Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications

European Union - Space of Regeneration, Learning and Innovation in the Context of Sustainable Multidisciplinary Research

Objective The Lisbon Strategy set a new goal for the EU economy: the transition to a knowledge based economy, competitive and sustainable at macro and regional levels, by creating the European Research Area – a geographic area without frontiers for researches, where scientific resources are better managed to create more jobs and improve Europe's competitiveness. That means an interaction between specific and multidisciplinary research network. Approach However, general research methodology sustains the importance of static and revolutionary specific criteria of ScientificResearch Programs but also reveals the natural process of multidisciplinary researches. In this context, the European Union could be regarded as a specific and multidisciplinary research area, as a network of flows, connections, relationships, interdependencies, and interferences between natural - experimental and social-humanistic research spheres (economics, management, sociology and complex systemsecology). Prior Work: In this respect some researchers suggested that both natural and social systemscould be considered as multidisciplinary complex adaptive systems consisting of specific cluster network connections ( in the form of biotic and abiotic nodes, respectively, the competitive and regional poles) with the ability to continuous self-organizing, learning and regenerating processespecially in crisis situations. Implications and Value Paper Utility The present paper might be useful to illustrate the contribution of technical-economic and socio-ecological researches to increasing the sustainability framework of European Research Area by considering the transition from the R&D approach (development through research process) to the L&D approach (development through learning process)

Evaluating platform architectures within ecosystems: modeling the relation to indirect value

This thesis establishes a framework for understanding the role of a supplier within the context of a business ecosystem. Suppliers typically define their business in terms of capturing value by meeting the demands of direct customers. However, the framework recognises the importance of understanding how a supplier captures indirect value by meeting the demands of indirect customers. These indirect customers increasingly use a supplier’s products and services over time in combination with those of other suppliers. This type of indirect demand is difficult for the supplier to anticipate because it is asymmetric to their own definition of demand. Customers pay the costs of aligning products and services to their particular needs by expending time and effort, for example, to link disparate social technologies or to coordinate healthcare services to address their particular condition. The accelerating tempo of variation in individual needs increases the costs of aligning products and services for customers. A supplier’s ability to reduce its indirect customers’ costs of alignment represents an opportunity to capture indirect value. The hypothesis is that modelling the supplier's relationship to indirect demands improves the supplier’s ability to identify opportunities for capturing indirect value. The framework supports the construction and analysis of such models. It enables the description of the distinct forms of competitive advantage that satisfy a given variety of indirect demands, and of the agility of business platforms supporting that variety of indirect demands. Models constructed using this framework are ‘triply-articulated’ in that they articulate the relationships among three sub-models: (i) the technical behaviours generating products and services, (ii) the social entities managing their supply, and (iii) the organisation of value defined by indirect customers’ demands. The framework enables the derivation from such a model of a layered analysis of the risks to which the capture of indirect value exposes the supplier, and provides the basis for an economic valuation of the agility of the supporting platform architectures. The interdisciplinary research underlying the thesis is based on the use of tools and methods developed by the author in support of his consulting practice within large and complex organisations. The hypothesis is tested by an implementation of the modeling approach applied to suppliers within their ecosystems in three cases: (a) UK Unmanned Airborne Systems, (b) NATO Airborne Warning and Control Systems, both within their respective theatres of operation, and (c) Orthotics Services within the UK's National Health Service. These cases use this implementation of the modeling approach to analyse the value of platforms, their architectural design choices, and the risks suppliers face in their use. The thesis has implications for the forms of leadership involved in managing such platform-based strategies, and for the economic impact such strategies can have on their larger ecosystem. It informs the design of suppliers’ platforms as system-of-system infrastructures supporting collaborations within larger ecosystems. And the ‘triple-articulation’ of the modelling approach makes new demands on the mathematics of systems modeling

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Using Information Systems in Innovation Networks: Uncovering Network Resources

In order to innovate, firms progressively combine complementary abilities through forming networks. Such innovation networks represent temporary assemblages of partners that, in collaboration, pursue new product developments. Existing theories suggest that successful participation in such networks depends on firms’ having certain firm-level dynamic capabilities (i.e., skill in sensing the network and its environment, learning about the network, and coordinating and integrating individual resources across the network). In this paper, we argue that firms also have to develop particular networking capabilities (i.e., they have to understand who they are partnering with, what each partner can contribute, and how exactly each partner can cooperate with others across the network). We show that inter-organizational information systems (IS) are vital for facilitating the development of these networking capabilities. IS are also vital in developing unique constellations of resources (i.e., physical, human, and organizational resources) that we term IS-embedded network resources. These resources are manifested in the IS and are unique to the innovation network because they go beyond resources at the firm level. Using three innovation networks as case studies, we provide empiric evidence on how IS support networking capabilities to arrive at unique resource constellations embedded in IS and how the set of IS-embedded network resources is a determining factor for competitive advantage in innovation networks

Collaborative decision making in complex work settings: a process of managing inter dependencies

There exists disparity between the conceptualization and occurrence of Collaborative Decision Making (CDM) in everyday work activities of complex work settings. Current notions in the field of Computer Supported Cooperative Work (CSCW) based on studies of decision making in groups typically portray CDM as an isolated event in which multiple personnel jointly undertake decision making. In the real world, however, decisions are made during work performance and interlaced with other processes and activities. Moreover, the complex work setting is a cooperative arrangement in which decision making is distributed. This research aims to alleviate the disparity by investigating how people in a complex working environment make decisions collaboratively. The original contribution to knowledge made by this thesis is the theory of CDM as a process of managing interdependencies. Field-studies conducted in an airport to examine the way CDM is undertaken during Air Traffic Control operations inform theory development. The study takes a qualitative approach and is guided by Grounded Theory Methodology (GTM). The findings of this research indicate that undertaking decision making in the cooperative arrangement of complex work settings requires managing the distributions and interconnections inherent in this setup. In addition, participation and contribution of personnel in decision making is found to be structured by the dependencies between their activities. These findings form the central focus of the theory leading to the depiction of CDM as a process of managing interdependencies. The theory presented in this thesis clarifies and extends existing views by explicating the differentiated process of CDM in the cooperative arrangement of a complex work setting. Based on this a new definition of CDM is formulated. In addition, a conceptual framework of ten parameters is derived to serve as a tool for analysing CDM taking place in a particular work setting. Application of this framework is demonstrated by analysing an aircraft accident report to draw insights about the occurrence of CDM in this setting