Pub Date : 2022-07-20DOI: 10.1007/s10619-022-07418-6
Jie Song, George Alter, H. Jagadish
{"title":"Structured data transformation algebra (SDTA) and its applications","authors":"Jie Song, George Alter, H. Jagadish","doi":"10.1007/s10619-022-07418-6","DOIUrl":"https://doi.org/10.1007/s10619-022-07418-6","url":null,"abstract":"","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":"40 1","pages":"373 - 408"},"PeriodicalIF":1.2,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47534159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-09DOI: 10.1007/s10619-022-07417-7
Maximilian E. Schüle, Harald Lang, M. Springer, A. Kemper, Thomas Neumann, Stephan Günnemann
{"title":"Recursive SQL and GPU-support for in-database machine learning","authors":"Maximilian E. Schüle, Harald Lang, M. Springer, A. Kemper, Thomas Neumann, Stephan Günnemann","doi":"10.1007/s10619-022-07417-7","DOIUrl":"https://doi.org/10.1007/s10619-022-07417-7","url":null,"abstract":"","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":"40 1","pages":"205 - 259"},"PeriodicalIF":1.2,"publicationDate":"2022-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42851273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.18445/20210625-130223-0
Jan Kristof Nidzwetzki, R. H. Güting
BBoxDB Streams is a distributed stream processing system, which allows the handling of multi-dimensional data. Multi-dimensional streams consist of n -dimensional elements, such as position data (e.g., two-dimensional positions of cars or three-dimensional positions of aircraft). The software is an enhancement of BBoxDB, a distributed key-bounding-box-value store that allows the handling of n -dimensional big data. BBoxDB Streams supports continuous range queries and continuous spatial joins; n -dimensional point and non-point data are supported. Operations in BBoxDB Streams are performed primarily on the bounding boxes of the data. With user-defined filters (UDFs), custom data formats can be decoded, and the bounding box-based operations are refined (e.g., a UDF decodes and performs intersection tests on the real geometries of WKT encoded stream elements). A unique feature of BBoxDB Streams is the ability to perform continuous spatial joins between stream elements and previously stored multi-dimensional big data. For example, the dynamic position of a car can be efficiently joined with the static spatial data of a street network.
{"title":"BBoxDB streams: scalable processing of multi-dimensional data streams","authors":"Jan Kristof Nidzwetzki, R. H. Güting","doi":"10.18445/20210625-130223-0","DOIUrl":"https://doi.org/10.18445/20210625-130223-0","url":null,"abstract":"BBoxDB Streams is a distributed stream processing system, which allows the handling of multi-dimensional data. Multi-dimensional streams consist of n -dimensional elements, such as position data (e.g., two-dimensional positions of cars or three-dimensional positions of aircraft). The software is an enhancement of BBoxDB, a distributed key-bounding-box-value store that allows the handling of n -dimensional big data. BBoxDB Streams supports continuous range queries and continuous spatial joins; n -dimensional point and non-point data are supported. Operations in BBoxDB Streams are performed primarily on the bounding boxes of the data. With user-defined filters (UDFs), custom data formats can be decoded, and the bounding box-based operations are refined (e.g., a UDF decodes and performs intersection tests on the real geometries of WKT encoded stream elements). A unique feature of BBoxDB Streams is the ability to perform continuous spatial joins between stream elements and previously stored multi-dimensional big data. For example, the dynamic position of a car can be efficiently joined with the static spatial data of a street network.","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":"40 1","pages":"559-625"},"PeriodicalIF":1.2,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45748731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1007/s10619-022-07408-8
Jan Kristof Nidzwetzki, R. H. Güting
{"title":"BBoxDB streams: scalable processing of multi-dimensional data streams","authors":"Jan Kristof Nidzwetzki, R. H. Güting","doi":"10.1007/s10619-022-07408-8","DOIUrl":"https://doi.org/10.1007/s10619-022-07408-8","url":null,"abstract":"","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":"40 1","pages":"559 - 625"},"PeriodicalIF":1.2,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42849318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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