68:(OLAP) such dimensions could be the subsidiaries a company has, the products the company offers, and time; in this setup, a fact would be a sales event where a particular product has been sold in a particular subsidiary at a particular time. In satellite image timeseries dimensions would be latitude and longitude coordinates and time; a fact (sometimes called measure) would be a pixel at a given space and time as taken by the satellite (following some processing that is not of concern here). Even though it is called a
72:(and the examples provided above happen to be 3-dimensional for brevity), a data cube generally is a multi-dimensional concept which can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. In any case, every dimension divides data into groups of cells whereas each cell in the cube represents a single measure of interest. Sometimes cubes hold only a few values with the rest being
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Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a data cube.
442:
Gray, Jim; Chaudhuri, Surajit; Bosworth, Adam; Layman, Andrew; Reichart, Don; Venkatrao, Murali; Pellow, Frank; Pirahesh, Hamid (January 1997). "Data Cube: A Relational
Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals".
123:
introduced management of massive data cubes with high-level user functionality combined with an efficient software architecture. Datacube operations include subset extraction, processing, fusion, and in general queries in the spirit of
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supports n-D arrays with a rich set of operations. All these have in common that arrays must fit into the main memory and are available only while the particular program maintaining them (such as image processing software) is running.
322:) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the
277:(Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional data cubes. This database category has been pioneered by the
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in 2008. In addition to the common data cube operations, the language knows about the semantics of space and time and supports both regular and irregular grid data cubes, based on the concept of
53:
of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte
360:(OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.
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A series of data exchange formats support storage and transmission of data cube-like data, often tailored towards particular application domains. Examples include
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Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which
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For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and
423:. Int. Workshop on Graphics Modeling, Visualization in Science & Technology. Darmstadt, Germany: Springer (published 1993). pp. 236–45.
336:, since a spectrally-resolved image is represented as a three-dimensional volume. Earth observation data cubes combine satellite imagery such as
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offers arbitrarily-indexed 1-D arrays and arrays of arrays, which allows the construction of higher-dimensional arrays, up to 15 dimensions.
154:
Around that time, a working group on Multi-Dimensional
Databases ("Arbeitskreis Multi-Dimensionale Datenbanken") was established at German
76:, i.e. undefined, while sometimes most or all cube coordinates hold a cell value. In the first case such data are called
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120:
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database language was extended with data cube functionality as "SQL – Part 15: Multi-dimensional arrays (SQL/MDA)".
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The data cube is used to represent data (sometimes called facts) along some dimensions of interest. For example, in
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Some years after, the data cube concept was applied to describe time-varying business data as data cubes by
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Harinarayan, Venky; Rajaraman, Anand; Ullman, Jeffrey D. (1996). "Implementing data cubes efficiently".
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In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a
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Proceedings of the 1996 ACM SIGMOD international conference on
Management of data – SIGMOD '96
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Kopp, Steve; Becker, Peter; Doshi, Abhijit; Wright, Dawn J.; Zhang, Kaixi; Xu, Hong (2019).
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Baumann, Peter (April 1992). "Language
Support for Raster Image Manipulation in Databases".
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which rank among the top 500 most cited computer science articles over a 25-year period.
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An industry standard for querying business data cubes, originally developed by
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The EarthServer initiative has established geo data cube service requirements.
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Multi-dimensional arrays have long been familiar in programming languages.
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clips and other data en masse with simple expressions derived from
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mathematics. Some languages (such as PDL) distinguish between a
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DIS 9075-15 Information technology – Database languages – SQL
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Graphics
Modeling and Visualization in Science and Technology
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of images and a data cube, while many (such as IDL) do not.
691:"Achieving the Full Vision of Earth Observation Data Cubes"
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are examples, allow the programmer to manipulate complete
665:"EarthServer - Big Datacube Analytics at Your Fingertips"
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195:
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84:, although there is no hard delineation between the two.
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in 1996, however without addressing data cubes as such.
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is a geo data cube analytics language issued by the
306:may be represented as an n-dimensional data cube.
720:
108:for statistical (in particular, business) data,
638:"Management of Multidimensional Discrete Data"
613:"Part 15: Multi-dimensional arrays (SQL/MDA)"
570:"Datenbank Rundbrief, Ausgabe 23, Mai 1999"
552:"Datenbank Rundbrief, Ausgabe 19, Mai 1997"
309:
27:. For database support for datacubes, see
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332:A data cube is also used in the field of
80:, and in the second case they are called
31:. For spatio-temporal geo datacubes, see
541:. 12 June 2009. Retrieved 21 March 2017.
534:500 Most Cited Computer Science Articles
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35:. For the Image Processing company, see
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329:geo data cube query language standard.
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41:In computer programming contexts, a
445:Data Mining and Knowledge Discovery
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14:
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112:for general scientific data, and
16:Multi-dimensional data structure
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385:Australian Geoscience Data Cube
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202:Web Coverage Processing Service
51:multi-dimensional ("n-D") array
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346:Geographic information system
429:10.1007/978-3-642-77811-7_19
390:Graph (discrete mathematics)
358:online analytical processing
221:MultiDimensional eXpressions
66:online analytical processing
7:
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156:Gesellschaft für Informatik
126:data manipulation languages
10:
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324:Open Geospatial Consortium
206:Open Geospatial Consortium
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18:
110:Hierarchical Data Format
588:"The DatabaseManifesto"
457:10.1023/A:1009726021843
395:Abstract semantic graph
310:Science and engineering
502:10.1145/233269.233333
352:Business intelligence
176:applications for the
334:imaging spectroscopy
302:; more generally, a
708:10.3390/data4030094
486:. pp. 205–16.
281:system since 1994.
141:Venky Harinarayan
139:, et al., and by
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482:. Vol. 25.
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166:image processing
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187:Standardization
145:Anand Rajaraman
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61:of image data.
55:data warehouses
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191:In 2018, the
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162:Datacube Inc.
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121:Peter Baumann
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23:concept, see
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673:. Retrieved
669:Earth server
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451:(1): 29–53.
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537:(501–600),
348:analytics.
320:color space
294:Mathematics
275:Array DBMSs
149:Jeff Ullman
59:time series
21:data mining
723:Categories
675:2017-03-31
650:2017-09-21
623:2018-05-27
598:2017-09-21
484:ACM SIGMOD
406:References
370:Array DBMS
342:Sentinel-2
318:(or other
29:Array DBMS
701:(3): 94.
619:. ISO/IEC
488:CiteSeerX
380:OLAP cube
338:Landsat 8
217:Microsoft
178:PC market
119:In 1992,
43:data cube
25:OLAP cube
539:CiteSeer
465:12502175
375:rasdaman
364:See also
279:rasdaman
174:software
170:hardware
137:Jim Gray
47:datacube
19:For the
520:3104453
233:Fortran
164:was an
94:Fortran
88:History
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304:tensor
300:matrix
265:vector
253:S-Lang
251:, and
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641:(PDF)
516:S2CID
461:S2CID
344:with
245:NumPy
219:, is
128:like
82:dense
74:empty
695:Data
671:. EU
645:VLDB
594:. EU
574:dblp
556:dblp
506:ISBN
340:and
327:WCPS
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263:and
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70:cube
57:and
45:(or
703:doi
498:doi
453:doi
425:doi
356:In
316:RGB
249:PDL
241:IDL
237:APL
196:SQL
193:ISO
130:SQL
106:MDX
98:APL
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