A transformation-based approach to business process management in the cloud

Business Process Management (BPM) has gained a lot of popularity in the last two decades, since it allows organizations to manage and optimize their business processes. However, purchasing a BPM system can be an expensive investment for a company, since not only the software itself needs to be purchased, but also hardware is required on which the process engine should run, and personnel need to be hired or allocated for setting up and maintaining the hardware and the software. Cloud computing gives its users the opportunity of using computing resources in a pay-per-use manner, and perceiving these resources as unlimited. Therefore, the application of cloud computing technologies to BPM can be extremely beneficial specially for small and middle-size companies. Nevertheless, the fear of losing or exposing sensitive data by placing these data in the cloud is one of the biggest obstacles to the deployment of cloud-based solutions in organizations nowadays. In this paper we introduce a transformation-based approach that allows companies to control the parts of their business processes that should be allocated to their own premises and to the cloud, to avoid unwanted exposure of confidential data and to profit from the high performance of cloud environments. In our approach, the user annotates activities and data that should be placed in the cloud or on-premise, and an automated transformation generates the process fragments for cloud and on-premise deployment. The paper discusses the challenges of developing the transformation and presents a case study that demonstrates the applicability of the approach

Genetic Programming for QoS-Aware Data-Intensive Web Service Composition and Execution

Web service composition has become a promising technique to build powerful enterprise applications by making use of distributed services with different functions. In the age of big data, more and more web services are created to deal with a large amount of data, which are called data-intensive services. Due to the explosion in the volume of data, providing efficient approaches to composing data-intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality on the Internet, web service composition is usually performed with end-to-end Quality of Service (QoS) properties which are adopted to describe the non-functional properties (e.g., response time, execution cost, reliability, etc.) of a web service. In addition, the executions of composite web services are typically coordinated by a centralized workflow engine. As a result, the centralized execution paradigm suffers from inefficient communication and a single point of failure. This is particularly problematic in the context of data-intensive processes. To that end, more decentralized and flexible execution paradigms are required for the execution of data-intensive applications. From a computational point of view, the problems of QoS-aware data-intensive web service composition and execution can be characterised as complex, large-scale, constrained and multi-objective optimization problems. Therefore, genetic programming (GP) based solutions are presented in this thesis to address the problems. A series of simulation experiments are provided to demonstrate the performance of the proposed approaches, and the empirical observations are also described in this thesis. Firstly, we propose a hybrid approach that integrates the local search procedure of tabu search into the global search process of GP to solving the problem of QoS-aware data-intensive web service composition. A mathematical model is developed for considering the mass data transmission across different component services in a data-intensive service composition. The experimental results show that our proposed approach can provide better performance than the standard GP approach and two traditional optimization methods. Next, a many-objective evolutionary approach is proposed for tackling the QoS-aware data-intensive service composition problem having more than three competing quality objectives. In this approach, the original search space of the problem is reduced before a recently developed many-objective optimization algorithm, NSGA-III, is adopted to solve the many-objective optimization problem. The experimental results demonstrate the effectiveness of our approach, as well as its superiority than existing single-objective and multi-objective approaches. Finally, a GP-based approach to partitioning a composite data-intensive service for decentralized execution is put forth in this thesis. Similar to the first problem, a mathematical model is developed for estimating the communication overhead inside a partition and across the partitions. The data and control dependencies in the original composite web service can be properly preserved in the deployment topology generated by our approach. Compared with two existing heuristic algorithms, the proposed approach exhibits better scalability and it is more suitable for large-scale partitioning problems

Integrating Mobile Tasks with Business Processes: A Self-Healing Approach

Process management technology constitutes a fundamental component of any service-driven computing environment. Process management facilitates both the composition of services at design time and their orchestration at run time. In particular, when applying the service paradigm to enterprise integration management, high flexibility is required. In this context, atomic as well as composite services representing the business functions should be quickly adaptable to cope with dynamic business changes. Furthermore, they should enable mobile and quick access to enterprise information. The growing maturity of smart mobile devices has fostered their prevalence in knowledge-intensive areas in the enterprise as well. As a consequence, process management technology needs to be enhanced with mobile task support. However, tasks hitherto executed stationarily, cannot be simply transferred in order to run on smart mobile devices. Many research groups focus on the partitioning of processes and the distributed execution of the resulting fragments on smart mobile devices. Opposed to this fragmentation concept, this chapter proposes an approach to enable the robust and flexible execution of single process tasks on smart mobile devices by provisioning self-healing techniques to address the smooth integration of mobile tasks with business processes

Partially distributed coordination with Reo and constraint automata

Algorithms and the Foundations of Software technolog

An MDA Approach to Business Process Model Transformations

We present in this work an MDA approach for the definition of transformations for business process models. These transformations are based on the use of two platform independent workflow universal languages –UML 2.0 Activity Diagrams and BPMN– and a platform specific language, the XPDL language. The first two languages are used in the definition of a horizontal transformation, while BPMN and XPDL are used in the definition of a vertical transformation. Although there are several options for a model transformation language, we have adhered to one of the principles of MDA, namely the use of standards, therefore adopting the QVT language, which is the transformation language proposed by the OMG. We also show, in this work, a practical case of an application of the transformations proposed here.Sociedad Argentina de Informática e Investigación Operativ