Trajectory Data Warehouses - Data Warehouse Systems: Design and Implementation - page 493

The data type system for temporal types follows the approach of [ 75 ].
The system Secondo developed by Guting et al. is described in [ 233 ]. An
SQL extension for spatiotemporal data is proposed in [ 223 ]. The view of
continuous fields as cubes was introduced in [ 69 ]. The GeoPKDD trajectory
data warehouse, its associated ETL process, and the double-counting problem
during aggregation are studied in [ 151 ]. A good discussion on trajectory
data warehouses is presented in [ 129 , 161 ]. Analysis tools for trajectory data
warehouses can be found in [ 167 ]. A survey on spatiotemporal aggregation is
given in [ 222 ], while a state-of-the-art analysis on trajectory aggregation is
provided in [ 7 ].
12.8 Review Questions
12.1 What are moving objects? How are they different from spatial objects?
12.2 Give examples of different types of moving objects, and for each of
these types, illustrate a scenario in which the analysis of them is
important.
12.3 What is a trajectory?
12.4 Discuss different criteria that can be used to segment movement. How
do analysis requirements impact on this segmentation?
12.5 What is the difference between continuous and discrete trajectories?
12.6 Define the terms trajectory databases and trajectory data warehouses.
Mention the main differences between the two concepts.
12.7 What are temporal types? How are they constructed?
12.8 Define valid time and transaction time.
12.9 Give an example of a temporal base type, a temporal spatial type,
and a temporal field type.
12.10 Give examples of operations associated with each of the temporal
types in the previous question.
12.11 Explain why traditional operations must be lifted for temporal types.
Illustrate this with examples.
12.12 Give a hint about how temporal types can be implemented in a
platform such as PostGIS. How does this implementation differ from
the abstract definition of temporal types?
12.13 Discuss how temporal types can be added to a multidimensional
schema.
12.14 Discuss the implications of including trajectories as dimensions or
measures in a data warehouse.
12.15 What does the term similarity of trajectories mean? State why this
concept is important in data warehouse context.
12.16 Comment on two different ways to include field types in a multidi-
mensional schema. Give examples of queries that take advantage of
one representation over the other.