Partially-Distributed Coordination with Reo (Technical Report)

Coordination languages, as Reo, have emerged for the specification and implementation of interaction protocols among concurrent entities. In this paper, we propose a framework for generating partially-distributed, partially-centralized implementations of Reo connectors to improve 1) build-time compilation and 2) run-time throughput and parallelism. Our framework relies on the definition of a new formal product operator on constraint automata (Reo's formal semantics), which enables the formally correct distribution of disjoint parts of a coordination scheme over different machines according to several possible motivations (e.g., performance, privacy, QoS constraints, resource availability, network topology). First, we describe the design and a proof-of-concept implementation of our framework. Then, in a case study, we show and explain how a generated connector implementation can be executed in the Cloud and supports Big Data coordination

Exploring How Usage-Focused Business Models Enable Circular Economy through Digital Technologies

Recent studies advocate that digital technologies are key enabling factors for the introduction of servitized business models. At the same time, these technologies support the implementation of the circular economy (CE) paradigm into businesses. Despite this general agreement, the literature still overlooks how digital technologies enable such a CE transition. To fill the gap, this paper develops a conceptual framework, based on the literature and a case study of a company implementing a usage-focused servitized business model in the household appliance industry. This study focuses on the Internet of Things (IoT), Big Data, and analytics, and identifies eight specific functionalities enabled by such technologies (improving product design, attracting target customers, monitoring and tracking product activity, providing technical support, providing preventive and predictive maintenance, optimizing the product usage, upgrading the product, enhancing renovation and end-of-life activities). By investigating how these functionalities affect three CE value drivers (increasing resource efficiency, extending lifespan, and closing the loop), the conceptual framework developed in this paper advances knowledge about the role of digital technologies as an enabler of the CE within usage-focused business models. Finally, this study shows how digital technologies help overcome the drawback of usage-focused business models for the adoption of CE pointed out by previous literatur

How will smart city production systems transform supply chain design: a product-level investigation

© 2016 Informa UK Limited, trading as Taylor & Francis Group.This paper is a first step to understand the role that a smart city with a distributed production system could have in changing the nature and form of supply chain design. Since the end of the Second World War, most supply chain systems for manufactured products have been based on ‘scale economies’ and ‘bigness’; in our paper we challenge this traditional view. Our fundamental research question is: how could a smart city production system change supply chain design? In answering this question, we develop an integrative framework for understanding the interplay between smart city technological initiatives (big data analytics, the industrial Internet of things) and distributed manufacturing on supply chain design. This framework illustrates synergies between manufacturing and integrative technologies within the smart city context and links with supply chain design. Considering that smart cities are based on the collaboration between firms, end-users and local stakeholders, we advance the present knowledge on production systems through case-study findings at the product level. In the conclusion, we stress there is a need for future research to empirically develop our work further and measure (beyond the product level) the extent to which new production technologies such as distributed manufacturing are indeed democratising supply chain design and transforming manufacturing from ‘global production’ to a future ‘city-oriented’ social materiality

A framework for effective management of water and sewer infrastructure

Abstract: From a municipal operational and maintenance perspective water and sewer infrastructure systems are complex systems with numerous components leading to recurring water pipe bursts and sewer blockages. This prompted the study to assess, analyze and quantify the characteristics of factors that compromise water distribution and sewer services reliability around Johannesburg (used as a case study). The aim of the study was firstly to investigate water and sewer infrastructure challenges by researching the relationship between operations, maintenance, design and construction. To develop a short-term framework for improving the day to day operations process based on data highlighting common failures. The common trend found in the literature case studies is the use of a water audit as a basis for assessing bursts, blockages, leaks and water losses. The water audit approach enables researchers to discover patterns in big data without formulating hypotheses by using a grading system (Lycett, A., 2013).This study considered various methods of managing water and sewer systems, developing a framework for addressing various types of infrastructure failures related to water and sewer. The study fills a gap by supporting effective project management of water leaks and sewer blockages by implementing quality management systems during construction to prevent recurring burst/blockages and post construction (maintenance plan linked to operations and complaint loggings from residents). In order to simplify understanding a Model Based Systems Engineering (MBSE) approach was used to develop and present a proposed framework. Failure Modes and Effects Analysis (FMEA) was used as a method to identify potential failures of a design, construction operations, maintenance, product and process identified. The FMEA was used as a continuation from the data to create the Framework The initial study focused on developing a short-term framework for improving the day to day operations process based on data findings. This is then to be escalated into a longer term framework over time

Explaining the Big Data adoption decision in Small and Medium Sized Enterprises: Cape Town case studies

Problem Statement: Small and Medium-Sized Enterprises (SMEs) play an integral role in the economy of developed and developing countries. SMEs are constantly searching for innovative technologies that will not only reduce their overhead costs but also improve product development, customer relations and profitability. Literature has revealed that some SMEs around the world have incorporated a fairly new technology called Big Data to achieve higher levels of operational efficiency. Therefore, it is interesting to observe the reasons why some organizations in developing countries such as South Africa are not adopting this technology as compared to other developed countries. A large portion of the available literature revealed that there isa general lack of in-depth information and understanding of Big Data amongst SMEs in developing countries such as South Africa. The main objective of this study is to explain the factors that SMEs consider during the Big Data decision process. Purpose of the study: This research study aimed to identify the factors that South African SMEs consider as important in their decision-making process when it comes to the adoption of BigData. The researcher used the conceptual framework proposed by Frambach and Schillewaert to derive an updated and adapted conceptual framework that explained the factors that SMEs consider when adopting Big Data. Research methodology: SMEs located in the Western Province of South Africa were chosen as the case studies. The interpretive research philosophy formed the basis of this research. Additionally, the nature of the phenomenon being investigated deemed it appropriate that the qualitative research method and research design be applied to this thesis. Due to constraints such as limited time and financial resources this was a cross-sectional study. The research strategy in this study was multiple in-depth case studies. The qualitative approach was deemed appropriate for this study. The researcher used two methods to collect data, namely, the primary research method and the secondary research method. The primary research method enabled the researcher to obtain rich data that could assist in answering the primary research questions, whilst the secondary research method included documents which supplemented the primary data collected. Data was analyzed using the NVivo software provided by the University of Cape Town. Key Findings: The findings suggest that the process that influences the decision to adopt Big Data by SMEs follows a three-step approach namely: 1.) Awareness, 2.) Consideration, 3.) Intention. This indicates that for Big Data to be adopted by SMEs there must be organizational readiness to go through the process. This study identified the main intention for SMEs to adopt Big Data is to ensure operational stability. Improved operational efficiency was identified as the supporting sub-theme. This study has raised awareness about the process that SMEs, academic researchers, IT practitioners and government need to place emphasis on to improve the adoption of Big Data by SMEs. Furthermore, this study has raised awareness about the opportunities and challenges that SMEs, academic researchers, IT practitioners and government need to place emphasis on to improve the adoption of Big Data by SMEs. Value of the study: The study adds value in both academia and the business industry as it provides more insight into the factors that SMEs consider in the Big Data adoption decision

Data as a design material: An analysis on the challenges of working with “big data” related technologies in an industrial context

In recent years, the ability to collect, store and analyse large datasets by private companies and government agencies has increased to the point where the term “big data” has been coined to describe the phenomena. Alongside “big data”, several data processing technologies are becoming more widespread due to their effectiveness and success in everyday products and services; these are artificial intelligence, with its subsets machine learning and deep learning, and data analytics amongst others. This study investigated the challenges designers face when working with new information and communication technologies in an industrial context. More specifically, it deals with “big data” and new data processing technologies and how designers engage with them as a design material when envisioning new products and services. The research questions were (1) what challenges are designers facing when working with “big data” in a data-rich industrial context? (2) how is working with “big data” and new data collecting and processing technologies different from other design materials? (3) how can designers overcome some of the challenges of working with data? This thesis adopted a research through design approach and data was collected between June 2015 and January 2016. Furthermore, a review of the material-centred design literature was used as a theoretical framework. To answer the research questions, this thesis investigated a six-month design project done for the energy company Vattenfall. Vattenfall was at the time going through a digitalisation phase and was interested in evaluating the possibility of combining their internal data with other data sources to explore new products and services. During the six-month period, I worked in Vattenfall’s Helsinki offices, designing different concepts under the supervision of the product development team and their programme manager as my direct supervisor. Data was gathered using different qualitative methods and focusing on three areas: the design practice, the design outcomes, and the interactions with the team and stakeholders. The key findings demonstrate how the practice of design in this new technological landscape faces multiple challenges. The main challenges being (a) the high level of complexity of these technologies, (b) the lack of education/experience of the designer to work in this context, (c) the lack of competence in the organization and (d) the missing frameworks and tools for collaboration between data experts and designers. Furthermore, it was also found and validated against the literature that these new technologies present different properties not comparable with previously well-studied ones like haptics, Bluetooth and RFID. Making existing frameworks and traditional approaches to exploring new digital materials hard to replicate. The results further suggest the need for developing novel concepts and frameworks to support new ways of understanding, describing and working with “big data” and its related technologies

Gender-Based Comparison of Students Personality Traits and Their Academic Achievement in Khyber Pakhtunkhwa, Pakistan

Theoretical framework of this study based on Big Five Personality Trait Theory (Cattell’s & Eysenck’s 1973). Objectives of the study were: i) to compare different personality traits of male and female secondary school students. ii) to compare the academic achievement of male and female secondary school students. Descriptive survey design was used for this study. All the secondary school students of Khyber Pakhtunkhwa, Pakistan constituted the population of the study. Out of 25 districts 2 districts were randomly selected (Bannu & Lakki Marwat). Out of 12009 students who were studying in 119 boys and 73 girls secondary schools of these districts 800 (400 male & 400 female) students of 10th class were selected through multistage random sampling method using proportional allocation technique as a sample of the study. A self developed questionnaire and result cards of the students were used as research instruments. Personally collected data was entered in SPSS-21. Pearson Product Moment Correlation and Chi-square were applied as statistical tools to achieve the objectives of the study. Keywords: Personality, Traits, Extroversion, Conscientiousness, Agreeableness, Neuroticism, Openness to Experienc

An Affordance Perspective Of RAs 2.0: Theorizing The New Generation Of Recommendation Agents

With the advent of rapid technological advancements in artificial intelligence (AI), data analytics, Internet of things (IoT), semantic web, cloud and mobile computing, coupled with the explosive growth of big data, a new generation of AI-driven recommendation agents (RAs) has emerged and continued to evolve and present possibilities to diverse application domains. However, extant information systems (IS) studies have predominantly focused on user perceptions and evaluations of traditional non-intelligent product-brokering recommendation agents (PRAs), supported by empirical studies on custom-built experimental RAs which heavily rely on explicit user preference-elicitations. As the nature of RAs has evolved from primarily ad-hoc, task-based and short-term transaction-focused product-brokering tools, to intelligent and autonomous assistants that foster long-term digital companionships with users, the need for a better understanding of the formation of trust, affection, attachment, commitment, and intimacy between users and the new generation of RAs has become a research and practical necessity. To fill the void of research in the new generation of intelligent RAs, this paper aims to study consumer responses to AI-driven RAs using an affordance perspective, making this research the first attempt in the IS discourse to link RA design artifacts, RA affordances, RA outcomes and user continuance intentions, and examine how actualized RA affordances influence user engagements with and evaluations of these highly personalized systems, which increasingly focus on user experiences and long-term relationships. In view of contributing to this understanding, this paper conceptually defines and typologizes the new generation RAs 2.0 that leverages the latest technological advancements in artificial intelligence and big data and employs an affordance-based lens to illuminate the symbiotic relationship between RA capabilities and a user’s goal and action. In addition, it proposes an overarching comprehensive framework for the key technological and affective affordances of RAs 2.0 provided by both mechanics and dynamics design artifacts, as well as the influences these affordances have on user engagement and evaluation, which in turn, affect one’s perceived digital companionship, perceived service quality and continuance intention. Moreover, this paper identifies potential areas of future research for scholars in the RA discourse and develops testable propositions derived from multiple theoretical perspectives. For practitioners, it also provides advice and important guidelines concerning the effective design and development of the new generation of RAs. Keywords: Recommendation agents (RAs), RAs 2.0, affordance, artificial intelligence (AI), deep learning (DL), digital companionshi