Results 1 - 10
of
1,769
Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals
, 1996
"... Abstract. Data analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional aggregates. Applications need the N-dimensional generalization of these op ..."
Abstract
-
Cited by 860 (11 self)
- Add to MetaCart
in more complex non-procedural data analysis programs. The cube operator treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensional cube. Super-aggregates are computed
Performance analysis of k-ary n-cube interconnection networks
- IEEE Transactions on Computers
, 1990
"... Abstmct-VLSI communication networks are wire-limited. The cost of a network is not a function of the number of switches required, but rather a function of the wiring density required to construct the network. This paper analyzes commu-nication networks of varying dimension under the assumption of co ..."
Abstract
-
Cited by 357 (18 self)
- Add to MetaCart
of constant wire bisection. Expressions for the latency, average case throughput, and hot-spot throughput of k-ary n-cube networks with constant bisection are derived that agree closely with experi-mental measurements. It is shown that low-dimensional networks (e.g., tori) have lower latency and higher hot
The Turn Model for Adaptive Routing
- JOURNAL OF ACM
, 1994
"... This paper presents a model for designing wormhole routing algorithms, A unique feature of the model is th~t lt is not based cm adding physical or virtual channels to direct networks (although it can be applied to networks with extra channels). Instead, the model is based [In analyzlng the directio ..."
Abstract
-
Cited by 361 (6 self)
- Add to MetaCart
topologies for wormhole routing, ~z-dimensional meshes and k-ary /z-cubes without extra channels. In such networks, just a quarter of the turns must be prohibited to prevent deadlock. The remaining three quarters of the turns allow routing to be fidaptwe, Adaptive routing algorithms are described for two-dimensional
Transmitting in the n-dimensional cube
- Discrete Applied Mathematics
, 1992
"... Alon, N., Transmitting in the n-dimensional cube, Discrete Applied Mathematics 37/38 (1992) 9-1 I. Motivated by a certain communication problem we show that for any integer n and for any sequence (a,,...,ak) of k = [n/21 binary vectors of length n, there is a binary vector z of length n whose Hammin ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
Hamming distance from a, is strictly bigger than k-i for all 1 5 i 5 k. The n-dimensional cube is the graph whose vertices are all 2 ” binary vectors of length n, in which two vertices are adjacent if and only if their Hamming distance is 1, i.e., they differ in precisely one coordinate. Suppose
Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
, 1998
"... We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a space-efficient data structure that would allow us to ..."
Abstract
-
Cited by 215 (9 self)
- Add to MetaCart
to search, given a query vector, for the closest or nearly closest vector in the database. We also address this problem when distances are measured by the L 1 norm, and in the Hamming cube. Significantly improving and extending recent results of Kleinberg, we construct data structures whose size
A Lower Bound on the Complexity of Approximate Nearest-Neighbor Searching on the Hamming Cube
- In Proc. 31th Annual ACM Symposium on Theory of Computing (STOC’99
, 1999
"... We consider the nearest-neighbor problem over the d-cube: given a collection of points in {0, 1} d , find the one nearest to a query point (in the L 1 sense). We establish a lower bound of###90 log d/ log log log d) on the worst-case query time. This result holds in the cell probe model with ( ..."
Abstract
-
Cited by 19 (3 self)
- Add to MetaCart
We consider the nearest-neighbor problem over the d-cube: given a collection of points in {0, 1} d , find the one nearest to a query point (in the L 1 sense). We establish a lower bound of###90 log d/ log log log d) on the worst-case query time. This result holds in the cell probe model
On homomorphisms from the Hamming cube to Z
- Israel J. Math
"... Write F for the set of homomorphisms from {0, 1} d to Z which send 0 to 0 (think of members of F as labellings of {0, 1} d in which adjacent strings get labels differing by exactly 1), and Fi for those which take on exactly i values. We give asymptotic formulae for |F | and |Fi|. In particular, we s ..."
Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets
"... Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, a ..."
Abstract
-
Cited by 198 (3 self)
- Add to MetaCart
, approximate answers to OLAP aggregation queries. In this paper, we present anovel method that provides approximate answers to high-dimensional OLAP aggregation queries in massive sparse data sets in a time-efficient and space-efficient manner. We construct a compact data cube, which is an approximate
Earth Mover Distance over High-Dimensional Spaces
, 2007
"... The Earth Mover Distance (EMD) between two equalsize sets of points in R d is defined to be the minimum cost of a bipartite matching between the two pointsets. It is a natural metric for comparing sets of features, and as such, it has received significant interest in computer vision. Motivated by re ..."
Abstract
-
Cited by 22 (8 self)
- Add to MetaCart
Hamming cube into ℓ1 must incur a distortion Ω(d), thus practically losing all distance information. We circumvent this roadblock by focusing on sets with cardinalities upperbounded by a parameter s, and achieve a distortion of only O(log s · log d). Since in applications the feature sets have bounded
Results 1 - 10
of
1,769