frequency of relationship mining within the EDM community.
Abstract: We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The most cited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.
1,217 citations
Authors
Showing all 6336 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrew G. Clark | 140 | 823 | 123333 |
Ming Li | 103 | 1669 | 62672 |
Joseph Sarkis | 101 | 482 | 45116 |
Arthur C. Graesser | 95 | 614 | 38549 |
Kevin J. Harrington | 85 | 682 | 33625 |
Kui Ren | 83 | 501 | 32490 |
Bart Preneel | 82 | 844 | 25572 |
Ming-Hui Chen | 82 | 525 | 29184 |
Yuguang Fang | 79 | 572 | 20715 |
Wenjing Lou | 77 | 311 | 29405 |
Bernard Lown | 73 | 330 | 20320 |
Joe Zhu | 72 | 231 | 19017 |
Y.S. Lin | 71 | 304 | 16100 |
Kevin Talbot | 71 | 268 | 15669 |
Christof Paar | 69 | 399 | 21790 |