We compare the performance of two database selection algorithms reported in the literature. Their performance is compared using a common testbed designed specifically for database selection techniques. The testbed is a decomposition of the TREC TIPSTER data into 236 subcollections. The databases from our testbed were ranked using both the gGlOSS and CORI techniques and compared to a baseline derived from TREC relevance judgements. We examined the degree to which CORI and gGlOSS approximate this baseline. Our results con rm our earlier observation that the gGlOSS Ideall ranks do not estimate relevancebased ranks well. We also nd that CORI is a uniformly better estimator of relevance-based ranks than gGlOSS for the test environment used in this study. Part of the advantage of the CORI algorithm can be explained by a strong correlation between gGlOSS and a size-based baseline SBR. We also nd that CORI produces consistently accurate rankings on testbeds ranging from 100 921 sites. However for a given level of recall, search e ort appears to scale linearly with the number of databases.

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Distributed searching is cast in three parts -database selection, query processing, and results merging. In this paper we examine the effect of database selection on retrieval performance. We look at retrieval performance in three different distributed retrieval testbeds and distill some general results. First we find that good database selection can result in better retrieval effectiveness than can be achieved in a centralized database. Second we find that good performance can be achieved when only a few sites are selected and that the performance generally increases as more sites are selected. Finally we find that when database selection is employed, it is not necessary to maintain collection wide information (CWI), e.g. global idf. Local information can be used to achieve superior performance. This means that distributed systems can be engineered with more autonomy and less cooperation. This work suggests that improvements in database selection can lead to broader improvements in retrieval performance, even in centralized (i.e. single database) systems. Given a centralized database and a good selection mechanism, retrieval performance can be improved by decomposing that database conceptually and employing a selection step.

We describe a testbed for database selection techniques and an experiment conducted using this testbed. The testbed is a decomposition of the TREC/TIPSTER data that allows analysis of the data along multiple dimensions, including collection-based and temporal-based analysis. We characterize the subcollections in this testbed in terms of number of documents, queries against which the document,s have been evaluated for relevance, and distribution of relevant documents.We then present initial results from a study conducted using this testbed that examines the effectiveness of the gGlOSS approach to database selection. The databases from our testbed were ranked using the gGl0S.S techniques and compared to the gGlOSS I&l(l) baseline and a baseline derived from TREC relevance judgements.We have examined the degree to which several gGlOSS estimate functions approximate these baselines. Our initial results confirm that the gGZOSS estimators are excellent predictors of the Ideal(Z) ranks but that the Ideal(l) ranks do not estimate relevance-based ranks well.

Clustering partitions a collection of objects into groups called clusters, such that similar objects fall into the same group. Similarity between objects is defined by a distance function satisfying the triangle inequality; this distance function along with the collection of objects describes a distance space. In a distance space, the only operation possible on data objects is the computation of distance between them. All scalable algorithms in the literature assume a special type of distance space, namely a k-dimensional vector space, which allows vector operations on objects.We present two scalable algorithms designed for clustering very large datasets in distance spaces. Our first algorithm BUBBLE is, to our knowledge, the first scalable clustering algorithm for data in a distance space. Our second algorithm BUBBLE-FM improves upon BUBBLE by reducing the number of calls to the distance function, which may be computationally very expensive. Both algorithms make only a single scan over the database while producing high clustering quality. In a detailed experimental evaluation, we study both algorithms in terms of scalability and quality of clustering. We also show results of applying the algorithms to a real-life dataset.use the term distance space to emphasize that only distance computations are possible between objects. We call an n-dimensional space a coordinate space to emphasize that vector operations like centroid computation, sum, and difference of vectors are possible.2 The edit distance between two strings is the number of simple edit operations required to transform one string into the other.

Clustering partitions a collection of objects into groups called clusters, such that similar objects fall into the same group. Similarity between objects is defined by a distance function satisfying the triangle inequality; this distance function along with the collection of objects describes a distance space. In a distance space, the only operation possible on data objects is the computation of distance between them. All scalable algorithms in the literature assume a special type of distance space, namely a k-dimensional vector space, which allows vector operations on objects. We present two scalable algorithms designed for clustering very large datasets in distance spaces. Our first algorithm BUBBLE is, to our knowledge, the first scalable clustering algorithm for data in a distance space. Our second algorithm BUBBLE-FM improves upon BUBBLE by reducing the number of calls to the distance function, which may be computationally very expensive. Both algorithms make only a single scan over the database while producing high clustering quality. In a detailed experimental evaluation, we study both algorithms in terms of scalability and quality of clustering. We also show results of applying the algorithms to a real-life dataset. use the term distance space to emphasize that only distance computations are possible between objects. We call an n-dimensional space a coordinate space to emphasize that vector operations like centroid computation, sum, and difference of vectors are possible. 2 The edit distance between two strings is the number of simple edit operations required to transform one string into the other.

The proliferation of online information resources increases the importance of effective and efficient information retrieval in a multicollection environment. Multicollection searching is cast in three parts: collection selection (also referred to as database selection), query processing and results merging. In this work, we focus our attention on the evaluation of the first step, collection selection.In this article, we present a detailed discussion of the methodology that we used to evaluate and compare collection selection approaches, covering both test environments and evaluation measures. We compare the CORI, CVV and gGlOSS collection selection approaches using six test environments utilizing three document testbeds. We note similar trends in performance among the collection selection approaches, but the CORI approach consistently outperforms the other approaches, suggesting that effective collection selection can be achieved using limited information about each collection.The contributions of this work are both the assembled evaluation methodology as well as the application of that methodology to compare collection selection approaches in a standardized environment.

As more online databases are integrated into digital libraries, the issue of quality control of the data becomes increasingly important, especially as it relates to the effective retrieval of information. Authority work, the need to discover and reconcile variant forms of strings in bibliographic entries, will become more critical in the future. Spelling variants, misspellings, and transliteration differences will all increase the difficulty of retrieving information. We investigate a number of approximate string matching techniques that have traditionally been used to help with this problem. We then introduce the notion of approximate word matching and show how it can be used to improve detection and categorization of variant forms. We demonstrate the utility of these approaches using data from the Astrophysics Data System and show how we can reduce the human effort involved in the creation of authority files.

As more online databases are integrated into digital libraries, the issue of quality control of the data becomes increasingly important, especially as it relates to the effective retrieval of information. The need to discover and reconcile variant forms of strings in bibliographic entries, i.e., authority work, will become more critical in the future. Spelling variants, misspellings, and transliteration differences will all increase the difficulty of retrieving information. Approximate string matching has traditionally been used to help with this problem. In this paper we introduce the notion of approximate word matching and show how it can be used to improve detection and categorization of variant forms.