Automated preprocessing of environmental data
| Autor: | Mauno Rönkkö, Ville Kotovirta, Venkatachalam Chandrasekar, Jani Heikkinen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
formal methods
010504 meteorology & atmospheric sciences Computer Networks and Communications Computer science ta1172 Cloud computing 02 engineering and technology computer.software_genre User requirements document 01 natural sciences Environmental data Set (abstract data type) reachability analysis data preprocessing 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences ta113 business.industry Hardware and Architecture 020201 artificial intelligence & image processing workflows Data mining Data pre-processing environmental informatics business computer Software |
| Zdroj: | Rönkkö, M, Heikkinen, J & Kotovirta, V 2015, ' Automated preprocessing of environmental data ', Future Generation Computer Systems, vol. 45, pp. 13-24 . https://doi.org/10.1016/j.future.2014.10.011 |
| DOI: | 10.1016/j.future.2014.10.011 |
| Popis: | In this article we discuss automated preprocessing of environmental data for further use. Environmental data is by default heterogeneous, as it may consist of data from sources such as weather stations, weather radars, chemical sensors, acoustic sensors, and off-line laboratory analysis. When integrating data from such heterogeneous sources, it needs to be processed in a context dependent manner. In addition, there is no single generic processing method; rather, several atomic methods need to be applied and in an appropriate sequence. Furthermore, the problem is complicated by the requirements set by the intended use of the data. The requirements influence not only the set of applicable methods but also the application sequence. In this article, we study automation of the selection and sequencing of preprocessing methods based on the user requirements. As the main contribution, we propose here the use of characterizations and a reachability algorithm to solve the selection and sequencing problem. In this article, we present the algorithm and argue for its correctness. We also discuss, how the algorithm is implemented as a cloud service, and illustrate the use of the service with simple case studies. A characterization based method for automated preprocessing of environmental data.A formalization of the preprocessing selection and sequencing problem.An algorithm solving the selection and sequencing problem.Simple case study implementation as a cloud service. |
| Databáze: | OpenAIRE |
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