Business And Scientific Workflows

Focuses on how to use web service computing and service-based workflow technologies to develop timely, effective workflows for both business and scientific fields Utilizing web computing and Service-Oriented Architecture (SOA), Business and Scientific Workflows: A Web Service Oriented Approach focuses on how to design, analyze, and deploy web service based workflows for both business and scientific applications in many areas of healthcare and biomedicine. It also discusses and presents the recent research and development results. This informative reference features application scenarios that include healthcare and biomedical applications, such as personalized healthcare processing, DNA sequence data processing, and electrocardiogram wave analysis, and presents: Updated research and development results on the composition technologies of web services for ever-sophisticated service requirements from various users and communities Fundamental methods such as Petri nets and social network analysis to advance the theory and applications of workflow design and web service composition Practical and real applications of the developed theory and methods for such platforms as personalized healthcare and Biomedical Informatics Grids The authors' efforts on advancing service composition methods for both business and scientific software systems, with theoretical and empirical contributions With workflow-driven service composition and reuse being a hot topic in both academia and industry, this book is ideal for researchers, engineers, scientists, professionals, and students who work on service computing, software engineering, business and scientific workflow management, the internet, and management information systems (MIS).

This is a timely book presenting an overview of the current state-of-the-art within established projects, presenting many different aspects of workflow from users to tool builders. It provides an overview of active research, from a number of different perspectives. It includes theoretical aspects of workflow and deals with workflow for e-Science as opposed to e-Commerce. The topics covered will be of interest to a wide range of practitioners.

Scientific workflows are one important means in the context of data-intensive science for reliable and efficient scientific data processing in distributed computing infrastructures such as Grids. A common trend is to adapt existing and established business workflow technologies instead of developing own technologies from scratch. This thesis provides a model-driven approach for scientific workflow engineering, in which domain-specific languages (DSLs) tailored for a certain scientific domain are used for scientific workflow modeling, and automated mapping techniques for technical execution are developed and evaluated. The Business Process Model and Notation (BPMN) is thereby used at the domain-specific layer and the Web Services Business Process Execution Language (BPEL) at the technical layer. The implementation uses the Eclipse Modeling Framework (EMf) and is evaluated in three application scenarios.

Creating scientific workflow applications is a very challenging task due to the complexity of the distributed computing environments involved, the complex control and data flow requirements of scientific applications, and the lack of high-level languages and tools support. Particularly, sophisticated expertise in distributed computing is commonly required to determine the software entities to perform computations of workflow tasks, the computers on which workflow tasks are to be executed, the actual execution order of workflow tasks, and the data transfer between them. Qin and Fahringer present a novel workflow language called Abstract Workflow Description Language (AWDL) and the corresponding standards-based, knowledge-enabled tool support, which simplifies the development of scientific workflow applications. AWDL is an XML-based language for describing scientific workflow applications at a high level of abstraction. It is designed in a way that allows users to concentrate on specifying such workflow applications without dealing with either the complexity of distributed computing environments or any specific implementation technology. This research monograph is organized into five parts: overview, programming, optimization, synthesis, and conclusion, and is complemented by an appendix and an extensive reference list. The topics covered in this book will be of interest to both computer science researchers (e.g. in distributed programming, grid computing, or large-scale scientific applications) and domain scientists who need to apply workflow technologies in their work, as well as engineers who want to develop distributed and high-throughput workflow applications, languages and tools.

Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc. Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data. In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.

In this book, the use of the industry standard BPEL, a Turing-complete workflow language for modeling business processes, is proposed for the modeling and the execution of scientific workflows. BPEL is well suited for the modeling of scientific workflows, but existing implementations of the standard lack important properties - such as availability, reliability, and scalability - that are necessary for the execution of scientific workflows. This work presents components that extend an existing implementation of the BPEL standard and eliminate the identified weaknesses. Those components include a provisioner which enables the system to automatically provision virtual machines from Cloud environments. It is steered by a multi-objective genetic scheduling algorithm that takes data flow dependencies and cost into account. A policy-based fault-tolerance component improves reliability and availability of the system by using a variety of mechanisms, including Cloud-based substituion of faulty servers. Lastly, a comprehensive suite of easy-to-use graphical development and modeling tools is presented.

"This book offers insight into practical and methodological issues related to collaborative e-research and furthers readers understanding of current and future trends in online research and the types of technologies involved"--Provided by publisher.

Currently, we see a variety of tools and techniques for specifying and implementing business processes. The problem is that there are still gaps and tensions between the different disciplines needed to improve business process execution and improvement in enterprises. Business process modeling, workflow execution and application programming are examples of disciplines that are hosted by different communities and that emerged separately from each other. In particular, concepts have not yet been fully elaborated at the system analysis level. Therefore, practitioners are faced again and again with similar questions in concrete business process projects: Which decomposition mechanism to use? How to find the correct granularity for business process activities? Which implementing technology is the optimal one in a given situation? This work offers an approach to the systematization of the field. The methodology used is explicitly not a comparative analysis of existing tools and techniques – although a review of existing tools is an essential basis for the considerations in the book. Rather, the book tries to provide a landscape of rationales and concepts in business processes with a discussion of alternatives.