Using Ontologies for the Design of Data Warehouses

Abstract

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.

Fuzzy Ontology-based Approach for the Requirements Query Imprecision Assessment in Data Warehouse Design Process near Negative Fuzzy Operator

“…So, we can conclude that works "Ref. [23,24,25]" have highlighted the imprecise requirements but they don't dealt rigorously with vagueness requirements expression, they presented the ontologies as remedies for incomplete, uncertain and unclear data. These works share the fact that they have not proposed approaches or solutions to achieve their observations.…”

“…These ontologies resemble database diagrams, but they are more flexible in the sense that they provide definitions, inaccurate and implied for the generated data which mean adding annotation data resources for obtaining Semantic DW, the authors in "Ref. [24]" show that requirements for data analyses are often unclear and uncertain, mainly because of incomplete decision processes are flexibly structured and poorly shared across large organizations, but also because of a difficult communication between users and analysts. They also differentiate between the managerial and strategic requirements, the authors in "Ref.…”

An Ontology based Hybrid Approach to Derive Multidimensional Schema for Data Warehouse

“…Ontology is a formal specification of an agreed conceptualization of a domain in the context of knowledge description. The use of ontology for the data warehouse design helps to solve the heterogeneity issues that arise in the data sources [13]. The data sources can be represented by means of ontologies and mapping these ontologies can provide integrated view that could help to access and exchange information in a semantically sound manner [14].…”

DC Proposal: Online Analytical Processing of Statistical Linked Data

“…Also, data may imply access restrictions that need to be considered. For that, the MDM could be enriched with information describing users, privileges and policies that serve to articulate an access control and audit (ACA) policy [10].…”

“…Semantic Web ontologies allow to make data self-descriptive; to represent consensus about the meaning of data; to find implicit knowledge and inconsistencies; and to ease the integration effort. Although usage of Semantic Web ontologies is not explicitly required by the Linked Data principles, we assume that expressive ontological structures will make it possible to overcome the challenges of SLD analysis [10].…”