Parallel multi-dimensional ROLAP indexing

Dehne, Frank; Eavis, Todd; Rau-Chaplin, Andrew

doi:10.1109/ccgrid.2003.1199356

Cited by 25 publications

(28 citation statements)

References 25 publications

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“…While Microsoft MOLAP server still has a slight advantage, we note that 1) the Microsoft DBMS benefits from years of optimization, and 2) MOLAP is ideally suited to the scale of the current test (i.e., 1-10 million records). Given that our DBMS framework is not constrained by the limits of array-based storage [23], these preliminary results suggest that the current DBMS−and the architecture it represents−does indeed have the potential to provide MOLAP-style performance with ROLAP-style scalability (we note that a number of legacy components in the codebase currently prevent true Terabyte scale testing. However, an ongoing software re-engineering effort is expected to remove these limitations in the coming year).…”

Section: H Competitive Query Performancementioning

confidence: 97%

Minimizing the MOLAP/ROLAP Divide: You Can Have Your Performance and Scale It Too

Eavis¹,

Taleb²

2013

Journal of Computing Science and Engineering

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Over the past generation, data warehousing and online analytical processing (OLAP) applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP's table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features from both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Specifically, it utilizes an OLAP algebra coupled with a domain specific query optimizer, to map user queries directly to the storage and indexing framework. Experimental results demonstrate that not only does the design improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.

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Section: H Competitive Query Performancementioning

confidence: 97%

Minimizing the MOLAP/ROLAP Divide: You Can Have Your Performance and Scale It Too

Eavis¹,

Taleb²

2013

Journal of Computing Science and Engineering

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“…Such multi-dimensional data comes in many forms [1-3, 5, 9, 18, 19, 21, 23, 25, 26, 32, 35] including spectral elements in a parallel high resolution atmospherical global circulation model [11], tissue microarray data in a co-operative Grid-based oncology system [34] or business oriented OLAP data [10].…”

Section: Introductionmentioning

confidence: 99%

“…And in distributed information systems they are used to partition multi-dimensional data in such a way that points that are close in Euclidian space are likely to be allocated to the same or neighbouring processors. The idea of using space-filling curves for partitioning has been key to applications in parallel [10], P2P [33] and grid computing [34] settings.…”

Section: Introductionmentioning

confidence: 99%

Compact Hilbert Indices for Multi-Dimensional Data

Hamilton

Rau-Chaplin

2007

First International Conference on Complex, Intelligent and Software Intensive Systems (CISIS'07)

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“…In the OLAP-Enabled Grid, we use R-Trees to index the data stored on each block of external memory, which is ordered on disk according to a multidimensional space-filling curve as in [2][3][4]. In those studies, we have observed that the time to answer a query is proportional to the amount of data which must be read from disk, which in turn depends on the selection ranges of the query and the materialized view which will be used to answer it.…”

mentioning

confidence: 99%

Implementing OLAP Query Fragment Aggregation and Recombination for the OLAP Enabled Grid

Lawrence

Dehne

Rau-Chaplin

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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In this paper we propose a new query processing method for the OLAP Enabled Grid, which blends sophisticated cache extraction techniques and data grid scheduling to efficiently satisfy OLAP queries in a distributed fashion. The heart of our approach is our query Fragment Aggregation and Recombination (FAR) strategy that partitions OLAP queries into subqueries which can be effectively answered by retrieving and aggregating multiple fragments of cached data from nearby grid sources, or as a last resort, more remote backend data warehouses. We have implemented and experimentally evaluated our query processing method and found that our strategy reduces query time between 50% and 60% for practical user cache sizes and network parameters.

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Parallel multi-dimensional ROLAP indexing

Cited by 25 publications

References 25 publications

Minimizing the MOLAP/ROLAP Divide: You Can Have Your Performance and Scale It Too

Minimizing the MOLAP/ROLAP Divide: You Can Have Your Performance and Scale It Too

Compact Hilbert Indices for Multi-Dimensional Data

Implementing OLAP Query Fragment Aggregation and Recombination for the OLAP Enabled Grid

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