caa a22 4500 463207173 CHVBK 20180405153129.0 cr unu---uuuuu 170326e20071001xx s 000 0 eng 10.1007/s10462-009-9100-2 doi (NATIONALLICENCE)springer-10.1007/s10462-009-9100-2 P2P case storage and retrieval with an unspecified ontology [Elektronische Daten] [Shlomo Berkovsky, Tsvi Kuflik, Francesco Ricci] Traditional approaches for similarity-based retrieval of structured data, such as Case-Based Reasoning (CBR), have been largely implemented using centralized storage systems. In such systems, when the cases contain both numeric and free-text attributes, similarity-based retrieval cannot exploit standard speedup techniques based on multi-dimensional indexing, and the retrieval is implemented by an exhaustive comparison of the case to be solved with the whole set of stored cases. In this work, we review current research on Peer-to-Peer (P2P) and distributed CBR techniques and propose a novel approach for storage of the case-base in a decentralized Peer-to-Peer environment using the notion of Unspecified Ontology to improve the performance of the case retrieval stage and build CBR systems that can scale up to large case-bases. We develop an algorithm for efficient retrieval of approximated most-similar cases, which exploits inherent characteristics of the unspecified ontology in order to improve the performance of the case retrieval stage in the CBR problem solving cycle. The experiments show that the algorithm successfully retrieves cases close to the most-similar cases, while reducing the number of cases to be compared. Hence, it improves the performance of the retrieval stage. Moreover, the distributed nature of our approach eliminates the computational bottleneck and single point of failure of the centralized storage systems. Springer Science+Business Media B.V., 2009 Case-Based Reasoning nationallicence Similarity-based retrieval nationallicence Peer-to-Peer nationallicence Unspecified ontology nationallicence Berkovsky Shlomo CSIRO, ICT Centre, Hobart, Australia aut Kuflik Tsvi Management Information Systems Department, University of Haifa, Haifa, Israel aut Ricci Francesco Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy aut Artificial Intelligence Review Springer Netherlands 28/3(2007-10-01), 227-255 0269-2821 28:3<227 2007 28 10462 https://doi.org/10.1007/s10462-009-9100-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-009-9100-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Berkovsky Shlomo CSIRO, ICT Centre, Hobart, Australia aut NATIONALLICENCE 700 1- Kuflik Tsvi Management Information Systems Department, University of Haifa, Haifa, Israel aut NATIONALLICENCE 700 1- Ricci Francesco Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 28/3(2007-10-01), 227-255 0269-2821 28:3<227 2007 28 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer