Beyond term indexing: A P2P framework for Web information retrieval
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Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and ...
We present a query-driven algorithm for the distributed indexing of large document collections within structured P2P networks. To cope with bandwidth consumption that has been identified as the major problem for the standard P2P approach with single term i ...
Image-based retrieval in large Earth observation archives is difficult, because one needs to navigate across thousands of candidate matches only with the proposition image as a guide. By using text as a query language, the retrieval system gains in usabili ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
Selection and aggregation of ranking criteria became an important topic in information retrieval as search is getting more specialized and as volume of electronically available information grows. In this context, document ranking has undergone a shift from ...
This lecture introduces systematically into the problem of managing large data collections in peer-to-peer systems. Search over large datasets has always been a key problem in peer-to-peer systems and the peer-to-peer paradigm has incited novel directions ...
In this paper, we present a query-driven indexing/retrieval strategy for efficient full text retrieval from large document collections distributed within a structured P2P network. Our indexing strategy is based on two important properties: (1) the generate ...
The Probalistic Latent Semantic Indexing model, introduced by T. Hofmann (1999), has engendered applications in numerous fields, notably document classification and information retrieval. In this context, the Fisher kernel was found to be an appropriate do ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
Efficient and effective search in large-scale data repositories requires complex indexing solutions deployed on a large number of servers. Web search engines such as Google and Yahoo! already rely upon complex systems to be able to return relevant query re ...