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GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemas

Data Warehouse (DW) is a dimensional database for providing decision support by means of on-line analytical processing (OLAP) techniques. Another technology also used to provide decision support is Geographical Information System (GIS). Much research aims at integrating these technologies, but there are still some open questions, particularly regarding the design of a geographical dimensional data schema. This paper discusses some related work and proposes GeoDWFrame that is a framework based on the star schema and has been specified as guidance for designing geographical dimensional schemas. Some experimental results are also given.

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.

Modelling and querying geographical data warehouses

A number of proposals for integrating geographical (Geographical Information Systems-GIS) and multidimensional (data warehouse-DW and online analytical processing-OLAP) processing are found in the database literature. However, most of the current approaches do not take into account the use of a GDW (geographical data warehouse) metamodel or query language to make available the simultaneous specification of multidimensional and spatial operators. To address this, this paper discusses the UML class diagram of a GDW metamodel and proposes its formal specifications. We then present a formal metamodel for a geographical data cube and propose the Geographical Multidimensional Query Language (GeoMDQL) as well. GeoMDQL is based on well-known standards such as the MultiDimensional eXpressions (MDX) language and OGC simple features specification for SQL and has been specifically defined for spatial OLAP environments based on a GDW. We also present the GeoMDQL syntax and a discussion regarding the taxonomy of GeoMDQL query types. Additionally, aspects related to the GeoMDQL architecture implementation are described, along with a case study involving the Brazilian public healthcare system in order to illustrate the proposed query language.

An open source and web based framework for geographic and multidimensional processing

The development of Business Intelligence (BI) systems has been the destination of high investments made by several enterprises. The motivation is because an efficient decision support environment brings them several business world advantages, mainly if it provides integrated functionalities for the geographical and/or multidimensional processing. The intended goal is to provide users with a system capable of processing both geographic and multidimensional data in a seamless way, by abstracting the complexity of separately querying and analyzing these data in a decision making process. However, this integration may not be fully achieved yet or may be built using proprietary technologies. This paper presents an open source and web based framework for geographic and multidimensional decision support. Our approach uses a geographical data warehouse, a metadata source, a query language and a geographical and multidimensional engine for processing queries sent by a web based client application.

A set of aggregation functions for spatial measures

A number of studies have been developed in recent years aimed at integrating pertinent concepts and technologies for analytical multidimensional (OLAP) and geographic (GIS) processing environments. This type of integrated environment has been identified as SOLAP (Spatial OLAP). However, due to the fact that these two technologies were conceived with different purposes in mind, the interaction of the two environments is not an easy task and even with so much research being developed, there remain unresolved issues that merit exploration. One such issue refers to aggregation functions for measures. These functions are currently used in the definition of multidimensional and geographic data cubes. The aim of this paper is to present a set of aggregation functions for geographic measures. We also show these functions in practice, by taking into account their use with a SOLAP architecture prototype. This SOLAP prototype is based on a model for Geographic Data Warehouse (GDW), a data cube model and a geographic multidimensional query language.

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.

Processing OLAP Queries over an Encrypted Data Warehouse Stored in the Cloud

Several studies deal with mechanisms for processing transactional queries over encrypted data. However, little attention has been devoted to determine how a data warehouse (DW) hosted in a cloud should be encrypted to enable analytical queries processing. In this article, we present a novel method for encrypting a DW and show performance results of this DW implementation. Moreover, an OLAP system based on the proposed encryption method was developed and performance tests were conducted to validate our system in terms of query processing performance. Results showed that the overhead caused by the proposed encryption method decreased when the proposed system was scaled out and compared to a non-encrypted dataset (46.62% with one node and 9.47% with 16 nodes). Also, the computation of aggregates and data groupings over encrypted data in the server produced performance gains (from 84.67% to 93.95%) when compared to their executions in the client, after decryption.

A Metamodel for the Specification of Geographical Data

The decision-making processes can be supported by many tools such as Data Warehouse (DW), On-Line Analytical Processing (OLAP) and Geographical Information System (GIS). Much research found in literature is aimed at integrating these technologies, although most of these approaches do not provide formal definitions for a Geographical Data Warehouse (GDW) nor there is a consensus regarding the design of spatial dimensional schemas for GDW. To address this, GeoDWFrame was proposed as a set of guidelines to design spatial dimensional schemas. However, there is not a metamodel that implements its specifications, nor there is a metamodel that integrates GDW concepts with some standards. Then, in this paper we propose GeoDWM, which is a formally specified metamodel, that extends GeoDWFrame with spatial measures. We have instantiated our formal metamodel based on CWM and OGC standards to assist the conceptual modelling needs found in the development of GDW applications. Finally, some issues concerning the development of a CASE tool that is based on the GeoDWM metamodel and that provides a means of validating the application conceptual data model are given together with a discussion on GDW conceptual design aspects and some descriptions for future work.