使用HBase优化时空决策支持的历史查询

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"这篇研究论文探讨了在大数据背景下，如何高效地进行时空决策支持系统（stDSS）的历史查询。作者X.Y. Chen, C. Zhang, B. Ge, 和 W.D. Xiao提出使用NoSQL数据库HBase替代传统后端存储系统来解决海量时空数据的处理挑战。" 在过去的十年间，随着空间-时间数据收集技术的快速发展和普及，数十亿乃至万亿级别的感知数据不断积累，这给时空决策支持系统带来了巨大的压力。传统的数据库由于难以应对如此庞大的数据量，常常导致分析平台的性能瓶颈。针对这一问题，该论文提出了使用HBase——一个广泛研究的NoSQL数据库，作为后端存储解决方案。 HBase是一种基于分布式文件系统的列式存储数据库，尤其适合处理大规模、高并发的数据查询。在时空数据场景中，它能够有效地处理空间和时间维度的数据，因为这类数据通常具有稀疏性和特定的访问模式。论文深入研究了如何优化HBase以支持高效的时空查询，包括空间索引的构建、时间序列数据的存储以及查询优化策略等。论文可能涵盖了以下几个关键点： 1. HBase的时空数据模型：介绍了如何将空间和时间信息融入HBase的行键和列族设计中，以便快速定位和检索数据。 2. 索引策略：讨论了如何构建空间索引（如R树或quadtree）以提高空间查询性能，同时结合时间戳信息优化查询效率。 3. 查询优化：提出了适应HBase特性的查询优化技术，如预聚合、数据分区和并行查询，以减少查询延迟和提升响应速度。 4. 性能评估与比较：通过实验对比了HBase与其他传统数据库在处理时空历史查询时的性能差异，证明了HBase在大数据场景中的优势。 5. 案例应用与未来展望：可能还包括了实际应用场景的案例分析，以及对进一步改进HBase和时空决策支持系统的未来研究方向的探讨。这篇论文为处理大量时空数据的历史查询提供了一种有效的方法，对大数据环境下的决策支持系统设计和优化具有重要的参考价值。

616 X.Y. Chen, C. Zhang, B. Ge, W.D. Xiao

2 Related Works

To overcome the drawbacks of traditional RDBMS, as an attractive alternative for large-

scale data processing, Cloud storage system currently adopts a hash-like approach to retrieve

data that only support simple keyword-based queries, but lacks various forms of information

search. For data processing operations, several cloud data managements (CDMs), such as HBase,

are developed. HBase, as NoSQL databases, is capable to handle large scale storage and high

insertion rate, however, it does not oﬀer much support for rich index functions. Many works

focus on this point and propose various approaches.

Nishimura et al. [5] address multidimensional queries for PaaS by proposing MD-HBase. It

uses k-d-trees and quad-trees to partition space and adopts Z-curve to convert multidimensional

data to a single dimension, and supports multi-dimensional range and nearest neighbor queries,

which leverages a multi-dimensional index structure layered over HBase. However, MD-HBase

builds index in the meta table, which does not index inner structure of regions, so that scan

operations are carried out to ﬁnd results, which reduces its eﬃciency.

Hsu et al. [6] propose a novel Key formulation scheme based on R

-tree, called KR

-tree,

and based on it, spatial query algorithm of kNN query and range query are designed. Moreover,

the proposed key formulation schemes are implemented on HBase and Cassandra. With the

experiment on real spatial data, it demonstrates that KR

-tree outperforms MD-HBase. KR

tree is able to balance the number of false-positive and the number of sub-queries so that it

improves the eﬃciency of range query and kNN query a lot. This work designs the index according

to the features found in experiments on HBase and Cassandra. However, it still does not consider

the inner structure of HBase.

Zhou et al. [7] propose an eﬃcient distributed multi-dimensional index (EDMI), which con-

tains two layers: the global layer divides the space into many subspaces adopting k-d-tree, and

in the local layer, each subspace is associated to a Z-order preﬁx R-tree (ZPR-tree). ZPR-tree

can avoid the overlap of MBRs and obtain better query performance than other Packed R-trees

and R

∗

-tree. This paper experimentally evaluates EDMI based on HBase for point, range and

kNN query, which veriﬁes its superiority. Compared with MD-HBase, EDMI uses ZPR-tree in

the bottom layer, while MD-HBase employs scan operation, so that EDMI provides a better

performance.

Han et al. [8] propose HGrid data model for HBase. HGrid data model is based on a hybrid

index structure, combining a quad-tree and a regular grid as primary and secondary indices,

supports eﬃcient performance for range and kNN queries. This paper also formulates a set of

guidelines on how to organize data for geo-spatial applications in HBase. This model does not

outperform all its competitors in terms of query response time. However, it requires less space

than the corresponding quad-tree and regular-grid indices.

HBaseSpatial, a scalable spatial data storage based on HBase, proposed by Zhang et al. [9].

Compared with MongoDB and MySQL , experimental results show it can eﬀectively enhance the

query eﬃciency of big spatial data and provide a good solution for storage. But this model does

not compare with other distributed index method.

All the previous works we have mentioned above only consider the spatial query. For moving

objects, a certain type of geo-spatial applications, requires high update rate and eﬃcient real-

time query on multi-attributes such as time-period and arbitrary spatial dimension. Du et al. [10]

present hybrid index structure based on HBase, using R-tree for indexing space and applying

Hilbert curve for traversing approaching space. It supports eﬃcient multi-dimensional range

queries and kNN queries, especially it is adept at skewing data compared with MD-HBase and

-tree. As this work focus on moving objects, it is diﬀerent for our goal, and it also does not

take the inner structure of HBase into account.

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