36572676
An efficient algorithm for hyperspherical range query processing in high-dimensional data space
Recently, similarity search on high-dimensional feature vectors has become an important search paradigm for various multimedia retrieval applications. The technique used is to map the data items as points into a high-dimensional feature space. The feature space is usually indexed using a multi-dimensional index structure. Similarity search then corresponds to the hyperspherical range query that returns all objects within a threshold level of similarity to the query object [1]. The hyperspherical range query for similarity search can be defined as follows:
6127186
An efficient nearest neighbor search in high-dimensional data spaces
Similarity search in multimedia databases requires an efficient support of nearest neighbor search on a large set of high-dimensional points. A technique applied for similarity search in multimedia databases is to transform important properties of the multimedia objects into points of a high-dimensional feature space. The feature space is usually indexed using a multidimensional index structure. Then, similarity search corresponds to a range search which returns all objects within a threshold level of similarity to the query objects, and a k-nearest neighbor search that returns the k most similar objects to the query object. Initially, traditional multidimensional data structures (e.g., R-tree [1], kd-tree [5]), which were designed for indexing low-dimensional spatial data, were used for indexing high-dimensional feature vectors. However, recent research activities [10,9,8] reported
36612071
SPY-TEC: An efficient indexing method for similarity search in high-dimensional data spaces
Most of all index structures based on the R-tree have failed to support eAcient indexing mechanisms for similarity search in high-dimensional data spaces. This is due to the fact that most of the index structures commonly use balanced split strategy in order to guarantee storage utilization and the shape of queries for similarity search is a hypersphere in high-dimensional spaces. In this paper, we propose the Spherical Pyramid-Technique (SPY-TEC), an eAcient indexing method for similarity search in high-dimensional data space. The SPY-TEC is based on a special partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into one-dimensional space as the Pyramid-Technique [14] does. Thus, we are able to use a B a -tree to manage the transformed one-dimensional data. We also propose the algorithms to process hyperspherical range queries on the data space partitioned by this partitioning strategy. Finally, we show that the SPY-TEC clearly outperforms other related techniques including the Pyramid-Technique in processing hyperspherical range queries through various experiments using synthetic and real data. ” 2000 Elsevier Science B.V. All rights reserved.
10484740
Labeling Points on a Single Line
In this paper, we consider a map labeling problem where the points to be labeled are restricted on a line. It is known that the 1d-4P and the 1d-4S unit-square label placement problem and the Slope-4P unit-square label placement problem can both be solved in linear time and the Slope-4S unit-square label placement problem can be solved in quadratic time in Ref. [8]. We extend the result to the following label placement problem: Slope-4P fixed-height (width) label or elastic label placement problem and present a linear time algorithm for it provided that the input points are given sorted. We further show that if the points are not sorted, the label placement problems have a lower bound of Ω(n log n), where n is the input size, under the algebraic computation tree model. Optimization versions of these point labeling problems are also considered.
728532
Performance and correctness of the ATM ABR rate control scheme
We study both the correctness and performance of the source/destination protocol of the available bit rate (ABR) service in asynchronous transfer mode (ATM) networks. Although the basic source/destination protocol for congestion management is relatively simple, the protocol specification has to cope with several "real-world" cases such as failures and delayed/lost feedback which may introduce complexity. Rigorous proofs of the correct functioning of the protocol based on a formal specification is necessary. We use a formal extended finite state machine (EFSM) model to show that the ABR source/destination protocol is free of live-locks, so that under all conditions both resource management (RM) and data cells will be transmitted. We also show that the network options of explicit forward congestion indication (EFCI) and explicit rate (ER) interoperate correctly. We use the understanding of the informal English description of the source/destination behavior and of our EFSM model to derive conditions that ensure that the source transmission rate is stable in the presence of delayed or lost feedback RM cells, especially under the operation of a source rule that requires the reduction of the source rate under these conditions. We arrive at bounds on the number of consecutive RM cell losses tolerated while the rate remains stable. We also provide a worst-case analysis of the delay in turning around RM cells at the destination station and the worst-case inter-departure time of forward RM cells from the source.
6583254
Rectilinear Path Problems among Rectilinear Obstacles Revisited
Efficient algorithms are presented for finding rectilinear collision-free paths between two given points among a set of rectilinear obstacles. The results improve the time complexity of previous results for finding the shortest rectilinear path, the minimum-bend shortest rectilinear path, the shortest minimum-bend rectilinear path and the minimum-cost rectilinear path. For finding the shortest rectilinear path, a graph-theoretic approach is used and an algorithm is obtained with $O(m\log t+ t\log^{3/2} t)$ running time, where $t$ is the number of extreme edges of given obstacles and $m$ is the number of obstacle edges. Based on this result an $O(N\log N+(m+N)\log t + (t+N)\log^2 (t+N))$ running time algorithm for computing the $L_1$ minimum spanning tree of given $N$ terminals among rectilinear obstacles is obtained. For finding the minimum-bend shortest path, the shortest minimum-bend rectilinear path, and the minimum-cost rectilinear path, we devise a new dynamic-searching approach and derive algorithms that run in $O(m\log^2m)$ time using $O(m\log m)$ space or run in $O(m\log^{3/2}m)$ time and space.
19612327
Instruction level redundant number computations for fast data intensive processing in asynchronous processors
Instruction level parallelism (ILP) is strictly limited by various dependencies. In particular, data dependency is a major performance bottleneck of data intensive applications. In this paper we address acceleration of the execution of instruction codes serialized by data dependencies. We propose a new computer architecture supporting a redundant number computation at the instruction level. To design and implement the scheme, an extended data-path and additional instructions are also proposed. The architectural exploitation of instruction level redundant number computations (IL-RNC) makes it possible to eliminate carry propagations. As a result execution of instructions which are serialized due to inherent data dependencies is accelerated. Simulations have been performed with data intensive processing benchmarks and the proposed architecture shows about a 1.2-1.35 fold speedup over a conventional counterpart. The proposed architecture model can be used effectively for data intensive processing in a microprocessor, a digital signal processor and a multimedia processor.
15124242
Solving the all-pair shortest path query problem on interval and circular-arc graphs
In this paper, we study the following all-pair shortest path query problem: Given the interval model of an unweighted interval graph of n vertices, build a data structure such that each query on the shortest path (or its length) between any pair of vertices of the graph can be processed eeciently (both sequentially and in parallel). We show that, after sorting the input intervals by their endpoints, a data structure can be constructed sequentially in O(n) time and O(n) space; using this data structure, each query on the length of the shortest path between any two intervals can be answered in O(1) time, and each query on the actual shortest path can be answered in O(k) time, where k is the number of intervals on that path. Furthermore, this data structure can be constructed optimally in parallel, in O(log n) time using O(n= log n) CREW PRAM processors; each query on the actual shortest path can be answered in O(1) time using k processors. Our techniques can be extended to solving the all-pair shortest path query problem on circular-arc graphs, both sequentially and in parallel, in the same complexity bounds. As an immediate consequence of our results, we improve by a factor of n the space complexity of the previously best known sequential all-pair shortest path algorithm for unweighted interval graphs.