This paper essentially presents an exploration of the relationship between organizational culture and information systems management. Three contributions are offered namely the findings of a study of the organizational culture and information management competencies of five organizations in the Netherlands, with particular reference to the reliability of the measurements tool that was used, as well as an exploratory study of the relationship between organizational culture and the ability of an organization to manage its information systems. A brief review of the literature reveals that these two concepts in combination have been studied extensively, but that their conceptualization are somewhat fragmented in nature. In an effort to study the relationship using a more inclusive frame of reference the paper then presents a description of two models that were used the foundation for the design of a measurement tool to investigate the topic. The results provides a description of the general culture and information systems management abilities of the organizations and also suggest that the measurement tool is indeed reliable. Further analysis reveals that several variables from within each of the two main concepts, organizational culture and information systems management, are correlated.
Additions to the book "Systems Design and Engineering" by Bonnema et.al. Subjects were chosen based on the Systems Engineering needs for Small and Medium Enterprises, as researched in the SESAME project. The
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The research described in this paper provides insights into tools and methods which are used by professional information workers to keep and to manage their personal information. A literature study was carried out on 23 scholar papers and articles, retrieved from the ACM Digital Library and Library and Information Science Abstracts (LISA). The research questions were: - How do information workers keep and manage their information sources? - What aims do they have when building personal information collections? - What problems do they experience with the use and management of their personal collections? The main conclusion from the literature is that professional information workers use different tools and approaches for personal information management, depending on their personal style, the types of information in their collections and the devices which they use for retrieval. The main problem that they experience is that of information fragmentation over different collections and different devices. These findings can provide input for improvement of information literacy curricula in Higher Education. It has been remarked that scholar research and literature on Personal Information Management do not pay a lot of attention to the keeping and management of (bibliographic) data from external documentation. How people process the information from those sources and how this stimulates their personal learning, is completely overlooked. [The original publication is available at www.elpub.net]
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
Family Dairy Tech Sustainable and affordable stable management systems for family dairy farms in India. An example of Dutch technology that is useful to an ?emerging economy?. Summary Problem The demand for dairy products in India is increasing. Small and medium-sized family farmers want to capitalize on this development and the Indian government wants to support them. Dutch companies offer knowledge and a wide range of products and services to improve dairy housing systems and better milk quality, in which India is interested. However, the Dutch technology is sophisticated and expensive. For a successful entry into this market, entrepreneurs have to develop affordable and robust (?frugal?) systems and products adapted to the Indian climate and market conditions. The external question is therefore: ?How can Dutch companies specialised on dairy housing systems adapt their products and offer these on the Indian market to contribute to sustainable and profitable local dairy farming??. Goal Since 2011, VHL University of Applied Sciences (VHL) is collaborating with a college and an agricultural information center Krishi Vigyan Kendra (KVK), Baramati, Pune district, Maharashtra State India. In this region many small-scale dairy farmers are active. Within this project, KVK wants to support farmers to scale up their farm form one or a few cows up to 15 to 100 cows, with a better milk quality. In this innovative project, VHL and Saxion Universities of Applied Sciences, in collaboration with KVK and several Dutch companies want to develop integrated solutions for the growing number of dairy farms in the State of Maharashtra, India. The research questions are: 1. "How can, by smart combinations of existing and new technologies, the cow-varieties and milk- and stable-management systems in Baramati, India, for family farmers be optimized in an affordable and sustainable way?" 2. "What are potential markets in India for Dutch companies in the field of stable management and which innovative business models can support entering this market?" Results The intended results are: 1. A design of an integral stable management system for small and medium-sized dairy farms in India, composed of modified Dutch technologies. 2. A cattle improvement programme for robust cows that are adapted to the conditions of Maharashtra. 3. An advice to Dutch entrepreneurs how to develop their market position in India for their technologies. 4. An advice to Indian family farmers how they can increase their margins in a sustainable way by employing innovative technologies.
