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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 other 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 of 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 ConventionalMining 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 on temporal transportation network data.

● Lose some insights from the structural characteristics of the data.

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Challenges forData 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.