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Building an Earth Observations data cube: lessons learned from the Swiss data cube (SDC) on generating Analysis Ready data (ARD)

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Big Earth data 2017年第1期1卷 100-117页

作者： Gregory Giuliani Bruno Chatenoux Andrea De Bono Denisa Rodila Jean-Philippe Richard Karin Allenbach Hy Dao Pascal Peduzzi Institute for Environmental Sciences/g D-geneva University of genevagenevaSwitzerland Institute for Environmental Sciences/EnviroSPaCE University of genevagenevaSwitzerland Institute for Environmental Sciences/Environmental governance and territorial Development University of genevagenevaSwitzerland Science Division United Nations Environment ProgrammegenevaSwitzerland

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental *** of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible ***,the full information potential of EO data has not been yet *** remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing *** data cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready *** and regular provision of Analysis Ready data(ARD)will significantly reduce the burden on EO data ***,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging *** paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services *** approach has been tested and validated generating Landsat ARD while building the Swiss data cube.

关键词： data cube Earth Observations Landsat automatic processing analysis ready data

Freecube:有效减小data cube体积

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FreeCube:有效减小Data Cube体积

基于多维压缩数组的并行data cube算法

第二十届全国数据库学术会议

作者：孙延凡陈红王珊中国人民大学数据与知识工程研究所教育部数据仓库与商务智能工程研究中心

1引言data cube是OLAP中最具影响的操作之一,它是对关系操作GROUP BY的多维扩展,对维的所有可能组合做相应的GROUP BY操作。datacube是一个开销很大的操作,并且操作的结果非常庞大,尤其是在基本关系表中的维和元组数比较多

关键词： OLAP data cube

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基于多维压缩数组的并行Data Cube算法

PMC： Select Materialized Cells in data cubes

第十九届全国数据库学术会议

作者：李盛恩李翠平王珊杜小勇中国科学院计算技术研究所中国人民大学信息学院

1前言data cube是OLAP中的一个重要技术,它将一组基本查询Cuboid的结果实例化,以加快查询响应时间。基本查询是一个GroupBy-Aggregate操作,一个有n个属性的关系可以有2个基本查询。data cube计算既是I/O密集型的又是CPU密集型的,寻找一... 详细信息

1前言data cube是OLAP中的一个重要技术,它将一组基本查询Cuboid的结果实例化,以加快查询响应时间。基本查询是一个GroupBy-Aggregate操作,一个有n个属性的关系可以有2个基本查询。data cube计算既是I/O密集型的又是CPU密集型的,寻找一个高效的算法是很有实际意义的,目前已经提出了若干个算法。

关键词： data warehouse OLAP data cube

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Journal of Computer Science & Technology 2006年第2期21卷 297-304,F0003页

作者：李红松黄厚宽 School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 P.R. China

QC-Tree is one of the most storage-efficient structures for data cubes in an MOLAP system. Although QC- Tree can achieve a high compression ratio, it is still a fully materialized data cube. In this paper, an improved structure PMC is presented allowing us to materialize only a part of the cells in a QC-Tree to save more storage space. There is a notable difference between our partially materialization algorithm and traditional materialized views selection algorithms. In a traditional algorithm, when a view is selected, all the cells in this view are to be materialized. Otherwise, if a view is not selected, all the cells in this view will not be materialized. This strategy results in the unstable query performance. The presented algorithm, however, selects and materializes data in cell level, and, along with further reduced space and update cost, it can ensure a stable query performance. A series of experiments are conducted on both synthetic and real data sets. The results show that PMC can further reduce storage space occupied by the data cube, and can shorten the time to update the cube.

关键词： data warehousing data cube multidimensional database partial materialization

Cloud-based storage and computing for remote sensing big data:a technical review

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International Journal of Digital Earth 2022年第1期15卷 1417-1445页

作者： Chen Xu Xiaoping Du Xiangtao Fan Gregory Giuliani Zhongyang Hu Wei Wang Jie Liu Teng Wang Zhenzhen Yan Junjie Zhu Tianyang Jiang Huadong Guo Key Lab of Digital Earth Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute for Environmental Sciences/GRID-Geneva University of GenevaGenevaSwitzerland Institute for Marine and Atmospheric research Utrecht (IMAU) Utrecht UniversityUtrechtThe Netherlands State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China Tianyi Cloud People’s Republic of China Telecom Co.LtdGuangzhouPeople’s Republic of China Department of Computer Science and Technology Tsinghua UniversityBeijingPeople's Republic of China

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and *** the progress of computer technologies,conventional computing implementations have become technically inefficient for processing *** computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past *** study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer ***,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,*** storage,metadata management,data homogeneity,and computing ***,we introduce state-of-the-art cloud-based data management technologies for RSBD *** unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data *** data models are suggested,***,ARD,data cubes,and composite ***,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing ***,we categorize the architectures of mainstream RSBD *** research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

关键词： Remote sensing big data cloud computing data cube analysis ready data parallel computing data model

Area and shape distortions in open-source discrete global grid systems

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Big Earth data 2022年第3期6卷 256-275页

作者： Alexander Kmoch Ivan Vasilyev Holger Virro Evelyn Uuemaa Department of Geography Institute of Ecology and Earth SciencesUniversity of TartuTartuEstonia

A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data cube *** facilitates integration and aggregation of multi-resolution data from various sources to rapidly calculate spatial *** calculated normalized area and compactness for cell geometries from 5 open-source DGGS implementations-Uber H3,Google S2,RiskAware OpenEAGGR,rHEALPix by Landcare Research New Zealand,and DGGRID by Southern Oregon University-to evaluate their suitability for a global-level statistical data *** conclude that the rHEALPix and OpenEAGGR and DGGRID ISEA-based DGGS definitions are most suitable for global statistics because they have the strongest guarantee of equal area preservation-where each cell covers almost exactly the same area on the *** H3 has the smallest shape distortions,but Uber H3 and Google S2 have the largest variations in cell ***,they provide more mature software library *** provides excellent functionality to construct grids with desired geometric properties but as the only implementation does not provide functions for traversal and navigation within a grid after its construction.

关键词： Coordinate reference systems spatial indexing big spatial data analytics data cube DGGS

Development of Geological data Warehouse

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Journal of China University of Geosciences 2003年第3期14卷 261-264页

作者： LiZhenhua HuGuangdao ZhangZhenfei DepartmentofComputerScience&Technology ChinaUniversityofGeosciencesWuhan430074 FacultyofEarthResources ChinaUniversityofGeosciencesWuhan430074

data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.

关键词： data warehouse (DW) geological data warehouse (GDW) space control points data cube

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

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China Communications 2017年第11期14卷 39-47页

作者： Hongman Wang Mengqi Zeng Zijie Xiong Fangchun Yang Institute of Network Technology Engineering Research Center of Information Network Ministry of EducationBeijing University of Posts and Telecommunications Beijing China

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

关键词： elevator group accidents apriori multi-dimensional association rules data cube edge computing

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一种提高BUC算法效率的有效方法

一种提高BUC算法效率的有效方法