Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Automated data analysis of unstructured grey literature in health research: A mapping review

\ua9 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. The amount of grey literature and ‘softer’ intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research

Linking social media, medical literature, and clinical notes using deep learning.

Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic medical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, and our key is finding concepts in the medical text. The National Library of Medicine (NLM) introduces a Unified Medical Language System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and apply supervised and semi-supervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer learning. The results include an integrated, end-to-end, web-based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts

Qualitätskontrolle mittels semantischer Technologien in digitalen Bibliotheken

Controlled content quality especially in terms of indexing is one of the major ad-vantages of using digital libraries in contrast to general Web sources or Web search engines. Therefore, more and more digital libraries offer corpora related to a specialized domain. Beyond simple keyword based searches the resulting infor-mation systems often rely on entity centered searches. For being able to offer this kind of search, a high quality document processing is essential. However, considering today’s information flood the mostly manual effort in ac-quiring new sources and creating suitable (semantic) metadata for content indexing and retrieval is already prohibitive. A recent solution is given by automatic genera-tion of metadata, where mostly statistical techniques like e.g. document classifica-tion and entity extraction currently become more widespread. But in this case neglecting quality assurance is even more problematic, because heuristic genera-tion often fails and the resulting low-quality metadata will directly diminish the quality of service that a digital library provides. Thus, the quality assessment of information system’s metadata annotations used for subsequent querying of collections has to be enabled. In this thesis we discuss the importance of metadata quality assessment for information systems and the benefits gained from controlled and guaranteed quality.Eine kontrollierte Qualität der Metadaten ist einer der wichtigsten Vorteile bei der Verwendung von digitalen Bibliotheken im Vergleich zu Web Suchmaschinen. Auf diesen hochqualitativen Inhalten werden immer mehr fachspezifische Portale durch die digitalen Bibliotheken erzeugt. Die so entstehenden Informationssysteme bieten oftmals neben einer simplen Stichwortsuche auch Objekt zentrierte Suchen an. Um solch eine Objekt-Suche zu ermöglichen, ist aber eine hochqualitative Verarbeitung der zugrunde liegenden Dokumente notwendig. Betrachtet man hingegen die heutige Informationsflut, so stellt man fest, dass der Aufwand für eine manuelle Erschließung von neuen Quellen und die Erzeugung von (semantischen) Metadaten für die Indexierung schon heute unerschwinglich ist. Eine aktuelle Lösung für dieses Problem ist die zumeist automatische Erzeugung von (semantischen) Metadaten, durch statistische Methoden, wie die automatische Dokumenten Klassifizierung Entitäten Extraktion. Aber bei der Verwendung sol-cher Methoden ist die Vernachlässigung der Qualität noch problematischer, da eine heuristische Erzeugung oftmals fehlerbehaftet ist. Diese schlechte Qualität der so erzeugten Metadaten wird dabei direkt die Servicequalität einer digitalen Biblio-thek herabmindern. Somit muss eine Qualitätsbewertung der Metadaten garantiert werden. In dieser Arbeit diskutieren wir die Bedeutung von Metadaten Qualität für Digitale Bibliotheken und die Chancen die aus kontrollierter und garantierter Qua-lität gewonnen werden können