Alberto Salguero, Francisco Araque and Cecilia Delgado
Department of Software Engineering
E.T.S.I.I.T., University of Granada, Granada (Andalucía), Spain
Keywords: Data Warehouse, Ontology, Integration, GIS.
Abstract: One of the most complex issues of the integration and transformation interface is the case where there are
multiple sources for a single data element in the enterprise Data Warehouse (DW). There are many facets
due to the number of variables that are needed in the integration phase. However we are interested in the
integration of temporal and spatial problem due to the nature of DWs. This paper presents our ontology
based DW architecture for temporal integration on the basis of the temporal and spatial properties of the
data and temporal characteristics of the data sources. The proposal shows the steps for the transformation of
the native schemes of the data sources into the DW scheme and end user scheme and the use of an ontology
model as the common data model.
The ability to integrate data from a wide range of
data sources is an important field of research in data
engineering. Data integration is a prominent theme
in many areas and enables widely distributed,
heterogeneous, dynamic collections of information
sources to be accessed and handled.
Many information sources have their own
information delivery schedules, whereby the data
arrival time is either predetermined or predictable. If
we use the data arrival properties of such underlying
information sources, the Data Warehouse
Administrator (DWA) can derive more appropriate
rules and check the consistency of user requirements
more accurately. The problem now facing the user is
not the fact that the information being sought is
unavailable, but rather that it is difficult to extract
exactly what is needed from what is available. It
would be extremely useful to have an approach
which determines whether it would be possible to
integrate data from two data sources.
The use of DW and Data Integration has been
proposed previously in many fields. In (Haller et al.,
2000) the Integrating Heterogeneous Tourism
Information data sources is addressed using three-
tier architecture. In (Vassiliadis, 2001) a framework
for quality-oriented DW management is exposed,
where special attention is paid to the treatment of
metadata. The problem of the little support for
automatized tasks in Data Warehousing is
considered in (Thalhammer, 2001), where the DW is
used in combination with event/condition/action
(ECA) rules to get an active DW. Nevertheless, none
of the previous works encompass the aspects of the
integration of the data according to the temporal and
the spatial parameters of the data.
In this article a solution to this problem is
proposed: a DW architecture for data integration on
the basis of the temporal and the spatial properties of
the data and temporal characteristics of the sources
and their extraction methods. This architecture give
as result the necessary data for the later refreshment
of the DW. The use of a data model based on
ontologies is proposed as a common data model to
deal with the data sources schemes integration.
Although it is not the first time the ontology model
has been proposed for this purpose (Skotas and
Simitsis, 2006), in this case the work has been
focused on the integration of spatio-temporal data.
Moreover, to our knowledge this is the first time the
metadata storage capabilities of some ontology
definition languages has been used in order to
improve the DW data refreshment process design.
The remaining part of this paper is organized as
follows. In Section 2, some basic concepts as well as
our previously related works are revised; in section 3
our architecture is presented; Finally, Section 4
summarizes the conclusions of this paper.
Salguero A., Araque F. and Delgado C. (2008).
In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 497-500
DOI: 10.5220/0001710904970500
Inmon (Inmon, 2002) defined a Data Warehouse as
“a subject-oriented, integrated, time-variant, non-
volatile collection of data in support of
management’s decision-making process.” A DW is a
database that stores a copy of operational data with
an optimized structure for query and analysis. A
federated database system (FDBS) is formed by
different component database systems; it provides
integrated access to them: they co-operate (inter-
operate) with each other to produce consolidated
answers to the queries defined over the FDBS.
Generally, the FDBS has no data of its own as the
DW has.
We have extended the Sheth & Larson five-level
FDBS architecture (Sheth & Larson, 1990), which is
very general and encompasses most of the
previously existing architectures. In this architecture
three types of data models are used: first, each
component database can have its own native model;
second, a canonical data model (CDM) which is
adopted in the FDBS; and third, external schema can
be defined in different user models.
In order to carry out the integration process, it
will be necessary to transfer the data of the data
sources, probably specified in different data models,
to a common data model, that will be the used as the
model to design the scheme of the warehouse. In
our case, we have decided to use an ontological
model as canonical data model.
An ontology is a controlled vocabulary that
describes objects and the relations between them in a
formal way, and has a grammar for using the
vocabulary terms to express something meaningful
within a specified domain of interest. They allow the
use of automatic reasoning methods. We have
extended OWL with temporal and spatial elements.
We call this OWL extension STOWL.
The ontology data models are a good option as a
CDM but they are too generals. Extending any of the
languages for defining ontologies seem the most
suitable option. Among all of them, OWL language
is selected as the base for defining the CDM.
Firstly an ontology in the OWL language will be
built to define the generic temporal primitives found
of interest. Then the spatial primitives will also be
incorporated. Finally, the information that describes
the characteristics of data to integrate, i.e. the
metadata which will be useful to design the data
extracting, loading and refreshing processes, will be
incorporated to the data source scheme using the
annotation properties of OWL. Annotation
properties are a special kind of OWL properties
which can be used to add information (metadata—
data about data) to classes, individuals and
object/datatype properties.
The result will be an ontology, expressed in
OWL, defining the spatial and temporal primitives
which will be used to build the schemes of the data
sources and a set of properties which will allow the
addition of information about the data sources
characteristics. We call STOWL (Spatio-Temporal
OWL) to this base ontology.
3.1 Temporal Annotation of Data
Due to the nature of the DW the annotation
properties will usually refer to the temporal
characteristics of data, so a set of annotation
properties is defined to describe the sources
according to some of the temporal concepts studied
in (Araque et al., 2007a). All these properties can be
associated directly to the ontology viewed as a
resource or individually to each concept defined in
the ontology.
Figure 1: Extraction Time definition in STOWL.
STOWL defines, for instance, the Extraction
Time of a change in a data source as shown in figure
1. The Extraction Time parameter can be defined as
the time expended in extracting a data change from
the source. Some examples of the temporal
parameters (Araque et al., 2006) that we consider of
interest for the integration process are: Granularity,
Availability, Extraction Time , Transaction time,
Storage time, Temporal Reference System
3.2 Spatial Annotation of Data
After reviewing various spatial data models we have
chosen the model proposed by the Open GIS
Consortium. This standard, called Geography
Markup Language (GML), is an XML grammar
written in XML Schema for the modelling,
transportation and storage of geographic
information. GML is often used as a communication
protocol between a large set of GIS applications,
both commercial and open source. It has been
ICEIS 2008 - International Conference on Enterprise Information Systems
Figure 2: Functional architecture.
necessary the translation of GML, written in XML
Schema, to OWL.
GML defines several kinds of entities (such as
features, geometrical entities, topological entities…)
in form of a object hierarchy. As well as the
temporal properties they have been incorporated to
STOWL in order to support the inclusion of spatial
metadata in the data sources schemes.
We have also considered spatial characteristics
like, for instance, the Coordinate Reference System
(CRS) and the the Spatial Granularity (SGr).
concepts. A CRS provides a method for assigning
values to a location. All the data in a data source
must share the same CRS. In this case, because we
are dealing with spatiality, it is common to work
with granules like meter, kilometer, country…
As with the temporal data source features, the
annotation properties are used to describe the source
spatial metadata.
Taking paper (Araque et al., 2006) as point of
departure, we propose the reference architecture in
figure 2. In this figure, the data flow as well as the
metadata flow are illustrated. Metadata flow
represents how all the data that refers to the data, i.e.
the schemes of the data sources, the rules for
integrating the data…, are populated through the
architecture. Following are explained the involved
Native Schema. Initially we have the different data
source schemes expressed in its native schemes.
Each data source will have, a scheme, the data
inherent to the source and the metadata of its
scheme. In the metadata we will have huge
temporal information about the source: temporal
and spatial data on the scheme, metadata on
availability of the source.
Preintegration. In the Preintegration phase, the
semantic enrichment of the data source native
schemes is made by the conversion processor. In
addition, the data source temporal and spatial
metadata are used to enrich the data source scheme
with temporal and spatial properties. We obtain the
component scheme (CS) expressed in the CDM, in
our case using STOWL (OWL enriched with
temporal and spatial elements). From the CS the
negotiation processor generates the export schemes
(ES) also expressed in STOWL. The ES represents
the part of a component scheme which is available
for the DW designer. It is expressed in the same
CDM as the Component Scheme. For security or
privacy reasons part of the CS can be hidden.
Integration. The DW scheme corresponds to the
integration of multiple ES according to the DW
designer needs. It is expressed in an enriched CDM
(STOWL) so that temporal and spatial concepts
could be expressed straightforwardly. This process
is made by the Schema Integration Processor which
suggests how to integrate the Export Schemes,
helping to solve semantic heterogeneities (out of the
scope of this paper), and defining the Extracting,
Transforming and Loading processes (ETL).
The integration processor consist of two modules
which have been added to the reference architecture
in order to carry out the integration of the temporal
and spatial properties of data, considering the data
source extraction method used:
The Temporal and Spatial Integration Processor
uses the set of semantic relations and the conformed
schemes obtained during the detection phase of
similarities (Oliva and Saltor, 1996). This phase is
part of the integration methodology of data schemes.
As a result, we obtain data in form of rules about the
integration possibilities existing between the
originating data from the data sources (minimum
resultant granularity…).
The Metadata Refreshment Generator
determines the most suitable parameters to carry out
the refreshment of data in the DW scheme. As result,
the DW scheme is obtained along with the
Refreshment Metadata necessary to update the DW
according to the data extraction method and other
spatio-temporal properties of a the data sources.
Data Warehouse Refreshment. After the schema
integration and once the DW scheme is obtained, its
maintenance and update will be necessary. Each
Data Integration Processor is responsible of doing
the incremental capture of its corresponding data
source and transforming them to solve the semantic
heterogeneities. Each Data Integration Processor
accesses to its corresponding data source according
to the temporal and spatial requirements obtained in
the integration stage. A global Data Integrator
Processor uses a parallel, fuzzy data integration
algorithm to integrate the data (Araque et al.,
In this paper we have presented a DW architecture
for the integration on the basis of the temporal and
spatial properties of the data as well as the temporal
and the spatial characteristics of the data sources.
We have described the modules introduced to the
Sheth and Larson reference architecture. These
modules are responsible of checking the temporal
and the spatial parameters of data sources and
determine the best refreshing parameters according
to the requirements. This parameters will be used to
design the DW refreshment process, made up by the
extracting, transforming and loading data processes.
We used STOWL as the Canonical Data Model.
All the data sources schemes will be translated to
this one. STOWL is an OWL extension including
spatial, temporal and metadata elements for the
precise definition of the extracting, transforming and
loading data processes.
This work has been supported by the
Research Program under project GR2007/07-2 and
by the Spanish Research Program under projects
EA-2007-0228 and TIN2005-09098-C05-03.
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ICEIS 2008 - International Conference on Enterprise Information Systems