OBJECTIVES

The goal of data lifecycle management is to provide a high point of
observation to capture the long-term trends within the management of data
and thus anticipate and automate in a cost-effective manner important
operations, such as data migration, data integration, data archival
and provenance tracking.
While many of these problems have been tackled individually, a global
view of the problem is required to achieve optimality, and to
guarantee global properties. Examples of such efforts are the use of
data provenance in scientific workflows aiming at managing data flows
in their completeness, the introduction of service oriented
architectures in enterprises, and, with the success of mashups, the
study of novel solutions to solve the problem of correlating different
modules.

The Dali workshop focuses on methods for improving and automating the
management of data throughout its complete lifecycle. Recent research
in information preservation, data and workflow provenance, and data
curation has already broaden the scope trying to capture complex data
manipulation processes, but more work is needed.