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Semantic-aware data quality assessment for image big data
ارزیابی کیفیت داده های آگاهانه معنایی برای داده های بزرگ تصویر-2020 Data quality (DQ) assessment is essential for realizing the promise of big data by judging the value of
data in advance. Relevance, an indispensable dimension of DQ, focusing on ‘‘fitness for requirement’’,
can arouse the user’s interest in exploiting the data source. It has two-level evaluations: (1) the amount
of data that meets the user’s requirements; (2) the matching degree of these relevant data. However,
there lack works of DQ assessment at dimension of relevance, especially for unstructured image
data which focus on semantic similarity. When we try to evaluate semantic relevance between an
image data source and a query (requirement), there are three challenges: (1) how to extract semantic
information with generalization ability for all image data? (2) how to quantify relevance by fusing the
quantity of relevant data and the degree of similarity comprehensively? (3) how to improve assessing
efficiency of relevance in a big data scenario by design of an effective architecture?
To overcome these challenges, we propose a semantic-aware data quality assessment (SDQA)
architecture which includes off-line analysis and on-line assessment. In off-line analysis, for an image
data source, we first transform all images into hash codes using our improved Deep Self-taught Hashing
(IDSTH) algorithm which can extract semantic features with generalization ability, then construct
a graph using hash codes and restricted Hamming distance, next use our designed Semantic Hash
Ranking (SHR) algorithm to calculate the importance score (rank) for each node (image), which takes
both the quantity of relevant images and the degree of semantic similarity into consideration, and
finally rank all images in descending order of score. During on-line assessment, we first convert
the user’s query into hash codes using IDSTH model, then retrieve matched images to collate their
importance scores, and finally help the user determine whether the image data source is fit for his
requirement. The results on public dataset and real-world dataset show effectiveness, superiority and
on-line efficiency of our SDQA architecture. Keywords: Semantic-aware | Quality assessment | Image big data | IDSTH | SHR |
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