STUDY AND IMPLEMENTATION OF MOVING OBJECT DATA GENERATORS FOR ROAD NETWORKS

Master’s Plan B presentationPradeep Nagalla

Adviser: Prof. Mohamed MokbelUniversity of Minnesota

Outline

Motivation Goal of Project Introduction to data generators Comparison of different data

generators Implementation of web-based data

generator Introduction to BerlinMOD Extending BerlinMOD to work with

any map Results and Contributions

2

Motivation

The evaluation of spatio-temporal databases requires the definition of suitable benchmarks.

Having a consistent way to generate scalable and representative moving object data will be very helpful.

However the current data generators have their limitations. (ease of use, type of data generated etc.)

3

Goal of Project

To study the existing data generators and develop and extend a data generator which is easy to use and generate data for any map and for any format.

Develop a wrapper around BerlinMOD and make it web-based. No need to install any software Easy to generate data for any part of the map of

Minnesota Users can visualize the generated data

4

Introduction to a Data generator

A tool for systematically generating spatial data in a representative and scalable manner.

It provides well-defined datasets for experimental evaluations.

Because database systems are used in practice, the best dataset would be real data. Then why use a generator?

5

Survey of data generators

The initial data generators dealt only with generating data in an unconstrained environment. This kind of data is not good for systems representing data in road networks.

Another issue is generating representative data. That means any good generator should try to model real-life data (traffic jams, traffic signals, children crossing road) as closely as possible. 6

Survey of data generators (cont…)

GSTD Generate sets of moving point or rectangular

data that follow an extended set of distributions. Main drawback is that this generator does not

allow for specifying a road network. Not currently available

SUMO It is a microscopic traffic simulator which

performs a time-discrete, space-continuous simulation of traffic.

Requires the user to carefully specify trip data. (starting and ending locations)

7

Survey of data generators (cont…)

Oporto This is a realistic data generator that

satisfies the demands of “smoothly” moving object applications.

The data generated in this way has unrestricted 2-D movement.

Also, we can generate spatio-temporal data for huge number of objects observed for a long period of time (trajectories).

8

Survey of data generators (cont…)

Brinkhoff generator It is a network based generator of moving

objects. Combines real data (the actual road network) with user-defined parameters and produces the data.

One of the few generators that allow for visualizing of the data and the road network.

Disadvantage is that the generator is not so flexible.

Except the parameters specified in the program if we want to do anything else, we need to go and modify the source code.

9

Survey of data generators (cont…)

BerlinMOD One of the widely used generators out there.

Overcomes the problems discussed in the previous ones.

Built on top of the Secondo DBMS. Has a wide range of features including an interface for dispalying the road networks and the data generated.

2 advantages over Brinkhoff generator Allows for both trip-based and trajectory-based

approach. BerlinMOD gives more parameters to play with.

10

Web-based data generator

Developed with the goal to generate moving object data for any map.

User can select a rectangular region in the map and generate data only in it.

User can see the animation of moving points on road network.

No special software to install. It can be used with the help of a web browser.

11

Web-based data generator (cont…)

12

Web-based data generator (cont…)

13

Web-based data generator (cont…)

14

Web-based data generator (cont…)

Developed using MapServer and OpenLayers framework.

The map data has been obtained from US Census

Disadvantages Currently, only supports the road

network of Minnesota, though it can be extended easily.

But more serious problem is the data generated is not representative. Does not follow any pattern. 15

Extending BerlinMOD generator BerlinMOD is a benchmark for

STDBMS. It is intended as a tool for comparing the performance of different STDBMS.

It consists of data generation tool and set of benchmark queries. We did not deal with the queries in my project.

The data is generated not using a dedicated data generator program, but using a script for the extensible Secondo DBMS.

16

Extending BerlinMOD generator (cont…)

Older approach New approach

17

BerlinMOD Script

Secondo DBMS

Output data

BerlinMOD Script

Secondo DBMS

Output data

Data converter

Input map, road network

Extending BerlinMOD generator (cont…)

There is also an optimizing component which we added to BerlinMOD.

The script is split up into 2 parts One is for reading in the road network

and storing it. The second script allows to generate

data for the same road network, multiple times.

18

High level view of steps involved in Data conversion layer

C

19

Extract shp files from Tiger Maps

Convert shp to secondo format

in Secondo window

Edges.zip, cousub.zip

Edit the secondo files in to

BerlinMOD format using perl

script

C

Run BerlinMOD first script

Streets.data, homeRegions.data, workRegions.data

Preprocess the data

Adjust the parameters and

run second script

Selecting desired highways and assigning maximum speed

Data is generated

Number of vehicles, number of days, output format (CSV, SHP, Database)

Extending BerlinMOD generator (cont…)

Experiments The data generated consisted of some

amount of corrupted data. This can be filtered out by using a final script.

Experiments results can be seen on next page.

20

Speed of data generation

Number of vehicles Time taken in seconds

20 91

200 629

2000 4689 (78 minutes)

21

Number of days Time taken in seconds

14 541

21 590

28 580

Below is the running time for the experiments. The data is generated for 14 days.

The data is generated for 200 vehicles.

Combining BerlinMOD & Web-based generator

21

Output data

Selected Map,

Input parameters,

Notification Email

Send Email to User,

Animate output data

WebClient

BerlinMODApacheServer

Formatted input

Combining BerlinMOD & Web-based generator (contd…)

1. User enters the parameters ( Number of vehicles, Number of days)

2. Selects the interested area in map either by mouse-click or through drop-down list.

3. Once output is generated, he will be notified by email.

4. Can also see the animated data using the generator.

23

Contributions (summary)

A survey report on different spatio-temporal data generators.

Development of a naive web-based generator.

Simplify usage of BerlinMOD generator by preparing good and extensive documentation.

Extended BerlinMOD to generate data for any map.

Developed a wrapper around BerlinMOD using the web-based generator.

24

References

1. Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)

2. Saglio, J.-M., Moreira, J.: Oporto: a realistic scenario generator for moving objects. Geoinformatica 5(1), 71–93 (2001)

3. Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: SSD, pp. 147–164 (1999)

4. Tzouramanis, T., Vassilakopoulos, M., Manolopoulos, Y.: On the generation of time-evolving regional data. Geoinformatica 6(3), 207–231 (2002). doi:10.1023/A:1019705618917. ISSN 1384-6175

5. SMARTEST. Final Report for Publication. Technical report, European Commission Transport RTD Programme of the 4th Framework Programme, 1999, Project Reference: RO-97-SC.1059. http://www.its.leeds.ac.uk/projects/smartest/finrep.PDF

6. SMARTEST Project Web Site. http://www.its.leeds.ac.uk/projects/smartest/ (1999)

7. Gidófalvi, G., Pedersen, T.B.: ST-ACTS: A spatio-temporal activity simulator. In: de By, R.A., Nittel, S., (eds.), GIS, pp. 155–162. ACM Press, New York (2006)

25

References

8. Güting, R.H., de Almeida, V.T., Ansorge, D., Behr, T., Ding, Z., Höse, T., Hoffmann, F., Spiekermann, M., Telle, U.: Secondo: An extensible DBMS platform for research prototyping and teaching. In: ICDE, pp. 1115–1116 (2005)

9. Krajzewicz, D., Hertkorn, G., Rössel, C.,Wagner, P.: SUMO(Simulation of Urban MObility): an open-source traffic simulation. In: Proceedings of the 4th Middle East Symposium on Simulation and Modelling (MESM2002), pp. 183–187. SCS European Publishing House (2002)

10. Vazirgiannis, M., Wolfson O.: A Spatiotemporal Model and Language for Moving Objects on Road Networks. In: Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, pp. 20 – 35. ACM Press, New York (2001).

11. Giannotti, F., Mazzoni,A., Puntoni, S., Renso, C.: Synthetic generation of cellular network positioning data. In: GIS ’05: Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, pp. 12–20. ACM Press, New York (2005)

12. Dungten C., Behr T., Guting R.H.: BerlinMOD: a benchmark for moving object databases. In the VLDB Journal. April 2009.

26

References

13. Wolfson, O., Chamberlain, S., Dao, S., Jiang, L., Mendez, G.: Cost and imprecision in modeling the position of moving objects. In: ICDE, pp. 588–596 (1998)

14. Wolfson, O., Sistla, A.P., Chamberlain, S., Yesha, Y.: Updating and querying databases that track mobile units. Distrib. Parallel Databases 7(3), 257–387 (1999)

15. Wolfson, O., Xu, B., Chamberlain, S., Jiang, L.: Moving objects databases: issues and solutions. In: Statistical and Scientific Database Management, pp. 111–122 (1998)

16. Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Querying the uncertain position of moving objects. Lect. Notes Comput. Sci. 1399, 310–337 (1998)

17. Wolfson, O., Cao H., Lin H., Trajcevski G., Zhang F., Rishe N.: Management of Dynamic Location Information in DOMINO. EDBT 2002:769-771

18. Wolfson, O., Chamberlain, S., Dao, S., Jiang, L., Mendez, G.: Cost and imprecision in modeling the position of moving objects. In: ICDE, pp. 588–596 (1998)

27

Thank You

28