An efficient index structure for distributed k-nearest neighbours query processing

Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for k-NN query over moving objects to tackle the increasingly large scale of data. This approach includes a new grid-based index called Block Grid Index (BGI), and a distributed k-NN query algorithm based on BGI. There are three advantages of our approach: (1) BGI can be easily constructed and maintained in a distributed setting; (2) the algorithm is able to return the results set in only two iterations. (3) the efficiency of k-NN query is improved. The efficiency of our solution is verified by extensive experiments with millions of nodes.

[1]  Yutaka Ishibashi,et al.  Efficient Large-scale Medical Data (eHealth Big Data) Analytics in Internet of Things , 2017, 2017 IEEE 19th Conference on Business Informatics (CBI).

[2]  Yannis Manolopoulos,et al.  Fast Nearest-Neighbor Query Processing in Moving-Object Databases , 2003, GeoInformatica.

[3]  Yufei Tao,et al.  Continuous Nearest Neighbor Search , 2002, VLDB.

[4]  Wei Wu,et al.  Distributed Processing of Moving K-Nearest-Neighbor Query on Moving Objects , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[5]  Nick Roussopoulos,et al.  Nearest neighbor queries , 1995, SIGMOD '95.

[6]  Tao Jiang,et al.  Secure and efficient k-nearest neighbor query for location-based services in outsourced environments , 2017, Science China Information Sciences.

[7]  Luis Gravano,et al.  Evaluating Top-k Selection Queries , 1999, VLDB.

[8]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[9]  Beng Chin Ooi,et al.  Efficient Processing of k Nearest Neighbor Joins using MapReduce , 2012, Proc. VLDB Endow..

[10]  Ying Xia,et al.  Grid-based k-Nearest Neighbor Queries over Moving Object Trajectories with MapReduce , 2017 .

[11]  Christian S. Jensen,et al.  Parallel main-memory indexing for moving-object query and update workloads , 2012, SIGMOD Conference.

[12]  Jianliang Xu,et al.  Grid-partition index: a hybrid method for nearest-neighbor queries in wireless location-based services , 2005, The VLDB Journal.

[13]  Peter Triantafillou,et al.  Scaling k-Nearest Neighbours Queries (The Right Way) , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[14]  Philip S. Yu,et al.  Distributed Processing of Spatial Alarms: A Safe Region-Based Approach , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[15]  Xiaohui Yu,et al.  Scalable Distributed Processing of K Nearest Neighbor Queries over Moving Objects , 2015, IEEE Transactions on Knowledge and Data Engineering.

[16]  Feifei Li,et al.  Efficient parallel kNN joins for large data in MapReduce , 2012, EDBT '12.

[17]  RoussopoulosNick,et al.  Nearest neighbor queries , 1995 .

[18]  Ahmed Eldawy,et al.  A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data , 2013, Proc. VLDB Endow..

[19]  Hanan Samet,et al.  Distance browsing in spatial databases , 1999, TODS.

[20]  Nick Roussopoulos,et al.  K-Nearest Neighbor Search for Moving Query Point , 2001, SSTD.

[21]  Beng Chin Ooi,et al.  Indexing the Distance: An Efficient Method to KNN Processing , 2001, VLDB.

[22]  Shingo Yamaguchi,et al.  Implementation of parallel model checking for computer-based test security design , 2016, 2016 7th International Conference on Information and Communication Systems (ICICS).

[23]  Hans-Peter Kriegel,et al.  Optimal multi-step k-nearest neighbor search , 1998, SIGMOD '98.

[24]  Ling Liu,et al.  MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System , 2004, EDBT.

[25]  Shweta Tripathi,et al.  Hadoop Based Defense Solution to Handle Distributed Denial of Service (DDoS) Attacks , 2013 .