DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks
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| Názov: | DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks |
|---|---|
| Autori: | Havers, Bastian, 1991, Duvignau, Romaric, 1989, Najdataei, Hannaneh, 1988, Gulisano, Vincenzo Massimiliano, 1984, Papatriantafilou, Marina, 1966, Koppisetty, Ashok Chaitanya |
| Zdroj: | AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2 Molnbaserade produkter och produktion (FiC) HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman Future Generation Computer Systems. 107:1-17 |
| Predmety: | Streaming data, Clustering, Compression, Edge computing, Fog computing |
| Popis: | The growing interest in data analysis applications for Cyber–Physical Systems stems from the large amounts of data such large distributed systems sense in a continuous fashion. A key research question in this context is how to jointly address the efficiency and effectiveness challenges of such data analysis applications. DRIVEN proposes a way to jointly address these challenges for a data gathering and distance-based clustering tool in the context of vehicular networks. To cope with the limited communication bandwidth (compared to the sensed data volume) of vehicular networks and data transmission's monetary costs, DRIVEN avoids gathering raw data from vehicles, but rather relies on a streaming-based and error-bounded approximation, through Piecewise Linear Approximation (PLA), to compress the volumes of gathered data. Moreover, a streaming-based approach is also used to cluster the collected data (once the latter is reconstructed from its PLA-approximated form). DRIVEN's clustering algorithm leverages the inherent ordering of the spatial and temporal data being collected to perform clustering in an online fashion, while data is being retrieved. As we show, based on our prototype implementation using Apache Flink and thorough evaluation with real-world data such as GPS, LiDAR and other vehicular signals, the accuracy loss for the clustering performed on the gathered approximated data can be small (below 10%), even when the raw data is compressed to 5-35% of its original size, and the transferring of historical data itself can be completed in up to one-tenth of the duration observed when gathering raw data. |
| Popis súboru: | electronic |
| Prístupová URL adresa: | https://research.chalmers.se/publication/515315 https://research.chalmers.se/publication/515427 https://research.chalmers.se/publication/515427/file/515427_Fulltext.pdf |
| Databáza: | SwePub |
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| Items | – Name: Title Label: Title Group: Ti Data: DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Havers%2C+Bastian%22">Havers, Bastian</searchLink>, 1991<br /><searchLink fieldCode="AR" term="%22Duvignau%2C+Romaric%22">Duvignau, Romaric</searchLink>, 1989<br /><searchLink fieldCode="AR" term="%22Najdataei%2C+Hannaneh%22">Najdataei, Hannaneh</searchLink>, 1988<br /><searchLink fieldCode="AR" term="%22Gulisano%2C+Vincenzo+Massimiliano%22">Gulisano, Vincenzo Massimiliano</searchLink>, 1984<br /><searchLink fieldCode="AR" term="%22Papatriantafilou%2C+Marina%22">Papatriantafilou, Marina</searchLink>, 1966<br /><searchLink fieldCode="AR" term="%22Koppisetty%2C+Ashok+Chaitanya%22">Koppisetty, Ashok Chaitanya</searchLink> – Name: TitleSource Label: Source Group: Src Data: <i>AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2 Molnbaserade produkter och produktion (FiC) HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman Future Generation Computer Systems</i>. 107:1-17 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Streaming+data%22">Streaming data</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering%22">Clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Compression%22">Compression</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Fog+computing%22">Fog computing</searchLink> – Name: Abstract Label: Description Group: Ab Data: The growing interest in data analysis applications for Cyber–Physical Systems stems from the large amounts of data such large distributed systems sense in a continuous fashion. A key research question in this context is how to jointly address the efficiency and effectiveness challenges of such data analysis applications. DRIVEN proposes a way to jointly address these challenges for a data gathering and distance-based clustering tool in the context of vehicular networks. To cope with the limited communication bandwidth (compared to the sensed data volume) of vehicular networks and data transmission's monetary costs, DRIVEN avoids gathering raw data from vehicles, but rather relies on a streaming-based and error-bounded approximation, through Piecewise Linear Approximation (PLA), to compress the volumes of gathered data. Moreover, a streaming-based approach is also used to cluster the collected data (once the latter is reconstructed from its PLA-approximated form). DRIVEN's clustering algorithm leverages the inherent ordering of the spatial and temporal data being collected to perform clustering in an online fashion, while data is being retrieved. As we show, based on our prototype implementation using Apache Flink and thorough evaluation with real-world data such as GPS, LiDAR and other vehicular signals, the accuracy loss for the clustering performed on the gathered approximated data can be small (below 10%), even when the raw data is compressed to 5-35% of its original size, and the transferring of historical data itself can be completed in up to one-tenth of the duration observed when gathering raw data. – Name: Format Label: File Description Group: SrcInfo Data: electronic – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/515315" linkWindow="_blank">https://research.chalmers.se/publication/515315</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/515427" linkWindow="_blank">https://research.chalmers.se/publication/515427</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/515427/file/515427_Fulltext.pdf" linkWindow="_blank">https://research.chalmers.se/publication/515427/file/515427_Fulltext.pdf</link> |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.future.2020.01.050 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Streaming data Type: general – SubjectFull: Clustering Type: general – SubjectFull: Compression Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Fog computing Type: general Titles: – TitleFull: DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Havers, Bastian – PersonEntity: Name: NameFull: Duvignau, Romaric – PersonEntity: Name: NameFull: Najdataei, Hannaneh – PersonEntity: Name: NameFull: Gulisano, Vincenzo Massimiliano – PersonEntity: Name: NameFull: Papatriantafilou, Marina – PersonEntity: Name: NameFull: Koppisetty, Ashok Chaitanya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 0167739X – Type: issn-locals Value: SWEPUB_FREE – Type: issn-locals Value: CTH_SWEPUB Numbering: – Type: volume Value: 107 Titles: – TitleFull: AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2 Molnbaserade produkter och produktion (FiC) HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman Future Generation Computer Systems Type: main |
| ResultId | 1 |