Efficient processing of k nearest neighbor joins using mapreduce pdf

Efficient processing of allknearestneighbor queries in. Meanwhile, applications continue to witness a quick exponential in some cases increase in the. Pdf given a point p and a set of points s, the knn operation finds the k. Rankreduce is composed of a pre processing step and a single mapreduce n. Meanwhile, applications continue to witness a quick exponential in some cases increase in the amount. The knn join is a primitive operation and is widely used in many data mining applications. Indexbased joins, such as index nested loop join and join using bitmap indices, have been shown to outperform scanbased joins under high join selectivity or for analyzing read mostly data 16, 10. It is a combination of the k nearest neighbor and join queries and is. Given the increasing volume of data, it is difficult to perform a knn join on a.

Using mapreduce for efficient parallel processing of. Conclusion in this paper, we study the problem of ef. Pdf k nearest neighbour joins for big data on mapreduce. In this example the k nearest neighbour classification method supervised machine learning is applied to some sample data about car types and buyer characteristics, so that it classifies a buyer with a likely car model. Recent work has focused on implementing efficient solutions using the mapreduce programming model because it is suitable for large scale data processing. Therefore, how to improve the join query processing algorithm based on the mapreduce has been an urgent problem. One such query is the all k nearest neighbor query, or k nearest neighbor join, that takes as input two datasets and, for each object of the first one, returns the k nearest neighbors from the second one. Other spatial operations can be added to the operations layer using a similar approach of the implementation of basic spatial operations. Efficient largescale distancebased join queries in. Spatialhadoop is an efficient mapreduce diskbased distributed spatial query processing system. Efficient processing of the most representative and studied dbjqs over largescale spatial datasets is a challenging task, and is the main target of this paper. He obtained his bsc 1st class honors and phd from monash university, australia, in 1985 and, year 2012, pages. Comparing mapreducebased k nn similarity joins on hadoop for highdimensional data. Parallel knn queries for big data based on voronoi diagram using mapreduce.

The knearest neighbor algorithm using mapreduce paradigm. K nearest neighbor join knn join is a new operation proposed recently 5. Pdf efficient processing of k nearest neighbor joins. As a combination of the k nearest neighbor query and the join operation, knn join is an expensive operation. Efficient processing of k nearest neighbor joins using mapreduce by wei lu, yanyan shen, su chen, beng chin ooi professor of computer science at the national university of singapore nus.

An efficient twotable join query processing based on. Given two datasets, say r and s, r k nn join s nds the k nearest neighbors in s for each tuple in r. It is a combination of the k nearestneighbor and join queries and is computationally demanding. Especially for solving highdimensional text data processing, semantic web and search engine are required to pay attention to proximity searches and text relevance analysis. Find the point closest to x in the training data set. Produce approximate nearest neighbors using locality sensitive hashing. Hence, how to execute knn joins efficiently on large. This cited by count includes citations to the following articles in scholar. Suppose we want to answer a reverse nearest neighbor query rnn. An efficient algorithm for approximated selfsimilarity. Efficient processing of k nearest neighbor joins using. Reduce computations in k nearest neighbor search by using kdtrees. The k nearest neighbor queries knn queries, designed to find k.

Parallel knn queries for big data based on voronoi diagram. K nearest neighbour joins for big data on mapreduce. Recent works have focused on implementing efficient solutions using the. Knearest neighbor classifier is one of the introductory supervised classifier, which every data science learner should be aware of. Efficient processing of allknearestneighbor queries in the. Voronoibased geospatial query processing with mapreduce. In this paper, we put forward twotable join query processing and optimization strategies for the above problems. Efficient processing of allknearestneighbor queries in the mapreduce programming framework. Compare and contrast supervised and unsupervised learning tasks. Processing knearest neighbor queries in high dimensional data has received a lot of attention by researchers in recent years. Spatial query processing a spatial object basically includes its geometry and can. Processing k nearest neighbors queries on top of mapreduce. Pdf solutions for processing k nearest neighbor joins.

Introduction the k nearest neighbor graph k nng for a set of objects v is a directed graph with vertex set v and an edge from each v. The ones marked may be different from the article in the profile. In highdimensional spaces and because of the curse of dimensionality 17, dataindependent hashbased approximate k. With its setatime nature, knn join can be used to ef. A popular model nowadays for largescale data processing is the sharednothing cluster on a number of commodity machines using mapreduce. Yanyan shen, kaushik chakrabarti, surajit chaudhuri, bolin ding, lev novik. Efcient processing of hammingdistancebased similarity.

By exploiting voronoi diagrambased partitioning met hod, our proposed approach is able. However, it is an expensive operation because it combines the knn query and the join operation, whereas most existing methods assume. A mapreducebased knearest neighbor approach for big data. In cloud computing environments parallel knn queries for big data is an important issue. It will bring more time overhead and io cost at shuffle stage, which will result in low efficiency. Numerous modern applications, from social networking to astronomy, need efficient answering of queries on spatial data. Present join implementations on mapreduce are mainly scan based. Although its relevance is questionable on some applications 118, many studies focused on join processing using mapreduce 5,17,29,97,4, including k nearest neighbor joins 89 or similarity. The efficient use of knn join has proven good results in finding the objects from two data sets prevailed in the. Since it involves both the join and the nn search, performing knn joins efficiently is a challenging task. Efficient parallel knn joins for large data in mapreduce. We run the algorithm 100 times on each dataset, computing 1, 2, 8 and 16 nearest neighbors selfsimilarity join. Given the increasing volume of data, it is difficult to perform a knn join.

Jestes, efficient parallel knn joins for large data in. This is a class project to build a recommendation system for the yahoo music dataset using the formulas used in the paper titled efficient processing of k nearest neighbor joins using mapreduce. The primary application of a knn join is k nearest neighbor. C efficient processing of k nearest neighbor joins using mapreduce. Rankreduce processing knearest neighbor queries on top. Pdf solutions for processing k nearest neighbor joins for big. Since it involves both the join and the nn search, performing knn joins e. Given a query point q and a set of data points p, rnn query retrieves all the data points that have q as their nearest neighbors 9.

When the dimensionality increases the distance between the closest and the farthest neighbor decreases rapidly, for most of the datasets 6, which is also known as the curse of dimensionality. Rankreduce processing knearest neighbors queries on. Knn classifier, introduction to knearest neighbor algorithm. The operation combines each point of one dataset with its k nearest neighbors in another dataset. Several techniques have been proposed to process the knn or similar joins using the mapreduce framework 7, 11, 4, 12.

Efficient processing of k nearest neighbor joins using mapreduce. We investigate the k nearest neighbor knn join in road networks to determine the k nearest neighbors nns from a dataset s to every object in another dataset r. All experiments were executed on a machine with debian 4. Hence, the capability to naturally express indexbased joins on mapreduce is. For simplicity, this classifier is called as knn classifier. Big datanosql, monogdb, java k nearest neighbour joins for big data on mapreduce.

Citeseerx document details isaac councill, lee giles, pradeep teregowda. Efficient parallel knn joins for large data in mapreduceedbt2012 efficient parallel setsimilarity joins using mapreducesigmod2010 parallel top k similarity join algorithms using mapreduceicde2012. Optional improving efficiency through multiple tables. In this work we design a new parallel knn algorithm. Efficient parallel knn joins for large data in mapreduce papers and talks source code overview. Sorry, we are unable to provide the full text but you may find it at the following locations. This is a java program designed to work with the mapreduce framework. Solutions for processing k nearest neighbor joins for big data on mapreduce. K nearest neighbour implementation for hadoop mapreduce. In data mining applications and spatial and multimedia databases, a useful tool is the knn join, which is to produce the k nearest neighbors nn, from a dataset s, of every point in a dataset r. Now nearest neighbor rule asks to assign the label of y.