HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data

Chmiel, Jan; Morzy, Tadeusz; Wrembel, Robert

doi:10.1007/978-3-642-03730-6_8

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Furthermore, new functionalities have been proposed for it, such as binning, encoding and compression techniques [32-34, 36, 37]. Other related proposals focus on organizing hierarchically Bitmap indices for indexing dimensional data [43,44]. However, these proposals do not investigate how the Bitmap index should handle spatial data, which is the objective of the indices proposed in this paper.…”

Section: Related Workmentioning

confidence: 96%

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The SB-index and the HSB-index: efficient indices for spatial data warehouses

et al. 2011

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Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial Geoinformatica (2012) 16:165-205 objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSBindex mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

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Section: Related Workmentioning

confidence: 96%

“…As a result, the performance of the Bitmap index is not drastically affected by the number of indexed dimensions. Therefore, the Bitmap index is frequently used to index warehouse data [30,42,43]. On the other hand, high cardinality has been seen as one of the Bitmap index' main drawback.…”

Section: The Projection and The Star-join Bitmap Indicesmentioning

confidence: 99%

The SB-index and the HSB-index: efficient indices for spatial data warehouses

et al. 2011

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“…Morzy et al in [8] proposed hierarchical bitmap index for indexing set-valued attributes. Later, Chmiel et al in [5] extended that concept to present hierarchically-organized bitmap index for indexing dimensional data. Bender et al proposed the cache oblivious B-Trees [1] that performs the optimal search across different hierarchical memories with varying memory levels, cache size, and cache line size.…”

Section: Related Workmentioning

confidence: 99%

Using evolving storage structures for data storage

Rahman

2010

Proceedings of the 8th International Conference on Frontiers of Information Technology

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Different data storage structures suit different data management scenarios, therefore, a universal data storage structure is not possible. Furthermore, each data storage structure has different resource consumption because of their unique execution complexity. Self-tuning data management systems need a mechanism to select appropriate data storage structure and to adapt it with changing data management requirements. We propose Evolving Storage Structures for self-tuning data management system that supports customization and adaptation with changing data management needs.

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“…Morzy et al in [21] proposed a hierarchical bitmap index for indexing set-valued attributes. Later, Chmiel et al in [11] extended that concept to present hierarchically-organized bitmap indexes for indexing dimensional data. ECOS uses hierarchical organization differently.…”

Section: Related Workmentioning

confidence: 99%

ECOS: Evolutionary Column-Oriented Storage

Rahman

Schallehn

Saake

2011

Lecture Notes in Computer Science

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As DBMS has grown more powerful over the last decades, they have also become more complex to manage. To achieve efficiency by database tuning is nowadays a hard task carried out by experts. This development inspired the ongoing research on self-tuning to make database systems more easily manageable. In this report, we present a customizable self-tuning storage manager, we termed as Evolutionary Column-Oriented Storage (ECOS). The capability of self-tuning data management with minimal human intervention, which is the main design goal for ECOS, is achieved by dynamically adjusting the storage structures of a column-oriented DBMS according to data size and access characteristics. It is based on the Decomposed Storage Model (DSM) with support for customization at the table-level using five different variations of DSM. Furthermore, it also proposes fine-grained customization of storage structures at the column-level. It uses hierarchicallyorganized storage structures for each column to enable autonomic selection of the suitable storage structure along the hierarchy (as hierarchy-level increases) using an evolution mechanism. Moreover, for ECOS we proposed the concept of an evolution path that provides a reduction of human intervention for database maintenance. We evaluated ECOS empirically using a custom micro benchmark showing performance improvement.

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HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data

Cited by 7 publications

References 19 publications

The SB-index and the HSB-index: efficient indices for spatial data warehouses

The SB-index and the HSB-index: efficient indices for spatial data warehouses

Using evolving storage structures for data storage

ECOS: Evolutionary Column-Oriented Storage

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