A model-driven method for the systematic literature review of qualitative empirical research

This paper explores a model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of previous qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further researc

Value-driven partner search for <i>Energy from Waste</i> projects

Energy from Waste (EfW) projects require complex value chains to operate effectively. To identify business partners, plant operators need to network with organisations whose strategic objectives are aligned with their own. Supplier organisations need to work out where they fit in the value chain. Our aim is to support people in identifying potential business partners, based on their organisation’s interpretation of value. Value for an organisation should reflect its strategy and may be interpreted using key priorities and KPIs (key performance indicators). KPIs may comprise any or all of knowledge, operational, economic, social and convenience indicators. This paper presents an ontology for modelling and prioritising connections within the business environment, and in the process provides means for defining value and mapping these to corresponding KPIs. The ontology is used to guide the design of a visual representation of the environment to aid partner search

Bayesian spectral modeling for multiple time series

We develop a novel Bayesian modeling approach to spectral density estimation for multiple time series. The log-periodogram distribution for each series is modeled as a mixture of Gaussian distributions with frequency-dependent weights and mean functions. The implied model for the log-spectral density is a mixture of linear mean functions with frequency-dependent weights. The mixture weights are built through successive differences of a logit-normal distribution function with frequency-dependent parameters. Building from the construction for a single spectral density, we develop a hierarchical extension for multiple time series. Specifically, we set the mean functions to be common to all spectral densities and make the weights specific to the time series through the parameters of the logit-normal distribution. In addition to accommodating flexible spectral density shapes, a practically important feature of the proposed formulation is that it allows for ready posterior simulation through a Gibbs sampler with closed form full conditional distributions for all model parameters. The modeling approach is illustrated with simulated datasets, and used for spectral analysis of multichannel electroencephalographic recordings (EEGs), which provides a key motivating application for the proposed methodology

A Model-Driven Method for the Systematic Literature Review of Qualitative Empirical Research

This paper explores a new model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further research

Correction. Brownian models of open processing networks: canonical representation of workload

Due to a printing error the above mentioned article [Annals of Applied Probability 10 (2000) 75--103, doi:10.1214/aoap/1019737665] had numerous equations appearing incorrectly in the print version of this paper. The entire article follows as it should have appeared. IMS apologizes to the author and the readers for this error. A recent paper by Harrison and Van Mieghem explained in general mathematical terms how one forms an ``equivalent workload formulation'' of a Brownian network model. Denoting by $Z(t)$ the state vector of the original Brownian network, one has a lower dimensional state descriptor $W(t)=MZ(t)$ in the equivalent workload formulation, where $M$ can be chosen as any basis matrix for a particular linear space. This paper considers Brownian models for a very general class of open processing networks, and in that context develops a more extensive interpretation of the equivalent workload formulation, thus extending earlier work by Laws on alternate routing problems. A linear program called the static planning problem is introduced to articulate the notion of ``heavy traffic'' for a general open network, and the dual of that linear program is used to define a canonical choice of the basis matrix $M$. To be specific, rows of the canonical $M$ are alternative basic optimal solutions of the dual linear program. If the network data satisfy a natural monotonicity condition, the canonical matrix $M$ is shown to be nonnegative, and another natural condition is identified which ensures that $M$ admits a factorization related to the notion of resource pooling.Comment: Published at http://dx.doi.org/10.1214/105051606000000583 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling of IoT devices in Business Processes: A Systematic Mapping Study

[EN] The Internet of Things (IoT) enables to connect the physical world to digital business processes (BP). By using the IoT, a BP can, e.g.: 1) take into account real-world data to take more informed business decisions, and 2) automate and/or improve BP tasks. To achieve these benefits, the integration of IoT and BPs needs to be successful. The first step to this end is to support the modeling of IoT-enhanced BPs. Although numerous researchers have studied this subject, it is unclear what is the current state of the art in terms of current modeling solutions and gaps. In this work, we carry out a Systematic Mapping Study (SMS) to find out how current solutions are modelling IoT into business processes. After studying 600 papers, we identified and analyzed in depth a total of 36 different solutions. In addition, we report on some important issues that should be addressed in the near future, such as, for instance the lack of standardization.This research has been funded by Internal Funds KU Leuven (Interne Fondsen KU Leuven) and the financial support of the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.Torres Bosch, MV.; Serral, E.; Valderas, P.; Pelechano Ferragud, V.; Grefen, P. (2020). Modeling of IoT devices in Business Processes: A Systematic Mapping Study. IEEE. 221-230. https://doi.org/10.1109/CBI49978.2020.00031S22123

Statistical Fourier Analysis: Clarifications and Interpretations

This paper expounds some of the results of Fourier theory that are essential to the statistical analysis of time series. It employs the algebra of circulant matrices to expose the structure of the discrete Fourier transform and to elucidate the filtering operations that may be applied to finite data sequences. An ideal filter with a gain of unity throughout the pass band and a gain of zero throughout the stop band is commonly regarded as incapable of being realised in finite samples. It is shown here that, to the contrary, such a filter can be realised both in the time domain and in the frequency domain. The algebra of circulant matrices is also helpful in revealing the nature of statistical processes that are band limited in the frequency domain. In order to apply the conventional techniques of autoregressive moving-average modelling, the data generated by such processes must be subjected to antialiasing filtering and sub sampling. These techniques are also described. It is argued that band-limited processes are more prevalent in statistical and econometric time series than is commonly recognised.

Heuristic Approaches for Generating Local Process Models through Log Projections

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create so-called local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), special session on Process Mining, part of the Symposium Series on Computational Intelligence (SSCI