M-Grid : A distributed framework for multidimensional indexing and querying of location based big data

The widespread use of mobile devices and the real time availability of user-location information is facilitating the development of new personalized, location-based applications and services (LBSs). Such applications require multi-attribute query processing, handling of high access scalability, support for millions of users, real time querying capability and analysis of large volumes of data. Cloud computing aided a new generation of distributed databases commonly known as key-value stores. Key-value stores were designed to extract value from very large volumes of data while being highly available, fault-tolerant and scalable, hence providing much needed features to support LBSs. However complex queries on multidimensional data cannot be processed efficiently as they do not provide means to access multiple attributes. In this thesis we present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set of nodes in a P-Grid overlay network which provides fault-tolerance and efficient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to efficiently manage multi-dimensional data in a key-value store. We propose algorithms to dynamically process range and k nearest neighbor (kNN) queries on linearized values. This removes the overhead of maintaining a separate index table. Our approach is completely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that MGrid achieves a performance improvement of three orders of magnitude in comparison to MapReduce and four times to that of MDHBase scheme --Abstract, pages iii-iv

H.: An efficient nearest neighbor algorithm for p2p settings

New Peer-to-Peer (P2P) applications like P2P jobemployee seeker networks and P2P virtual cities, for application domains such as collaborative urban planning and forming virtual communities, are about to emerge. An important component in these applications is spatial data, i.e., data with locational components. Many requests initiated on spatial data involve finding the spatial objects that are nearest to a query location. In this paper, we propose an efficient algorithm that finds the spatial objects that are nearest to a given query location on a P2P network in the order of their minimum distance to the query point. The proposed algorithm makes use of a distributed spatial index that does not rely on the use of a central server. The algorithm is designed to be more efficient by utilizing the parallel nature of the P2P network. A demonstration of the proposed algorithm was implemented as a prototype P2P application that finds events and places of interest in a city