Profiling with Big Data: Identifying Privacy Implication for Individuals, Groups and Society

User profiling using big data raises critical issues regarding personal data and privacy. Until recently, privacy studies were focused on the control of personal data; due to big data analysis, however, new privacy issues have emerged with unidentified implications. This paper identifies and investigates privacy threats that stem from data-driven profiling using a multi-level approach: individual, group and society, to analyze the privacy implications stemming from the generation of new knowledge used for automated predictions and decisions. We also argue that mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Finally, this paper discusses privacy threats resulting from the cumulative effect of big data profiling

An Overview of Next-generation Manufacturing Execution Systems:How important is MES for Industry 4.0?

The purpose of this paper is to understand the evolution of manufacturing execution systems (MES) in the digital transformation era. Theoretical propositions made on MES (based on literature survey) were empirically examined using three case studies in Danish companies. Findings gave an overview of Industry 4.0 ready MES and identified its role in factories of the future. It is a first attempt to analyze the concepts behind next-generation MES to give a primer on ‘MES as a digital twin', via first iteration of results from cross-case synthesis of collected data. The paper also maps the current MES research pertaining to Industry 4.0 into key groups to highlight its significance in digital manufacturing

Context-aware Knowledge-based Systems: A Literature Review

Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services’ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed

Multi-agent Manufacturing Execution System (MES):Concept, architecture & ML algorithm for a smart factory case

Smart factory of the future is expected to support interoperability on the shop floor, where information systems are pivotal in enabling interconnectivity between its physical assets. In this era of digital transformation, manufacturing execution system (MES) is emerging as a critical software tool to support production planning and control while accessing the shop floor data. However, application of MES as an enterprise information system still lacks the decision support capabilities on the shop floor. As an attempt to design intelligent MES, this paper demonstrates one of the artificial intelligence (AI) applications in the manufacturing domain by presenting a decision support mechanism for MES aimed at production coordination. Machine learning (ML) was used to develop an anomaly detection algorithm for multi-agent based MES to facilitate autonomous production execution and process optimization (in this paper switching the machine off after anomaly detection on the production line). Thus, MES executes the ‘turning off’ of the machine without human intervention. The contribution of the paper includes a concept of next-generation MES that has embedded AI, i.e., a MES system architecture combined with machine learning (ML) technique for multi-agent MES. Future research directions are also put forward in this position paper