Abstract
The advent of the Big Data era has led to the heterogeneity of data from multiple sources, and traditional database management techniques are overstretched in the face of the increasing complexity and variability of data. As a result, the concept of data spaces has been developed. The multiple and heterogeneous nature of data in the current context makes it necessary to provide a variety of query methods in the data space. As heterogeneous data contains various types of data structures, and traditional information retrieval mainly targets text documents to establish indexing relationships, it cannot provide queries to meet the needs of multiple sources of heterogeneous data. Therefore, this paper compares the current advanced algorithms used in cross-modal retrieval based on the data space to further understand cross-modal retrieval.
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This work was supported by the National Key R&D Program of China (grant number 2020YFB1707801).
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Cui, X., Niu, D., Feng, J. (2023). Cross-Modal Retrieval Based on Deep Hashing in the Context of Data Space. In: Xu, Z., Alrabaee, S., Loyola-González, O., Cahyani, N.D.W., Ab Rahman, N.H. (eds) Cyber Security Intelligence and Analytics. CSIA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-031-31775-0_37
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