Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics

Fang, Ziquan; Chen, Lu; Gao, Yunjun; Lu, Pan; Jensen, Christian S.

doi:10.1007/s00778-021-00652-x

Cited by 23 publications

(10 citation statements)

References 33 publications

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“…Therefore, several Spark-based spatial (or spatio-temporal) data processing systems have been developed in the last few years. These systems include SpatialSpark [268], GeoSpark [269], Loca-tionSpark [234,235], Simba [264], STARK [114], SparkGIS [26], TrajSpark [282], Elcano [84], DiStRDF [174], DITA [221], UlTraMan [75], Dragoon [93], Beast [79], and GeoTrellis [76]. The detailed feature matrix of these systems is presented in Table 4.…”

Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

“…Besides, this new batch of data is loaded as RDDs in Spark, which are immutable, and any updates on RDD create a new RDD, which is costly. Dragoon [93] is a hybrid system for processing both historical (oline) and streaming (online) trajectories. The oline module of Dragoon is similar to UITraMan, but Dragoon has utilized Chronicle Map in such a way that it works for both historical and streaming trajectories.…”

Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

“…The big spatial data processing systems (except Dragoon [93]), which are developed based on Hadoop, Spark, or other platforms, can only store and process historical static spatial data. However, a wide range of location-based services require real-time processing of spatial data streams.…”

Section: Other Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Spatio-temporal Data Analytics Systems

2022

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

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A Survey on Spatio-temporal Data Analytics Systems

2022

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“…To overcome this shortcoming, a Spark-based hybrid and efficient framework named Dragoon [85] was proposed for both offline and online trajectory data analytics. Dragoon manages to decrease storage overhead up to doubled compared with the state-the-art big trajectory data management system Ultraman [86], while maintaining a similar offline trajectory query performance.…”

Section: Big Data Toolsmentioning

confidence: 99%

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Jiang

Luo

2022

ASI

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Big data have been used widely in many areas, including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted to improve the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized, and off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.

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“…Therefore, several Spark-based spatial (or spatio-temporal) data processing systems have been developed in the last few years to alleviate these limitations. These systems include SpatialSpark [260], GeoSpark [261], LocationSpark [224,225], Simba [257], STARK [112], SparkGIS [18], TrajSpark [272], Elcano [71], DiStRDF [164], DITA [210], UlTraMan [63], Dragoon [82], and GeoTrellis [64]. The detailed feature matrix of these systems is presented in Table 4.…”

Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

Alam¹,

Torgo²,

Bifet³

2021

Preprint

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatiotemporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), ( 2) big spatio-temporal data processing infrastructures, and (3) programming languages and software tools for processing spatio-temporal data. Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems. This survey also portrays the importance and future of spatial and spatio-temporal data analytics. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Spatialtemporal systems; Parallel and distributed DBMSs.

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Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics

Cited by 23 publications

References 33 publications

A Survey on Spatio-temporal Data Analytics Systems

A Survey on Spatio-temporal Data Analytics Systems

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

A Survey on Spatio-temporal Data Analytics Systems

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