CrossFlow: Cross-Organizational Workflow Management for Service Outsourcing in Dynamic Virtual Enterprises

In this report, we present the approach to cross-organizational workflow management of the CrossFlow project. CrossFlow is a European research project aiming at the support of cross-organizational workflows in dynamic virtual enterprises. The cooperation in these virtual enterprises is based on dynamic service outsourcing specified in electronic contracts. Service enactment is performed by dynamically linking the workflow management infrastructures of the involved organizations. Extended service enactment support is provided in the form of cross-organizational transaction management and process control, advanced quality of service monitoring, and support for high-level flexibility in service enactment. CrossFlow technology is realized on top of a commercial workflow management platform and applied in two real-world scenarios in the contexts of a logistics and an insurance company

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

FLOWViZ: framework for phylogenetic processing

Final project to obtain the Master Degree in Computer Science and EngineeringO aumento do risco epidemiológico e o constante crescimento da população mundial contribuiu para que se fizesse um forte investimento na análise filogenética, de modo a monitorizar doenças e a conceber tratamentos e medicação rápidos e eficazes. A análise filogenética utiliza grandes quantidades de informação, que deve ser analisada e processada para se extrair conhecimento, utilizando técnicas adequadas e, atualmente, software especializado e algoritmos, de modo a produzir resultados eficazes e rápidos. Estes algoritmos já são fornecidos por um grande conjunto de frameworks e ferramentas disponíveis gratuitamente, um bom exemplo é a framework de inferência filogenética PHYLOViZ[23]. A maioria das técnicas de análise utilizadas na inferência filogenética tendem a formar, topologicamente, pipelines de trabalho - procedimentos constituídos por passos, cujos fluxos de dados são dependentes entre si. Apesar de ser possível executar pipelines de trabalho manualmente, como tem sido feito há várias décadas, atualmente, já não é fazível, dado que os datasets utilizados são volumosos, tornando a sua análise manual contraproducente. A transição manual entre passos necessita também que haja interação humana para que cada passo receba os dados necessários, o que pode também estar sujeito ao erro humano. Por isso, foi construído software que reduzisse a interação humana e que automatizasse estes procedimentos. Este tipo de software é designado por sistemas de workflow - software que permite os utilizadores criarem workflows, através de uma Domain-Specific Language (DSL)[13], onde estes procedimentos são traduzidos para scripts, especificandose o grupo de tarefas, com os seus parâmetros e dependências de dados. Existem atualmente várias soluções de sistemas de workflow, que diferem na sua linguagem e estruturação de workflows, o que leva a que exista uma grande heterogeneidade de software, mas que piora também a partilha destes procedimentos. Por isso, quando se partilham workflows, é necessário despender-se tempo a traduzir pipelines de trabalho para a linguagem específica do sistema de workflow que vai executar a pipeline partilhada. Este problema levou a que fosse criada a Common Workflow Language (CWL)[2] - um novo standard que permite executar workflows entre vários sistemas de workflow. No entanto, nem todos os sistemas suportam este novo standard. Este projeto pretende construir uma framework, recorrendo a um projeto existente - PHYLOViZ e ao seu conjunto de ferramentas de inferência filogenética. Esta framework, permitirá ligar frameworks de inferência filogenética a sistemas de workflow, dando ao utilizador liberdade para construir os seus workflows personalizados, recorrendo à framework e às ferramentas do utilizador, fornecidas remotamente, que poderão ser geridas através de uma interface intuitiva. Tudo isto, fornecerá automatização de workflows e uma análise filogenética mais rápida e eficaz. Este projeto foi financiado, no contexto de uma bolsa de estudo da Fundação para a Ciência e a Tecnologia (FCT) com referência UIDB/50021/2020, no projeto NGPHYLO PTDC/CCI-BIO/29676/2017 e num projeto do IPL - IPL/2021/DIVA_ISEL.The increasing risk of epidemics and a fast-growing world population has contributed to a great investment in phylogenetic analysis, in order to track numerous diseases and conceive effective medication and treatments. Phylogenetic analysis requires large quantities of information to be analyzed and processed for knowledge extraction, using adequate techniques and, nowadays, specific software and algorithms, to deliver results as efficiently and fast as possible. These algorithms and techniques are already provided by a great set of free and available frameworks and tools, such as PHYLOViZ[23]. Most of the applied techniques and algorithms used for phylogenetic inference tend to form work pipelines - procedures formed by steps, which typically have an intrinsic dependency between them. Although it is possible to execute work pipelines manually, as it has been done for decades, nowadays, is not feasible, as genomic datasets are very large, and the respective analysis is time-consuming. The transition between steps also needs human interaction and each step must receive the matching data, correctly, which can introduce human error. Because of this, software were made to ease and reduce manual interaction, so these procedures could be automated. This type of software is typically referred as a workflow system - software which allows users to create workflows, on top of a provided Domain-Specific Language (DSL)[13], where procedures are translated into scripts, through the definition of a group of steps and their specific parameters and dependencies. There are already many software solutions available, which differ in their Domain-Specific Language and workflow structuring, leading to a great software heterogeneity, but also low workflow shareability - as users work on different workflow systems. Thus, when they share workflows with others, time needs to be spent converting and adapting certain workflows to a specific workflow system, so work pipelines can be executed, making workflow sharing a difficult task. This lead to the creation of the Common Workflow Language (CWL)[2] - a new standard which provides a way to execute workflows and work pipelines among diferente workflow systems. However, not every system supports this new standard. This project aims to build a framework on top of an already existing project - PHYLOViZ, which provides a set of state-of-the-art tools for phylogenetic inference. The developed framework, will link phylogenetic inference web frameworks with workflow systems, giving the user freedom to build its workflows, using the provided web framework’s or its remote tools, through a user-friendly web interface. Resulting in workflow automation, task scheduling and a more efficient and faster phylogenetic analysis. The project was supported by funds, under the context of a student grant of Fundação para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020, for a INESCID’s project - NGPHYLO PTDC/CCI-BIO/29676/2017 and a Polytechnic Institute of Lisbon project - IPL/2021/DIVA_ISEL.N/

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

SmartQC: An Extensible DLT-Based Framework for Trusted Data Workflows in Smart Manufacturing

Recent developments in Distributed Ledger Technology (DLT), including Blockchain offer new opportunities in the manufacturing domain, by providing mechanisms to automate trust services (digital identity, trusted interactions, and auditable transactions) and when combined with other advanced digital technologies (e.g. machine learning) can provide a secure backbone for trusted data flows between independent entities. This paper presents an DLT-based architectural pattern and technology solution known as SmartQC that aims to provide an extensible and flexible approach to integrating DLT technology into existing workflows and processes. SmartQC offers an opportunity to make processes more time efficient, reliable, and robust by providing two key features i) data integrity through immutable ledgers and ii) automation of business workflows leveraging smart contracts. The paper will present the system architecture, extensible data model and the application of SmartQC in the context of example smart manufacturing applications.Comment: 33 Pages, 9 Figures, Under Peer Review Proces