DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis

Qin, Jiwei; Ma, Long; Liu, Qing

doi:10.3390/info10020077

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Qin et al [29] proposed a storage and partitioning model for massive trajectory data management for HBase and implemented a co-processor-based multi-level index structure to accelerate spatio-temporal queries. Subsequently, to handle the similarity query problem of large-scale trajectory data, DFTHR [30], a distributed trajectory similarity query framework based on HBase and Redis, was proposed that ensures efficient trajectory query processing. Li et al [31] extended Geomesa to implement TrajMesa, which supports the storage management of massive trajectories with multiple trajectory query functions.…”

Section: Storage and Querying Of Trajectorymentioning

confidence: 99%

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

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Section: Storage and Querying Of Trajectorymentioning

confidence: 99%

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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“…There have been some works on distributed trajectory analysis [3], [22], [30]. However, most of them [3], [31]- [33] use coordinate-based distance for trajectory comparison, which eases the development of the parallel computation since trajectories can be easily divided into subsets based on regions they are located. Among these coordinate-based trajectory analyses works, many [34]- [36] utilize MapReduce; very few [32], [33] takes advantage of Apache Spark which has been shown to be much more efficient than MapReduce.…”

Section: Related Workmentioning

confidence: 99%

Find Another Me Across the World -- Large-scale Semantic Trajectory Analysis Using Spark

Cai¹,

Lin²

2022

Preprint

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In today's society, location-based services are widely used which collect a huge amount of human trajectories. Analyzing semantic meanings of these trajectories can benefit numerous real-world applications, such as product advertisement, friend recommendation, and social behavior analysis. However, existing works on semantic trajectories are mostly centralized approaches that are not able to keep up with the rapidly growing trajectory collections. In this paper, we propose a novel large-scale semantic trajectory analysis algorithm in Apache Spark. We design a new hash function along with efficient distributed algorithms that can quickly compute semantic trajectory similarities and identify communities of people with similar behavior across the world. The experimental results show that our approach is more than 30 times faster than centralized approaches without sacrificing any accuracy like other parallel approaches.

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“…Redis is an open source data storage system preferred by most internet companies due to its ability to save data on server cache [65]. Redis works on an asynchronous storage system by keeping data in memory [66]. It stores data as key-value and quickly retrieves records by taking the word-pair and the timestamp of the insertion frequency as value [67].…”

Section: Introductionmentioning

confidence: 99%

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

2020

Turk J Elec Eng & Comp Sci

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Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor to gather data from visitors of the hotels and the analysis was supported with an optimization approach through the deep autoencoder network. According to the data density gathered from the structure developed with distributed computing and the API software in C# language, server group numbers were increased to list the most affordable hotel in the desired times. Thanks to the developed architecture and software, response times of the servers were significantly reduced. In detail, it was seen that the HAProxy responded 11 times faster than NetScaler as the new architecture responded 1160 times faster than the old one. Also, the HashSet system in the newly developed architecture responded 18 times faster than the List system and as general, the new architecture was found to be 9 times faster than the old architecture.

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DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis

Cited by 7 publications

References 25 publications

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Find Another Me Across the World -- Large-scale Semantic Trajectory Analysis Using Spark

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

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