1 Shashi Shekhar McKnight Distinguished Uninversity Professor University of Minnesota www.cs.umn.edu/~shekhar , www.spatial.cs.umn.edu Spatio-Temporal Networks: A GIS Perspective A Provocation at Visualizing Network Dynamics Workshop (11/4-6/2008) Supporting NATO Research Task Group IST-059/RTG- 025 Outline Brief overview of my research group Recent NGA NURI Grant Network Dynamics Representation Provocation: Time Aggregated Graphs
Spatio-Temporal Networks: A GIS Perspective A Provocation at Visualizing Network Dynamics Workshop (11/4-6/2008) Supporting NATO Research Task Group IST-059/RTG-025. - PowerPoint PPT Presentation
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Shashi ShekharMcKnight Distinguished Uninversity Professor
University of Minnesotawww.cs.umn.edu/~shekhar, www.spatial.cs.umn.edu
Spatio-Temporal Networks: A GIS PerspectiveA Provocation at Visualizing Network Dynamics Workshop (11/4-
6/2008)
Supporting NATO Research Task Group IST-059/RTG-025
OutlineBrief overview of my research groupRecent NGA NURI GrantNetwork Dynamics RepresentationProvocation: Time Aggregated Graphs
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Spatial Databases: Example Projects
only in old plan
Only in new plan
In both plans
Evacutation Route Planning
Parallelize Range Queries
Storing graphs in disk blocksShortest Paths
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Spatial Data Mining: Example Projects
Nest locations Distance to open water
Vegetation durability Water depth
Location prediction: nesting sites Spatial outliers: sensor (#9) on I-35
Co-location Patterns Tele connections
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1. BooksSpatial Databases: A Tour, Prentice Hall, 2003
Encyclopedia of GIS, Springer, 2008
Service Activities
2. Journals GeoInformatica: An Intl. Journal on Advances in Computer Sc. for GIS
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Outline
•Brief overview of my research
•Recent NGA NURI Grant
•Network Dynamics – Representations
•Provocation: Time Aggregated Graphs
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Dynamic Purpose aware Graph Data Models for Representing and Reasoning about Composite Networks
Investigators: Shashi Shekhar,(U Minnesota) Start Date: August 2008
into single purpose networks 2. Role ( network entities, e.g. bridge
)is a bridge an obstacle or a link ?
3. Time aggregated graphs
Manhattan Money Laundering Incident
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Adding Roles, Purposes to Network Data Model
Proposed Extension Existing Graph model (Oracle)
Primitive Analysis Questions:•What is overall purpose of each component network?•What are network-element role-types (e.g. nodes, edges, obstacles, etc.) ?•What are instances of each element role-types? •What are the operations on element-types, roles, purposes and network?
Approach: Purpose Aware Graphs (PAG)Tasks:•T1: Conceptual Model for PAG T2: Data types, Operators•T3: Query Processing algorithms T4:Purpose and Role Taxonomy•T5: Validation
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Challenge 2: Time-variant, Fluid Networks
Syria's Suspected Nuclear Facility Source: New York Times and Digital Globe
Basic Modelling Questions:•What is the variation of the role of a node or an edge over time?•Where is a purpose changed or where does re-purposing occur?•What are the nodes and edges that causes the re-purposing of a network?•What are the nodes and edges that are part of a series of re-purposing?
Tasks•G1: Event and Process Model for DPAG•G2: Data type, query operators on DPAG•G3: Algorithms for DPAG•G4: Storage and Access Methods for DPAG•G5: Validation
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Outline
•Brief overview of my research
•Recent NGA NURI Grant
•Network Dynamics – Representations
•Provocation: Time Aggregated Graphs
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Motivation
Delays at signals, turns, Varying Congestion Levels travel time changes.
1) Transportation network Routing
2) Crime Analysis
Identification of frequent routes (i.e.) Journey to Crime
3) Dynamic Social Network Analysis
Emerging leaders or dense sub-networks, Cells with increased chatter,
4) Knowledge discovery from Sensor data.
Spreading Hotspots
9 PM, November 19, 2007
4 PM, November 19, 2007Sensors on Minneapolis Highway
Network periodically report time varying traffic
7 PM, November 19, 2007
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Problem Definition
Input : a) A Spatial Network b) Temporal changes of the network topology
and parameters.
Objective : Minimize storage and computation costs.
Output : A model that supports efficient correct algorithms for computing the query results.
Constraints : (i) Predictable future (ii) Changes occur at discrete instants of time, (iii) Logical & Physical independence,
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Challenges in Representation
Conflicting Requirements
Expressive Power
Storage Efficiency New and alternative semantics for common graph operations. What is the best start time ?
Shortest Paths are time dependent. Emerging, Dissipating, periodic, spreading, …
Time Aggregated Graphs B. George, S. Shekhar, Time Aggregated Graphs for Modeling Spatio-temporal Networks-An Extended
Abstract, Proceedings of Workshops (CoMoGIS) at International Conference on Conceptual Modeling, (ER2006) 2006. (Best Paper Award)
B. George, S. Kim, S. Shekhar, Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results, Proceedings of International Symposium on Spatial and Temporal Databases (SSTD07), July, 2007.
B. George, J. Kang, S. Shekhar, STSG: A Data Model for Representation and Knowledge Discovery in Sensor Data, Proceedings of Workshop on Knowledge Discovery from Sensor data at the International Conference on Knowledge Discovery and Data Mining (KDD) Conference, August 2007. (Best Paper Award).
B. George, S. Shekhar, Modeling Spatio-temporal Network Computations: A Summary of Results, Proceedings of Second International Conference on GeoSpatial Semantics (GeoS2007), 2007.
B. George, S. Shekhar, Time Aggregated Graphs for Modeling Spatio-temporal Networks, Journal on Semantics of Data, Volume XI, Special issue of Selected papers from ER 2006, December 2007.
B. George, J. Kang, S. Shekhar, STSG: A Data Model for Representation and Knowledge Discovery in Sensor Data, Accepted for publication in Journal of Intelligent Data Analysis.
B. George, S. Shekhar, Routing Algorithms in Non-stationary Transportation Network, Proceedings of International Workshop on Computational Transportation Science, Dublin, Ireland, July, 2008.
B. George, S. Shekhar, S. Kim, Routing Algorithms in Spatio-temporal Databases, Transactions on Data and Knowledge Engineering (In submission).
Evacuation Planning Q Lu, B. George, S. Shekhar, Capacity Constrained Routing Algorithms for Evacuation Planning: A
Summary of Results, Proceedings of International Symposium on Spatial and Temporal Databases (SSTD05), August, 2005.
S. Kim, B. George, S. Shekhar, Evacuation Route Planning: Scalable Algorithms, Proceedings of ACM International Symposium on Advances in Geographic Information Systems (ACMGIS07), November, 2007.
Q Lu, B. George, S. Shekhar, Capacity Constrained Routing Algorithms for Evacuation Planning, International Journal of Semantic Computing, Volume 1, No. 2, June 2007.