How Information Systems Can Help In Alarm Alert Detection
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How Information Systems Can Help in Alarm/Alert Detection
Alarm or alert detection remains an issue in various areas from nature, i.e. flooding, animals or earthquake, to software systems. Liveness, dynamicity, reactivity of alarm systems: how to ensure the warning information reach the right destination at the right moment and in the right location, still being relevant for the recipient, in spite of the various and successive filters of confidentiality, privacy, firewall policies, etc.? Also relevant in this context are to technical contingency issues: material failure, defect of connection, break of channels, independence of information routes and sources? Alarms with crowd media, (mis)information vs. rumours: how to make the distinction? The prediction of natural disasters (floods, avalanches, etc.), health surveillance (affectionate fevers of cattle, pollution by pesticides, etc.), air, sea and land transport, or space surveillance to prevent Risks of collisions between orbital objects involve more and more actors within Information Systems, one of whose purposes is the dissemination of alerts. By expanding the capabilities and functionality of such national or international systems, social networks are playing a growing role in dissemination and sharing, eg. with the support of systems like the Google Alert (https://www.google.fr/alerts) which concerns the publication of contents online. Recently, the Twitter microblogging platform announced a broadcast service, designed to help government organizations with alerts to the public. The proper functioning of such systems depends on fundamental properties such as resilience, liveliness and responsiveness: any alert must absolutely reach the right recipient at the right time and in the right place, while remaining relevant to him, despite the various constraints. on the one hand to external events, such as hardware failures, connection faults, breaks in communication channels, on the other hand to confidentiality, such as the collection and use of personal data (with or without the consent of the user), or the disparity of access policies (generation according to industrial, technological, security constraints, management of internal / external policies, etc.) between actors. This book opens the discussion on the "procrastination", the dynamics and the reactivity of the alert systems, but also the problems of confidentiality, filtering of information, and the means of distinguishing information and rumor. - Presents alarm or alert detection in all its aspects - Finds a solution so that the alert information reaches the right destination - Find relevance to various technical issues
Web Information Systems and Mining
The volume LNCS 7529 constitutes the refereed proceedings of the International Conference on Web Information Systems and Mining, WISM 2012, held in Chengdu, China, in October 2012. The 87 revised full papers presented were carefully reviewed and selected from 418 submissions. The papers are organized in topical sections on applications of Web information systems; applications of Web mining; e-government and e-commerce; information security; intelligent networked systems; management information systems; mobile computing; semantic Web and ontologies; Web information extraction; Web intelligence; Web interfaces and applications; and XML and semi-structured data.
Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare
This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments. This book offers comprehensive coverage of the most essential topics, including: Introduction to e-monitoring for healthcare Case studies based on big data and healthcare Intelligent learning analytics in healthcare sectors using machine learning and IoT Identifying diseases and diagnosis using machine learning and IoT Deep learning architecture and framework for healthcare using IoT Knowledge discovery from big data of healthcare-related processing Big data and IoT in healthcare Role of IoT in sustainable healthcare A heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters