An Efficient Implementation for MOLAP Basic Data Structure and Its Evaluation

Hasan, K. M. Azharul; Tsuji, Tomohiro; Higuchi, Ken

doi:10.1007/978-3-540-71703-4_26

Cited by 33 publications

(17 citation statements)

References 10 publications

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“…The basic idea concerning HOMD has been proposed in [14], but it never deals with the overflow problem, which is the main topic of this paper. It only describes usual HOMD(HI), namely chunking and splitting are never presented.…”

Section: Related Workmentioning

confidence: 99%

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History offset implementation scheme for large scale multidimensional data sets

Tsuji

Kuroda

Higuchi

2008

Proceedings of the 2008 ACM Symposium on Applied Computing

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A novel implementation scheme for large scale multidimensional datasets is proposed and evaluated. The scheme implements a multidimensional dataset by employing multidimensional arrays. Using multidimensional arrays provides many advantages, but suffers from some problems. The proposed history offset implementation scheme solves these problems by an efficient scheme of record encoding based on the notion of extendible array. In this paper, some solutions are presented against an essential problem of address space overflow in handling large scale multidimensional datasets. Our proposed schemes will be proved to exhibit good performance in space and time costs.

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Section: Related Workmentioning

confidence: 99%

“…However, binary searching is necessary in chunk arrays, and this causes the deterioration of element accessing performance. [14] has proposed simple and efficient method of encoding multidimensional dataset. This encoding is based on the extension history of the underlying extendible array and the offset value in its subarray.…”

Section: Introductionmentioning

confidence: 99%

History offset implementation scheme for large scale multidimensional data sets

Tsuji

Kuroda

Higuchi

2008

Proceedings of the 2008 ACM Symposium on Applied Computing

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“…They also uses conventional multidimensional array. Multidimensional extendible array is used in [12] and a data structure is proposed to retrieve, compress and reorganize data for fast access and less memory. So, none of these works evaluate the performance of EKA scheme on MOLAP cube and incremental aggregation.…”

Section: Related Workmentioning

confidence: 99%

“…The idea of extendible array solves the problem of extendibility. So, an efficient extendible array is necessary for efficient incremental aggregation [12]. Extendible Karnaugh Array (EKA) is an efficient scheme which has better performance than other data structures.…”

Section: Introductionmentioning

confidence: 99%

Incremental aggregation scheme based on Extendible Karnaugh Arrays

Hasan

Rabbi

Ahsan

2014

2014 17th International Conference on Computer and Information Technology (ICCIT)

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Data is increasing so rapidly that new data warehousing approaches are required to process and analyze data. Aggregation of data incrementally is needed to fast access of data and compute aggregation functions. Multidimensional arrays are generally used for this purpose. But some disadvantages such as address space requirement is large and processing time is comparatively slow in case of aggregation. For this purpose we use Extendible Karnaugh Array (EKA). EKA is an efficient scheme which has better performance than other data structures that we have tested in our research. In this research work we use EKA as basic structure for implementing incremental aggregation of data and evaluate its performance over other approaches. We use Multidimensional Online Analytical Processing (MOLAP) which stores data in optimized multi-dimensional array storage, rather than in a relational database. We create MOLAP data cube using Traditional Multidimensional Array (TMA) and EKA scheme and compare incremental aggregation with Relational Online Analytical Processing (ROLAP). The effective outcome of EKA structure for incremental aggregation on MOLAP structure is shown by some experimental results.

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“…It is customized for the multidimensional model, the data model considered by MOLAP [35]. Therefore, A-Store only deals with Selection-Projection-Join-Grouping-Aggregation (SPJGA) queries on star/ snowflake schemas, which are actually the most common cases for OLAP applications.…”

Section: The Query Processing Modelmentioning

confidence: 99%

Virtual Denormalization via Array Index Reference for Main Memory OLAP

Zhang

Zhou

Zhang

et al. 2016

IEEE Trans. Knowl. Data Eng.

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Abstract-Denormalization is a common tactic for enhancing performance of data warehouses, though its side-effect is quite obvious. Besides being confronted with update abnormality, denormalization has to consume additional storage space. As a result, this tactic is rarely used in main memory databases, which regards storage space, i.e., RAM, as scarce resource. Nevertheless, our research reveals that main memory database can benefit enormously from denormalization, as it is able to remarkably simplify the query processing plans and reduce the computation cost. In this paper, we present A-Store, a main memory OLAP engine customized for star/snowflake schemas. Instead of generating fully materialized denormalization, A-Store resorts to virtual denormalization by treating array indexes as primary keys. This design allows us to harvest the benefit of denormalization without sacrificing additional RAM space. A-Store uses a generic query processing model for all SPJGA queries. It applies a number of state-of-the-art optimization methods, such as vectorized scan and aggregation, to achieve superior performance. Our experiments show that A-Store outperforms the most prestigious MMDB systems significantly in star/snowflake schema based query processing.

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An Efficient Implementation for MOLAP Basic Data Structure and Its Evaluation

Cited by 33 publications

References 10 publications

History offset implementation scheme for large scale multidimensional data sets

History offset implementation scheme for large scale multidimensional data sets

Incremental aggregation scheme based on Extendible Karnaugh Arrays

Virtual Denormalization via Array Index Reference for Main Memory OLAP

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