Thiago Luís Lopes Siqueira

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

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 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 HSB-index 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.

The impact of spatial data redundancy on SOLAP query performance

Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.

A spatial bitmap-based index for geographical data warehouses

In this paper we propose the Spatial Bitmap Index (SB-index), which is an index based on Bitmap and Minimum Bounding Rectangle (MBR) to provide efficient query processing in Geographical Data Warehouses. The SB-index is built on the primary key of a spatial dimension table, and maintains the MBR of a given spatial attribute. Query processing requires a scan on the index, which compares both the query spatial predicate and the current MBR. This scan supplies a set of candidate solutions to a refinement step that evaluates each candidate. Finally, only the index entries from objects that satisfy the spatial predicate must be accessed, in order to answer the submitted query. Comparisons between the SB-index and the star-join indexed with R-tree and GiST showed significantly improvement of 25% up to 95% with regards to the query processing time. This performance gain occurs since SB-index restricts a set of candidates and avoids the star-join calculation.

Benchmarking spatial data warehouses

Spatial data warehouses (SDW) enable analytical multidimensional queries together with spatial analysis. Mainly, three operations are related to SDW query processing performance: (i) joining large fact tables and large spatial and non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to different spatial granularity levels. Several techniques to improve the query processing performance over SDW have been proposed in the literature. However, we identified the lack of a benchmark to carry out a controlled experimental evaluation of such techniques and, principally, to effectively measure the costs of the aforementioned three complex operations. In this paper, we propose a novel spatial data warehouse benchmark, called Spadawan, to provide performance evaluation environments for SDW and enable a further investigation on spatial data redundancy. The Spadawan benchmark is available at http://gbd.dc.ufscar.br/spadawan.

Modeling vague spatial data warehouses using the VSCube conceptual model

Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model.

Towards Vague Geographic Data Warehouses

Currently, geographic data warehouses provide a means of carrying out spatial analysis together with agile and flexible multidimensional analytical queries over huge volumes of data. However, they do not enable the representation and neither the analysis over real world phenomena that have uncertain locations or vague boundaries, which are denoted by vague spatial objects. In this paper, we introduce the vague geographic data warehouse (vGDW) and its spatially-enabled components at the logical level: attributes, measures, dimensions, hierarchies and queries. We base the vGDW on exact models to represent vague spatial objects. In addition, we combine the fuzzy model with the exact model in relational vGDW to improve the expressiveness of the queries. Finally, a case study is presented to validate our contributions.

Efficient processing of drill-across queries over geographic data warehouses

Drill-across SOLAP queries (spatial OLAP queries) allow for strategic decision-making through the use of numeric measures from distinct fact tables that share dimensions and by the evaluation of spatial predicates. Despite the importance of these queries in geographic data warehouses (GDWs), there is a lack of research aimed at their study. In this paper, we investigate three challenging aspects related to the efficient processing of drill-across SOLAP queries over GDWs: (i) the design of a GDW schema to enable the performance evaluation of drill-across SOLAP query processing; (ii) the definition of classes of drill-across SOLAP queries to be issued over the proposed GDW schema; and (iii) the analysis of different approaches to process drill-across SOLAP queries, as follows: star-join computation, materialized views and a new proposed approach based on the SB-index, which is named DrillAcrossSB. We conclude that the DrillAcrossSB approach highly speedups the processing of drill-across SOLAP queries from 39% up to 98%.

Querying Vague Spatial Information in Geographic Data Warehouses

Non-redundant geographic data warehouse (GDW) schemas have been recognized as an essential issue in the GDW design. However, little attention has been devoted to the study of how the handling of vague spatial data affects query performance and storage requirements in GDW. In this paper we investigate the query processing performance over nonredundant GDW schemas that are based on different spatial representation approaches for handling spatial data uncertainty. Further, we analyze the indexing issue, aiming at improving query performance on a nonredundant GDW with vague spatial data. We concluded that the adaptation of an existing index for GDW aiming at handling uncertain spatial data does not satisfy completely the performance requirements. Therefore, there is a need for new index structures for processing vague objects in GDW.