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.

Cognitive style factors affecting database query performance

As end-user computing becomes more pervasive, an organization's success increasingly depends on the ability of end-users, usually in managerial positions, to extract appropriate data from both internal and external sources. Many of these data sources include or are derived from the organization's accounting information systems. Managerial end-users with different personal characteristics and approaches are likely to compose queries of differing levels of accuracy when searching the data contained within these accounting information systems. This research investigates how cognitive style elements of personality influence managerial end-user performance in database querying tasks. A laboratory experiment was conducted in which participants generated queries to retrieve information from an accounting information system to satisfy typical information requirements. The experiment investigated the influence of personality on the accuracy of queries of varying degrees of complexity. Relying on the Myers–Briggs personality instrument, results show that perceiving individuals (as opposed to judging individuals) who rely on intuition (as opposed to sensing) composed queries more accurately. As expected, query complexity and academic performance also explain the success of data extraction tasks.

How does your model grow? An empirical investigation of the effects of ontological clarity and application domain size on query performance

Semantic data models provide a map of the components of an information system. The characteristics of these models affect their usefulness for various tasks (e.g., information retrieval). The quality of information retrieval has obvious important consequences, both economic and otherwise. Traditionally, data base designers have produced parsimonious logical data models. In spite of their increased size, ontologically clearer conceptual models have been shown to facilitate better performance for both problem solving and information retrieval tasks in experimental settings. The experiments producing evidence of enhanced performance for ontologically clearer models have, however, used application domains of modest size. Data models in organizational settings are likely to be substantially larger than those used in these experiments. This research used an experiment to investigate whether the benefits of improved information retrieval performance associated with ontologically clearer models are robust as the size of the application domains increase. The experiment used an application domain of approximately twice the size as tested in prior experiments. The results indicate that, relative to the users of the parsimonious implementation, end users of the ontologically clearer implementation made significantly more semantic errors, took significantly more time to compose their queries, and were significantly less confident in the accuracy of their queries.

Dissecting query performance in logical data models: Parsimony vs. greater ontological clarity

Even when data repositories exhibit near perfect data quality, users may formulate queries that do not correspond to the information requested. Users’ poor information retrieval performance may arise from either problems understanding of the data models that represent the real world systems, or their query skills. This research focuses on users’ understanding of the data structures, i.e., their ability to map the information request and the data model. The Bunge-Wand-Weber ontology was used to formulate three sets of hypotheses. Two laboratory experiments (one using a small data model and one using a larger data model) tested the effect of ontological clarity on users’ performance when undertaking component, record, and aggregate level tasks. The results indicate for the hypotheses associated with different representations but equivalent semantics that parsimonious data model participants performed better for component level tasks but that ontologically clearer data model participants performed better for record and aggregate level tasks.

Fast regridding of large, complex geospatial datasets

In the earth sciences, data are commonly cast on complex grids in order to model irregular domains such as coastlines, or to evenly distribute grid points over the globe. It is common for a scientist to wish to re-cast such data onto a grid that is more amenable to manipulation, visualization, or comparison with other data sources. The complexity of the grids presents a significant technical difficulty to the regridding process. In particular, the regridding of complex grids may suffer from severe performance issues, in the worst case scaling with the product of the sizes of the source and destination grids. We present a mechanism for the fast regridding of such datasets, based upon the construction of a spatial index that allows fast searching of the source grid. We discover that the most efficient spatial index under test (in terms of memory usage and query time) is a simple look-up table. A kd-tree implementation was found to be faster to build and to give similar query performance at the expense of a larger memory footprint. Using our approach, we demonstrate that regridding of complex data may proceed at speeds sufficient to permit regridding on-the-fly in an interactive visualization application, or in a Web Map Service implementation. For large datasets with complex grids the new mechanism is shown to significantly outperform algorithms used in many scientific visualization packages.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.