Buckets: Aggregative, Intelligent Agent Archival Objects for Digital Libraries
Michael L. Nelson
nelson-phd-proposal
10/28/98
Abstract
The goal of research projects is to produce information. This information is often instantiated in several forms, differentiated by semantic types (report, software, video, datasets, etc.). A given semantic type can be further differentiated by syntactic representations as well (PostScript version, PDF version, Word version, etc.). Although the information was created together and subtle relationships can exist between them, different semantic instantiations are generally segregated along currently obsolete media boundaries. Reports are placed in report archives, software might go into a software archive, but most of the data and supporting materials are likely to be kept in informal personal archives or discarded altogether. Rather than stretch the definition of "report" to include arbitrary data objects, we define a container construct in which data objects reside. Buckets provide an archive-independent aggregative container construct in which all related semantic and syntactic data types and objects can be logically grouped together, archived, and manipulated as a single object. Buckets, not archives or repositories, negotiate presentation and terms and conditions for accessing their contents. Furthermore, buckets are active archival objects and can communicate with each other, people, or arbitrary network services. We propose a blackboard-like communication system that enables buckets to send and receive messages asynchronously. The Bucket Matching System (BMS) is a generalized communication system to enable intelligent agent applications for buckets, including similarity matching, metadata scrubbing, and computational tasks.