BBoxDB流:多维数据流的可扩展处理

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Distributed and Parallel Databases Pub Date : 2022-05-02 DOI:10.18445/20210625-130223-0
Jan Kristof Nidzwetzki, R. H. Güting
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引用次数: 1

摘要

BBoxDB Streams是一个分布式流处理系统,它允许处理多维数据。多维流由n维元素组成,例如位置数据(例如,汽车的二维位置或飞机的三维位置)。该软件是BBoxDB的增强版,BBoxDB是一种分布式键绑定框值存储,允许处理n维大数据。BBoxDB Streams支持连续范围查询和连续空间连接;支持N维点和非点数据。BBoxDB Streams中的操作主要在数据的边界框上执行。使用用户定义过滤器(UDF),可以对自定义数据格式进行解码,并对基于边界框的操作进行细化(例如,UDF对WKT编码流元素的实际几何图形进行解码并执行交叉测试)。BBoxDB Streams的一个独特功能是能够在流元素和先前存储的多维大数据之间执行连续的空间连接。例如,汽车的动态位置可以有效地与街道网络的静态空间数据相结合。
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BBoxDB streams: scalable processing of multi-dimensional data streams
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.
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来源期刊
Distributed and Parallel Databases
Distributed and Parallel Databases 工程技术-计算机:理论方法
CiteScore
3.50
自引率
0.00%
发文量
17
审稿时长
>12 weeks
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.
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