Advanced Information Systems Engineering Workshops: CAiSE by Lazaros Iliadis, Michael Papazoglou, Klaus Pohl

By Lazaros Iliadis, Michael Papazoglou, Klaus Pohl

This booklet constitutes the completely refereed lawsuits of 5 foreign workshops held in Thessaloniki, Greece, along with the twenty sixth overseas convention on complicated 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 likelihood and information in info structures (APSIS), the 1st overseas Workshop on Advances in companies layout in accordance with the suggestion of potential, the second one foreign Workshop on Cognitive features of data structures Engineering (COGNISE), the 3rd Workshop on New new release firm and enterprise Innovation platforms (NGEBIS), and the 4th foreign Workshop on details platforms safeguard Engineering (WISSE).

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Additional info for Advanced Information Systems Engineering Workshops: CAiSE 2014 International Workshops, Thessaloniki, Greece, June 16-20, 2014. Proceedings

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As we mentioned in Sec. 2, the order of promising models can be estimated by investigating the ACF and PACF of the data. This will significantly reduce the number of possible candidate models to just a few. In this paper, we deliberately used 99 ARMA models to generally prove our proposed approach, but in practice much fewer models have to be investigated. Threat to Validity: The first threat to validity is the accuracy in capturing the evolution of design models. As discussed in Sec. 2, difference metrics have advantages compared to static software metrics.

Zhou, He, Sun: Traffic predictability based on ARIMA/GARCH model. In: 2nd Conf. gr Abstract. The Mahalanobis - Taguchi (MT) strategy is a statistical methodology combining various mathematical concepts and is used for diagnosis and classification in multidimensional systems. MT is a very efficient method and has been applied to a wide range of disciplines so far. However, its feature selection phase, which uses experimental designs (orthogonal arrays), is susceptible to improvement. In this paper, we propose a methodology that incorporates MT and a Genetic Algorithm (MT-GA), with the latter being used both for optimizing the feature selection step of MT and for determining the most suitable training set.

In the case that we have periodic or seasonal effect, the predictions are usually considered with a length equal to k times (usually k=1,2) of the length of the period. Since our data does not have any periodic effect, again, hold-out sets with length of 3 are justifiable. e. how our results can be generalized. Our sample set of open source Java projects are quite diverse with respect to application domains, number of revisions, number of elements etc. The process described in this paper is independent of a specific programming language, though.

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