57,267 research outputs found
Development and evaluation of clustering techniques for finding people
Typically in a large organisation much expertise and knowledge is held informally within employees' own memories. When employees leave an organisation many documented links that go through that person are broken and no mechanism is usually available to overcome these broken links. This match making problem is related to the problem of finding potential work partners in a large and distributed organisation. This paper reports a comparative investigation into using standard information retrieval techniques to group employees together based on their webpages. This information can, hopefully, be subsequently used to redirect broken links to people who worked closely with a departed employee or used to highlight people, say indifferent departments, who work on similar topics. The paper reports the design and positive results of an experiment conducted at Risø National Laboratory comparing four different IR searching and clustering approaches using real users' web pages
Peer to Peer Information Retrieval: An Overview
Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom
Efficient XML Keyword Search based on DAG-Compression
In contrast to XML query languages as e.g. XPath which require knowledge on
the query language as well as on the document structure, keyword search is open
to anybody. As the size of XML sources grows rapidly, the need for efficient
search indices on XML data that support keyword search increases. In this
paper, we present an approach of XML keyword search which is based on the DAG
of the XML data, where repeated substructures are considered only once, and
therefore, have to be searched only once. As our performance evaluation shows,
this DAG-based extension of the set intersection search algorithm[1], [2], can
lead to search times that are on large documents more than twice as fast as the
search times of the XML-based approach. Additionally, we utilize a smaller
index, i.e., we consume less main memory to compute the results
Sorting a Low-Entropy Sequence
We give the first sorting algorithm with bounds in terms of higher-order
entropies: let be a sequence of length containing distinct elements
and let (H_\ell (S)) be the th-order empirical entropy of , with
(n^{\ell + 1} \log n \in O (m)); our algorithm sorts using ((H_\ell (S) + O
(1)) m) comparisons
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