scholarly journals Optimism analysis of parallel queries in databases through multicores

2011 ◽  
Vol 3 (1) ◽  
pp. 156-164
Author(s):  
P Mohankumar ◽  
P Kumaresan ◽  
J Vaideeswaran
Keyword(s):  
Parallel Queries

A proof of Beigel's cardinality conjecture

1992 ◽  
Vol 57 (2) ◽  
pp. 677-681 ◽  
Author(s):  
Martin Kummer
Keyword(s):  
Complexity Theory ◽  
Structural Complexity ◽  
Halting Problem ◽  
Parallel Queries

In 1986, Beigel [Be87] (see also [Od89, III.5.9]) proved the nonspeedup theorem: if A, B ⊆ ω, and as a function of 2n variables can be computed by an algorithm which makes at most n queries to B, then A is recursive (informally, 2n parallel queries to a nonrecursive oracle A cannot be answered by making n sequential (or “adaptive”) queries to an arbitrary oracle B). Here, 2n cannot be replaced by 2n − 1. In subsequent papers of Beigel, Gasarch, Gill, Hay, and Owings the theory of “bounded query classes” has been further developed (see, for example, [BGGOta], [BGH89], and [Ow89]). The topic has also been studied in the context of structural complexity theory (see, for example, [AG88], [Be90], and [JY90]).If A ⊆ ω and n ≥ 1, let . Beigel [Be87] stated the powerful “cardinality conjecture” (CC): if A, B ⊆ ω, and can be computed by an algorithm which makes at most n queries to B, then A is recursive. Owings [Ow89] verified CC for n = 1, and, for n 1, he proved that A is recursive in the halting problem. We prove that CC is true for all n.

Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce

2016 ◽  
pp. 163-182
Author(s):  
Wei Yan
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Parallel Queries ◽  
Massive Spatial Data ◽  
Nearest Neighbor Queries

Parallel queries of k Nearest Neighbor for massive spatial data are an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, is a useful tool widely adopted by many applications including knowledge discovery, data mining, and spatial databases. In cloud computing environments, MapReduce programming model is a well-accepted framework for data-intensive application over clusters of computers. This chapter proposes a parallel method of kNN queries based on clusters in MapReduce programming model. Firstly, this chapter proposes a partitioning method of spatial data using Voronoi diagram. Then, this chapter clusters the data point after partition using k-means method. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on k-means clusters using MapReduce programming model. Finally, extensive experiments evaluate the efficiency of the proposed approach.

Multi-dimensional resource scheduling for parallel queries

1996 ◽  
Vol 25 (2) ◽  
pp. 365-376 ◽  
Author(s):  
Minos N. Garofalakis ◽  
Yannis E. Ioannidis
Keyword(s):  
Resource Scheduling ◽  
Parallel Queries

scholarly journals ON THE COMPLEXITY OF COMPUTING OPTIMAL SOLUTIONS

1991 ◽  
Vol 02 (03) ◽  
pp. 207-220 ◽  
Author(s):  
ZHI-ZHONG CHEN ◽  
SEINOSUKE TODA
Keyword(s):  
Polynomial Time ◽  
General Result ◽  
Optimization Problems ◽  
Randomized Algorithm ◽  
Optimal Solution ◽  
Maximum Clique ◽  
Optimal Solutions ◽  
Np Optimization ◽  
Parallel Queries ◽  
Class Of Functions

We study the computational complexity of computing optimal solutions (the solutions themselves, not just their cost) for NP optimization problems where the costs of feasible solutions are bounded above by a polynomial in the length of their instances (we simply denote by NPOP such an NP optimization problem). It is of particular interest to find a computational structure (or equivalently, a complexity class) which. captures that complexity, if we consider the problems of computing optimal solutions for NPOP’s as a class of functions giving those optimal solutions. In this paper, we will observe that [Formula: see text] the class of functions computable in polynomial-time with one free evaluation of unbounded parallel queries to NP oracle sets, captures that complexity. We first show that for any NPOP Π, there exists a polynomial-time bounded randomized algorithm which, given an instance of Π, uses one free evaluation of parallel queries to an NP oracle set and outputs some optimal solution of the instance with very high probability. We then show that for several natural NPOP’s, any function giving those optimal solutions is at least as computationally hard as all functions in [Formula: see text]. To show the hardness results, we introduce a property of NPOP’s, called paddability, and we show a general result that if Π is a paddable NPOP and its associated decision problem is NP-hard, then all functions in [Formula: see text] are computable in polynomial-time with one free evaluation of an arbitrary function giving optimal solutions for instances of Π. The hardness results are applications of this general result. Among the NPOP’s, we include MAXIMUM CLIQUE, MINIMUM COLORING, LONGEST PATH, LONGEST CYCLE, 0–1 TRAVELING SALESPERSON, and 0–1 INTEGER PROGRAMMING.

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