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Multimedia Databases and Querying techniques

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  1. Multimedia Databases and Querying techniques By: Rohit Kulkarni CS 2310 – Spring 2008

  2. Agenda • Background • Problem Definition • Challenges

  3. Background • What is MMDBMS? • Normalization framework • What is data fusion? • The general problem • Querying technique

  4. MMDBMS architecture

  5. 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

  6. 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

  7. Problem definitions • Extended Dependencies • The relational model • Similarity theory • Tuple distance function

  8. 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’’)

  9. Inference rules for MFDs • Reflexive rule • Augmentation rule • Transitive rule • Decomposition rule • Union rule • Pseudotransitive rule

  10. 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 are 1MNF, 2MNF , 3MNF and 4MNF

  11. Sensor data fusion • Background: • Need for Multiple Sensors:- data from a single sensor yields poor results in object recognition • Sensor Management Model

  12. Sensor Management Model

  13. sensor data fusion • Problems: • Association of objects from different Sensors • Tracking

  14. 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

  15. 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

  16. 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).

  17. 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

  18. 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.

  19. 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.

  20. Is there a moving vehicle present in the given area and in the given time interval?

  21. 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)

  22. Experimental Prototype

  23. Challenges • Handle large number of different sensors • Replacing manual query with a semi-automatic or fully automatic query refinement process

  24. 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!

  25. References • Intelligent Querying Techniques for Sensor Data Fusion byShi-Kuo Chang, GennaroCostagliola, ErlandJungert and Karin Camara • A Normalization Framework for Multimedia Databases byS.K. Chang, V. Deufemia, G. Polese • Querying distributed Multimedia databases and data sources for sensor data fusion by S.K.Chang, GennaroCostagliola, ErlandJungert 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 OlenaPunska, 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, GennaroCostagliola, ErlandJungert and Francesco Orciuoli

  26. Multimedia Database and Querying techniques By: Rohit Kulkarni CS 2310 – Spring 2008 • Thank you