Modeling A Green Decision Support System for Data Center Sustainability

The objective of this dissertation is developing more energy efficient data centers while focusing on the environment as well as meeting the increasing computing needs. Reliability of data centers will be the number one priority for management; however, the focus will be to implement a design by incorporating free cooling, applying thermal profiling, utilizing data mining, and continuing virtualization to create more efficient green data centers that are good for the environment. Since the fall of 2009, electrical consumption patterns were measured in the main data center for the servers and the air-conditioners at Montclair State University (MSU) to quantify the carbon footprint and the electrical costs. An important outcome of this work is to build a Decision Support System (DSS) for green computing in data centers. A DSS is a computer based application to assist in providing solutions with respect to decision-making to multifaceted problems. In summary, building on our measurements, the objective is to design a DSS for data centers to enhance energy efficiency, reduce the carbon footprint, and promote sustainability science across disciplines

Towards Human Digital Twins for Improving Customer Experience

Applications of digital twin (DT) technology have gained momentum in IS research and cognate disciplines. Several studies have documented how DTs create value in contexts such as manufacturing or smart cities through virtual monitoring and decision-making. While these contexts benefit from DTs of products or production steps, this research is the first to investigate the potentials of human DTs to improve customer experience (CX) (i.e., customer twins). Drawing on a structured literature review, we derive new conceptualizations of DTs as (i) virtual mirrors that depict a physical entity and its interactions in virtual space, and (ii) virtual orchestrators which extend the virtual mirror by also simulating potential virtual interactions. These new conceptualizations, by applying them to human DTs, enable us to discuss DT’s implications to approach current CX potentials. The results of the discussion indicate that human DTs can support CX management to improve CX throughout the whole customer journey

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

The COVID 19 black swan event has disrupted every aspect of life in unprecedented ways, causing organizations to scramble to effectively sense and respond to the tumultuous business environment. Business intelligence and analytics (BI&A) capability has gained attention as a key weapon in the arsenal needed to combat turbulent times and to adjust to the post-pandemic new normal. Post-pandemic BI&A trends point to changes in organizational priorities for BI&A infrastructure that influence the traditional view of BI&A architecture and its role within an organization. As a result, new challenges and opportunities are emerging. This paper identifies and examines twelve key post-pandemic BI&A trends from industry practice and six major research themes. It also proposes an initial set of research questions that could inspire future research in BI&A in the post-pandemic new normal

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications