SISYPHUS: The implementation of a chunk-based storage manager for OLAP data cubes

Karayannidis, Nikos; Sellis, Timos

doi:10.1016/s0169-023x(02)00178-7

Cited by 15 publications

(11 citation statements)

References 14 publications

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“…The other domain where array chunking has been predominantly used is in multidimensional on-line analytical processing algorithm (MOLAP) [19,5,9,14,12]. In [19], the method of computing the CUBE over a multi-dimensional data model was introduced.…”

Section: Related Workmentioning

confidence: 99%

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Optimal chunking of large multidimensional arrays for data warehousing

Otoo

Rotem

Seshadri

2007

Proceedings of the ACM Tenth International Workshop on Data Warehousing and OLAP

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Very large multidimensional arrays are commonly used in data intensive scientific computations as well as on-line analytical processing applications referred to as MOLAP. The storage organization of such arrays on disks is done by partitioning the large global array into fixed size sub-arrays called chunks or tiles that form the units of data transfer between disk and memory. Typical queries involve the retrieval of sub-arrays in a manner that access all chunks that overlap the query results. An important metric of the storage efficiency is the expected number of chunks retrieved over all such queries. The question that immediately arises is "what shapes of array chunks give the minimum expected number of chunks over a query workload?" The problem of optimal chunking was first introduced by Sarawagi and Stonebraker [15] who gave an approximate solution. In this paper we develop exact mathematical models of the problem and provide exact solutions using steepest descent and geometric programming methods. Experimental results, using synthetic and real life workloads, show that our solutions are consistently within than 2.0% of the true number of chunks retrieved for any number of dimensions. In contrast, the approximate solution of [15] can deviate considerably from the true result with increasing number of dimensions and also may lead to suboptimal chunk shapes.

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Section: Related Workmentioning

confidence: 99%

“…Persistent storage organization of multi-dimensional arrays is typically done by partitioning them into coarse-grained hyper-rectangular blocks called chunks or tiles which form the units of array transfers between disk and memory [15,16,5,9]. A chunk is defined by the index range of values along each dimension.…”

Section: Introductionmentioning

confidence: 99%

Optimal chunking of large multidimensional arrays for data warehousing

Otoo

Rotem

Seshadri

2007

Proceedings of the ACM Tenth International Workshop on Data Warehousing and OLAP

View full text Add to dashboard Cite

show abstract

“…The other domain where array chunking has been predominantly used is in multidimensional on-line analytical processing algorithm (MOLAP) [18,5,9,13,12]. In [18], the method of computing the CUBE over a multi-dimensional data model was introduced.…”

Section: Related Workmentioning

confidence: 99%

“…Not only is BESS applicable to MOLAP data sets, but can be applied to scientific multi-dimensional sparse array data. Variations of the chunking concepts for the storage schemes MOLAP data sets are also proposed in the SISYPHUS storage manager [9].…”

Section: Related Workmentioning

confidence: 99%

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Chunking of Large Multidimensional Arrays

Rotem¹,

Otoo²,

Seshadri³

2007

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Very large multidimensional arrays are commonly used in data intensive scientific computations as well as on-line analytical processing applications referred to as MOLAP. The storage organization of such arrays on disks is done by partitioning the large global array into fixed size sub-arrays called chunks or tiles that form the units of data transfer between disk and memory. Typical queries involve the retrieval of sub-arrays in a manner that accesses all chunks that overlap the query results. An important metric of the storage efficiency is the expected number of chunks retrieved over all such queries. The question that immediately arises is "what shapes of array chunks give the minimum expected number of chunks over a query workload?" The problem of optimal chunking was first introduced by Sarawagi and Stonebraker [14] who gave an approximate solution. In this paper we develop exact mathematical models of the problem and provide exact solutions using steepest descent and geometric programming methods. Experimental results, using synthetic and real life workloads, show that our solutions are consistently less than 2.0% of the true number of chunks retrieved for any number of dimensions. In contrast, the approximate solution of [14] can deviate considerably from the true result with increasing number of dimensions.

show abstract