An Approach to Integrate Heterogeneous Data Sources

To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information, normally scattered across disparate data sources, in an accurate and timely manner. To interoperate data sources which differ structurally and semantically, particular problems occur, for example, problems of changing schema in data sources will affect the integrated schema. In this paper, conflicts between heterogeneous systems are investigated and existing approaches to integration are reviewed. This research introduces a new mediated approach employing the Mediated Data Integration Mediator (MeDInt), and wrapping techniques as the main components for the integration of databases and legacy systems. The MeDInt mediator acts as an intermediate medium transforming queries to subqueries, integrating result data and resolving conflicts. Wrappers then transform sub-queries to specific local queries so that each local system is able to understand the queries. This framework is currently being developed to make the integration process more widely accessible by using standard tools. A prototype is implemented to demonstrate the model

A Mediator to Integrate Databases and Legacy Systems: The Mediated Data Integration (MeDInt) Mediator

To interoperate data sources which differ structurally and semantically, particular problems occur, for example, problems of changing schema in data sources will affect the integrated schema. In this paper, conflicts between heterogeneous systems are investigated and existing approaches to integration are reviewed. We propose a new mediated approach employing the Mediated Data Integration Mediator (MeDInt), and wrapping techniques as the main components for the integration of databases and legacy systems. The MeDInt mediator acts as an intermediate medium transforming queries to sub-queries, integrating result data and resolving conflicts. Wrappers then transform sub-queries to specific local queries so that each local system is able to understand the queries. This framework is currently being developed to make the integration process more widely accessible by using standard tools. A prototype is implemented to demonstrate the model

Significance of Semantic Reconciliation in Digital Forensics

Digital forensics (DF) is a growing field that is gaining popularity among many computer professionals, law enforcement agencies and other stakeholders who must always cooperate in this profession. Unfortunately, this has created an environment replete with semantic disparities within the domain that needs to be resolved and/or eliminated. For the purpose of this study, semantic disparity refers to disagreements about the meaning, interpretation, descriptions and the intended use of the same or related data and terminologies. If semantic disparity is not detected and resolved, it may lead to misunderstandings. Even worse, since the people involved may not be from the same neighbourhood, they may not be aware of the existence of the semantic disparities, and probably might not easily realize it. The aim of this paper, therefore, is to discuss semantic disparity in DF and further elaborates on how to manage it. In addition, this paper also presents the significance of semantic reconciliation in DF. Semantic reconciliation refers to reconciling the meaning (including the interpretations and descriptions) of terminologies and data used in digital forensics. Managing semantic disparities and the significance of semantic reconciliation in digital forensics constitutes the main contributions of this paper. Keywords: Digital forensics, semantic disparity, managing semantic disparity, semantic reconciliation, significance of semantic reconciliatio

An Approach to Resolve Data Model Heterogeneities in Multiple Data Sources

To gain a competitive advantage, it is imperative for executives to be able to obtain one unique view of information, normally scattered across disparate data sources, in an accurate and timely manner. To interoperate data sources which differ structurally and semantically, particular problems occur, for example, problems of changing schema in data sources affect the integrated schema. This paper presents an approach to resolve data model heterogeneities in databases and legacy systems through mediation and wrapping techniques. The system is well supported by the mediated data model (MDM), a semantically-rich data model which can describe and represent heterogeneous data schematically and semanticall

M2ORM2: A Model for the Transparent Management of Relationally Persistent Objects

Object-oriented application development often involves storing application objects in a relational database. Sometimes it is desirable to develop the persistent classes and the relational database in an independent way, and to use an object persistent manager to connect them in a suitable way. This paper introduces {M2ORM2, a model for describing meet-in-the-middle mappings between object schemas and relational schemas, to support the transparent management of object persistence by means of relational databases

The mediated data integration (MeDInt) : An approach to the integration of database and legacy systems

The information required for decision making by executives in organizations is normally scattered across disparate data sources including databases and legacy systems. To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. Particular problems occur when applying traditional integration approaches, for example, the global schema needs to be recreated when the component schema has been modified. This research investigates the following heterogeneities between heterogeneous data sources: Data Model Heterogeneities, Schematic Heterogeneities and Semantic Heterogeneities. The problems of existing integration approaches are reviewed and solved by introducing and designing a new integration approach to logically interoperate heterogeneous data sources and to resolve three previously classified heterogeneities. The research attempts to reduce the complexity of the integration process by maximising the degree of automation. Mediation and wrapping techniques are employed in this research. The Mediated Data Integration (MeDint) architecture has been introduced to integrate heterogeneous data sources. Three major elements, the MeDint Mediator, wrappers, and the Mediated Data Model (MDM) play important roles in the integration of heterogeneous data sources. The MeDint Mediator acts as an intermediate layer transforming queries to sub-queries, resolving conflicts, and consolidating conflict-resolved results. Wrappers serve as translators between the MeDint Mediator and data sources. Both the mediator and wrappers arc well-supported by MDM, a semantically-rich data model which can describe or represent heterogeneous data schematically and semantically. Some organisational information systems have been tested and evaluated using the MeDint architecture. The results have addressed all the research questions regarding the interoperability of heterogeneous data sources. In addition, the results also confirm that the Me Dint architecture is able to provide integration that is transparent to users and that the schema evolution does not affect the integration