danielzhangau/Adv-Techn-for-High-Dim-Data

spatial & multimedia databases: multidimensional data management concepts, theories, and technologies, focusing on data access methods and similarity query processing for spatial, multimedia, and Web-based databases, with particular emphasis on video indexing and search.

Advanced Techniques for High Dimensional Data

UQ INFS

Spatial & multimedia databases: multidimensional data management concepts, theories and technologies, focusing on data access methods and similarity query processing for spatial, multimedia and Web-based databases, with particular emphasis on video

Topics Covered:

1. Spatial data types and spatial databases
   • RDBMS data types: numbers, strings and dates
   • SDBMS data types: points, lines and polygons
     • Usages are very similar compared with relational data types
     • Indexing and processing are quite different
   • Spataial data types can help us to understand how other data types should be managed and processed
     • Such as multimedia data
     • Feature extraction and embedding can transform many type of data into vector data
   • Spatial Data Relations
     • Topological, Direction, Metric

   

   - Spatial Data Operations - Basic Spatial Operations - Selection, Projection, Amalgamation - Containment, Region, Intersection, Overlay, Fusion, Windowing, Clipping, Centre, Boundary - Area, Perimeter, Distance - Other Spatial Operations - Nearest Neighbours, Similarity Search, Skyline Queries - Complex Spatial Queries - Multiple predictions/operations, spatial sub-queries
2. Spatial indexing mechanisms
   • Purpose:
     • Efficiency in processing spatial selection, join and other spatial operations
   • Two strategies to organize space and objects
     • Map spatial objects into 1D space and use a standard index structure (B-tree)
     • Dedicated external data structures (R-tree & Quad-tree)
   • Basic ideas
     • Approximation
       • Bounding box, Grids
     • Hierachical Data Organization
3. Spatial alogrithm and query processing
   • KNN Query Processing
   • Skyline Query Processing
4. Spatiotemporal data management
   • Definitions: moving objects and trajectories
   • Spatiotemporal queries
     • Spatiotemporal point and window queries
     • Trajectory similarity queries - and similarity measures
       • Lock-step Windows, LCSS, EDR, DTW, ...
   • Spatiotemporal indexing methods
     • Simple ways of indexing spatiotemporal and issues
       • 3D R-Tree
       • HR-Tree
       • TPR-Tree
5. High-dimensional indexing and search
   • Dimensionality Curse
     • Many interesting observations
       • Number of partitions, Central hollow/ High overlapping, Nearest Neighbor is not clear...
     • The performance degrades rapidly as dimensionality increases, and eventually underperforms linear scan
     • However, linear scan needs to search the whole data file - affected volumn is 100%
   • Example Indexes: Why do they still exist?
     • R-tree -> X-tree
     • Z-order -> Pyramid
     • Grid -> VA-file
     • Cluster -> iDistance
6. Multimedia databases
   • Data representations
     • Image: basic format, abstraction and features (colour, texture and shape)
     • Video: structure
   • Text-based vs Content-based search
   • Similarity measures
     • P-norm, ViTri, mean distance and DTW
     • Query results evaluation: Precision and Recall
7. Route Planning in Road Network
   • Index Free
     • Dijkstra's, Bi-Dijkstra's, A*, Bi-A*, Landmark
   • Index based
     • Contraction Hierarchy, 2 Hop Labeling
       

What I learned:

• Identify the applications and features of complex data types, including spatial and multimedia data.
• Acquire both principles and hand-on experiences of implementing existing advanced spatial and multimedia processing techniques and databases.
• Examine the major issues of spatial and multimedia data management systems.
• Analyze and evaluate existing research methods, creative to identify and define problems, and innovative for their solutions for multimedia database management.