Integration of BPM systems

New technologies have emerged to support the global economy where for instance suppliers, manufactures and retailers are working together in order to minimise the cost and maximise efficiency. One of the technologies that has become a buzz word for many businesses is business process management or BPM. A business process comprises activities and tasks, the resources required to perform each task, and the business rules linking these activities and tasks. The tasks may be performed by human and/or machine actors. Workflow provides a way of describing the order of execution and the dependent relationships between the constituting activities of short or long running processes. Workflow allows businesses to capture not only the information but also the processes that transform the information - the process asset (Koulopoulos, T. M., 1995). Applications which involve automated, human-centric and collaborative processes across organisations are inherently different from one organisation to another. Even within the same organisation but over time, applications are adapted as ongoing change to the business processes is seen as the norm in today’s dynamic business environment. The major difference lies in the specifics of business processes which are changing rapidly in order to match the way in which businesses operate. In this chapter we introduce and discuss Business Process Management (BPM) with a focus on the integration of heterogeneous BPM systems across multiple organisations. We identify the problems and the main challenges not only with regards to technologies but also in the social and cultural context. We also discuss the issues that have arisen in our bid to find the solutions

A comprehensive evaluation of module detection methods for gene expression data

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods

Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept

Whole genome sequencing is increasingly recognized as the most informative approach for characterization of bacterial isolates. Success of the routine use of this technology in public health laboratories depends on the availability of well-characterized and verified data analysis methods. However, multiple subtyping workflows are now often being used for a single organism, and differences between them are not always well described. Moreover, methodologies for comparison of subtyping workflows, and assessment of their performance are only beginning to emerge. Current work focuses on the detailed comparison of WGS-based subtyping workflows and evaluation of their suitability for the organism and the research context in question. We evaluated the performance of pipelines used for subtyping of Neisseria meningitidis, including the currently widely applied cgMLST approach and different SNP-based methods. In addition, the impact of the use of different tools for detection and filtering of recombinant regions and of different reference genomes were tested. Our benchmarking analysis included both assessment of technical performance of the pipelines and functional comparison of the generated genetic distance matrices and phylogenetic trees. It was carried out using replicate sequencing datasets of high- and low-coverage, consisting mainly of isolates belonging to the clonal complex 269. We demonstrated that cgMLST and some of the SNP-based subtyping workflows showed very good performance characteristics and highly similar genetic distance matrices and phylogenetic trees with isolates belonging to the same clonal complex. However, only two of the tested workflows demonstrated reproducible results for a group of more closely related isolates. Additionally, results of the SNP-based subtyping workflows were to some level dependent on the reference genome used. Interestingly, the use of recombination-filtering software generally reduced the similarity between the gene-by-gene and SNP-based methodologies for subtyping of N. meningitidis. Our study, where N. meningitidis was taken as an example, clearly highlights the need for more benchmarking comparative studies to eventually contribute to a justified use of a specific WGS data analysis workflow within an international public health laboratory context