By Lazaros Iliadis, Michael Papazoglou, Klaus Pohl
This publication constitutes the completely refereed complaints of 5 foreign workshops held in Thessaloniki, Greece, along with the twenty sixth overseas convention on complex details platforms Engineering, CAiSE 2014, in June 2014.
The 24 complete and 8 brief papers have been conscientiously chosen from sixty three submissions.
The 5 workshops have been the 1st foreign Workshop on complicated chance and records in info platforms (APSIS), the 1st foreign Workshop on Advances in companies layout according to the concept of strength, the second one overseas Workshop on Cognitive elements of knowledge platforms Engineering (COGNISE), the 3rd Workshop on New new release company and enterprise Innovation structures (NGEBIS), and the 4th foreign Workshop on details structures protection Engineering (WISSE).
Read or Download Advanced Information Systems Engineering Workshops: CAiSE 2014 International Workshops, Thessaloniki, Greece, June 16-20, 2014. Proceedings PDF
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Additional resources for Advanced Information Systems Engineering Workshops: CAiSE 2014 International Workshops, Thessaloniki, Greece, June 16-20, 2014. Proceedings
Papazoglou, and K. ): CAiSE 2014 Workshops, LNBIP 178, pp. 16–30, 2014. c Springer International Publishing Switzerland 2014 An Evolutionary Improvement of the Mahalanobis – Taguchi Strategy 17 Feature selection as a procedure can inﬂuence the classiﬁcation process in various ways.  indicate that the choice of features used to represent patterns that are presented to a classiﬁer aﬀects, among other things, the accuracy of the learned classiﬁcation algorithm, the time needed for learning a classiﬁcation function, the number of examples needed for learning and the cost associated with the features.
We applied this approach to several software systems. The evaluation showed that our models could very accurately predict the actual amount of changes in the last 3 revisions of each system; usually with error rates as low as 3% and less. This information can be used to control the time and budget allocated for a project in the near future or to plan regression testing activities. In our own work, we use these stochastic models to conﬁgure a test model generator which generates realistic histories of models; these histories are used to test and evaluate various tools which support model driven development.
Finally, we compute the Mahalanobis distance (MD) values only for the healthy cases using the Gram-Schmidt orthogonalization process. Step 2: Validation of the scale. In the next step, we compute the MD values for the “abnormal” cases (observations that do not belong to the “healthy” group). The values of the variables in these “abnormal” cases are standardized by using the mean values and standard deviations of the corresponding variables from the “healthy” group. The eﬀectiveness of the scale is validated with the use of a simple rule: the MD values for the “abnormal” observations must be higher than those for the “healthy” ones (they must be further from the center of the group of “healthy” cases than any of the “healthy” cases are).