Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
On the fusion of documents from multiple collection information retrieval systems
Journal of the American Society for Information Science
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
The impact of database selection on distributed searching
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Database merging strategy based on logistic regression
Information Processing and Management: an International Journal
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 27th International Conference on Very Large Data Bases
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
STARTS: Stanford Protocol Proposal for Internet Retrieval and Search
STARTS: Stanford Protocol Proposal for Internet Retrieval and Search
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Engineering a multi-purpose test collection for web retrieval experiments
Information Processing and Management: an International Journal
Shadow document methods of resutls merging
Proceedings of the 2004 ACM symposium on Applied computing
The FedLemur project: Federated search in the real world
Journal of the American Society for Information Science and Technology
Capturing collection size for distributed non-cooperative retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
From uncertain inference to probability of relevance for advanced IR applications
ECIR'03 Proceedings of the 25th European conference on IR research
Results merging algorithm using multiple regression models
ECIR'07 Proceedings of the 29th European conference on IR research
Hi-index | 0.00 |
The problem of results merging in distributed information retrieval environments has gained significant attention the last years. Two generic approaches have been introduced in research. The first approach aims at estimating the relevance of the documents returned from the remote collections through ad hoc methodologies (such as weighted score merging, regression etc.) while the other is based on downloading all the documents locally, completely or partially, in order to calculate their relevance. Both approaches have advantages and disadvantages. Download methodologies are more effective but they pose a significant overhead on the process in terms of time and bandwidth. Approaches that rely solely on estimation on the other hand, usually depend on document relevance scores being reported by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging algorithms, need a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced is based on adaptively downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. Thus it reconciles the above two approaches, combining their strengths, while minimizing their drawbacks, achieving the limited time and bandwidth overhead of the estimation approaches and the increased effectiveness of the download. The proposed algorithm is tested in a variety of settings and its performance is found to be significantly better than the former, while approximating that of the latter.