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https://hdl.handle.net/20.500.14279/35355| Title: | A Multiple Compression Approach using Attribute-based Signatures | Authors: | Costa, Marios Costa, Constantinos Chrysanthis, Panos Herodotou, Herodotos Stavrakis, Efstathios Nikolaou, Nikolas |
Major Field of Science: | Engineering and Technology | Field Category: | Computer and Information Sciences | Issue Date: | 10-Feb-2025 | Source: | Open Research Europe, 2025 | Link: | https://open-research-europe.ec.europa.eu/articles/5-49/v1 | Abstract: | With the increasing volume of data collected for advanced analytical and AI applications, data storage remains a significant challenge. Despite advancements in storage technologies, the cost of maintaining vast datasets continues to grow. Compression techniques have been widely used to address this issue, but existing systems primarily rely on a single, typically lossless method, which limits adaptability to varying data characteristics. | URI: | https://hdl.handle.net/20.500.14279/35355 | DOI: | 10.12688/openreseurope.19247.1 | Rights: | CC0 1.0 Universal | Type: | Article | Affiliation : | Cyprus University of Technology Algolysis Ltd Rinnoco Ltd |
Funding: | Grant Information: This work is implemented under the programme of social cohesion “THALIA 2021-2027” co-funded by the European Union, through Research and Innovation Foundation, under project COMPASS - CONCEPT/0823/0002, and is also partially supported by the European Union’s Horizon Europe program for Research and Innovation through the HYPER-AI project under Grant No. 101135982. The views, findings, conclusions, or recommendations expressed in this material are solely those of the author(s) and do not necessarily represent those of the sponsors. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
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