MULTIMEDIA DATABASES AND QUERYING TECHNIQUESBy: Rohit Kulkarni

CS 2310 – Spring 2008

AGENDA

Background

Problem Definition

Challenges

BACKGROUND

What is MMDBMS?

Normalization framework

What is data fusion?

The general problem

Querying technique

MMDBMS ARCHITECTURE

REQUIREMENTS FOR MMDBMS

Traditional DBMS capabilities Huge capacity for storage management Information retrieval capabilities Media integration, composition and

representation Multimedia query support Multimedia interface and interactivity Performance

ISSUES IN MMDBMS

Multimedia data modeling Multimedia object storage Multimedia integration, presentation and

QOS Multimedia indexing, retrieval and browsing Multimedia query support Distributed multimedia database

management System support

PROBLEM DEFINITIONS

Extended Dependencies

The relational model

Similarity theory

Tuple distance function

AN EXAMPLE

Define a functional dependency between attributes FINGERPRINT and PHOTO of police database, and use the fingerprint matching function FINGERCODE for comparing digital fingerprint [JPH00], and the similarity technique used by QBIC for comparing photo images, we would write as follows

FINGERPRINTFINGERCODE(t’) PHOTOQBIC(t’’)

INFERENCE RULES FOR MFDS

Reflexive rule Augmentation rule Transitive rule Decomposition rule Union rule Pseudotransitive rule

NORMAL FORMS IN MULTIMEDIA DATABASES

Normal forms are used to derive database schemes that prevent manipulation anomalies

Similar anomalies can arise in multimedia database

Types of normal forms are1MNF, 2MNF , 3MNF and 4MNF

SENSOR DATA FUSION

Background:

Need for Multiple Sensors:- data from a single sensor yields poor results in object recognition

Sensor Management Model

SENSOR MANAGEMENT MODEL

SENSOR DATA FUSION

Problems:

Association of objects from different Sensors

Tracking

NEED FOR QUERY TECHNIQUE

• Problem with existing query techniques

• Why not SQL?

• To support the retrieval and fusion of multimedia information from multiple sources and distributed databases, a spatial/temporal query language called QL has been proposed

FEATURES OF QL

Easy to learn as syntax is similar to SQL

Allows user to specify queries for both Multimedia data sources and Multimedia databases

Supports multiple sensor sources and systematic modification of queries

OPERATOR CLASSES

The operators in QL can be categorized with respect to their functionality.

The two main classes are: transformational operators (the σ‑operators) fusion operators (the ‑operators).

TRANSFORMATIONAL OPERATORS

Definition: A σ‑operator is defined as an operator to be applied to any multi-dimensional source of objects in a specified set of intervals along a dimension. The operator projects the source along that dimension to extract clusters

TRANSFORMATIONAL OPERATORS (CONTD)

As an example, if we write a σ‑expression for extracting the video frame sequences in the time intervals [t1-t2] and [t3-t4] from a video source VideoR.

The expression will be

is σtime([t1-t2], [t3-t4]) VideoR

where VideoR is projected along the time dimension to extract clusters (frames in this case) whose projected positions along the time dimension are in the specified intervals.

FUSION OPERATORS

Much more complex as it deals with Sensor data fusion

Requires input data in different time periods from multiple sensors

The output of the fusion‑operator is some kind of high level, qualitative representation of the fused object, and may include object type, attribute values and status values.

IS THERE A MOVING VEHICLE PRESENT IN THE GIVEN AREA AND IN THE GIVEN TIME INTERVAL?

IS THERE A MOVING VEHICLE PRESENT IN THE GIVEN AREA AND IN THE GIVEN TIME INTERVAL?

• Corresponding query:type,position, direction

(motion(moving) type(vehicle)

xy(*)

(T)T mod 10 = 0 and T>t1 and T <t2

media_sources (video)media_sources

ype (vehicle) xyz(*)

(T) T>t1 and T<t2

media_sources(laser_radar) media_sources)

EXPERIMENTAL PROTOTYPE

CHALLENGES

Handle large number of different sensors

Replacing manual query with a semi-automatic or fully automatic query refinement process

APPLICATIONS OF MMDBMS

Education- digital libraries, training, presentation

Healthcare- telemedicine, health information management

Entertainment- interactive TV, video on demand

Information dissemination- news, TV broadcasting

And many more!

REFERENCES

Intelligent Querying Techniques for Sensor Data Fusion by Shi-Kuo Chang, Gennaro Costagliola, Erland Jungert and Karin Camara

A Normalization Framework for Multimedia Databases by S.K. CHANG, V. DEUFEMIA, G. POLESE

Querying distributed Multimedia databases and data sources for sensor data fusion by S.K.Chang, Gennaro Costagliola, Erland Jungert and Francesco Orciuoli

Multimedia database management-requirements and issues by Donald A. Adjeroh and Kingsley C. Nwosu

Fuzzy Queries in Multimedia database system by Ronald Fagin Bayesian Approaches to Multi-Sensor data fusion by Olena

Punska, St. John’s CollegeMultimedia database management-requirements and issues by Donald A. Adjeroh and Kingsley C. Nwosu Querying distributed Multimedia databases and data sources for sensor data fusion by S.K.Chang, Gennaro Costagliola, Erland Jungert and Francesco Orciuoli

MULTIMEDIA DATABASE AND QUERYING TECHNIQUES

By: Rohit KulkarniCS 2310 – Spring 2008

Thank you