.png){kind=icon}
.png){kind=icon}
Return
A data reuse strategy based on deep learning for high dimensional data's pattern and instance similarity
.png){kind=icon}
PRE
AI
DOI：10.1007/s00607-021-00964-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Data reuse strategy is an effective method to save storage space and improve data utilization in data management. In view of the successful application of deep learning in the field of text mining, a data reuse strategy based on deep learning is proposed for high dimensional data's pattern and instance similarity. With traditional feature analysis and deep learning model of convolutional neural network, the pattern similarity of data dimension is analyzed so as to optimize the similar dimension pairs among high dimensional data sets. Combining inner-attention mechanism, a semantic similarity model IA-LSTM is designed for instance similarity, which can build the association mapping among data entities by the calculation of the similarity of short text. Based on the pattern and instance similarity in the proposed strategy, reusable data entities are discovered, and column storage is designed to improve data reuse efficiency.
Keywords:
Data reuse
High dimensional data
Deep learning
Pattern similarity
Instance similarity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.