Project Summary: The RECLAIM project aims to establish the theoretical and algorithmic foundations for a new generation of data access methods and indexing structures that efficiently manage and retrieve information in compressed and machine learning-based data environments. Modern data management systems increasingly rely on compression and learned representations to reduce storage and energy costs, yet these techniques disrupt traditional access paths and indexing principles. RECLAIM will address this challenge by developing (i) compression-aware access methods that optimize retrieval using compression metadata and signatures, and (ii) model-driven indexing mechanisms capable of efficiently querying data reconstructed or predicted by machine learning models. The research will advance the state of the art through analytical modeling, algorithmic design, and experimental validation, progressing from low to medium technological readiness. The project is implemented by a strong partnership between the Cyprus University of Technology (CUT) and Rinnoco Ltd, combining academic excellence and industrial expertise. The outcomes will strengthen Cyprus' position in advanced data management research, promote open science, and lay the groundwork for future applied innovations in data-intensive systems.

Details:

Programme	PILLAR II. SUSTAINABLE RTDI SYSTEM PROGRAMME - EXCELLENCE HUBS
Proposal Number	EXCELLENCE/0925/0331
Proposal Title	Retrieval and Compression-aware Data Layer for Advanced Information Management
Proposal Acronym	RECLAIM
Partners	Cyprus University of Technology (CUT) and Rinnoco Ltd
TRL	TRL 1-3
Funding	The project is implemented under the programme of social cohesion “THALIA 2021- 2027” co-funded by the European Union, through Research and Innovation Foundation.

Research Focus: Compression-aware retrieval, learned and model-driven indexing, analytical modeling, algorithmic design, and experimental validation for advanced information management.