We study the problem of computing the join of n relations in mul-tiple rounds of MapReduce. We introduce a distributed and gen-eralized version of Yannakakis’s algorithm, called GYM. GYM takes as input any generalized hypertree decomposition (GHD) of a query of width w and depth d, and computes the query in O(d+log(n)) rounds andO(n (IN w+OUT)2 M) communication cost, where M is the memory available per machine in the cluster and IN and OUT are the sizes of input and output of the query, respec-tively. M is assumed to be IN 1 , for some constant > 1. Using GYM we achieve two main results: (1) Every width-w query can be computed in O(n) rounds of MapReduce with O(n (IN w+OUT)2
Similarity Joins are recognized to be among the most useful data processing and analysis operations....
k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every ...
International audienceGiven a point p and a set of points S, the kNN operation finds the k closest p...
We study the problem of computing the join of n relations in mul-tiple rounds of MapReduce. We intro...
Multiround algorithms are now commonly used in distributed data processing systems, yet the extent t...
We optimize multiway equijoins on relational tables using degree information. We give a new bound th...
Join has always been one of the most expensive queries to carry out in terms of the amount of time ...
k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every ...
In this paper, a novel multi join algorithm to join multiple relations will be introduced. The novel...
Multi-way Theta-join queries are powerful in describing com-plex relations and therefore widely empl...
For over a decade, MapReduce has become the leading programming model for parallel and massive proce...
MapReduce is with no doubt the parallel computation paradigm which has managed to interpret and serv...
The increasing amount of available spatial data leads to the development and spread of big data syst...
We present three novel algorithms for performing multi-dimensional joins and an in-depth survey and ...
Multi-way Theta-join queries are powerful in describing complex relations and therefore widely emplo...
Similarity Joins are recognized to be among the most useful data processing and analysis operations....
k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every ...
International audienceGiven a point p and a set of points S, the kNN operation finds the k closest p...
We study the problem of computing the join of n relations in mul-tiple rounds of MapReduce. We intro...
Multiround algorithms are now commonly used in distributed data processing systems, yet the extent t...
We optimize multiway equijoins on relational tables using degree information. We give a new bound th...
Join has always been one of the most expensive queries to carry out in terms of the amount of time ...
k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every ...
In this paper, a novel multi join algorithm to join multiple relations will be introduced. The novel...
Multi-way Theta-join queries are powerful in describing com-plex relations and therefore widely empl...
For over a decade, MapReduce has become the leading programming model for parallel and massive proce...
MapReduce is with no doubt the parallel computation paradigm which has managed to interpret and serv...
The increasing amount of available spatial data leads to the development and spread of big data syst...
We present three novel algorithms for performing multi-dimensional joins and an in-depth survey and ...
Multi-way Theta-join queries are powerful in describing complex relations and therefore widely emplo...
Similarity Joins are recognized to be among the most useful data processing and analysis operations....
k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every ...
International audienceGiven a point p and a set of points S, the kNN operation finds the k closest p...