Distributed moving object data management in MobilityDB

Bakli, Mohamed; Sakr, Mahmoud; Zimányi, Esteban

doi:10.1145/3356999.3365467

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…In various applications, efficient and effective analytic systems are necessary for IoT big data. There are systems for efficiently analyzing ordinary IoT data [16], but these systems focus on specific data types (e.g., trajectory data) [17], [18]. Such systems are useful in many applications because IoT devices are typically deployed for specific applications (e.g., traffic surveillance).…”

Section: A Motivation Of Surveymentioning

confidence: 99%

A Survey on IoT Big Data Analytic Systems: Current and Future

Sasaki

2022

IEEE Internet Things J.

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The Internet of Things (IoT) has become widespread around the world. Since a large number of diverse devices such as vehicles, household electrical appliances, smart phones, and environmental sensors are connected to the Internet, we can obtain a large volume of diverse IoT data, known as IoT big data. The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We explore Hadoop-and Spark-based batch processing systems for spatio-temporal and trajectory data. We also review fog-and edge-aware stream processing systems. Although many existing systems can efficiently and effectively analyze specific data and tasks, no system exists that can handle all characteristics of IoT big data: volume, velocity, variety, veracity, and variability.We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems.

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Section: A Motivation Of Surveymentioning

confidence: 99%

A Survey on IoT Big Data Analytic Systems: Current and Future

Sasaki

2022

IEEE Internet Things J.

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“…This support includes spatio-temporal data types, indexing techniques, and query operations. Recently, MobilityDB emerged as a distributed system by integrating with Citus [65] for processing massive trajectory data [28]. The spatial data analysis platform CARTO [53] also uses PostgreSQL/PostGIS underneath as a spatial database server.…”

Section: Spatial Relational Databasesmentioning

confidence: 99%

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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“…This support includes spatio-temporal data types, indexing techniques, and query operations. Recently, MobilityDB emerged as a distributed system by integrating with Citus [55] for processing massive trajectory data [20]. CARTO [44] provides spatial analysis and mapping services for a wide range of application areas for many organizations by developing APIs, libraries, and tools.…”

Section: Spatial Relational Databasesmentioning

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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Distributed moving object data management in MobilityDB

Cited by 13 publications

References 18 publications

A Survey on IoT Big Data Analytic Systems: Current and Future

A Survey on IoT Big Data Analytic Systems: Current and Future

A Survey on Spatio-temporal Data Analytics Systems

A Survey on Spatio-temporal Data Analytics Systems

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