When decision support systems fail: insights for strategic information systems from Formula

Decision support systems (DSS) are sophisticated tools that increasingly take advantage of big data and are used to design and implement individual - and organization - level strategic decisions . Yet, when organizations excessively rely on their potential the outcome may be decision - making failure, particularly when such tools are applied under high pressure and turbulent conditions. Partial understanding and unidimensional interpretation can prevent learning from failure. Building on a practice perspective, we study an iconic case of strategic failure in Formula 1 racing. Our approach, which integrates the decision maker as well as the organizational and material context , identifies three interrelated sources of strategic failure that are worth investigation for decision - makers using DSS and big data: (1) t he situated nature and affordances of decision - making ; (2) t he distributed nature of cognition in decision - making; and (3) the performativity of the DSS. We outline specific research questions and their implications for firm performance and competitive advantage. Finally, we advance an agenda that can help close timely gaps in strategic IS research

Assessing sheep behavior through low-power microcontrollers in smart agriculture scenarios

Automatic animal monitoring can bring several advantages to the livestock sector. The emergence of low-cost and low-power miniaturized sensors, together with the ability of handling huge amounts of data, has led to a boost of new intelligent farming solutions. One example is the SheepIT solution that is being commercialized by iFarmtec. The main objectives of the solution are monitoring the sheep’s posture while grazing in vineyards, and conditioning their behaviour using appropriate stimuli, such that they only feed from the ground or from the lower branches of the vines. The quality of the monitoring procedure has a linear correlation with the animal condition capability of the solution, i.e., on the effectiveness of the applied stimuli. Thus, a Real-Time mechanism capable of identifying animal behaviour such as infraction, eating, walking or running movements and standing position is required. On a previous work we proposed a solution based on low-power microcontrollers enclosed in collars wearable by sheep. Machine Learning techniques have been rising as a useful tool for dealing with big amounts of data. From the wide range of techniques available, the use of Decision Trees is particularly relevant since it allows the retrieval of a set of conditions easily transformed in lightweight machine code. The goal of this paper is to evaluate an enhanced animal monitoring mechanism and compare it to existing ones. In order to achieve this goal, a real deployment scenario was availed to gather relevant data from sheep’s collar. After this step, we evaluated the impact of several feature transformations and pre-processing techniques on the model learned from the system. Due to the natural behaviour of sheep, which spend most of the time grazing, several pre-processing techniques were tested to deal with the unbalanced dataset, particularly resorting on features related with stateful history. Albeit presenting promising results, with accuracy over 96%, these features resulted in unfeasible implementations. Hence, the best feasible model was achieved with 10 features obtained from the sensors’ measurements plus an additional temporal feature. The global accuracy attained was above 91%. Howbeit, further research shall assess a way of dealing with this kind of unbalanced datasets and take advantage of the insights given by the results achieved when using the state’s history.publishe

Technology, governance, and a sustainability model for small and medium-sized towns in Europe

New and cutting-edge technologies causing deep changes in societies, playing the role of game modifiers, and having a significant impact on global markets in small and medium-sized towns in Europe (SMSTEs) are the focus of this research. In this context, an analysis was carried out to identify the main dimensions of a model for promoting innovation in SMSTEs. The literature review on the main dimensions boosting the innovation in SMSTEs and the methodological approach was the application of a survey directed to experts on this issue. The findings from the literature review reflect that technologies, governance, and sustainability dimensions are enablers of SMSTEs’ innovation, and based on the results of the survey, a model was implemented to boost innovation, being this the major add-on of this research.info:eu-repo/semantics/publishedVersio

Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft

To share or not to share: Publication and quality assurance of research data outputs. A report commissioned by the Research Information Network

A study on current practices with respect to data creation, use, sharing and publication in eight research disciplines (systems biology, genomics, astronomy, chemical crystallography, rural economy and land use, classics, climate science and social and public health science). The study looked at data creation and care, motivations for sharing data, discovery, access and usability of datasets and quality assurance of data in each discipline