Data Virtualization (DV) has become an important method to store and handle data cost-efficiently. However, it is unclear what kind of data and when data should be virtualized or not. We applied a design science approach in the first stage to get a state of the art of DV regarding data integration and to present a concept matrix. We extend the knowledge base with a systematic literature review ...

Introduction In major corporations the winds of globalisation and the efforts to seek further process efficiencies are accompanied by the frequent and massive deployment of large ICT platforms and business redesigns. ERP packages are one example of this drive to wire into software and hardware new routines, processes and practices in ways that are aligned with the prevailing globalisation and r...

The growing volume, variety and complexity of data being collected for scientific purposes presents challenges for data integration. For data to be truly useful, scientists need not only to be able to access it, but also be able to interpret and use it. Doing this requires semantic context. Semantic Data Integration is an active field of research, and this chapter describes the current challeng...

This article introduces some of the most popular techniques of data integration, that allow the combination of information coming from various sources. The illustration focuses, in particular, on the Bayesian generalized Heckman methodology and on the data calibration methodology based on vines and nonparametric Bayesian networks.

In this paper we present the first truly practical formalism for querying and integrating data using the functorial data model. In our formalism, database schemas and instances are multi-sorted equational theories of a certain form. Schemas denote categories, and instances denote their initial (term) algebras. The instances on a schema S form a category, S–Inst, and a morphism of schemas F : S→...

DEFINITION Data Integration (or Information Integration) is the problem of finding and combining data from different sources. Viewbased Data Integration is a framework that solves the data integration problem for structured data by integrating sources into a single unified view. This integration is facilitated by a declarative mapping language that allows the specification of how each source re...

Todays world relies heavily on spatial data to manage the natural and built environments. Monitoring and controlling the impact of human activities on the environment and the impact of natural environmental changes (including natural hazards) on the built environment is a major policy driver for many jurisdictions. The mitigation of natural hazards including tsunamis, earthquakes and landslides...

15 Biomedical research networks need to integrate research data among their members and with 16 external partners. To support such data sharing activities, adequate information technology 17 infrastructure is necessary. To facilitate the establishments of such an infrastructure, we 18 developed a reference model for requirements. The reference model consists of five reference 19 goals and 15 re...

In this paper we propose and experimentally evaluate a data integration approach where the uncertainty generated during the comparison and merging of the input data sources is included into the resulting mediated schema, and can be used to provide richer answers to the users. We describe a system implementing our method, and use it to empirically study the impact of uncertainty management on th...

Primary care data is the single richest source of routinely collected health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites with different health record systems and integration with clinical trial and other types of medical data [1]. Data interoperability is therefore of utmost importance, and is typically implemented using a set...