Towards better measures: evaluation of estimated resource description quality for distributed IR

An open problem for Distributed Information Retrieval systems (DIR) is how to represent large document repositories, also known as resources, both accurately and efficiently. Obtaining resource description estimates is an important phase in DIR, especially in non-cooperative environments. Measuring the quality of an estimated resource description is a contentious issue as current measures do not provide an adequate indication of quality. In this paper, we provide an overview of these currently applied measures of resource description quality, before proposing the Kullback-Leibler (KL) divergence as an alternative. Through experimentation we illustrate the shortcomings of these past measures, whilst providing evidence that KL is a more appropriate measure of quality. When applying KL to compare different QBS algorithms, our experiments provide strong evidence in favour of a previously unsupported hypothesis originally posited in the initial Query-Based Sampling work

A retrieval evaluation methodology for incomplete relevance assessments

In this paper we a propose an extended methodology for laboratory based Information Retrieval evaluation under in complete relevance assessments. This new protocol aims to identify potential uncertainty during system comparison that may result from incompleteness. We demonstrate how this methodology can lead towards a finer grained analysis of systems. This is advantageous, because the detection of uncertainty during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections

Adaptive query-based sampling for distributed IR

No abstract available

Updating collection representations for federated search

To facilitate the search for relevant information across a set of online distributed collections, a federated information retrieval system typically represents each collection, centrally, by a set of vocabularies or sampled documents. Accurate retrieval is therefore related to how precise each representation reflects the underlying content stored in that collection. As collections evolve over time, collection representations should also be updated to reflect any change, however, a current solution has not yet been proposed. In this study we examine both the implications of out-of-date representation sets on retrieval accuracy, as well as proposing three different policies for managing necessary updates. Each policy is evaluated on a testbed of forty-four dynamic collections over an eight-week period. Our findings show that out-of-date representations significantly degrade performance overtime, however, adopting a suitable update policy can minimise this problem

Augmenting entry: the possibilities for utilizing geo-referenced information to improve mobile calendar applications

Today's mobile communication devices often offer extensive calendar facilities. However the use of these is often very limited through cumbersome interfaces and inappropriate designs for small devices. Prompted by previous work in mobile calendar usability, this paper discusses how augmentation of calendar entries with mobile spatial information could provide potential advantages and improve the usability of an electronic calendar

An evaluation of resource description quality measures

An open problem for Distributed Information Retrieval is how to represent large document repositories (known as resources) efficiently. To facilitate resource selection, estimated descriptions of each resource are required, especially when faced with non-cooperative distributed environments. Accurate and efficient Resource description estimation is required as this can have an affect on resource selection, and as a consequence retrieval quality. Query-Based Sampling (QBS) has been proposed as a novel solution for resource estimation, with proceeding techniques developed therafter. However, the challenge to determine if one QBS technique is better at generating resource description than another is still an unresolved issue. The initial metrics tested and deployed for measuring resource description quality were the Collection Term Frequency ratio (CTF) and Spearman Rank Correlation Coefficient (SRCC). The former provides an indication of the percentage of terms seen, whilst the later measures the term ranking order, although neither consider the term frequency, which is important for resource selection. We re-examine this problem and consider measuring the quality of a resource description in context to resource selection, where an estimate of the probability of a term given the resource is typically required. We believe a natural measure for comparing the estimated resource against the actual resource is the Kullback-Leibler Divergence (KL) measure. KL addresses the concerns put forward previously, by not over-representing low frequency terms, and also considering term order. In this paper, we re-assess the two previous measures alongside KL. Our preliminary investigation revealed that the former metrics display contradictory results. Whilst, KL suggested a different QBS technique than that prescribed in, would provide better estimates. This is a significant result, because it now remains unclear as to which technique will consistently provide better resource descriptions. The remainder of this paper details the three measures, the experimental analysis of our preliminary study and outlines our points of concern along with further research directions