Using Ontologies for the Design of Data Warehouses

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.Comment: 15 pages, 2 figure

Semantic integrity in data warehousing : a framework for understanding : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Business Studies in Information Systems at Massey University, Palmerston North, New Zealand

Data modelling has gathered an increasing amount of attention by data warehouse developers as they come to realise that important implementation decisions such as data integrity, performance and meta data management, depend on the quality of the underlying data model. Not all organisations model their data but where they do, Entity-Relationship (E-R) modelling, or more correctly relational modelling, has been widely used. An alternative, dimensional modelling, has been gaining acceptance in recent years and adopted by many practitioners. Consequently, there is much debate over which form of modelling is the most appropriate and effective. However, the dimensional model is in fact based on the relational model and the two models are not so different that a debate is necessary. Perhaps, the real focus should be on how to abstract meaning out of the data model. This research explores the importance of semantic integrity during data warehouse design and its impact on the successful use of the implemented warehouse. This has been achieved through a detailed case study. Consequently, a conceptual framework for describing semantic integrity has been developed. The purpose of the framework is to provide a theoretical basis for explaining how a data model is interpreted through the meaning levels of understanding, connotation and generation, and also how a data model is created from an existing meaning structure by intention, generation and action. The result of this exploration is the recognition that the implementation of a data warehouse may not assist with providing a detailed understanding of the semantic content of a data warehouse

Proximal business intelligence on the semantic web

This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to improve specific information access and transcoding but not on how the information can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology language and then re-used to provide the invisibility of pervasive access; uncovering more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO

Enterprise modeling:process and REA value chain perspective

The paper focuses on enterprise business value chain modeling as an alternative to business process modeling. Well known REA methodology proposed by McCarthy and Geerts is used as the basic modeling framework. The research presented in the paper results in a generic semantic enterprise model using REA ontology. This rather static model is then converted into UML activity, sequence and state diagrams thus achieving dynamic view of the REA model. The dynamic REA view connects the process model and the value chain perspectives. It is shown that by using REA model transition called dynamization not only process models at task level can be achieved but also a consistency check of the REA model can be accomplished. By means of step by step value chain modeling of the enterprise a consistent process model can be reached preserving all advantages of the typical business process modeling methodsProcess model; Value chain model; REA; Production planning

An Ontological Approach to Representing the Product Life Cycle

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from The Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry

Some Ontological Issues of the REA Framework in Relation to Enterprise Business Process

The aim of the paper is to describe using REA framework to model enterprise planning not only at the operational level but also at the policy level. Using policy level enlarges the possibility of the models on the base of the REA framework because the policy level in this way represents metalevel of the model. The policy level of the REA framework itself is comprised both of the entities related by typification, grouping and policy relationships and of the Commitment entity with the fulfillment relationship. This entity may be viewed as either a sublayer or a middle layer of the REA framework. The Commitment entity belongs to the fundamental entities of the policy level but has some specifications that are expressed by the fulfillment relationship. This many-to-many relationship forms the link to the operational level. In the paper we discuss the problem and suggest some solution that moves the Commitment entity closer to the typification and grouping semantic abstractions.REA ontology; enterprise business process; semantic abstractions

SOME ONTOLOGICAL ISSUES OF THE REA FRAMEWORK IN RELATION TO ENTERPRISE BUSINESS PROCESS

The aim of the paper is to describe using REA framework to model enterprise planning not only at the operational level but also at the policy level. Using policy level enlarges the possibility of the models on the base of the REA framework because the policy level in this way represents metalevel of the model. The policy level of the REA framework itself is comprised both of the entities related by typification, grouping and policy relationships and of the Commitment entity with the fulfilment relationship. This entity may be viewed as either a sub layer or a middle layer of the REA framework. The Commitment entity belongs to the fundamental entities of the policy level but has some specifications that are expressed by the fulfilment relationship. This many-to-many relationship forms the link to the operational level. In the paper we discuss the problem and suggest some solution that moves the Commitment entity closer to the typification and grouping semantic abstractions.REA ontology, enterprise business process, semantic abstractions

Integration of Biological Sources: Exploring the Case of Protein Homology

Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration

Exploiting a perdurantist foundational ontology and graph database for semantic data integration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The view of reality that is inherent to perdurantist philosophical ontologies, often termed four dimensional (4D) ontologies, has not been widely adopted within the mainstream of information system design practice. However, as the closed world of enterprise systems is opened to Internet scale Semantic Web and Open Data information sources, there is a need to better understand the semantics of both internal and external data and how they can be integrated. Philosophical foundational ontologies can help establish this understanding and there is, therefore, an emerging need to research how they can be applied to the problem of semantic data integration. Therefore, a prime objective of this research was to develop a framework through which to apply a 4D foundational ontology and a graph database to the problem of semantic data integration, and to assess the effectiveness of the approach. The research employed design science, a methodology which is applicable to undertaking research within information systems as it encompasses methods through which the research can be undertaken and the resultant artefacts evaluated. This methodology has a number of discrete stages: problem awareness; a core design-build-evaluate iterative cycle through which the research is conducted; and a conclusion stage. The design science research was conducted through the development of a number of artefacts, the prime being the 4D-Semantic Extract Load (4D-SETL) framework. The effectiveness of the framework was assessed by applying it to semantically interpret and integrate a number of large scale datasets and to instantiate a prototype graph database warehouse to persist the resultant ontology. A series of technical experiments confirmed that directly reflecting the model patterns of 4D ontology within a prototype data warehouse proved an effective means of both structuring and semantically integrating complex datasets and that the artefacts produced by 4D-SETL could function at scale. Through illustrative scenario, the effectiveness of the approach is described in relation to the ability of the framework to address a number of weaknesses in current approaches. Furthermore the major advantages of the 4D-SETL are elaborated; which include ability of the framework is to combine foundational, domain and instance level ontological models in a single coherent system that dispensed with much of the translation normally undertaken between conceptual, logical and physical data models. Additionally, adopting a perdurantist realist foundational ontology provided a clear means of establishing and maintaining the identity of physical objects as their constituent temporal and spatial parts unfold over the course of tim