Mapcube Shashi Shekhar Computer Science Department, AHPCRC University of Minnesota [email protected]@cs.umn.edu (612) 624-8307 shekhar.

Mapcube

Shashi Shekhar

Computer Science Department, AHPCRCUniversity of Minnesota

[email protected](612) 624-8307

http://www.cs.umn.edu/~shekharhttp://www.cs.umn.edu/research/shashi-group/

Biography Highlights

7/01-now : Professor, Dept. of CS, U. of MN 12/89-6/01 : Asst./Asso. Prof. of CS, U of MN Ph.D. (CS), M.B.A., U of California, Berkeley (1989) Member: CTS(since 1990),Army Center, CURA Author: “A Tour of Spatial Database” (Prentice Hall,

2002) and 100+ papers in Journals, Conferences Editor: Geo-Information(2002-onwards), IEEE

Transactions on Knowledge and Data Eng.(96-00) Program chair: ACM Intl Conf. on GIS (1996) Tech. Advisor: UNDP(1997-98), ESRI(1995), MNDOT

GuideStar(1993-95 on Genesis Travlink) Grants: FHWA, MNDOT, NASA, ARMY, NSF, ... Supervised 7+ Ph.D Thesis (placed at Oracle, IBM

TJ Watson Research Center etc.), 30+ MS. Thesis

Research Interests

Knowledge and Data Engineering Spatial Database Management Spatial Data Mining(SDM) and

Visualization Geographic Information System Application Domains : Transportation,

Climatology, Defence Computations

Spatial Data Mining, SDBMS

Historical Examples London Cholera (1854) Dental health in Colorado

Current Examples Environmental justice Crime mapping - hot spots (NIJ) Cancer clusters (CDC) Habitat location prediction (Ecology) Site selection, assest tracking, spatial

outliers

Project: Traffic Database

System

Sponsor and time-period: MNDOT, 1998-1999 Students: Xinhong Tan, Anuradha Thota Contributions to Transportation Domain

Reduce response of queries from hours to minutesPerformance tuning (table design, index selection)

Contributions to Computer ScienceGUI design for extracting relevant summaries Evaluate technologies with large dataset

Map of Station in Mpls

Gui Design

http://www.cs.umn.edu/research/shashi-group/TMC/html/gui.html

Existing Table

Fivemin

DetectorReadDateTimeDayofweekVolumeOccupancyValiditySpeed

Benchmark Queries1. Get 5-min Volume, occupancy for detector ID =

10 on Oct. 1st, 1997 from 7am to 8am2. Get 5-min volume, Occupancy for detector ‘5’ on

Aug1 1997.3. Get 5-min volume, Occupancy for detector ‘5’ on

Aug1 1997 from 6.30am to 7.30am.4. Get average 5-min volume, occupancy, for

Monday in Aug1997 between 8.00 - 8.05,8.05-8.10 …… 9.00

5. Get maximum volume, Occupancy for detector ‘5’ on Aug1 1997 from 6am to 7am

6. Get the average of AM rushhour hourly volume for a set of stations on highway I35W-NB with milepoint between 0.0 and 4.0 from Oct. 1st, 1997 to Oct. 5th , 1997

Conclusion

Examples of the Query

Example1: Query description:

Get 5-min Volume, occupancy for detector ID = 10 on Oct. 1st, 1997 from 7am to 8am

SQL statement: SELECT readdate, time, xtan.fivemin.detector, occupancy,

volume FROM xtan.fivemin, xtan.datetime WHERE ReadDate = to_date('01-OCT-97', 'DD-MON-YYYY') AND time BETWEEN '0705' AND '0800' AND xtan.fivemin.Detector = '10' AND xtan.fivemin.

Examples of the Query

Query result 1:

Examples of the Query Example2:

Query description: Get the average of AM rushhour hourly volume for a set of

stations on highway I35W-NB with milepoint between 0.0 and 4.0 from Oct. 1st, 1997 to Oct. 5th , 1997

SQL statement: SELECT hour, xtan.v_stat_hour.station, avg(volume) FROM tan.v_stat_hour, xtan.statrdwy WHERE ReadDate BETWEEN to_date('01-OCT-97','DD-

MON-YYYY') AND to_date('05-OCT-97','DD-MON-YYYY') AND hour BETWEEN '06' AND '09' AND statrdwy.route = 'I35W-I' AND statrdwy.mp >= 0.0 AND statrdwy.mp <= 4.0 AND xtan.v_stat_hour.station = statrdwy.station GROUP BY xtan.v_stat_hour.station, hour

Examples of the Query

Query result 2:

Project: Traffic Data Visualization

Sponsor and time-period: USDOT/ITS Inst., 2000-2001 Students: Alan Liu, CT Lu Contributions to Transportation Domain

Allow intuitive browsing of loop detector data Highlight patterns in data for further study

Contributions to Computer Science Mapcube - Organize visualization using a dimension lattice Visual data mining, e.g. for clustering

Motivation for Traffic Visualization

Transportation Manager How the freeway system performed yesterday? Which locations are worst performers?

Traffic Engineering Where are the congestion (in time and space)? Which of these recurrent congestion? Which loop detection are not working properly? How congestion start and spread?

Traveler, Commuter What is the travel time on a route? Will I make to destination in time for a meeting? Where are the incident and events?

Planner and Research How much can information technique to reduce congestion? What is an appropriate ramp meter strategy given specific

evolution of congestion phenomenon?

Dimensions

Available• TTD : Time of Day

• TDW : Day of Week

• TMY : Month of Year• S : Station, Highway, All Stations

Others• Scale, Weather, Seasons, Event types,

…

Comparison with IWEDA

Summary of IWEDA Weather Visualizations Dimension = system of components, time slot, space User chooses a system component and a timeslot

A cell in the matrix of systems x timeslots Select a component from the system User gets a weather map

It is querying a time slice Possibilities with mapcube

Other visualizations are facilitated Changes in weather for a day for a location Changes in weather for a day for a given route …

Possibilities with Spatial Data Mining Co-location of micro phenomena with terrain types Spatial outliers or discontinuities Hotspots, e.g., tornado alley

Mapcube : Which Subset of Dimensions ?

TTDTDWS

TTDTDW TDWS STTD

TTD TDWS

TTDTDWTMYS

Next Project

Data Fusion levels and Mapcube

Different Sub-cubes help with different data fusion levels Level 0: Single Sensor

Local weather as a function of time Level 1: Correlating Multiple Sensors

Map of spatial variation in weather Space-time plot for a route for a day

Level 2: Interpret, Aggregate Detect spatial discontinuities, spatial outliers Group sensors with similar weather measurements Group timeslots with similar weather measurements

Singleton Subset : TTD

X-axis: time of day; Y-axis: Volume

For station sid 138, sid 139, sid 140, on 1/12/1997

Configuration:

Trends:

Station sid 139: rush hour all day long

Station sid 139 is an S-outlier

Singleton Subset: TDW

Configuration: X axis: Day of week; Y axis: Avg. volume.For stations 4, 8, 577Avg. volume for Jan 1997

Trends:Friday is the busiest day of weekTuesday is the second busiest day of week

Singleton Subset: S

Configuration:

X-axis: I-35W South; Y-axis: Avg. traffic volume

Avg. traffic volume for January 1997

Trends?:

High avg. traffic volume from Franklin Ave to Nicollet Ave

Two outliers: 35W/26S(sid 576) and 35W/TH55S(sid 585)

Dimension Pair: TTD-TDW

Evening rush hour broader than morning rush hour Rush hour starts early on Friday. Wednesday - narrower evening rush hour

Configuration:

Trends:

X-axis: time of date; Y-axis: day of Week f(x,y): Avg. volume over all stations for Jan 1997, except Jan 1, 1997

Dimension Pair: S-TTD

Configuration: X-axis: Time of Day Y-axis: Route f(x,y): Avg. volume over all stations for

1/15, 1997

Trends: 3-Cluster

• North section:Evening rush hour• Downtown area: All day rush

hour• South section:Morning rush hour

S-Outliers • station ranked 9th

• Time: 2:35pm Missing Data

Dimension Pair: TDW-S

Busiest segment of I-35 SW is b/w Downtown MPLS & I-62

Saturday has more traffic than Sunday Outliers – Route branch

Configuration: X-axis: stations; Y-axis: day of week

f(x,y): Avg. volume over all stations for Jan-Mar 1997

Trends:

Post Processing of cluster patterns

Clustering Based Classification:

Class 1: Stations with Morning Rush Hour

Class 2: Stations Evening Rush Hour

Class 3: Stations with Morning + Evening Rush Hour

Triplet: TTDTDWS: Compare Traffic Videos

Configuration: Traffic volume on Jan 9 (Th) and 10 (F), 1997

Trends: Evening rush hour starts earlier on Friday Congested segments: I-35W (downtown Mpls – I-62);

I-94 (Mpls – St. Paul); I-494 ( intersection I-35W)

Size 4 Subset: TTDTDWTMYS(Album)

Configuration: Outer: X-axis (month of year); Y-axis (route) Inner: X-axis (time of day); Y-axis (day of week)

Trends:

Morning rush hour: I-94 East longer than I-35 W North Evening rush hour: I-35W North longer than I-94 East Evening rush hour on I-94 East: Jan longer than Feb