Managing Process Model Complexity - Part I: Concrete Syntax

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with process model complexity in order to improve the understanding of a process model by stakeholders and process analysts. Features for dealing with this complexity can be classified in two categories: 1) those that are solely concerned with the appearance of the model, and 2) those that in essence change the structure of the model. In this paper we focus on the former category and present a collection of patterns that generalize and conceptualize various existing features. The paper concludes with a detailed analysis of the degree of support of a number of state-of-the-art languages and language implementations for these patterns.

Managing process model complexity via abstract syntax modifications

As a result of the growing adoption of Business Process Management (BPM) technology different stakeholders need to understand and agree upon the process models that are used to configure BPM systems. However, BPM users have problems dealing with the complexity of such models. Therefore, the challenge is to improve the comprehension of process models. While a substantial amount of literature is devoted to this topic, there is no overview of the various mechanisms that exist to deal with managing complexity in (large) process models. It is thus hard to obtain comparative insight into the degree of support offered for various complexity reducing mechanisms by state-of-the-art languages and tools. This paper focuses on complexity reduction mechanisms that affect the abstract syntax of a process model, i.e. the structure of a process model. These mechanisms are captured as patterns, so that they can be described in their most general form and in a language- and tool-independent manner. The paper concludes with a comparative overview of the degree of support for these patterns offered by state-of-the-art languages and language implementations.

Managing process model complexity via concrete syntax modifications

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: 1) those that are solely concerned with the visual representation of the model and 2) those that change its inner structure. While significant attention is paid to the latter category in the BPM literature, this paper focuses on the former category. It presents a collection of patterns that generalize and conceptualize various existing mechanisms to change the visual representation of a process model. Next, it provides a detailed analysis of the degree of support for these patterns in a number of state-of-the-art languages and tools. This paper concludes with the results of a usability evaluation of the patterns conducted with BPM practitioners.

Does increased hydrochemical model complexity decrease robustness?

The aim of this study was, within a sensitivity analysis framework, to determine if additional model complexity gives a better capability to model the hydrology and nitrogen dynamics of a small Mediterranean forested catchment or if the additional parameters cause over-fitting. Three nitrogen-models of varying hydrological complexity were considered. For each model, general sensitivity analysis (GSA) and Generalized Likelihood Uncertainty Estimation (GLUE) were applied, each based on 100,000 Monte Carlo simulations. The results highlighted the most complex structure as the most appropriate, providing the best representation of the non-linear patterns observed in the flow and streamwater nitrate concentrations between 1999 and 2002. Its 5% and 95% GLUE bounds, obtained considering a multi-objective approach, provide the narrowest band for streamwater nitrogen, which suggests increased model robustness, though all models exhibit periods of inconsistent good and poor fits between simulated outcomes and observed data. The results confirm the importance of the riparian zone in controlling the short-term (daily) streamwater nitrogen dynamics in this catchment but not the overall flux of nitrogen from the catchment. It was also shown that as the complexity of a hydrological model increases over-parameterisation occurs, but the converse is true for a water quality model where additional process representation leads to additional acceptable model simulations. Water quality data help constrain the hydrological representation in process-based models. Increased complexity was justifiable for modelling river-system hydrochemistry. Increased complexity was justifiable for modelling river-system hydrochemistry.

Model complexity affects transient population dynamics following a dispersal event: A case study with pea aphids

Stage-structured population models predict transient population dynamics if the population deviates from the stable stage distribution. Ecologists’ interest in transient dynamics is growing because populations regularly deviate from the stable stage distribution, which can lead to transient dynamics that differ significantly from the stable stage dynamics. Because the structure of a population matrix (i.e., the number of life-history stages) can influence the predicted scale of the deviation, we explored the effect of matrix size on predicted transient dynamics and the resulting amplification of population size. First, we experimentally measured the transition rates between the different life-history stages and the adult fecundity and survival of the aphid, Acythosiphon pisum. Second, we used these data to parameterize models with different numbers of stages. Third, we compared model predictions with empirically measured transient population growth following the introduction of a single adult aphid. We find that the models with the largest number of life-history stages predicted the largest transient population growth rates, but in all models there was a considerable discrepancy between predicted and empirically measured transient peaks and a dramatic underestimation of final population sizes. For instance, the mean population size after 20 days was 2394 aphids compared to the highest predicted population size of 531 aphids; the predicted asymptotic growth rate (λmax) was consistent with the experiments. Possible explanations for this discrepancy are discussed. Includes 4 supplemental files.

Irish Credit Unions: Differential Regulation based on Business Model Complexity

This study examines the business model complexity of Irish credit unions using a latent class approach to measure structural performance over the period 2002 to 2013. The latent class approach allows the endogenous identification of a multi-class framework for business models based on credit union specific characteristics. The analysis finds a three class system to be appropriate with the multi-class model dependent on three financial viability characteristics. This finding is consistent with the deliberations of the Irish Commission on Credit Unions (2012) which identified complexity and diversity in the business models of Irish credit unions and recommended that such complexity and diversity could not be accommodated within a one size fits all regulatory framework. The analysis also highlights that two of the classes are subject to diseconomies of scale. This may suggest credit unions would benefit from a reduction in scale or perhaps that there is an imbalance in the present change process. Finally, relative performance differences are identified for each class in terms of technical efficiency. This suggests that there is an opportunity for credit unions to improve their performance by using within-class best practice or alternatively by switching to another class.

NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

Background: Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. Results: We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. Conclusions: NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems. The source code for NETGEM is available from https://github.com/vjethava/NETGEM

(Semi-)Predictive Discretization During Model Selection

In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also independent of the metric used in the continuous space. Our experiments with gene expression data show that discretization plays a crucial role regarding the resulting network structure.