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.
| Submissions: | November 15, 2010 |
| Notification: | December 15, 2010 |
| Event: | April 11, 2011-April 11, 2011 |