USING ONTOLOGY META DATA FOR DATA WAREHOUSING

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.

1 INTRODUCTION

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.

497

Salguero A., Araque F. and Delgado C. (2008).

USING ONTOLOGY META DATA FOR DATA WAREHOUSING.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 497-500

DOI: 10.5220/0001710904970500

 SciTePress

2 DATA WAREHOUSE

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.

3 DATA SOURCES ANNOTATION

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

498

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.

4 DW ARCHITECTURE

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

component.

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,

USING ONTOLOGY META DATA FOR DATA WAREHOUSING

499

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.,

2007b).

5 CONCLUSIONS

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.

ACKNOWLEDGEMENTS

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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