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A novel method for parallel indexing of real time geospatial big data generated by IoT devices

机译：IOT设备生成的实时地理空间大数据并行索引的一种新方法

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摘要

IoT produces a huge amount of big data as it comprises billions of devices that are interconnected with each other through internet. Today's majority of the big data part is about geospatial data and every year it increases rapidly. In order to process such massive real time geospatial big data, we must have scalable, efficient indexing method. R Tree and its variants have emerged as most efficient, widely accepted and have adopted indexing method for the management and processing of geospatial data.Current literature on parallel construction of R Tree indexes of geospatial data has disadvantages, that all the methods considered only two dimensional geospatial data and all are based on MapReduce framework. As the number of dimension increases, complexity of index creation is also increases along with this MapReduce framework has lots of disadvantages such as, it works only on static data, consumes a lot of disk space and time, which leads to high latency and fault tolerance of the entire system. In order to overcome these issues, a novel method for parallel construction of R Tree and its variants, use of the Apache Spark (in-memory and on-disk computation) based on the IoT Zetta platform is proposed. The main purpose of using Apache Spark is to index real time geospatial data for continuously updating the position of aircraft in real time while indexing it in R tree and its variants, so that spatial range query can fetch real time results and Apache Spark is much faster as compared to MapReduce framework. The extensive experimental results show that our parallel generated R tree and its variants retains similar properties as of sequential generated R tree and its variants with the excellent scalability and reducing a significant amount of time for the construction of index, index updating & executing spatial range query over geospatial data by exploiting the latest parallelism framework. (C) 2018 Elsevier B.V. All rights reserved.
机译：IoT产生大量大数据，因为它包括数十亿个设备，这些设备通过Internet互相连接。今天大部分大数据部分是关于地理空间数据，而且每年它都会迅速增加。为了处理如此大规模的实时地理空间大数据，我们必须具有可扩展，高效的索引方法。 R树及其变体已经出现为最有效，广泛接受，并采用了地理空间数据的管理和处理的索引方法。关于地理空间数据的R树指数的并联建设的电流文献具有缺点，所有方法都仅考虑二维地理空间数据和所有基于MapReduce框架。随着尺寸的数量增加，索引创建的复杂性也会随着诸如静态数据的许多缺点而增加，索引创建的复杂性也随之而来，它仅适用于静态数据，消耗大量磁盘空间和时间，这导致高延迟和容错整个系统。为了克服这些问题，提出了一种基于物联网Zett平台的Apache Spark（内存中和磁盘计算）的平行构建的新方法。使用Apache Spark的主要目的是索引实时地理空间数据，以便在索引R树及其变体中实时索引飞机位置，以便空间范围查询可以取得实时结果，Apache Spark更快与MapReduce框架相比。广泛的实验结果表明，我们的并行生成的R树及其变体保持了与顺序生成的R树及其变体具有出色的可扩展性的类似特性，并减少了索引的构建的大量时间，指数更新和执行空间范围查询通过利用最新的并行框架来实现地理空间数据。 （c）2018年elestvier b.v.保留所有权利。

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