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    Wyświetlanie 1-5 z 5
    Tytuł:
    An algorithm for data quality assessment in predictive toxicology
    Autorzy:
    Malazizi, L.
    Neagu, D.
    Chaudhry, Q.
    Powiązania:
    https://bibliotekanauki.pl/articles/1943267.pdf
    Data publikacji:
    2007
    Wydawca:
    Politechnika Gdańska
    Tematy:
    QSAR models
    data quality
    data cleaning
    Opis:
    Lack of the quality of the information that is integrated from heterogeneous sources is an important issue in many scientific domains. In toxicology the importance is even greater since the data is used for Quantitative Structure Activity Relationship (QSAR) modeling for prediction of chemical toxicity of new compounds. Much work has been done on QSARs but little attention has been paid to the quality of the data used. The underlying concept points to the absence of the quality criteria framework in this domain. This paper presents a review on some of the existing data quality assessment methods in various domains and their relevance and possible application to predictive toxicology, highlights number of data quality deficiencies from experimental work on internal data and also proposes some quality metrics and an algorithm for assessing data quality concluded from the results.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2007, 11, 1-2; 103-115
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    GeoWebCln: An intensive cleaning architecture for geospatial metadata
    Autorzy:
    Kumari Sheoran, Savita
    Parmar, Vinti
    Powiązania:
    https://bibliotekanauki.pl/articles/2054577.pdf
    Data publikacji:
    2022-03-31
    Wydawca:
    Uniwersytet im. Adama Mickiewicza w Poznaniu
    Tematy:
    spatial data
    spatial metadata
    data cleaning
    spatial database
    GIS
    Opis:
    Developments in big data technology, wireless networks, Geographic information system (GIS) technology, and internet growth has increased the volume of data at an exponential rate. Internet users are generating data with every single click. Geospatial metadata is widely used for urban planning, map making, spatial data analysis, and so on. Scientific databases use metadata for computations and query processing. Cleaning of data is required for improving the quality of geospatial metadata for scientific computations and spatial data analysis. In this paper, we have designed a data cleaning tool named as GeoWebCln to remove useless data from geospatial metadata in a user-friendly environment using the Python console of QGIS Software.
    Źródło:
    Quaestiones Geographicae; 2022, 41, 1; 51-62
    0137-477X
    2081-6383
    Pojawia się w:
    Quaestiones Geographicae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data cleaning of medical data sets
    Autorzy:
    Widera, A.
    Widera, M.
    Feige, D.
    Horoba, K.
    Stankiewicz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333299.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    czyszczenie danych
    zmaterializowane widoki
    odkrywanie wiedzy w bazach danych medycznych
    data cleaning
    materialised views
    knowledge discovery in medical databases
    Opis:
    Each database system evolves during the time. If the primary database schema was designed only to store the limited scope of abstraction classes then the database system improvement process is performed in traditional way (using alter table, update table and create table commands). Anyhow it could be impossible, from the engineering point of view, or too expensive from the economic point of view. Transferring the data from one database schema to another database schema one has to perform an additional step called Data Cleaning. This paper present a basic sketch for the data cleaning theory based on the materialised views idea and corresponding data cleaning environment. The proposed methodology is suitable not only for the data verification but also for the reengineering of the broken references between data fields, recreation of missing rows and data types conversion.
    Źródło:
    Journal of Medical Informatics & Technologies; 2004, 8; MM129-140
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Decision support and maintenance system for natural hazards, processes and equipment monitoring
    System wspomagania decyzji dla monitorowania zagrożeń naturalnych, procesów i urządzeń
    Autorzy:
    Kozielski, M.
    Sikora, M.
    Wróbel, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/300692.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    decision support system
    prediction
    expert system
    data cleaning
    process monitoring
    device monitoring
    hazard
    system wspomagania decyzji
    czyszczenie danych
    predykcja
    system ekspertowy
    monitorowanie procesów
    monitorowanie urządzeń
    monitorowanie zagrożeń
    Opis:
    This paper presents the DISESOR integrated decision support system and its applications. The system integrates data from different monitoring and dispatching systems and contains such modules as data preparation and cleaning, analytical, prediction and expert system. Architecture of the system is presented in the paper and a special focus is put on the presentation of two issues: data integration and cleaning, and creation of prediction model. The work contains also two case studies presenting the examples of the system application.
    W pracy przedstawiono zintegrowany system wspomagania decyzji DISESOR oraz jego zastosowania. System pozwala na integrację danych pochodzących z różnych systemów monitorowania i systemów dyspozytorskich. Struktura systemu DISESOR składa się z modułów realizujących: przygotowanie i czyszczenie danych, analizę danych, zadania predykcyjne oraz zadania systemu ekspertowego. W pracy przedstawiono architekturę systemu DISESOR, a szczególny nacisk został położony na zagadnienia związane z integracją i czyszczeniem danych oraz tworzeniem modeli predykcyjnych. Działanie systemu przedstawione zostało na dwóch przykładach analizy dla danych rzeczywistych.
    Źródło:
    Eksploatacja i Niezawodność; 2016, 18, 2; 218-228
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Are safety data sheets for cleaning products used in Norway a factor contributing to the risk of workers exposure to chemicals?
    Autorzy:
    Suleiman, Abdulqadir M.
    Svendsen, Kristin V.
    Powiązania:
    https://bibliotekanauki.pl/articles/2178899.pdf
    Data publikacji:
    2014-10-01
    Wydawca:
    Instytut Medycyny Pracy im. prof. dra Jerzego Nofera w Łodzi
    Tematy:
    cleaning workers
    safety data sheets
    cleaning products
    health information
    constituent components
    Safety measures
    Opis:
    Objectives: Cleaning products are considered less hazardous than those used in other sectors. Suppliers and distributors are less conscientious when it comes to informing users on health risks. The aim of the study was to elaborate on the usefulness and clarity of information in the safety data sheets (SDS) for cleaning products, and considering if the use of these SDSs can be seen as a risk factor towards occupational exposure to hazardous chemicals in the sector. Material and Methods: Safety data sheets were selected based on the risk level of the product assigned in an industrial sector scheme. 320 SDSs for cleaning products were reviewed. Constituent components found in the products over a given threshold were listed and available information thereof used to assess the perceived non-hazard consideration of the chemicals. Results: The contents of the SDSs was generic and mostly incomplete. Safety measures and health information lacked sufficient specificity despite varying compositions and concentrations of components. There is generally incompatibility between mentioned sections on the suggested non-hazardous nature of the products and health effects. Not all substances used in these products have harmonized classifications, which makes them open to various classification of the products and the suggested safety measures. This results in different companies classifying similar products differently. Risk management measures and suggested personal protective equipment (PPEs) are given haphazardly. Physical properties relevant to risk assessment are not included. Conclusions: The safety data sheets are ambiguous, and they lack relevant and important information. Inadequate information and risk assessment concerning the products can lead to workers being exposed to hazardous chemicals. Underestimation of the hazard contribution of the components of the products and the insufficient, non-objective mention of appropriate control and protective measures are the major contributing elements. There is a need to test the products in order to establish health effects and product specific safety measures.
    Źródło:
    International Journal of Occupational Medicine and Environmental Health; 2014, 27, 5; 840-853
    1232-1087
    1896-494X
    Pojawia się w:
    International Journal of Occupational Medicine and Environmental Health
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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