Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

12 1. Introduction and Problem Statement
1.1 Overview of Thesis and Contributions
This section provides an overview of the following chapters.
Chapter 2 presents a comparative study between array databases and OLAP, and
devotes special attention to data structures and operations. It starts with a discussion
of existing approaches for data modeling, storage management and query processing
in both array databases and the data warehousing/OLAP environment. Existing
pre-aggregation and related techniques are also discussed in both application domains.
From this study, one can observe similarities with regards to data structures and operations
between both application domains. This suggests that array databases can benefit
from pre-aggregation schemes to accelerate the computation of aggregate queries.
Chapter 3 describes fundamental operations in GIS and remote-sensing imaging
applications. The selection of operations is based on a thorough review of existing
surveys regarding GIS operations, international standards, and on feedback from GIS
practitioners. To better understand the structural characteristics of common queries in
array databases, such operations evolved using a proven array model. This allowed
us to identify a set of operations requiring data summarization (aggregation) and the
candidate operations to be supported by pre-aggregation techniques.
Chapter 4 deals with the computation of aggregate queries in array databases using
pre-aggregated data. The proposed pre-aggregation framework distinguishes different
types of pre-aggregates and shows that such a distinction is useful in finding an optimal
solution that reduces the cost of the CPU required for the computation of aggregate
queries. A cost-model is used to assess the benefit of using pre-aggregated data
for computing aggregate queries. The measurements on real-life raster image datasets
show that the computation of aggregate queries is always faster with our algorithms
in comparison to traditional methods.
Chapter 5 considers the problem of offering pre-aggregation support to non-standard
aggregate operations in GIS and remote-sensing imaging applications. A discussion
is presented on the issues found while attempting to provide pre-aggregation support
for all non-standard aggregate operations as well as the motivation for focusing on
scaling operations. The framework and cost model presented in Chapter 4 are adapted
to support scaling operations. Experiments covering 2D, 3D, and 4D show how our
pre-aggregation approach not only generalizes the most common approach for 2D, but
it also helps reduce computational times for 2D, 3D, and 4D datasets.
Chapter 6 presents a summary of our findings and outlines future lines of research.
1.2 Publications Related to this Thesis
A number of papers have been published that relate to the work described in this
thesis. Doctoral workshops provided a platform to discuss the feasibility of the proposed
research and an opportunity to receive feedback from experts in computer science
[6] and the GIS scientific community [5]. Participation in those workshops led
to a refinement of the research objectives outlined in Chapter 1. The study and algebraic
modeling of geo-raster operations reported in Chapter 3 are presented in [7, 8].