Integrated Modelling of Business Process Models and Business Rules: A Research Agenda

Process models are the basis for a wide range of critical activities within an organisation. It is not surprising then that process models, and the act of process modelling, have been the focus of much research over the last two decades. Recent research indicates, however, that common process modelling notations lack sufficient representation for capturing business rules. Although the need for business processes and business rules to be modelled in an integrated manner is well established, the body of knowledge on integrated modelling of the two is limited. In this paper our aim is to review the state of related research and develop a research agenda, based on a systematic review of related literature, to advance research in this field. We present a consolidated view of the benefits of rule and process model integration, together with an overview of current related approaches, and a research agenda going forward

Knowledge based recursive non-linear partial least squares (RNPLS)

Producción CientíficaSoft sensors driven by data are very common in industrial plants to perform indirect measurements of difficult to measure critical variables by using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the colinearity of the predictor variables, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed in many applications to model the linear relations between process variables, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and time-varying characteristics of the processes. In this work, a new knowledge-based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is set up by carrying out the PLS regression over the augmented input matrix, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the system’s nature, and takes into account the available knowledge about the process, which is provided by expert knowledge or emulated using software tools. Then, the recursive exponential weighted PLS is used to modify and adapt the model according to the process changes. This RNPLS algorithm has been tested using two case studies according to the available knowledge, a real industrial evaporation station of the sugar industry, where the expert knowledge about the process permits the formulation of the relationships, and a simulated wastewater treatment plant, where the necessary knowledge about the process is obtained by a software tool. The results show that the methodology involving knowledge regarding the process is able to adjust the process changes, providing highly accurate predictions.Este trabajo forma parte del proyecto de investigación: MINECO/FEDER: DPI2015-67341-C2-2-R

Estimating hidden semi-Markov chains from discrete sequences.

International audienceThis article addresses the estimation of hidden semi-Markov chains from nonstationary discrete sequences. Hidden semi-Markov chains are particularly useful to model the succession of homogeneous zones or segments along sequences. A discrete hidden semi-Markov chain is composed of a nonobservable state process, which is a semi-Markov chain, and a discrete output process. Hidden semi-Markov chains generalize hidden Markov chains and enable the modeling of various durational structures. From an algorithmic point of view, a new forward-backward algorithm is proposed whose complexity is similar to that of the Viterbi algorithm in terms of sequence length (quadratic in the worst case in time and linear in space). This opens the way to the maximum likelihood estimation of hidden semi-Markov chains from long sequences. This statistical modeling approach is illustrated by the analysis of branching and flowering patterns in plants

Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR(∞) Models *

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101814/1/1468-2354.t01-1-00016.pd

Estimation of a Dynamic Panel Data: The Case Of Corporate Investment in Chile

In this paper I discuss about the estimation of Dynamic Panel Data model, showing that we can reduce the finite-sample bias of the Arellano-Bond estimator by truncation of the number of lags used in this estimator. We check our theoretical result in an empirical application using a panel of Chilean firms.

Prediction of Term Structure with Potentially Misspecified Macro-Finance Models near the Zero Lower Bound

In this paper, we study the forecasting performances of the affine term structure model (ATSM) and the quadratic term structure model (QTSM) with macro-finance features under the zero interest rate policy of Japan. As both the two models can be potentially misspecified, we adopt the pptimal pooling prediction scheme following the recent work by Geweke and Amisano (2011). We find that the QTSM provides a more realistic statistical description when bond yields are close to the zero lower bound. The ATSM gives a good fit to the macroeconomic variables and bond yields simultaneously, however, it predicts a large probability of negative interest rates and hence is not appropriate for the forecasting of bond yields. The Markov-switching prediction pool dominates individual models as well as the static and dynamic pools. Our results suggest that both of the ATSM and QTSM macro-finance models are potentially misspecified and one should use a combination of the two models for the prediction of future bond yields during different time periods. Our analysis sheds light on the macro-finance modeling using US data amid the Federal Reserve’s zero interest rate policy since December 2008

Fiscal News, Uncertainty, and the Business Cycle

The recent "Great Recession" has thrown macroeconomic research into a state of disarray and has clearly shown the need to go beyond traditional business cycle explanations. However, many of the recently proposed business cycle explanations rely on factors that are not directly observed by the econometrician. One promising way to deal with this issue of unobserved state variables has been the use of structural estimation. The present work contributes to the literature on non-traditional business cycle explanations by using structural macroeconomic modeling and structural estimation to analyze the role of fiscal news (Chapter 1), policy risk (Chapter 2), and terms of trade uncertainty (Chapter 3) for explaining macroeconomic fluctuations. Chapter 1 investigates the role of news about fiscal policy, and in particular the anticipation of tax rate changes, for macroeconomic fluctuations in the United States. To deal with the problem that news shocks are not observed by the econometrician, we resort to structural estimation of a New Keynesian DSGE model. We find that while fiscal policy accounts for 12 to 20 percent of output variance at business cycle frequencies, the anticipated components hardly matter for explaining fluctuations of real variables. In contrast, anticipated capital tax shocks do explain a sizable part of inflation and nominal interest rate fluctuations, accounting for 5 to 15 percent of their total variance. Consistent with earlier studies, we find that news shocks account for 20 percent of output variance, driven by news about stationary TFP and non-stationary investment-specific technology. Chapter 2 analyzes the role of policy risk in explaining business cycle fluctuations by using an estimated New Keynesian model featuring policy risk as well as uncertainty about technology. To deal with the unobserved state "uncertainty", we directly measure uncertainty from aggregate time series by structurally estimating a stochastic volatility model using Sequential Monte Carlo Methods. While we find considerable evidence of policy risk in the data, we show that the "pure uncertainty"-effect of policy risk is unlikely to play a major role in business cycle fluctuations. In the estimated model, output effects are relatively small due to i) dampening general equilibrium effects that imply a low amplification and ii) counteracting partial effects of uncertainty. Chapter 3 analyzes the effects of terms of trade uncertainty on Chilean business cycles through the lens of a small open economy DSGE model. My findings are fourfold. First, there is considerable evidence for time-varying terms of trade uncertainty in the Chilean data, with the variance of terms of trade shocks more than doubling in a short period of time. Second, I show that the ex-ante and ex-post effects of increased terms of trade uncertainty can account for about one fifth of Chilean output fluctuations at business cycle frequencies. Third, I find that a two-standard deviation terms of trade risk shock, i.e. a 54 percent increase in uncertainty, leads to a 0.1 percent drop in output. The fact that terms of trade uncertainty more than doubled during the recent commodities boom suggests that the contribution of terms of trade risk during this more recent period may have been substantial. Finally, I show that the economic mechanisms that attenuated the negative output effects of uncertainty in Chapter 2 also dampen the negative impact of terms of trade uncertainty

BPMN – A Logical Model and Property Analysis

Business Process Modeling Notation has become a powerful and widely accepted visual language for modeling business processes. Despite its expressive power and high usability, a weak point of BPMN is the lack of formal semantics and difficulties with assuring correctness of the overall process. In this paper an attempt is made towards investigation and development of foundations for a logical, declarative model for BPMN. Such model should enable formal analysis of desired properties referring to correct operation of Business Processes modeled with use of BPMN