An approach for semantic integration of heterogeneous data sources

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

Adaptive Query Processing in Data Grids

The data grid integrates wide-area autonomous data sources and provides users with a unified data query and processing infrastructure. Adaptive data query and processing is required by data grids to provide better quality of services (QoS) to users and applications in spite of dynamically changing resources and environments. Existing AQP techniques can only meet partially data grid requirements. Some existing work is either addressing domain-specific or single-node query processing problems. Data grids provide new mechanisms for monitoring and discovering data and resources in a cross-domain wide area. Data query in grids can benefit from this information and provide better adaptability to the dynamic nature of the grid environment. In this work, an adaptive controller is proposed that dynamically adjusts resource shares to multiple data query requests in order to meet a specified level of service differentiation. The controller parameters are automatically tuned at runtime based on a predefined cost function and an online learning method. Simulation results show that our controller can meet given QoS differentiation targets and adapt to dynamic system resources among multiple data query processing requests while total demand from users and applications exceeds system capability.

A relational data harmonization approach to XML

There are numerous approaches for integrating data from heterogeneous data sources. A common background assumption is that the data sources remain quite stable and are known in advance. Hence an integration system can be built to manipulate them. In practice there is, however, often a demand for supporting ad hoc information needs concerning unexpected autonomous data sources containing volatile data. A different approach is therefore needed. We propose that semantically similar data are harmonized when extracting data from XML-based data sources. We introduce a constructor algebra, which is a powerful tool in the harmonization of XML data. This algebra is able to form for any XML data source a unique relational representation, called an XML relation. We demonstrate that the XML relation representation supports grouping and aggregation of data needed, for example, in OLAP (online analytical processing) -style applications.

Semantic Integration of Manufacturing Data Sources

In order to gain insight into business processes, multiple autonomous data sources residing in the manufacture enterprise need to integrate not only on storage and access methods but also capturing the meaning of data to get a coherent and meaningful data views for different applications requirements. This paper presents a semantic-based architecture for the integration of heterogeneous manufacturing data sources. The integration is realized on a semantic level by the explicit presentation of data semantics with ontology and relationships between ontologies. During applications usage, heterogeneous data sources which represent relations of relevance are dynamically organized in terms of their semantics. The paper discusses some major problems in the architecture: unified schema transformation, semi-automatic ontology generation and mediation.

A NEGOTIATION FRAMEWORK FOR ORGANIZATIONAL INFORMATION SYSTEMS

In the last few years there has been a growing need for organizations to build cooperative information systems. These organizations and their processes that manipulate vast quantities of information are based on heterogeneous, distributed and autonomous data sources. However, without appropriated techniques of negotiation, any execution of organizational information systems would yield to disjoining and error-prone behavior, while requiring excessive effort to build and maintain. In this paper, we present a flexible negotiation framework, which is based on social constraints and conversation plans. The infrastructure of this framework may well fit the negotiation needs of organizational information systems in a highly dynamic and unpredictable environment. This framework has been implemented as a negotiation service in our cooperative application environment. Some examples are given from manufacturing applications.