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

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Taming the snake in paradise: combining institutional design and leadership to enhance collaborative innovation

The growing expectations to public services and the pervasiveness of wicked problems in times characterized by growing fiscal constraints call for the enhancement of public innovation, and new research suggests that multi-actor collaboration in networks and partnerships is superior to hierarchical and market-based strategies when it comes to spurring such innovation. Collaborative innovation seems ideal as it builds on diversity to generate innovative public value outcomes, but there is a catch since diversity may clash with the need for constructing a common ground that allows participating actors to agree on a joint and innovative solution. The challenge for collaborative innovation – taming the snake in paradise – is to nurture the diversity of views, ideas and forms of knowledge while still establishing a common ground for joint learning. While we know a great deal about the dynamics of the mutually supportive processes of collaboration, learning and innovation, we have yet to understand the role of institutional design and leadership in spurring collaborative innovation and dealing with this tension. Building on extant research, the article draws suitable cases from the Collaborative Governance Data Bank and uses Qualitative Comparative Analysis to explore how multiple constellations of institutional design and leadership spur collaborative innovation. The main finding is that, even though certain institutional design features reduce the need for certain leadership roles, the exercise of hands-on leadership is more important for securing collaborative innovation outcomes than hands-off institutional design

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

Smart Systems and Collaborative Innovation Networks for Productivity Improvement in SMEs

The adoption of Smart Manufacturing Systems in manufacturing companies is often seen as a strategy towards achieving improvements in productivity. However, there is little evidence to indicate that UK manufacturing SMEs are prepared for the implementation of such systems. Through the employment of a triangulation research approach involving the detailed examination of 36 UK manufacturing SMEs from three manufacturing sectors, this study investigates the level of awareness and understanding within SMEs of Smart Manufacturing Systems. The development of a profiling tool is shown and is subsequently used to audit company awareness and understanding of the key technologies, collaborative networks and systems of SMS. Further information obtained from semi-structured interviews and observations of manufacturing operations provide further contextual information. The findings indicate that whilst the priority technologies and systems differ between manufacturing sectors, the key issues around the need for developing appropriate collaborative networks and knowledge management systems are common to all sectors

Cyborgs as Frontline Service Employees: A Research Agenda

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose This paper identifies and explores potential applications of cyborgian technologies within service contexts and how service providers may leverage the integration of cyborgian service actors into their service proposition. In doing so, the paper proposes a new category of ‘melded’ frontline service employees (FLEs), where advanced technologies become embodied within human actors. The paper presents potential opportunities and challenges that may arise through cyborg technological advancements and proposes a future research agenda related to these. Design/methodology This study draws on literature in the fields of services management, Artificial Intelligence [AI], robotics, Intelligence Augmentation [IA] and Human Intelligence [HIs] to conceptualise potential cyborgian applications. Findings The paper examines how cyborg bio- and psychophysical characteristics may significantly differentiate the nature of service interactions from traditional ‘unenhanced’ service interactions. In doing so, we propose ‘melding’ as a conceptual category of technological impact on FLEs. This category reflects the embodiment of emergent technologies not previously captured within existing literature on cyborgs. We examine how traditional roles of FLEs will be potentially impacted by the integration of emergent cyborg technologies, such as neural interfaces and implants, into service contexts before outlining future research directions related to these, specifically highlighting the range of ethical considerations. Originality/Value Service interactions with cyborg FLEs represent a new context for examining the potential impact of cyborgs. This paper explores how technological advancements will alter the individual capacities of humans to enable such employees to intuitively and empathetically create solutions to complex service challenges. In doing so, we augment the extant literature on cyborgs, such as the body hacking movement. The paper also outlines a research agenda to address the potential consequences of cyborgian integration