Business Process Modeling Notations Techniques: A Comparative Study Using AHP

The rapid evolution of information systems has triggered drastic changes in business schemes. This phenomenon has led to the rise of Business Process Management. Business Process Management consists of the concepts, methods, techniques and software tools that assist the life cycle of business processes. The implementation of BPM solutions is not an easy task due to the existence of different Business Process Modelling (BPM) techniques. Thus, organizations seek for BPM to make informed decisions about the appropriate technique that fits their needs. In this research, we proposed a new comparison model for selecting the most appropriate Modelling technique using a Multi-Criteria Decision Making Technique, which is Analytical Hierarchy Process (AHP). Precisely, we compare four BPM techniques: BPMN, RAD, IDEF3 and EPC in term of three main criteria which are: Direct Representation, Automation, and Open standards. The results show a ranking list of the selected techniques. According to our analysis, BPMN represents the best technique compared with the designated criteria, followed by Event-driven Process Chain, then RAD and finally IDEF3

Forecasting and Granger Modelling with Non-linear Dynamical Dependencies

Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed of a set of input kernels and a set of output kernels learned in the cone of positive semi-definite matrices. In addition to superior predictive performance in the presence of strong non-linearities, our method also recovers the hidden dynamic relationships between the series and thus is a new alternative to existing graphical Granger techniques.Comment: Accepted for ECML-PKDD 201

Automatic Time Series Forecasting: The forecast Package for R

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.

A Hybrid Method for E-Process Selection

A number of e-Processes (i.e. software processes for developing e-Commerce information systems) are available in industry. It is difficult to select the best suited e-Process for a case at hand. At the same time this selection is important because functionality and quality of any system under development will depend on the instantiated software process. The knowledge required for the selection task cannot be easily realized. That task can be considered as an instance of multi attribute decision making and several of the attributes to consider are likely to conflict with each other. An efficient and effective approach is needed to selecting software processes for developing e-commerce systems. In this paper we propose such an approach. It is hybrid as it rests on case-based reasoning, multi attribute decision making, and social choice methods. To demonstrate how our approach works we briefly discuss a case study

Automatic time series forecasting: the forecast package for R.

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series, R.

Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model

This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model. This selection is a notoriously difficult problem even in linear spatial models and is even more difficult in a quantile regression setup. The proposal is illustrated by an empirical example and manages to produce tractable models. One important feature of the proposed methodology is that by allowing different degrees and forms of spatial dependence across quantiles it further relaxes the usual quantile restriction attributable to the linear quantile regression. In this way we can obtain a more robust, with regard to potential functional misspecification, model, but nevertheless preserve the parametric rate of convergence and the established inferential apparatus associated with the linear quantile regression approach

Permutation based decision making under fuzzy environment using Tabu search

One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method

Survival Models for the Duration of Bid-Ask Spread Deviations

Many commonly used liquidity measures are based on snapshots of the state of the limit order book (LOB) and can thus only provide information about instantaneous liquidity, and not regarding the local liquidity regime. However, trading in the LOB is characterised by many intra-day liquidity shocks, where the LOB generally recovers after a short period of time. In this paper, we capture this dynamic aspect of liquidity using a survival regression framework, where the variable of interest is the duration of the deviations of the spread from a pre-specified level. We explore a large number of model structures using a branch-and-bound subset selection algorithm and illustrate the explanatory performance of our model