Advanced computing on cloud computing infrastructures can only become viable alternative for the enterprise if these infrastructures can provide proper levels of nonfunctional properties (NPFs). A company that focuses on service-oriented architectures (SOA) needs to know what configuration would provide the proper levels for individual services if they are deployed in the cloud. In this paper we present an approach for performance evaluation of cloud computing configurations. While cloud computing providers assure certain service levels, this it typically done for the platform and not for a particular service instance. Our approach focuses on NFPs of individual services and thereby provides a more relevant and granular information. An experimental evaluation in Amazon Elastic Compute Cloud (EC2) verified our approach.

Purpose: – This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today ' s business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big-Data-based approaches that aim to gain visibility into process performance. Design/methodology/approach: – Authors present a cloud-based solution that leverages (BD) technology to provide essential insights into business process improvement. The proposed solution is aimed at measuring and improving overall business performance, especially in very large and complex cross-organisational business processes, where this type of visibility is hard to achieve across heterogeneous systems. Findings: – Three different (BD) approaches have been undertaken based on Hadoop and HBase. We introduced first, a map-reduce approach that it is suitable for batch processing and presents a very high scalability. Secondly, we have described an alternative solution by integrating the proposed system with Impala. This approach has significant improvements in respect with map reduce as it is focused on performing real-time queries over HBase. Finally, the use of secondary indexes has been also proposed with the aim of enabling immediate access to event instances for correlation in detriment of high duplication storage and synchronization issues. This approach has produced remarkable results in two real functional environments presented in the paper. Originality/value: – The value of the contribution relies on the comparison and integration of software packages towards an integrated solution that is aimed to be adopted by industry. Apart from that, in this paper, authors illustrate the deployment of the architecture in two different settings.

Purpose: The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach: This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings: A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications: Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value: The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.

Blockchain technology has the potential to drive innovations across various industries, businesses, and use cases. It is broadly recognized that innovation is a vital source of competitive advantage in a rapidly changing environment. High expectations surround blockchain’s potential for contributing to sustainable economic and social development. However, current blockchain projects still show high failure rates. The associated side effects of such failure rates generate a negative impact on economic and social sustainability performances such as corporate governance, risk management, finance management, human resources and culture management, and competitiveness. This paper assesses adoption models and their risk and success factors. Building on a novel, integrated adoption model to operationalize, measure and manage blockchain-driven business innovation in a sustainable way, we assessed its applicability with an empirical study across 20 industry sectors and 125 business leaders. The results reveal that the developed adoption model holds the potential to support the sustainable usage of blockchain technology for business innovations, not limited to a specific industry or use case. Further case studies and industry activities can be carried out to continue its validation in future works.