Abstract.In vague queries, a user enters a value that represents some real world object and expects as the result the set of database values that represent this real world object even with not exact matching. The problem appears in databases that collect data from different sources or databases were different users enter data directly. Query engines usually rely on the use of some type of similarity metric to support data with inexact matching. The problem of building query engines to execute vague queries has been already studied, but an important problem still remains open, namely that of defining the threshold to be used when a similarity scan is performed over a database column. From the bibliography it is known that the threshold depends on the similarity metrics and also on the set of values being queried. Thus, it is unrealistic to expect that the user supplies a threshold at query time. In this paper we propose a process for estimation of recall/precision values for several thresholds for a database column. The idea is that this process is started by a database administrator in a pre-processing phase using samples extracted from database. The meta-data collected by this process may be used in query processing in the optimization phase. The paper describes this process as well as experiments that were performed in order to evaluate it.

This paper presents a method for assessing the quality of similarity functions. The scenario taken into account is that of approximate data matching, in which it is necessary to determine whether two data instances represent the same real world object. Our method is based on the semi-automatic estimation of optimal threshold values. We propose two methods for performing such estimation. The first method is an algorithm based on a reward function, and the second is a statistical method. Experiments were carried out to validate the techniques proposed. The results show that both methods for threshold estimation produce similar results. The output of such methods was used to design a grading function for similarity functions. This grading function, called discernability, was used to compare a number of similarity functions applied to an experimental data set.

Abstract. Retrieval queries that combine structural constraints with keyword search are placing new challenges on retrieval systems. This paper presents TReX-a new retrieval system for XML. TReX uses structural summaries to efficiently retrieve elements given structural constraints. TReX can efficiently return either all the answers to a given query or only the top-k answers. In this paper, we discuss our participation in the annual Initiative for the Evaluation of XML Retrieval (INEX) workshop in the ad-hoc track. Specifically, we investigate the use of summaries and the flexibility they provide when dealing with structural constraints. We present an algorithm for retrieval using summaries. Finally, experimental results are presented showing that TReX answers queries efficiently and effectively.

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