Annotatie

The purpose of the 7th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2008) and the 2nd IEEE/ACIS International Workshop on e-Activity (IWEA 2008) to be held on May 14 16, 2008 in Portland, Oregon, U.S.A. is to bring together scientists, engineers, computer users, and

Flaptekst

Thepurposeofthe 7thIEEE/ACISInternationalConferenceonComputerandInfor- tion Science (ICIS2008)and the 2nd IEEE/ACISInternationalWorkshop on e-Activity (IWEA 2008) to be held on May 14¿16, 2008 in Portland, Oregon, U.S.A. is to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas and research results about all aspects (theory, applications and tools) of computer and information science; and to discuss the practical challenges - countered along the way and the solutions adopted to solve them. In January, 2008 one of editors of this book approached in house editor Dr. Thomas Ditzingeraboutpreparingavolumecontainingextendedandimprovedversionsofsome of the papers selected for presentation at the conference and workshop. Upon receiving Dr. Ditzinger¿s approval, conference organizers selected 23 outstanding papers from ICIS/IWEA 2008, all of which you will nd in this volume of Springer¿s Studies in Computational Intelligence. In chapter 1, Fabio Perez Marzullo et al. describe a model driven architecture (MDA) approachfor assessing database performance.The authorspresent a pro ling technique that offers a way to assess performance and identify aws, while performing software construction activities. In chapter 2, authorsHuy Nguyen Anh Pham and EvangelosTriantaphyllouoffera new approachfortesting classi cation algorithms,and present thisapproachthroughrean- ysis of the Pima Indian diabetes dataset, one of the most well-known datasets used for this purpose. The new method put forth by the authors is dubbed the Homogeneity- Based Algorithm(HBA), and it aims to optimally control the over ttingand overgen- alization behaviors that have proved problematic for previous classi cation algorithms on this dataset.