High-Level Languages for Geospatial Analysis of Big Data

2021 ◽  
pp. 62-81
Author(s):  
Symphorien Monsia ◽  
Sami Faiz
Keyword(s):  
Big Data ◽  
Query Language ◽  
Research Work ◽  
Temporal Data ◽  
Data Systems ◽  
High Level Language ◽  
Gis Tools ◽  
Spatio Temporal ◽  
Big Data Systems ◽  
High Level

In recent years, big data has become a major concern for many organizations. An essential component of big data is the spatio-temporal data dimension known as geospatial big data, which designates the application of big data issues to geographic data. One of the major aspects of the (geospatial) big data systems is the data query language (i.e., high-level language) that allows non-technical users to easily interact with these systems. In this chapter, the researchers explore high-level languages focusing in particular on the spatial extensions of Hadoop for geospatial big data queries. Their main objective is to examine three open source and popular implementations of SQL on Hadoop intended for the interrogation of geospatial big data: (1) Pigeon of SpatialHadoop, (2) QLSP of Hadoop-GIS, and (3) ESRI Hive of GIS Tools for Hadoop. Along the same line, the authors present their current research work toward the analysis of geospatial big data.

scholarly journals A Review of Polyglot Persistence in the Big Data World

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 141 ◽  
Author(s):  
Pwint Phyu Khine ◽  
Zhaoshun Wang
Keyword(s):  
Big Data ◽  
Distributed Systems ◽  
Query Language ◽  
Point Of View ◽  
Data Systems ◽  
Data Intensive ◽  
Soft State ◽  
Eventual Consistency ◽  
Big Data Systems ◽  
The Relationship

The inevitability of the relationship between big data and distributed systems is indicated by the fact that data characteristics cannot be easily handled by a standalone centric approach. Among the different concepts of distributed systems, the CAP theorem (Consistency, Availability, and Partition Tolerant) points out the prominent use of the eventual consistency property in distributed systems. This has prompted the need for other, different types of databases beyond SQL (Structured Query Language) that have properties of scalability and availability. NoSQL (Not-Only SQL) databases, mostly with the BASE (Basically Available, Soft State, and Eventual consistency), are gaining ground in the big data era, while SQL databases are left trying to keep up with this paradigm shift. However, none of these databases are perfect, as there is no model that fits all requirements of data-intensive systems. Polyglot persistence, i.e., using different databases as appropriate for the different components within a single system, is becoming prevalent in data-intensive big data systems, as they are distributed and parallel by nature. This paper reflects the characteristics of these databases from a conceptual point of view and describes a potential solution for a distributed system—the adoption of polyglot persistence in data-intensive systems in the big data era.

HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems

2019 ◽  
Vol 34 (6) ◽  
pp. 1167-1184
Author(s):  
Rui Ren ◽  
Jiechao Cheng ◽  
Xi-Wen He ◽  
Lei Wang ◽  
Jian-Feng Zhan ◽  
...  
Keyword(s):  
Big Data ◽  
Performance Data ◽  
Data Systems ◽  
Data Correlation ◽  
Performance Diagnosis ◽  
Spatio Temporal ◽  
Big Data Systems

scholarly journals Wireless Sensor Networks for Big Data Systems

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1565 ◽  
Author(s):  
Beom-Su Kim ◽  
Ki-Il Kim ◽  
Babar Shah ◽  
Francis Chow ◽  
Kyong Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Sensor Networks ◽  
Large Scale ◽  
Research Work ◽  
Sensor Nodes ◽  
Data Sources ◽  
Wireless Sensor ◽  
Data Systems ◽  
Big Data Systems

Before discovering meaningful knowledge from big data systems, it is first necessary to build a data-gathering infrastructure. Among many feasible data sources, wireless sensor networks (WSNs) are rich big data sources: a large amount of data is generated by various sensor nodes in large-scale networks. However, unlike typical wireless networks, WSNs have serious deficiencies in terms of data reliability and communication owing to the limited capabilities of the nodes. Moreover, a considerable amount of sensed data are of no interest, meaningless, and redundant when a large number of sensor nodes is densely deployed. Many studies address the existing problems and propose methods to overcome the limitations when constructing big data systems with WSN. However, a published paper that provides deep insight into this research area remains lacking. To address this gap in the literature, we present a comprehensive survey that investigates state-of-the-art research work on introducing WSN in big data systems. Potential applications and technical challenges of networks and infrastructure are presented and explained in accordance with the research areas and objectives. Finally, open issues are presented to discuss promising directions for further research.

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