Content deleted Content added
m link distributed processing |
m add link |
||
| (7 intermediate revisions by 6 users not shown) | |||
Line 1:
{{short description|System that provides database services specifically for arrays}}
An '''
[[File:Euclidean neighborhood in n-D arrays.png|thumb|150px|alt=Euclidean neighborhood of elements in arrays|Euclidean neighborhood of elements in arrays]]
Line 18 ⟶ 19:
First significant work in going beyond BLOBs has been established with PICDMS.<ref>Chock, M., Cardenas, A., Klinger, A.: Database structure and manipulation capabilities of a picture database management system (PICDMS). IEEE ToPAMI, 6(4):484–492, 1984</ref> This system offers the precursor of a 2-D array query language, albeit still procedural and without suitable storage support.
A first declarative query language suitable for multiple dimensions and with an algebra-based semantics has been published by [[Peter Baumann (computer scientist)|Baumann]], together with a scalable architecture.<ref>Baumann, P.: [http://www.informatik.uni-trier.de/~ley/db/journals/vldb/vldb3.html#Baumann94 On the Management of Multidimensional Discrete Data]. VLDB Journal 4(3)1994, Special Issue on Spatial Database Systems, pp. 401–444</ref><ref>Baumann, P.: [http://www.informatik.uni-trier.de/~ley/db/conf/ngits/ngits99.html#Baumann99 A Database Array Algebra for Spatio-Temporal Data and Beyond]. Proc.
In terms of Array DBMS implementations, the [[rasdaman]] system has the longest implementation track record of n-D arrays with full query support. [[Oracle Spatial|Oracle GeoRaster]] offers chunked storage of 2-D raster maps, albeit without SQL integration. [[TerraLib]] is an open-source GIS software that extends object-relational DBMS technology to handle spatio-temporal data types; while main focus is on vector data, there is also some support for rasters. Starting with version 2.0, [[Postgis|PostGIS]] embeds raster support for 2-D rasters; a special function offers declarative raster query functionality. [[SciQL]] is an array query language being added to the [[MonetDB]] DBMS. [[Michael Stonebraker#SciDB|SciDB]] is a more recent initiative to establish array database support. Like SciQL, arrays are seen as an equivalent to tables, rather than a new attribute type as in rasdaman and PostGIS.
For the special case of [[sparse matrix|sparse data]], [[OLAP]] data cubes are well established; they store cell values together with their ___location{{snd}} an adequate compression technique in face of the few locations carrying valid information at all{{snd}} and operate with SQL on them. As this technique does not scale in density, standard databases are not used today for dense data, like satellite images, where most cells carry meaningful information; rather, proprietary ad
Generally, Array DBMSs are an emerging technology. While operationally deployed systems exist, like [[Oracle Spatial|Oracle GeoRaster]], [[Postgis|PostGIS 2.0]] and [[rasdaman]], there are still many open research questions, including query language design and formalization, query optimization, parallelization and [[distributed processing]], and scalability issues in general. Besides, scientific communities still appear reluctant in taking up array database technology and tend to favor specialized, proprietary technology.
Line 30 ⟶ 31:
=== Conceptual modeling ===
Formally, an array ''A'' is given by a (total or partial) function ''A'': ''X'' → ''V'' where ''X'', the ''___domain'' is a ''d''-dimensional integer interval for some {{math|''d'' > 0}} and ''V'', called ''range'', is some (non-empty) value set; in set notation, this can be rewritten as {{math|{{mset| (''p'',''v'') | ''p''
Following established database practice, an array query language should be [[Declarative programming|declarative]] and safe in evaluation.
Line 39 ⟶ 40:
The '''marray''' operator creates an array over some given ___domain extent and initializes its cells:
<syntaxhighlight lang="
marray index-range-specification
values cell-value-expression
Line 46 ⟶ 47:
where ''index-range-specification'' defines the result ___domain and binds an iteration variable to it, without specifying iteration sequence. The ''cell-value-expression'' is evaluated at each ___location of the ___domain.
'''Example:'''
<syntaxhighlight lang="
marray p in [10:20,40:50]
values A[p]
Line 53 ⟶ 54:
This special case, pure subsetting, can be abbreviated as
<syntaxhighlight lang="
A[10:20,40:50]
</syntaxhighlight>
This subsetting keeps the dimension of the array; to reduce dimension by extracting slices, a single slicepoint value is indicated in the slicing dimension.
'''Example:'''
<syntaxhighlight lang="
A[*:*,*:*,100]
</syntaxhighlight>
Line 67 ⟶ 68:
The above examples have simply copied the original values; instead, these values may be manipulated.
'''Example:'''
<syntaxhighlight lang="
marray p in ___domain(A)
values log( A[p] )
Line 74 ⟶ 75:
This can be abbreviated as:
<syntaxhighlight lang="
log( A )
</syntaxhighlight>
Line 81 ⟶ 82:
The '''condense''' operator aggregates cell values into one scalar result, similar to SQL aggregates. Its application has the general form:
<syntaxhighlight lang="
condense condense-op
over index-range-specification
Line 90 ⟶ 91:
'''Example:''' "The sum over all values in A."
<syntaxhighlight lang="
condense +
over p in sdom(A)
Line 97 ⟶ 98:
A shorthand for this operation is:
<syntaxhighlight lang="
add_cells( A )
</syntaxhighlight>
Line 106 ⟶ 107:
'''Example:''' "A histogram over 8-bit greyscale image A."
<syntaxhighlight lang="
marray bucket in [0:255]
values count_cells( A = bucket )
Line 118 ⟶ 119:
Array storage has to accommodate arrays of different dimensions and typically large sizes. A core task is to maintain spatial proximity on disk so as to reduce the number of disk accesses during subsetting. Note that an emulation of multi-dimensional arrays as nested lists (or 1-D arrays) will not per se accomplish this and, therefore, in general will not lead to scalable architectures.
Commonly arrays are partitioned into sub-arrays which form the unit of access. Regular partitioning where all partitions have the same size (except possibly for boundaries) is referred to as ''chunking''.<ref>[[Sunita Sarawagi|Sarawagi, S.]], [[Michael Stonebraker|Stonebraker, M.]]: Efficient Organization of Large Multidimensional Arrays. Proc. ICDE'94, Houston, USA, 1994, pp. 328-336</ref> A generalization which removes the restriction to equally sized partitions by supporting any kind of partitioning is ''tiling''.<ref>Furtado, P., Baumann, P.: [http://www.informatik.uni-trier.de/~ley/db/conf/icde/icde99.html#FurtadoB99 Storage of Multidimensional Arrays based on Arbitrary Tiling]. Proc. ICDE'99, March 23–26, 1999, Sydney, Australia, pp. 328–336</ref> Array partitioning can improve access to array subsets significantly: by adjusting tiling to the access pattern, the server ideally can fetch all required data with only one disk access.
Compression of tiles can sometimes reduce substantially the amount of storage needed. Also for transmission of results compression is useful, as for the large amounts of data under consideration networks bandwidth often constitutes a limiting factor.
Line 133 ⟶ 134:
The following are representative domains in which large-scale multi-dimensional array data are handled:
*Earth sciences: geodesy / mapping, [[remote sensing]], geology, oceanography, hydrology, atmospheric sciences, cryospheric sciences
*Space sciences: Planetary sciences, astrophysics (optical and radio telescope observations, cosmological simulations)
*Life sciences: gene data, confocal microscopy, CAT scans
| |||