Designing MMDBs


Designing MMDBs

characteristics of multimedia data that have impacts on the design of MMDBs
include : the huge size of MMDBs, temporal nature, richness of content,
complexity of representation and subjective interpretation. The major
challenges in designing multimedia databases arise from several
requirements they need to satisfy such as the following:
• Manage different types of input, output, and storage devices.
Data input can be from a variety of devices such as scanners, digital
camera for images, microphone, MIDI devices for audio, video cameras.
Typical output devices are high-resolution monitors for images and video,
and speakers for audio.
• Handle a variety of data compression and storage formats.
The data encoding has a variety of formats even within a single
application. For instance, in medical applications, the MRI images of brain
has lossless or very stringent quality of lossy coding technique, while the
X-ray images of bones can be less stringent. Also, the radiological image
data, the ECG data, other patient data, etc. have widely varying formats.

• Support different computing platforms and operating systems.
Different users operate computers and devices suited to their needs and
tastes. But they need the same kind of user-level view of the database.
• Integrate different data models. Some data such as numeric and
textual data are best handled using a relational database model, while
some others such as video documents are better handled using an objectoriented
database model. So these two models should coexist together in
MMDBs.
• Offer a variety of user-friendly query systems suited to
different kinds of media. From a user point of view, easy-to-use
queries and fast and accurate retrieval of information is highly desirable.
The query for the same item can be in different forms. For example, a
portion of interest in a video can be queried by using either

1) a few sample video frames as an example,
2) a clip of the corresponding audio track or
3) a textual description using keywords
􀂉 Handle different kinds of indices. The inexact and subjective nature
of multimedia data has rendered keyword-based indices and exact and
range searches used in traditional databases ineffective. For example, the
retrieval of records of persons based on social security number is
precisely defined, but the retrieval of records of persons having certain
facial features from a database of facial images requires, content-based
queries and similarity-based retrievals. This requires indices that are
content dependent, in addition to key-word indices.
􀂉 Develop measures of data similarity that correspond well with
perceptual similarity. Measures of similarity for different media types
need to be quantified to correspond well with the perceptual similarity of
objects of those data types. These need to be incorporated into the search
process

􀂉 Provide transparent view of geographically distributed data.
MMDBs are likely to be a distributed nature. The media data
resides in many different storage units possibly spread out geographically.
This is partly due to the changing nature of computation and computing
resources from centralized to networked and distributed.
• Adhere to real-time constraints for the transmission of media
data. Video and audio are inherently temporal in nature. For example, the
frames of a video need to be presented at the rate of at least 30
frames/sec. for the eye to perceive continuity in the video.
• Synchronize different media types while presenting to user. It
is likely that different media types corresponding to a single multimedia
object are stored in different formats, on different devices, and have
different rates of transfer. Thus they need to be periodically synchronized
for presentation.

On-Going Research Projects in
Multimedia Databases
• Multimedia database management
(NSF, Fuji Electric, AT&T)
– Video modeling and management
– Multimedia document management
• Distributed multimedia systems
(NSF, AFRL, IBM, Intel, Siemens)
• High-performance multimedia database
architecture for storage management
(NSF, AT&T)




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