An extra spatial hierarchical schema in key-value store

Zheng, Kun; Zheng, Kang; Fang, Fan Tao; Zhang, Miao; Li, Qi; Wang, Yanghui; Zhao, Wenyu

doi:10.1007/s10586-018-2270-4

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…HBase does not have special geospatial functions to support geospatial data storage and querying [62]. However, researchers developed some methods and applications for processing geospatial data in HBase [63][64][65][66][67][68][69][70], such as a geographical database with geohash-based spatial indexes [63,71], big spatial data processing with Apache Spark [72], a geospatial data model [64], and a new spatial query method based on primary keys' indexing [73]. Additionally, an open source suite of tools, GeoMesa, was designed to implement large-scale geospatial analytics and querying in the cloud or in conjunction with the HBase and Cassandra databases [74].…”

Section: Apache Hbasementioning

confidence: 99%

“…The third research direction is to build storage models/schemas of spatial data in HBase [67,73,[81][82][83]. Wang et al proposed a Z storage schema with row keys based on a Z curve for massive spatial vector data in HBase [67].…”

Section: Apache Hbasementioning

confidence: 99%

“…The wide column databases, Cassandra and HBase, store data tables in columns instead of in rows, and they are open-source, non-relational, distributed databases. HBase does not support geospatial processing; however, through MapReduce [75,76,93] and by designing new index structures [69,70,78,79] and storage models/schemas [67,73,[81][82][83], the HBase database can now process geospatial data for different applications. As with Hbase, Cassandra can also use Hadoop MapReduce for geospatial data processing [59].…”

Section: Comparisons Of Geospatial Data Processing In Nosql Databasesmentioning

confidence: 99%

“…The research on document databases has mainly concentrated on index improvement [44,45], performance analysis [18,23], and practical applications [38][39][40]. Additionally, due to the high performance of data insertion and retrieval in HBase, many researchers have designed systems and applications for geospatial data based on HBase [63,73,79,82,94].…”

Section: Comparisons Of Geospatial Data Processing In Nosql Databasesmentioning

confidence: 99%

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State-of-the-Art Geospatial Information Processing in NoSQL Databases

Guo

Onstein

2020

IJGI

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Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.

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Section: Apache Hbasementioning

confidence: 99%

Section: Apache Hbasementioning

confidence: 99%

Section: Comparisons Of Geospatial Data Processing In Nosql Databasesmentioning

confidence: 99%

Section: Comparisons Of Geospatial Data Processing In Nosql Databasesmentioning

confidence: 99%

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State-of-the-Art Geospatial Information Processing in NoSQL Databases

Guo

Onstein

2020

IJGI

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“…For timely MVFD processing, researchers have developed various MVFD processing acceleration from different aspects. Existing studies mainly focus on cloud computing based MFVD processing [19,20,21]. Christoph et al [5] summarized them into five categories, namely parallelize-over-blocks (POB), parallelize-over-seeds (POS), adaptive load balancing, hybrid parallelism, NS extended memory hierarchies, where the latter three strategies are actually extended from the former two, as illustrated in Figure 1.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Massive Vector Field Data Processing in Edge Computing

Zheng

Fang

et al. 2019

Sensors

Self Cite

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The spread of the sensors and industrial systems has fostered widespread real-time data processing applications. Massive vector field data (MVFD) are generated by vast distributed sensors and are characterized by high distribution, high velocity, and high volume. As a result, computing such kind of data on centralized cloud faces unprecedented challenges, especially on the processing delay due to the distance between the data source and the cloud. Taking advantages of data source proximity and vast distribution, edge computing is ideal for timely computing on MVFD. Therefore, we are motivated to propose an edge computing based MVFD processing framework. In particular, we notice that the high volume feature of MVFD results in high data transmission delay. To solve this problem, we invent Data Fluidization Schedule (DFS) in our framework to reduce the data block volume and the latency on Input/Output (I/O). We evaluated the efficiency of our framework in a practical application on massive wind field data processing for cyclone recognition. The high efficiency our framework was verified by the fact that it significantly outperformed classical big data processing frameworks Spark and MapReduce.

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A Map Tile Data Access Model Based on the Jump Consistent Hash Algorithm

Wang

Yao

Chen

2022

IJGI

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Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current inefficiency of massive tile map data management and access, this paper proposes a massive tile map data access model that utilizes the jump consistent hash algorithm. Via the uniformity and consistency of a certain seed of a pseudo-random function, the algorithm can generate a storage slot for each tile data efficiently. By recording the slot information in the head of a row key, a uniform distribution of the tiles on the physical cluster nodes is achieved. This effectively solves the problem of hotspotting caused by the monotonicity of tile row keys in the data access process, thereby maximizing the random-access performance of a big data platform and greatly improving concurrent database access. Experiments show that this model can significantly improve the efficiency of tile map data access by more than 39% compared to a direct storage method, thereby confirming the model’s advantages in accessing massive tile map data on a big data GIS platform.

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An extra spatial hierarchical schema in key-value store

Cited by 3 publications

References 16 publications

State-of-the-Art Geospatial Information Processing in NoSQL Databases

State-of-the-Art Geospatial Information Processing in NoSQL Databases

Real-Time Massive Vector Field Data Processing in Edge Computing

A Map Tile Data Access Model Based on the Jump Consistent Hash Algorithm

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