Data Provenance and Data Management in eScience

The highly distributed scientific research enabled by 'escience' features complex interactions in its infrastructure and thus needs strong data provenance and management systems. This book explains the latest information tracking and authentication techniques.

eScience allows scientific research to be carried out in highly distributed environments. The complex nature of the interactions in an eScience infrastructure, which often involves a range of instruments, data, models, application, people and computational facilities, suggests there is a need for data provenance and data management (DPDM). The W3C Provenance Working Group defines the provenance of a resource as a record that describes entities and processes involved in producing and delivering or otherwise influencing that resource. It has been widely recognised that provenance is a critical issue to enable sharing, trust, authentication and reproducibility of eScience process.

Data Provenance and Data Management in eScience identifies the gaps between DPDM foundations and their practice within eScience domains including clinical trials, bioinformatics and radio astronomy. The book covers important aspects of fundamental research in DPDM including provenance representation and querying. It also explores topics that go beyond the fundamentals including applications. This book is a unique reference for DPDM with broad appeal to anyone interested in the practical issues of DPDM in eScience domains.

Recent research on Data Provenance and Data Management for eScience How to use advanced semantic and AI techniques to track and manage information which describe the life cycle of data items and products Written by leading experts in the field

Auteur
John Taylor ist Bassist und Gründungsmitglied von Duran Duran. Bis heute hat die Band mehr als 80 Millionen Tonträger verkauft und wurde dafür mit sechs Lifetime Achievement Awards belohnt, darunter BRIT- und MTV-Awards. Er lebt mit seiner Frau und den drei Kindern abwechselnd in Wiltshire/England und Los Angeles.

Contenu
Provenance Model for Randomized Controlled Trials.- Evaluating Workflow Trust Using Hidden Markov Modeling and Provenance Data.- Unmanaged Workflows: Their Provenance and Use.- Sketching Distributed Data Provenance.- A Mobile Cloud with Trusted Data Provenance Services for Bioinformatics Research.- Data Provenance and Management in Radio Astronomy: A Stream Computing Approach.- Using Provenance to Support Good Laboratory Practice in Grid Environments.