Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations

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    The Impact of Online Data Collection on Consumer Autonomy
    ( 2021-01-05) Olszak, Celina ; Sohaib, Osama
    Recent advancements in the field of Big Data are facilitating various business intelligence activities for businesses. However, we contend that online data collection can generate tensions for consumers. The Big Data collection can compromise consumers' sense of autonomy, the lack of which can be harmful to consumer privacy, data security, data confidentiality, and data ownership. This study presents preliminary results on the relationship between online data collection and online consumer autonomy in Australia. This study identifies open research questions for future research.
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    The adaptive spatio-temporal clustering method in classifying direct labor costs for the manufacturing industry
    ( 2021-01-05) Weichbroth, Paweł ; Kalinowski, Mateusz ; Baran, Jakub
    Employee productivity is critical to the profitability of not only the manufacturing industry. By capturing employee locations using recent advanced tracking devices, one can analyze and evaluate the time spent during a workday of each individual. However, over time, the quantity of the collected data becomes a burden, and decreases the capabilities of efficient classification of direct labor costs. However, the results obtained from performed experiments show that the existing clustering methods have failed to deliver satisfactory results by taking advantage of spatial data. In contrast to this, the adaptive spatio-temporal clustering (ASTC) method introduced in this paper utilizes both spatial and time data, as well as prior data concerning the position and working status of deployed machines inside a factory. The results show that our method outperforms the bucket of three well-known methods, namely DBSCAN, HDBSCAN and OPTICS. Moreover, in a series of experiments, we also validate the underlying assumptions and design of the ASTC method, as well as its efficiency and scalability. The application of the method can help manufacturing companies analyze and evaluate employees, including the productive times of day and most productive locations.
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    Business Analytics Capabilities for Organisational Resilience
    ( 2021-01-05) Al-Ghattas, Hussein ; Marjanovic, Olivera
    Nowadays, organizations are facing unique challenges created by different disruptions, including natural disasters, new technologies, regulatory changes, and more recently, a global pandemic. Consequently, the need to build, sustain, and continuously enhance Organizational Resilience (OR) is greater than ever. An ongoing process of building OR requires high-quality data and business analytics (BA) capabilities. In this paper we aim to investigate the yet-to-be explored link between BA and OR. We achieve this aim by conducting a multidisciplinary literature review on OR and BA, focusing on BA capabilities for OR. Based on our findings, we then propose a conceptual framework of BA capabilities for OR. In doing so, we also bring a well-established area of OR to the attention of BA researchers, as a critically important area for further BA research and practice.
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    Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations
    ( 2021-01-05) Janus, Piotr ; Ganzha, Maria ; Bicki, Artur ; Paprzycki, Marcin
    Today, data centers deal with fast growing data volumes. To deliver services, they deploy growing amount of heterogeneous hardware. As a result, it becomes practically impossible to apply human-based data center management. For instance, in a real-world data center, with 500+ computers, delivering data, computational, and network services, it becomes impossible to visualize, and understand, causal relationships among variables describing performance of monitored resources. However, it is possible to collect data describing behavior of individual nodes. Hence, such data may be used to analyze/model system performance. In particular, it may be applied to recognize and predict anomalies in system behavior. Furthermore, collected data should allow finding the cause(s) of anomalies. Therefore, “data-driven approaches” have been applied to the real-world data, to find, so called, Root Cause of anomalies.
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