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Big Data has been listed as one of the current and future research frontiers by Gartner. Large-sized companies are already investing on and leveraging big data. Small-sized and medium-sized enterprises (SMEs) can also leverage big data to gain a strategic competitive advantage but are often limited by the lack of adequate financial resources to invest on the technology and manpower. Several big data challenges still exist especially in computer architecture that is CPU-heavy but I/O poor. Cloud computing eliminates the need to maintain expensive computing hardware and software. Cloud computing resources and techniques can be leveraged to address the traditional problems associated with fault tolerance and low performance causing bottlenecks to using big data. SMEs can take advantage of cloud computing techniques to avail the advantages of big data without significant investments in technology and manpower. This paper explores the current trends in the area of big data using cloud resources and how SMEs can take advantage of these technological trends. The results of this study will benefit SMEs in identifying and exploring possible opportunities and also understanding the challenges in leveraging big data.

In the EU-funded project Embed4Auto, we have developed an Integrated Learning Environment (ILE) to support UML/SysML training. The ILE combines independent distance learning with collaborative problem solving and social networking. The learning environment incorporates a set of learning resources on the modeling languages UML2 and SysML, an educational modeling tool supporting class and sequence diagrams and a tool for sharing and discussing solutions to modeling exercises. First evaluations of the ILE have shown promising results.

Organisations wishing to engage in industrial collaborative networks will typically seek some guarantees concerning the reliability of their prospective partners before committing to cooperation. Evaluating reliability can encompass several aspects, but one of the most crucial things to consider from a cooperation perspective is whether the software systems that support the business processes of some collaborator actually behave as expected. For organisations that rely on a service-oriented computing infrastructure, this amounts to checking whether the functionality of the respective services is conformant to a given behavioural specification. Today’s state of the art lacks standardised methods for creating behavioural specifications of Web services, and also lacks tools for automating the process of behavioural conformance checking through testing. This paper presents a concrete method for creating formal specifications of Web service behaviour and utilising them within service registries for automated testing of service implementations in order to verify and certify their conformance.

Reliability and trust are fundamental prerequisites for the establishment of functional relationships among peers in a Collaborative Networked Organisation (CNO), especially in the context of Virtual Enterprises where economic benefits can be directly at stake. This paper presents a novel approach towards effective service discovery and selection that is no longer based on informal, ambiguous and potentially unreliable service descriptions, but on formal specifications that can be used to verify and certify the actual Web service implementations. We propose the use of Stream X-machines (SXMs) as a powerful modelling formalism for constructing the behavioural specification of a Web service, for performing verification through the generation of exhaustive test cases, and for performing validation through animation or model checking during service selection.