Results 1 
6 of
6
Brief Announcement: ART: SubLogarithmic Decentralized Range Query Processing with Probabilistic Guarantees
"... We focus on range query processing on largescale, typically distributed infrastructures. In this work we present the ART (Autonomous Range Tree) structure, which outperforms the most popular decentralized structures, including Chord (and some of its successors), BATON (and its successor) and SkipG ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
We focus on range query processing on largescale, typically distributed infrastructures. In this work we present the ART (Autonomous Range Tree) structure, which outperforms the most popular decentralized structures, including Chord (and some of its successors), BATON (and its successor) and SkipGraphs. ART supports the join/leave and range query operations in O(log log N) and O(log 2 b log N + A) expected w.h.p number of hops respectively, where the base b is a doubleexponentially power of two, N is the total number of peers and A  the answer size.
Achieving Spatial Adaptivity while Finding Approximate Nearest Neighbors
"... We present the first spatially adaptive data structure that answers approximate nearest neighbor (ANN) queries to points that reside in a geometric space of any constant dimension d. The Ltnorm approximation ratio is O(d 1+1/t), and the running time for a query q is O(d 2 lg δ(p, q)), where p is th ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
We present the first spatially adaptive data structure that answers approximate nearest neighbor (ANN) queries to points that reside in a geometric space of any constant dimension d. The Ltnorm approximation ratio is O(d 1+1/t), and the running time for a query q is O(d 2 lg δ(p, q)), where p is the result of the preceding query and δ(p, q) is the number of input points in a suitablysized box containing p and q. Our data structure has O(dn) size and requires O(d 2 n lg n) preprocessing time, where n is the number of points in the data structure. The size of the bounding box for δ depends on d, and our results rely on the Random Access Machine (RAM) model with word size Θ(lg n). 1
Dynamic 3sided Planar Range Queries with Expected Doubly Logarithmic Time
 Proceedings of ISAAC, 2009
"... Abstract. We consider the problem of maintaining dynamically a set of points in the plane and supporting range queries of the type [a, b] × (−∞, c]. We assume that the inserted points have their xcoordinates drawn from a class of smooth distributions, whereas the ycoordinates are arbitrarily distr ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
Abstract. We consider the problem of maintaining dynamically a set of points in the plane and supporting range queries of the type [a, b] × (−∞, c]. We assume that the inserted points have their xcoordinates drawn from a class of smooth distributions, whereas the ycoordinates are arbitrarily distributed. The points to be deleted are selected uniformly at random among the inserted points. For the RAM model, we present a linear space data structure that supports queries in O(log log n + t) expected time with high probability and updates in O(log log n) expected amortized time, where n is the number of points stored and t is the size of the output of the query. For the I/O model we support queries in O(log log B n + t/B) expected I/Os with high probability and updates in O(log B log n) expected amortized I/Os using linear space, where B is the disk block size. The data structures are deterministic and the expectation is with respect to the input distribution. 1
ART: sublogarithmic decentralized range query processing with probabilistic guarantees
, 2012
"... © Springer Science+Business Media New York 2012 Abstract We focus on range query processing on largescale, typically distributed infrastructures, such as clouds of thousands of nodes of shareddatacenters, of p2p distributed overlays, etc. In such distributed environments, efficient range query pro ..."
Abstract
 Add to MetaCart
© Springer Science+Business Media New York 2012 Abstract We focus on range query processing on largescale, typically distributed infrastructures, such as clouds of thousands of nodes of shareddatacenters, of p2p distributed overlays, etc. In such distributed environments, efficient range query processing is the key for managing the distributed data sets per se, and for monitoring