Enterprises today are presented with a plethora of data, tools and analytics techniques, but lack systems which help analysts to navigate these resources and identify best fitting solutions for their analytics problems. To support enterprise-level data analytics research, this paper presents Research Variable Ontology (RVO), an ontology designed to catalogue and explore essential data analytics design elements such as variables, analytics models and available data sources. RVO is specialised to support researchers with exploratory and predictive analytics problems, popularly practiced in economics and social science domains. We present the RVO design process, its schema, how it links and extends existing ontologies to provide a holistic view of analytics related knowledge and how data analysts at the enterprise level can use it. Capabilities of RVO are illustrated through a case study on House Price Prediction.

The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint and focus and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develop a generic process model and then evaluate and refine the model through three case studies and domain expert reviews. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration models that can be applied to given cloud adoption scenarios.

The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.

Internet-based technologies have opened new opportunities for conducting business within and across enterprises that were never possible a few years ago. This paper presents our experience in using Web services for prototyping a service-oriented architecture for capital market systems (CMSs). Our work exposes a world-class surveillance system's functionality into a number of Web services. Our work also includes benchmarking the performance of this legacy system and investigating the associated overheads of using SOAP as a wire format for Web services. Even though other research studies have tried to explain SOAP's performance inefficiency, there is lack of studies that evaluate SOAP in the context of a realistic business application. This initial investigation shows that system's integration opportunities introduced by Web services can outweigh the performance overheads. This occurs in some aspects of real-time CMSs that are not performance-demanding such as the dissemination of market alerts to the analysts.