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Top-k processing in Uncertain Databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty information makes traditional top-k processing techniques inapplicable to uncertain databases. The existing approaches are all based on the assumption that the underlying data are exact (or certain). We construct a framework that encapsulates...
The task of k-nearest neighbor search is to find the k nearest neighbors of a query vector in the data set. Due to the orthogonality of the Haar wavelet transform, the k nearest neighbors, searching in the spatial domain, are the same as that in the wavelet domain. In addition, the transform can compress the energy into a few wavelet coefficients with low computational complexity. Therefore, some...
Optimum subwindow search for object detection aims to find a subwindow so that the contained subimage is most similar to the query object. This problem can be formulated as a four dimensional (4D) maximum entry search problem wherein each entry corresponds to the quality score of the subimage contained in a subwindow. For n × n images, a naive exhaustive search requires O(n4) sequential computations...
This paper addresses the problem of finding the nearest neighbor (or one of the R-nearest neighbors) of a query object in a database which is only accessible through a comparison oracle. The comparison oracle, given two reference objects and a query object, returns the reference object closest to the query object. The oracle attempts to model the behavior of human users, capable of making statements...
Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful...
Retrieving the k-nearest neighbors of a query object is a basic primitive in similarity searching. A related, far less explored primitive is to obtain the dataset elements which would have the query object within their own k-nearest neighbors, known as the reverse k-nearest neighbor query. We already have indices and algorithms to solve k-nearest neighbors queries in general metric spaces; yet, in...
The size of the intermediate results produced while executing queries has a direct impact on query optimizers. Larger size of intermediate results requires more memory usage and more computational power to evaluate their join predicates. Furthermore, if memory size is not big enough, secondary storage will be needed. This paper proposes the Exhaustive Greedy (EG) algorithm to optimize the intermediate...
Two decision problems are presented that arise from reversing the operation of a distance-based indexing tree. Whereas similarity search finds points in the tree given a query point, reverse similarity search begins with a set of constraints like those defining a leaf and generates a point meeting the constraints. These problems derive from robust hashing, a technique used in similarity search and...
Two decision problems are presented that arise from reversing the operation of a distance-based indexing tree. Whereas similarity search finds points in the tree given a query point, reverse similarity search begins with a set of constraints like those defining a leaf and generates a point meeting the constraints. These problems derive from robust hashing, a technique used in similarity search and...
In 1983, Akhus proved that randomization can speedup local search. For example, it reduces the query complexity of local search over grid [1 : n]d from ominus(nd-1) to 0(d1/2nd/2). It remains open whether randomisation helps fixed-point computation. Inspired by the recent advances on the complexity of equilibrium computation, we solve this open problem by giving an asymptotically tight bound of (Omega(n))...
In this paper, we address one of the wireless sensor network query processing issues posed due to the lack of support for multiple sensor network queries. The objective of the paper is to provide efficient and effective support to multiple queries so that the set of queries are pre-processed before disseminating them into the sensor network. It is very important that only necessary works will be assigned...
The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method using compression technique to overcome the difficulty of 'curse of dimensionality'. The VA-file method combined with tree-based index structure can improve the querying efficiency, but it still succumbs to the 'curse of dimensionality'. In this paper, a new high-dimensional indexing structure called...
Many information processing and computing problems can be traced back to find the extreme value of a database or a function. Unfortunately, classical solutions suffer from high computational complexity if the database is unsorted or, equivalently, the function has many local minimum/maximum points. Proposed quantum computing-based solutions involve the repeated application of Grower's searching algorithm...
k-Nearest neighbor (KNNC) classifier is the most popular non-parametric classifier. But it requires much classification time to search k nearest neighbors of an unlabelled object point, which badly affects its efficiency and performance. In this paper, an adaptive k-nearest neighbors classifier (AKNNC) is proposed. The algorithm can find k nearest neighbors of the unlabelled point in a small hypersphere...
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