With the rise of Big Data, a data-driven approach to business is transforming enterprises. Companies today are thinking about and using data in a myriad new ways to drive business value; from reducing risk and fraud in the financial sector to bringing new pharmaceuticals to market more quickly at a higher level of efficacy. Retailers can track purchase patterns and consumer preferences more accurately to guide product and marketing strategies. Media companies can offer more accurate recommendations and create specialized promotions. Businesses of all kinds can identify new revenue opportunities and operational efficiencies. Big Data can mean different things to different organizations, but one theme remains constant: Big Data calls for a new way of thinking and combining data analytics with business process workflows. Until now businesses were limited to utilizing customer and business information contained within in-house systems. Now they are increasingly analyzing external data too, gaining new insights into customers, markets, supply chains and operations. Organisational silos and a dearth of data specialists are the main obstacles to putting big data to work effectively for decision-making. Big data analytics need to be combined with business processes to improve operations and offer innovative services to customers. Business processes need to be reengineered for big data analytics. In this paper we discuss how the combination of Big Data analytics with business process using reengineering can deliver the benefits to organizations and customers.

Serious games are beneficial for education in various computer science areas. Numerous works have reported the experiences of using games (not only playing but also development) in teaching and learning. Considering it could be difficult for teachers/students to prepare/develop a game from scratch during one semester, assistant educational materials would be crucial in the corresponding courses. Unfortunately, the literature shows that not many materials from educational game projects are shared. To help different educators identify suitable courseware and help students implement game development, it is worth further investigating and accumulating the educational resources from individual game projects. Following such an idea, this paper proposes a game development project of an object-oriented Sokoban solver, and exposes relevant educational materials. The documented system design can be viewed as a ready-to-use resource for education in object-oriented analysis and design (OOAD), while the Sokoban solver itself may be used as an assignment platform for teaching artificial intelligence (AI). Further documentation, platform, and APIs will be realized and shared in the future to facilitate others' educational activities. Overall, this work is supposed to inspire and encourage other researchers and educators to post available materials of more game projects for the purpose of sharing and reuse.

Cloud Computing has been increasingly accepted as a promising computing paradigm in industry, with one of the most common delivery models being Infrastructure as a Service (IaaS). An increasing number of providers have started to supply public IaaS services with different terminologies, definitions, and goals. As such, understanding the full scope of performance evaluation of candidate services would be crucial and beneficial for both service customers (e.g., cost-benefit analysis) and providers (e.g., direction of improvement). Given the numerous and diverse IaaS service features to be evaluated, a natural strategy is to implement different types of evaluation experiments separately. Unfortunately, it could be hard to fairly distinguish between different experimental types due to different environments and techniques that may be adopted by different evaluators. To overcome such obstacles, we have first established a novel taxonomy to help profile and clarify the nature of IaaS services performance evaluation and then built a three-layer conceptual model to generalize the existing performance evaluation practices. Using relevant elements/classifiers in the taxonomy and conceptual model, evaluators can construct natural language-style descriptions and experimental design blueprints to outline the evaluation scope and also to guide new evaluation implementations. In essence, the generated descriptions and blueprints abstractly define and characterize the actual evaluation work. This enables relatively fair and rational comparisons between different performance evaluations according to their abstract characteristics.

Benchmark suites are significant for evaluating various aspects of Cloud services from a holistic view. However, there is still a gap between using benchmark suites and achieving holistic impression of the evaluated Cloud services. Most Cloud service evaluation work intended to report individual benchmarking results without delivering summary measures. As a result, it could be still hard for customers with such evaluation reports to understand an evaluated Cloud service from a global perspective. Inspired by the boosting approaches to machine learning, we proposed the concept Boosting Metrics to represent all the potential approaches that are able to integrate a suite of benchmarking results. This paper introduces two types of preliminary boosting metrics, and demonstrates how the boosting metrics can be used to supplement primary measures of individual Cloud service features. In particular, boosting metrics can play a summary Response role in applying experimental design to Cloud services evaluation. Although the concept Boosting Metrics was refined based on our work in the Cloud Computing domain, we believe it can be easily adapted to the evaluation work of other computing paradigms.

Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to support high-performance and computationally intensive tasks, while little evaluation studies can be found to reveal GCE's scientific capabilities. Considering that fundamental performance benchmarking is the strategy of early-stage evaluation of new Cloud services, we followed the Cloud Evaluation Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon EC2, to help understand the elementary capability of GCE for dealing with scientific problems. The experimental results and analyses show both potential advantages of, and possible threats to applying GCE to scientific computing. For example, compared to Amazon's EC2 service, GCE may better suit applications that require frequent disk operations, while it may not be ready yet for single VM-based parallel computing. Following the same evaluation methodology, different evaluators can replicate and/or supplement this fundamental evaluation of GCE. Based on the fundamental evaluation results, suitable GCE environments can be further established for case studies of solving real science problems.

Given an increasing number of Cloud services available in the market, evaluating candidate Cloud services is crucial and beneficial for both service customers (e.g. cost benefit analysis) and providers (e.g. direction of improvement). When it comes to performing any evaluation, a suitable methodology is inevitably required to direct experimental implementations. Nevertheless, there is still a lack of a sound methodology to guide the evaluation of Cloud services. By borrowing the lessons from evaluation of traditional computing systems, referring to the guidelines for Design of Experiments (DOE), and summarizing the existing experiences of real experimental studies, we proposed a generic Cloud Evaluation Experiment Methodology (CEEM) for Cloud services evaluation. Furthermore, we have established a pre-experimental knowledge base and specified corresponding suggestions to make this methodology more practical in the Cloud Computing domain. Through evaluating the Google AppEngine Python runtime as a preliminary validation, we show that Cloud evaluators may achieve more rational and convincing experimental results and conclusions following such an evaluation methodology.

Cloud Computing, as one of the most promising computing paradigms, has become increasingly accepted in industry. Numerous commercial providers have started to supply public Cloud services, and corresponding performance evaluation is then inevitably required for Cloud provider selection or cost-benefit analysis. Unfortunately, inaccurate and confusing evaluation implementations can be often seen in the context of commercial Cloud Computing, which could severely interfere and spoil evaluation-related comprehension and communication. This paper introduces a taxonomy to help profile and standardize the details of performance evaluation of commercial Cloud services. Through a systematic literature review, we constructed the taxonomy along two dimensions by arranging the atomic elements of Cloud-related performance evaluation. As such, this proposed taxonomy can be employed both to analyze existing evaluation practices through decomposition into elements and to design new experiments through composing elements for evaluating performance of commercial Cloud services. Moreover, through smooth expansion, we can continually adapt this taxonomy to the more general area of evaluation of Cloud Computing.

Given the diversity of commercial Cloud services, performance evaluations of candidate services would be crucial and beneficial for both service customers (e.g. cost-benefit analysis) and providers (e.g. direction of service improvement). Before an evaluation implementation, the selection of suitable factors (also called parameters or variables) plays a prerequisite role in designing evaluation experiments. However, there seems a lack of systematic approaches to factor selection for Cloud services performance evaluation. In other words, evaluators randomly and intuitively concerned experimental factors in most of the existing evaluation studies. Based on our previous taxonomy and modeling work, this paper proposes a factor framework for experimental design for performance evaluation of commercial Cloud services. This framework capsules the state-of-the-practice of performance evaluation factors that people currently take into account in the Cloud Computing domain, and in turn can help facilitate designing new experiments for evaluating Cloud services.

With more and more availability of services, Web service composition (WSC) based SOA implementations have increasingly become a significant type of SOA projects in practice. However, effort estimation for such a type of SOA project can still be limited because of the numerous and various approaches to WSC. Through viewing WSC based SOA system from a perspective of mechanistic organization, this paper borrows Divide-and-Conquer (D&C) as the generic strategy to narrow down the problem of effort judgment for the entire SOA implementation to that for individual WSCs. Moreover, benefiting from an effort-oriented classification matrix and a set of effort-related hypotheses, we assign scores to effort factors of WSC assisted by a set of rules. These effort scores are used to facilitate qualitatively judging different effort between different types of WSC approaches, and eventually construct an effort checklist for WSC approaches. Finally, this effort checklist can be used together with D&C algorithm to realize the qualitative effort judgment for WSC based SOA implementations.

Many organizations are introducing Service Oriented Architecture (SOA) as part of their business transformation projects to take advantage of the proposed benefits associated with using SOA. However, in many cases organizations don't necessarily know on which projects introducing SOA would be of value and show real benefits to the organization. In this paper we outline a method and pilot metrics framework (PMF) to help organization's select from a set of candidate projects those which would be most suitable for piloting SOA. The PMF is used as part of a method based on identifying a set of benefit and risk criteria, investigating each of the candidate projects, mapping them to the criteria and then selecting the most suitable project(s). The paper outlines a case study where the PMF was applied in a large government organization to help them select pilot projects and develop an overall strategy for introducing SOA into their organization.

Product Derivation is one of the central activities in Software Product Lines (SPL). One of the main challenges of the process of product derivation is dealing with complexity, which is caused by the large number of artifacts and dependencies between them. Another major challenge is maximizing development efficiency and reducing time-to-market, while at the same time producing high quality products. One approach to overcome these challenges is to automate the derivation process. To this end, this paper focuses on one particular activity of the derivation process; the derivation of the product-specific architecture and describes how this activity can be automated using a model-driven approach. The approach derives the product-specific architecture by selectively copying elements from the product-line architecture. The decision, which elements are included in the derived architecture, is based on a product-specific feature configuration. We present a prototype that implements the derivation as a model transformation described in the Atlas Transformation Language (ATL). We conclude with a short overview of related work and directions for future research

The migration to Web services has the potential of providing significant value to keeping legacy systems and components operational in a networked environment, a number of critical issues still need to be addressed. These issues can be classified in three categories; a) program comprehension and analysis issues; b) legacy system migration issues and; c) technology and standards issues. This paper discusses and debates approaches for addressing these issues. The paper's objective is twofold. First, is to explore the current state of the art in methods, tools and technologies that can assist in carrying out the required work and to identify a taxonomy of current approaches as well as, their strengths and weaknesses. The second objective is to identify open issues and research opportunities in the area of migrating legacy systems in Web enabled environments

Today, software intensive systems are developed more and more using product line approaches. These approaches require the definition of a set of product line artifacts including the product line architecture that implicitly or explicitly specifies some degree of variability. This variability is used to instantiate concrete software product instances. A product line approach not only implies reuse of architecture-level design knowledge, it also facilitates reuse of implementation-level artefacts, such as source code and executable components. The use of software product lines can reduce the cost of developing new products significantly.

Much progress has been achieved in defining methods, techniques, and tools for software architecture reconstruction (SAR). However, less progress has been achieved in constructing reasoning frameworks from existing systems that support organizations in architecture analysis and design decisions. These reasoning frameworks are necessary, for example, to assemble existing components and deploy them in new system configurations. We propose a model-centric approach where this kind of reasoning is driven by the analysis of quality attribute scenarios. The scenarios and the related quality attribute models guide the SAR effort by focusing on the elicitation of model relevant artifacts. The approach further drives the model construction towards the analytical support of What If scenarios that explore responses stimulated by new requirements, such as new deployments of existing components. The paper provides two real-world case studies. The first case study introduces the model-centric reconstruction approach in the context of a large satellite tracking system. The second case study provides the construction of a time performance model for an existing embedded system in the automotive industry. The model allows us to perform cost-efficient predictions of component assemblies in new customer configurations. Copyright © 2005 John Wiley & Sons, Ltd.

Product lines evolve out of existing products. In order to evaluate the potential of creating a product line from existing products, it is necessary to 'mine' their architectures and analyze the commonalities and variabilities across those architectures. To manage the evaluation process in a disciplined way the paper introduces the MAP (Mining Architectures for Product Lines) method. MAP outlines a bottom-up approach for mining the architecture of the existing products, a top-down approach to mapping architectural styles and attributes onto the mined architectures and an approach to analyzing their commonalities and variabilities. It combines well-known architecture reconstruction and product line analysis techniques. A case study is presented showing the application of the method and its benefits are outlined.