The Mirror MMDBMS architecture

Screenshots accompanying the technical demo and poster presented at VLDB 99.

Index: Overview, Relevance feedback, Thesauri, Another example, Architecture, Concluding remarks.


This demo introduces the Mirror architecture for multimedia database management systems. Like any DBMS, a MMDBMS is a general-purpose software system that supports various applications; however, the support is targeted to applications in the specific domain of digital libraries. Three new requirements are identified for this domain: 1) multimedia objects are active objects, 2) querying is an interaction process, and 3) query processing uses multiple representations. Mirror's design therefore provides basic functionality for the management of both the content structure and the logical structure of multimedia objects. The inference network retrieval model, the basis of a well-known IR system, is adapted for multimedia retrieval. Other characteristics of the Mirror architecture are the support for distribution of both data and operations, and extensibility of data types and operations.


In these pages, we take a tour along an image retrieval application that has been constructed using the Mirror architecture. The system architecture and design rationale are further discussed in the paper published in the VLDB Proceedings, as well as various other papers, all linked from the Mirror web pages.

Relevance feedback

Interaction with users in a multimedia retrieval task is tricky, because the users cannot tell us precisely what their multimedia information needs are. This problem may be addressed by using relevance feedback given by the user.

Short-term learning tour


Relying completely on short-term interaction is not sufficient for multimedia information retrieval. As a solution, thesauri model background knowledge, which helps to 1) bootstrap the iterative process and 2) obtain quicker convergence.

Long-term learning tour

Another example

While searching or browsing, you may suddenly decide to look for something else. In this situation, querying will be completely driven by examples.

Changing your mind


Before we finish the tours, let's take a brief look at the underlying system architecture.

How it works


As illustrated in the screenshots above, the current implementation of the Mirror DBMS supports sufficient functionality to create a state-of-the-art image retrieval system. In preliminary experiments, we have applied the same functionality to music fragments as well. And, the same queries have been used to participate in the evaluation of text retrieval systems at TREC-8.

Expressing the retrieval problem as a small number of declarative queries enables the reuse of code, allows the combination of IR querying (as above) with traditional database queries, and allows the system to optimize during query processing.

In our future work, we will continue the development of the Mirror DBMS, focusing on the following two aspects:

The first aspects requires more attention to help us increase the size of the collections that can be handled. We believe that the declarative query specifications are extremely useful to gain understanding in how to distribute and parallellize query processing. The second aspect is necessary to improve our - still rather limited - understanding of interaction between user and retrieval system.
Index: Overview, Relevance feedback, Thesauri, Another example, Architecture, Concluding remarks.


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Last updated: December 9, 1999
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