Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Technology Scouting – a case study on the Deutsche Telekom Laboratories

Technology Intelligence has become an important field of study in which a variety of different methods are discussed, all aiming at identifying opportunities and threats arising from advances in technology. In this respect, Technology Scouting is a method which can lower the time lag between the advances in technology and their detection by methods such as patent or publication analysis. Furthermore, in an environment of increasing technological complexity and the globalization of R&D, the successful identification and usage of external sources of knowledge is becoming increasingly important. In the sourcing of technology the scouts can also play an important role in identifying valuable sources and facilitate the sourcing. Based on two case studies of the Deutsche Telekom and British Telecom, as well as a literature review, the paper proposes a definition of Technology Scouting, a generic process, and identifies the motivations of the actors in the process.technology intelligence, technological forecasting, technology foresight; technology scouting; technology exploration; strategic foresight; technology monitoring; technology scanning

A Survey on Economic-driven Evaluations of Information Technology

The economic-driven evaluation of information technology (IT) has become an important instrument in the management of IT projects. Numerous approaches have been developed to quantify the costs of an IT investment and its assumed profit, to evaluate its impact on business process performance, and to analyze the role of IT regarding the achievement of enterprise objectives. This paper discusses approaches for evaluating IT from an economic-driven perspective. Our comparison is based on a framework distinguishing between classification criteria and evaluation criteria. The former allow for the categorization of evaluation approaches based on their similarities and differences. The latter, by contrast, represent attributes that allow to evaluate the discussed approaches. Finally, we give an example of a typical economic-driven IT evaluation

The Impact of Disruptive Technologies in Finance and Accounting: A Systematic Literature Review

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe digital transition era, marked by a strong evolution of Information Technologies, and its massive expansion towards all products, services, and sectors, has changed all known methods for carrying out and conducting all sorts of professional practices. Within the scope of accounting activities and transactions related to accounting, various tasks have started to be automatized with the help of Artificial Intelligence and Machine Learning. Hence, no longer existing the need of spending time on some of the repetitive day-to-day tasks, professionals in these areas will have more time and freedom to perform predictive business analysis, to collect and report financial data, which will most likely become vital to assist decision-making and possible attraction of new investments. As such, there is a clear link between accounting and the emergence of disruptive technologies, which indicates an interesting research area for accounting information systems researchers. What is the impact of disruptive technologies in accounting practices? What is the role played by accountants to work alongside their digital colleagues? What are the skills that accountants may have to be future proof in an ever-changing digital environment? This dissertation aims to answer these questions by following a qualitative and exploratory approach, through a systematic literature review. The analysis reveals that the impact of disruptive technologies in finance and accounting can be summarized in four main domains, Strategic Management, Technology Innovation, Business Acumen and Operations and Accounting Provision. We review the content of recent academic literature regarding the relationship between disruptive technologies and accounting and highlight research gaps and opportunities for future research