Knowledge Discovery from Transportation Network Data Paper Review - - PowerPoint PPT Presentation

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Knowledge Discovery from Transportation Network Data

Paper Review

Jiang, W., Vaidya, J., Balaporia, Z., Clifton, C., and Banich, B. Knowledge Discovery from Transportation Network Data. In ICDE, 2005

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Outline

• Background.
• Experiments.

Structurally Similar Routes Temporally Repeated Routes

• Experiment results.
• Conventional techniques.
• New challenges.

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A natural application area for Data Mining

• Transportation and logistics are an important

sector of the economy.

• -Transportation consumes 60% of oil worldwide
• Data mining has lead to significant gains in
• ther areas
• Computer use is widespread in transportation

and logistics.

• -Inventory management, parcel tracking, and even on-

truck location sensors

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Existing Applications

Data Mining

• Mining with transactional characteristics of freight and

events.

• - i.e. classification on safety/accident records might

find that trucks are prone to accidents at 7:00 AM on east - west roads.

• - NO geometry of the network.

Network Structure

• Optimization
• - Finds solution (Minimize cost)

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Transportation Networks

• Graph problems
• Graph mining

i.e. Finding the frequent sub-graphs Algorithms * WARMR * AGM * SUBDUE * FSG

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Dataset

• Six months of origin-destination (OD) data from a large

third-party logistic company. 98,292 transactions.

• Represented as a directed graph by mapping locations

to vertices.

• Each transaction can then be represented as the edge
• f an OD pair.
• The edges are labeled with the other attributes of the

transaction: pickup date, delivery date, distance, hours, weight, and mode. (binning strategy)

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Mining Interests

• Structurally Similar Routes
• -Identify structurally similar patterns that occur in many

locations. Methods * SUBDUE * FSG

• Temporally Repeated Routes
• -Find patterns of routes repeated in time, rather

than space. Method * FSG

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Structurally Similar Routes

• We assign all vertices the same label.
• Three variants for edge labels: weight, distance,

and time.

• - OD_TD : TOTAL-DISTANCE
• - OD_GW : GROSS-WEIGHT
• - OD_TH : MOVE-TRANSIT-HOURS

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Experiments with SUBDUE (MDL principle)

SUBDUE: A substructure discovery system

Results:

• Took about 3.25 hours to handle a graph of 100

vertices and 561 edges to find the best 3 patterns of beam size 4.

• Would need 6 months on the complete graph.
• Results were trivial.

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• Significant traffic from node 2 to node 4 via node 3, but

not much return traffic (deadheading)

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Experiments with FSG

• FSG mines patterns across a set of graph

transactions.

• Divides the single graph into multiple distinct

sub-graphs, and treats each sub-graph as a separate transaction.

Breadth first partitioning Depth first partitioning Both may result in patterns being broken

across partitions

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Results

• Partition sizes; 400, 800, 1200 and 1600.
• Depth-first partitioning: 200 frequent patterns

were found with the minimum support 120.

• Breadth-first partitioning: 667 frequent patterns

were found with the minimum support 240.

• Had runtime and memory problems with lower

supports on the breadth-first partitions.

• FSG is not an appropriate tool to use for mining

recurrence patterns in a large single graph

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Temporally Repeated Routes

• FSG
• Exploits the temporal nature of the

transportation graph

• Partition each graph into a set of graph

transactions based on date

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Results

• Unable to run FSG on the entire data set due to

insufficient memory / swap space.

• Most were small patterns. (The following is the

biggest one)

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Patterns Discovered by Using Conventional Mining Algorithms

• Mapped the dataset into a standard

“transactional” representation.

• Used traditional data mining approaches.
• Used Weka for association rule mining,

instance (tuple) classification and cluster analysis on the transportation data.

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Evaluations of Conventional Algorithms

• Traditional data mining techniques have

produced interesting and meaningful results to summarize our data.

• Further experimentation is required to explore

the potential and limitations of these techniques

• n temporal transportation network data.
• Lose some insights from the structural

characteristics of the data.

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Challenges for Data Mining Research

• Handling the temporal aspects of graphs

(dynamic graphs).

• Incorporating the notion of events into a graph.
• Expanding graph mining techniques beyond

data similar to molecular structures.

• Determining what makes a graph pattern

interesting.