Multidimensional Data Indexing Research Articles

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.

Fast and adaptive indexing of multi-dimensional observational data

Sensing devices generate tremendous amounts of data each day, which include large quantities of multi-dimensional measurements. These data are expected to be immediately available for real-time analytics as they are streamed into storage. Such scenarios pose challenges to state-of-the-art indexing methods, as they must not only support efficient queries but also frequent updates. We propose here a novel indexing method that ingests multi-dimensional observational data in real time. This method primarily guarantees extremely high throughput for data ingestion, while it can be continuously refined in the background to improve query efficiency. Instead of representing collections of points using Minimal Bounding Boxes as in conventional indexes, we model sets of successive points as line segments in hyperspaces, by exploiting the intrinsic value continuity in observational data. This representation reduces the number of index entries and drastically reduces "over-coverage" by entries. Experimental results show that our approach handles real-world workloads gracefully, providing both low-overhead indexing and excellent query efficiency.

The skip-octree: a dynamic cloud storage index framework for multidimensional big data systems

Nowadays the information explosion have generated a large amount of data. To utilise these data, there is a trend of setup efficient data index, especially for multidimensional data indexing. However, most of the current cloud storage systems build data indexing based on the distributed hash DHT, where data are stored by the key-value. This module is not very suitable for range queries in multidimensional data indexing. To solve these problems, this paper builds a multidimensional data index based on skip lists and octree structure. Firstly, this architecture adopts the structure of octree to store data and establish the corresponding index mechanism, it can make use of the idea of different dimensional space partitioning to achieve a multi-dimensional index. Secondly, the relevant algorithms are designed to sustain the characteristics of this framework in this paper. Finally, the simulation experiment shows that the skip-octree architecture is feasible and efficient.

Современные методы поиска и индексации многомерных данных в приложениях моделирования больших динамических сцен

This paper is dedicated to review of recent methods for indexing of multidimensional data and their use for modeling of large-scale dynamic scenes. The problem arises in many application domains such as computer graphics systems, virtual and augmented reality systems, CAD systems, robotics, geographical information systems, project management systems, etc. Two fundamental approaches to the indexing of multidimensional data, namely object aggregation and spatial decomposition, have been outlined. In the context of the former approach balanced search trees, also referred as object pyramids, have been discussed. In particular, generalizations of B-trees such as R-trees, R*-trees, R+-trees have been discussed in conformity to indexing of large data located in external memory and accessible by pages. The conducted analysis shows that object aggregation methods are well suited for static scenes but very limited for dynamic environments. The latter approach assumes the recursive decomposition of scene volume in accordance with the cutting rules. Space decomposition methods are octrees, A-trees, bintrees, K-D-trees, X-Y-trees, treemaps and puzzle-trees. They support more reasonable compromise between performance of spatial queries and expenses required to update indexing structures and to keep their in concordant state under permanent changes in the scenes. Compared to object pyramids, these methods look more promising for dramatically changed environments. It is concluded that regular dynamic octrees are most effective for the considered applications of modeling of large-scale dynamic scenes.

A Dynamic Resource Management Scheme on P2P-Based Grid Systems

Efficient multi-dimensional data indexing mechanism is one of the fundamental requirement for Grid computing systems. Peer-to-peer organization of Grid resource discovery approach would have several desirable features, including high scalability, high reliability, self-organization and self-healing. Most of the research in this area only considers static resource.In this paper, we describe a tree vector indexing scheme for Grid systems, in order to support range queries and at the same time preserve the important attributes of grid systems.

A Multi-Dimensional Query Scheme in Structured Overlays

Structured peer-to-peer (P2P) systems are creating a large proportion of network traffic in today’s Internet. Peer-to-peer systems enable access to data spread over an extremely large number of machines. A P2P system typically involves thousands or millions of live peers in the network. Multi-dimensional data indexing has received much attention in a centralized database. In this paper, we propose and evaluate a multi-dimensional searching scheme in structured P2P networks. We present the design and implementation of a peer-to-peer index service for high dimensional data that is capable of handling complex queries. We design a VibIndex scheme in structured P2P overlay networks. We analyze this scheme’s performance and present simulation results. Our simulation results demonstrated the benefits of the proposed system and show that the approach is able to search efficiently.

BR-Tree: A Scalable Prototype for Supporting Multiple Queries of Multidimensional Data

Multidimensional data indexing has received much research attention recently in a centralized system. However, it remains a nascent area of research in providing an integrated structure for multiple queries on multidimensional data in a distributed environment. In this paper, we propose a new data structure, called BR-tree (Bloom-filter-based R-tree), and implement such a prototype in the context of a distributed system. The node in a BR-tree, viewed as an expansion from the traditional R-tree node structure, incorporates space-efficient Bloom filters to facilitate fast membership queries. The proposed BR-tree can simultaneously support not only existing point and range queries, but also cover and bound queries that can potentially benefit various data indexing services. Compared with previous data structures, BR-tree achieves space efficiency and provides quick response (lesO(log n)) on these four types of queries. Our extensive experiments in a distributed environment further validate the practicality and efficiency of the proposed BR-tree structure.

An efficient peer-to-peer indexing tree structure for multidimensional data

As one of the most important technologies for implementing large-scale distributed systems, peer-to-peer (P2P) computing has attracted much attention in both research and industrial communities, for its advantages such as high availability, high performance, and high flexibility to the dynamics of networks. However, multidimensional data indexing remains as a big challenge to P2P computing, because of the inefficiency in search and network maintenance caused by the complicated existing index structures, which greatly limits the scalability of applications and dimensionality of the data to be indexed. We propose SDI (Swift tree structure for multidimensional Data Indexing), a swift index scheme with a simple tree structure for multidimensional data indexing in large-scale distributed systems. While keeping the query efficiency in O ( log N ) in terms of routing hops, SDI has extremely low maintenance costs which is proved through theoretical analysis. Furthermore, SDI overcomes the root-bottleneck problem existing in most other tree-based distributed indexing systems. Extensive empirical study verifies the superiority of SDI in both query and maintenance performance.

Using state diagrams for hilbert curve mappings

The Hilbert Curve describes a method of mapping between one and n dimensions. Such mappings are of interest in a number of application domains including image processing and, more recently, in the indexing of multi-dimensional data. Relatively little work, however, has been devoted to techniques for mapping in more that 2 dimensions. This paper presents a technique for constructing state diagrams to facilitate mappings and is a specialization of an incomplete generic process described by Bially. Although the storage requirements for state diagrams increase exponentially with the number of dimensions, they are useful in up to about 9 dimensions.