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

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
and analysis of linearly connected data such as that found in transportation networks
junction table and an edge table
transportation networks where two or more different transportation modes are linked – e.g., roads and rail
In this paper, we describe a design for modeling multimodal networks that are persisted in relational databases
The network engine used in this paper can support Multiple network analysis algorithms, such as shortest path finding, travelling salesman problems

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
Within transportation networks, interstate highways are commonly
associated with the highest level of the hierarchy, state highways
A turn is not simply one restriction or penalty; instead it should be
regarded as a first-class entity with attribution.
use within network analysis algorithms

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
To capture real-world constraints, such
as one-way travel restrictions, height/weight limits, and time-of-day travel times
In each case, client applications
should be able to retrieve attribute values without knowledge of the underlying
storage

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
This leads to a 1:1 mapping between features participating in a network and the network elements used to represent the network connectivity. This approach works reasonably well for simpler planar network datasets

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
Thus, a point feature that is coincident with a road feature in one group and a subway feature in a second group will connect the two groups together in its role as a junction element
A point feature class, containing point feature p1, participates in both connectivity groups. On the right side of Fig. 2, the resulting connectivity is shown. Note that l1 (edges e11 and e12) and l2 (edges e21 and e22) are connected at point p1 (junction j3). There is no connectivity between line l1 and line l3 as they are in different groups and there is no point feature where they intersect.

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
For example, the endpoints of line features representing roads that comprise an underpass may have a z elevation value of 0, while the lines representing the overpass roads may have a value of 1. This logical vertical ordering can extend to support very complex highway interchanges.

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
The presence of turns can greatly impact the movement through a network
When traversing the network, the turn table is queried as necessary
An alternative approach is to employ a transition matrix that represents possible transitions at an intersection [10]. The matrix can be encoded into a bitmap for a smaller physical representation.

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. Leads to incorrect traversal in the subgraph
Algorithmic issues are also introduced by traversing edges in the new expanded subgraph
Leads to incorrect traversal in the subgraph
This highlights the fundamental problem with this approach, namely, the significant bloating of the network storage requirements. This adversely impacts both storage costs and traversal performance

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
The network attributes often are mapped to attributes found in the associated feature; during the process of building the network and establishing network connectivity, attribute values are read from the features and persisted into the network

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
The network attributes often are mapped to attributes found in the associated feature; during the process of building the network and establishing network connectivity, attribute values are read from the features and persisted into the network
the network is built immediately following network definition and creation. If the user chooses to edit the features in the network, the entire network will have to be rebuilt in order to guarantee correctness of the connectivity used during network analysis.

17. Workflow Class Type 1 Users Class Type 2 Users Purchases network data
Infrequently edits or modifies data
Only build process support is required to complete all analysis and persistence
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
If the organization is editing smaller datasets, the build operation can be staged on a more frequent basis
The need for incremental builds during frequent editing can be obviated if network connectivity is “live”, i.e., network connectivity is automatically re-generated after individual edits to the source data

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
explicit representation of network primitives and connectivity using primary and foreign keys

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
In this example,
there is a turn restriction at junction j0 when moving from edge e2 to edge e1. This is
shown on the left side of the figure. A forward star query at junction j0 from edge e2
will result in two edge-junction pairs being returned; namely (e3, j3), and (e4, j4). The
edge-junction pair (e1, j1) is not returned as it is not traversable from edge e2 at
junction j0 because of the turn restriction

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
A fundamental implementation choice is whether or not the tables representing the network elements (junctions and edges) contain any associated geometry

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
that cache network connectivity information on the client (or application server) and provide access to the information through a conventional API
It additionally provides a general method for accessing the values of the associated network attributes.

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
which is to find the edges and junctions connected to a given junction

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