High Performance Multimodal Networks

1. High Performance Multimodal Networks Presented By, Gautham Mani Authors, Erik G.Hoel, Wee-Liang Heng, Dale Honeycutt

2. Agenda • Introduction • Logical Model • Requirements for robust model • Existing approaches • Access Model • New Physical Representation • Future Work • Summary & Conclusion

3. Introduction • Networks are mainly used in spatial databases to support rapid navigation • Networks data models have been widely used for geographical data representation • Network data models are usually represented as a small collection of tables • Advances in this field have not been able to meet requirements of multimodal transportation systems • Turns and maneuvers modelling are met but at a very high cost • New design for modelling multimodal networks • Design supports sophisticated network models • Multiple network analysis algorithms, such as shortest path finding, travelling salesman problems are supported

4. Logical Model • Basic Definitions • Network : A connectivity graph of junctions and their connecting edges • Network Element : Collection of junctions and edges comprising of the network • Network Attributes : A set of numeric properties that all network elements have • Network Building : A process in which connectivity graph of a network is derived from the source data • Turns : A turn element models entering a junction from an edge and exiting from another

5. Logical Model • Primary requirements for any robust implementation of network data models • Multimodal models : A model in which two or more types of transportation modes are modeled • Hierarchical models : Hierarchy is used within network models to further control flow within the network • Turns and maneuvers : Two part turns and multi-part turns(maneuvers) for accurately modeling networks • Fast network navigation : The persisted representation must support fast retrieval of connectivity information

6. Logical Model • Primary requirements for any robust implementation of network data models (cont) • Z Elevations : Refine network connectivity with planar network datasets logical z-values are supplied • Rich attribution of network elements : Model that supports multiple attributes on a network element • Uniform attribute access model : Models should be insulted from details of attribute origin

7. Connectivity Model Connectivity in a network is generally based upon spatial coincidence of endpoints of line features and other point features Works for planar datasets In case of non planar network connectivity can partway along a linear feature. This is termed as mid-span connectivity Thus, 1-1 mapping is generalized into 1-many mapping

8. Multimodal Models A line class participates in one group Line features of one group is not connected to those of other connectivity groups To establish connectivity point features are allowed in one or more groups

9. Z-Elevation Also referred to as “z-levs” Component used for modeling overpass, underpass, etc with planar datasets. The elevation information is logical and is not related to geographic elevation

10. Turns & Maneuvers • Most network models face problem due to Turn restrictions and impedances • A turn table is a common method used in networks to model turns • Turn table can be augmented to add the impedance attribute • Transition matrix is an alternative approach • Issue : Performance overhead to access tables disjoint from network connectivity tables • Solution : Imbed turns within network connectivity information

11. Existing Approaches • Graph Modification – Node Expansion • Each junction is expanded where edges are used to represent turns • Advantage : Turns are represented within connectivity graph of the network • Disadvantage: For n edges there are n2 possible turns

12. Algorithmic issues are also introduced by traversing edges in the new expanded subgraph • Leads to incorrect traversal in the subgraph

13. Existing Approaches • Graph Modification – Line Graphs • Transformation of the original(or primal) graph, junction replaced with line • Advantage : Turns are represented within connectivity graph of the network • Disadvantage: Requires primal graph to be retained to perform certain operations

14. Maneuver • A turn that spans three or more edges is known as maneuver • Maneuvers can get complicated and difficult to handle • Adapting graph modification techniques is not advicable

15. Network Attributes • Numeric properties of network elements used to define the navigational context during an analysis • Examples: Travel time, one way restriction • Various types of attributes: • Cost • Descriptor • Restriction • Hierarchy • Network attributes are persisted along with the network elements • Reduces number of tables queried during network analysis, improves performance

16. Access Model • Multiple approaches exist for building, maintaining and navigating elements within a network • Approach 1 • The overhead is placed on the client application • The client application is responsible for determining connectivity • Appropriately set foreign keys used to specify connectivity in persisted representation • Approach 2 • Systems have mechanisms to perform geometric analysis • Automatically find connectivity and persist information • User workflow is used to choose when to establish or update persisted connectivity information

17. Workflow Class Type 1 Users Class Type 2 Users Actively edit and maintain their data Usually hugh organizations such as government, companies Build process is viable if done periodically Subset of users where only edited features is rebuilt using user-initiated builds • Purchases network data • Infrequently edits or modifies data • Only build process support is required to complete all analysis and persistence

18. Connectivity Queries • Standard SQL queries are employed for normalized relational representation • Middleware libraries are used for analysis • Drawback, analysis functionality development not possible at high level navigation

19. Connectivity Queries • Alternate approach involves using forward star adjacency query • Returns elements that are immediately reachable from another element in a network • Constrained by a set of restrictions that controls which elements are traversed • Preferred method for querying network connectivity due to performance reasons

20. New Physical Model • Standard Physical Implementation • Network topology can be implemented for relational database as normalized relational model • If network tables do not contain geometry, they are called logical networks • If network tables do not contain geometry, they are called spatial networks • Foreign keys are used in the edge table to represent network connectivity • Expensive in terms of server loading and performance overhead • Middleware based solutions have been proposed

21. Example of Edge & Junction Table

22. Alternative Object Model & Physical Implementation • Network is central component to the system • Allows client to build connectivity Persisted Network Representation using geometric analysis of features present in FeatureClass • NetworkElement provides an API that allows the direct navigation to other immediately traversable NetworkElements • Forward Star Queries can be issued by clients via Network component • ForwardStarCursor component is returned which can be used to index or iterate through returned traversable NetworkElements

23. Storage Representation • The network contains collection of tables within the database • The network contains metadata, junction, edges, turn elements, connectivity relation b/w them and necessary attributes

24. Junction& Edge Table Connectivity is represented as a set of foreign key tuples Representation is navigation based Designed for common adjacency query during network analysis Records are serialized, compressed and stored in BLOB tables

25. Turn Table Graph modification techniques have not been employed Effectively generalized to support maneuvers and forward star queries Associates each junction with a turn Turn table is queried with specified junction and edge Last edge information in the turn entry allows the pairing of the turn with correct outgoing edge

26. Network Building Algorithm • Extract the geometries of the features in the source data. The extracted coordinates and their feature parentage are stored in a vertex information table • Sort the vertex information table by coordinate values, so that coincident vertices are grouped together • Analyze each group of coincident vertices according to the connectivity model, and generate the appropriate junction elements. During this analysis, vertices that do not connect to other vertices are discarded, while the remaining vertices may be further partitioned into disjoint subsets • Re-sort the vertex information table by vertex, so that vertices from each line feature are re-grouped together • Scan the vertex information table, and generate edge elements connecting adjacent vertices on each line • Analyze turn features and generate associated turn elements • Populate the attribute values of the generated network elements

27. Implementation Results • The build times include geometric analysis of feature geometry to establish connectivity • Typical case geometry and connectivity analysis took 45% • Creation of persistent network elements took 30% • Population of network attribute took 25%

28. Future Work • Extending the network for dirty area support • Incorporating incremental build algorithm for frequent edits • Support current network model in distributed database environment • Make other performance improvements

29. Summary & Conclusion • Brief overview of logical model and several extensions to standard model • Reviewed a common physical database implementation that uses conventional notions • Issues with the approach were discussed • New Physical Implementation was introduced and its various features • Efficient and flexible mechanisms for navigating the network connectivity • Design is currently used in implementation of transportation networks in the ArcGIS