{"title":"BBoxDB streams: scalable processing of multi-dimensional data streams","authors":"Jan Kristof Nidzwetzki, R. H. Güting","doi":"10.18445/20210625-130223-0","DOIUrl":null,"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":1.5000,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Distributed and Parallel Databases","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18445/20210625-130223-0","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍:
Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including:
Availability and reliability;
Benchmarking and performance evaluation, and tuning;
Big Data Storage and Processing;
Cloud Computing and Database-as-a-Service;
Crowdsourcing;
Data curation, annotation and provenance;
Data integration, metadata Management, and interoperability;
Data models, semantics, query languages;
Data mining and knowledge discovery;
Data privacy, security, trust;
Data provenance, workflows, Scientific Data Management;
Data visualization and interactive data exploration;
Data warehousing, OLAP, Analytics;
Graph data management, RDF, social networks;
Information Extraction and Data Cleaning;
Middleware and Workflow Management;
Modern Hardware and In-Memory Database Systems;
Query Processing and Optimization;
Semantic Web and open data;
Social Networks;
Storage, indexing, and physical database design;
Streams, sensor networks, and complex event processing;
Strings, Texts, and Keyword Search;
Spatial, temporal, and spatio-temporal databases;
Transaction processing;
Uncertain, probabilistic, and approximate databases.