Title : ( Ada-Context: adaptive context-aware grid-based approach for curation of data streams )
Authors: Mostafa Mirzaie , Behshid Behkamal , Mohammad Allahbakhsh , Samad Paydar , Elisa Bertino ,
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Abstract
Stream processing and real-time applications have changed how data is collected and processed. However, data quality is crucial for its usefulness. In this paper, we introduce Ada-Context, an approach that uses external contextual information to improve data quality assessment. It involves offline and online analysis components and uses a grid structure to map streaming data to cells, enhancing performance of quality control in data streams. Results show that contextual data especially external context improves data cleansing accuracy and the grid design boosts quality control effectiveness for data streams.
Keywords
, Streaming data, Data quality, Contextual information, Data quality assessment, Grid-based clustering
@article{paperid:1102689,
author = {Mirzaie, Mostafa and Behkamal, Behshid and Allahbakhsh, Mohammad and Paydar, Samad and الیزا برتینو},
title = {Ada-Context: adaptive context-aware grid-based approach for curation of data streams},
journal = {Data Mining and Knowledge Discovery},
year = {2025},
volume = {39},
number = {3},
month = {March},
issn = {1384-5810},
keywords = {Streaming data; Data quality; Contextual information; Data quality assessment; Grid-based clustering},
}
%0 Journal Article
%T Ada-Context: adaptive context-aware grid-based approach for curation of data streams
%A Mirzaie, Mostafa
%A Behkamal, Behshid
%A Allahbakhsh, Mohammad
%A Paydar, Samad
%A الیزا برتینو
%J Data Mining and Knowledge Discovery
%@ 1384-5810
%D 2025
