Kevin Wellenzohn, Hannes Mitterer, J. Gamper, Michael H. Böhlen, Mourad Khayati
In this paper, we present a simple yet effective algorithm, called the Top-k Case Matching algorithm, for the imputation of miss- ing values in streams of time series data that are similar to each other. The key idea of the algorithm is to look for the k situations in the historical data that are most similar to the current situation and to derive the missing value from the measured values at these k time points. To efficiently identify the top-k most similar historical situations, we adopt Fagin’s Threshold Algorithm, yielding an al- gorithm with sub-linear runtime complexity with high probability, and linear complexity in the worst case (excluding the initial sort- ing of the data, which is done only once). We provide the results of a first experimental evaluation using real-world meteorological data. Our algorithm achieves a high accuracy and is more accurate and efficient than two more complex state of the art solutions.
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Pub Date : 1900-01-01DOI: 10.1007/3-540-29325-6_31
W. May
{"title":"Reasoning im und für das Semantic Web","authors":"W. May","doi":"10.1007/3-540-29325-6_31","DOIUrl":"https://doi.org/10.1007/3-540-29325-6_31","url":null,"abstract":"","PeriodicalId":151717,"journal":{"name":"Grundlagen von Datenbanken","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125454946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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