We review the major paradigms for approximate similarity queries and propose a classification schema that easily allows existing approaches to be compared along several independent coordinates. Then, we discuss the impact that scheduling of index nodes can have on performance and show that, unlike exact similarity queries, no provable optimal scheduling strategy exists for approximate queries. On the positive side, we show that optimal-on-the-average schedules are well-defined and that their performance is indeed the best among practical schedules
Abstract. A critical issue in large scale search engines is to efficiently handle sudden peaks of in...
Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological ap...
Approximate query processing based on multiple similarity metrics is prevalent and essential for man...
We review the major paradigms for approximate similarity queries and propose a classification schema...
In this article, we review the major paradigms for approximate similarity queries and propose a cla...
AbstractWe review the major paradigms for approximate similarity queries and propose a classificatio...
Approximate similarity queries are a practical way to obtain good, yet suboptimal, results from larg...
Abstract—We consider the problem of finding similar patterns in a time sequence. Typical application...
Metric databases are databases where a metric distance function is defined for pairs of database obj...
Abstract. Similarity queries searching for the most similar objects in a database compared to a give...
Similarity search is important for many data-intensive applications to identify a set of similar obj...
For an increasing number of modern database applica-tions, efficient support of similarity search be...
Abstract. The indexing algorithms and data structures for similarity searching in metric spaces seem...
The nearest- or near-neighbor query problems arise in a large variety of database applications, usua...
Today, a myriad of data sources, from the Internet to business operations to scientific instruments,...
Abstract. A critical issue in large scale search engines is to efficiently handle sudden peaks of in...
Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological ap...
Approximate query processing based on multiple similarity metrics is prevalent and essential for man...
We review the major paradigms for approximate similarity queries and propose a classification schema...
In this article, we review the major paradigms for approximate similarity queries and propose a cla...
AbstractWe review the major paradigms for approximate similarity queries and propose a classificatio...
Approximate similarity queries are a practical way to obtain good, yet suboptimal, results from larg...
Abstract—We consider the problem of finding similar patterns in a time sequence. Typical application...
Metric databases are databases where a metric distance function is defined for pairs of database obj...
Abstract. Similarity queries searching for the most similar objects in a database compared to a give...
Similarity search is important for many data-intensive applications to identify a set of similar obj...
For an increasing number of modern database applica-tions, efficient support of similarity search be...
Abstract. The indexing algorithms and data structures for similarity searching in metric spaces seem...
The nearest- or near-neighbor query problems arise in a large variety of database applications, usua...
Today, a myriad of data sources, from the Internet to business operations to scientific instruments,...
Abstract. A critical issue in large scale search engines is to efficiently handle sudden peaks of in...
Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological ap...
Approximate query processing based on multiple similarity metrics is prevalent and essential for man...