52 Slices
Medium 9781601322517

A Study on Information Connection Model using Rule-based Connection Platform

Hamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Information and Knowledge Engineering | IKE'13 |

29

A Study on Information Connection Model using Rulebased Connection Platform

Heeseok Choi , Jaesoo Kim

NTIS Center, Korea Institute of Science and Technology Information, Daejeon, Korea

Abstract - National Science & Technology Information

Service (NTIS) collects national R&D information through the connection system in real time with specialized institutions under government ministries for R&D information service.

However, because the information connection between the research management systems in each ministry (institution) and the NTIS is different, it is not easy to operate the connection system, and immediate data collection is thus not ensured. This study aims to propose an information connection model to be applied on the NTIS-like systems. To do this, we examine methods or styles of information connection and compare strength and weakness of connection methods. In this paper we also understand issues or characteristics of the methods through analyzing current information connection methods applied on the NTIS.

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Medium 9781601322517

Management of Knowledge on the Basis of Stochastic Mathematical Models

Hamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

34

Int'l Conf. Information and Knowledge Engineering | IKE'13 |

Management of Knowledge on the Basis of Stochastic

Mathematical Models

James William Brooks1, Dmitry Zhukov2, Irina Samoylo3 and Victoria Hodges4

1

Chancellor, Salem International University, Salem, West Virginia, USA

2

Professor, Consultant, Department of Medical and Biological Physics,

I.M. Sechenov First Moscow State Medical University, Moscow, Russia

3

Professor, Department of Medical and Biological Physics,

I.M. Sechenov First Moscow State Medical University, Moscow, Russia

4

Consultant, Department of Medical and Biological Physics,

I.M. Sechenov First Moscow State Medical University, Moscow, Russia

Abstract - This article discusses the questions of the use of stochastic models in the description of an educational process, which includes such parts as obtaining, loss (forgetting), and self-organization of educational information. The probability approach used by the authors led them to the deduction of differential equations of the second order of the type of the

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Medium 9781601322517

Time Interval Sequential Sequence Mining in Large Database

Hamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

240

Int'l Conf. Information and Knowledge Engineering | IKE'13 |

Time Interval Sequential Sequence Mining in Large Database

Kiran R. Amin, Member, IEEE and J. S. Shah

Abstract—“Time interval sequential sequence mining” mines sequential sequence from database with efficient support counting. It is used to find frequent subsequences occur with minimum support value. The sequential sequence mining focuses on sequence of events occurred frequently in given dataset unlike simple association rule mining. The sequence of the items plays major role. We use the order dataset where all events stored in some particular order. The traditional sequential sequence mining doesn’t care for the timing between the purchasing of items.

The goal of our research work is to develop and evaluate new

Time interval sequential sequence mining algorithms of

MySSM which efficiently produce sequential sequences in large database having significant improvement in execution Time and

Memory.

KeyWords--MySSM, GAS, CMEM and OUTR, Time Interval

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Medium 9781601322517

Session - Novel Methodologies and Applications

Hamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF
Medium 9781601322517

Discovery of Predictive Neighborly Rules from Neighborhood Systems

Hamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Information and Knowledge Engineering | IKE'13 |

119

Discovery of Predictive Neighborly Rules from

Neighborhood Systems

Ray Hashemi1, Azita Bahrami2, Mark Smith3, Nicholas R. Tyler4, Matthew Antonelli1, and

Sean Clapp1

1

Department of Computer Science

4

Department of Biology

Armstrong Atlantic State University

Savannah, GA, USA

2

IT Consultation

Savannah, GA, USA

3

Department of Computer Science

University of Central Arkansas

Conway, AR, USA

Abstract - The use of “data closeness” for clustering, concept generalization, and imprecise query processing has been frequently reported in the literature. In this paper, however, we introduce the use of “data closeness” for building a prediction tool. To do so, we: (1) Generate the workable neighborhood system for every record, Ri, of a training set, (2) build and expand the “record tree” for Ri using its workable neighborhood system, (3)

Extract a neighborly rule from each expanded record tree, and (4) Use the rules for prediction. The empirical results revealed that the predictive power of the neighborly rules is comparable with that of

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