Towards a Scalable, Performance-Oriented OLAP Storage Engine

Eavis, Todd; Taleb, Ahmad

doi:10.1007/978-3-642-29035-0_13

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…We note that the current paper significantly extends upon an earlier publication [1]. In particular, we have expanded upon our discussion of the storage engine itself, providing a much more thorough presentation of the individual elements of the system.…”

Section: Introductionmentioning

confidence: 88%

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: Introductionmentioning

confidence: 88%

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

Eavis¹,

Taleb²

2013

Journal of Computing Science and Engineering

Self Cite

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Towards a framework for large-scale multimedia data storage and processing on Hadoop platform

Lai

Chen

et al. 2013

J Supercomput

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Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study

2019

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When exploring big amounts of data without a clear target, providing an interactive experience becomes really difficult, since this tentative inspection usually defeats any early decision on data structures or indexing strategies. This is also true in the physics domain, specifically in high-energy physics, where the huge volume of data generated by the detectors are normally explored via C++ code using batch processing, which introduces a considerable latency. An interactive tool, when integrated into the existing data management systems, can add a great value to the usability of these platforms. Here, we intend to review the current state-of-the-art of interactive data exploration, aiming at satisfying three requirements: access to raw data files, stored in a distributed environment, and with a reasonably low latency. This paper follows the guidelines for systematic mapping studies, which is well suited for gathering and classifying available studies. We summarize the results after classifying the 242 papers that passed our inclusion criteria. While there are many proposed solutions that tackle the problem in different manners, there is little evidence available about their implementation in practice. Almost all of the solutions found by this paper cover a subset of our requirements, with only one partially satisfying the three. The solutions for data exploration abound. It is an active research area and, considering the continuous growth of data volume and variety, is only to become harder. There is a niche for research on a solution that covers our requirements, and the required building blocks are there.INDEX TERMS Big data applications, data analysis, data engineering, data exploration, database systems, interactive systems, systematic mapping study. APPENDIX RESULTS OF THE MAPPING STUDYSee Tables.

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Towards a Scalable, Performance-Oriented OLAP Storage Engine

Cited by 4 publications

References 12 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

Towards a framework for large-scale multimedia data storage and processing on Hadoop platform

Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study

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