ARES

Association Rules Extractor from Spatial data

 

A short description  
Architecture of the system  
Experiment: “Highway caracterization (Stockport)”   
Experiment: “Stepping Hill hospital”   
The distribution package
The Project team
Related publications
Acknowledgments

 

LACAM @ Dipartimento di Informatica - Università degli Studi di Bari - Via Orabona, 4 -70126 Bari

A short description

ARES is a spatial association rule discovery system.

Theproblem solved by ARES can be formally stated asfollows:

Given

Find strong multi-level spatial association rules.

Reference objects are the main subject of the description, while task-relevant objects are spatial objects that are relevant for the task in hand and are spatially related to the former. For instance, we may be interested in lookingfor spatial association rules that relate properties of some large towns (reference objects) with properties of other spatial objects, such as waterbodies and motorways (task-relevant objects). Each Rk is typically a map layer and spatial hierarchies capture is-a relations among locations on the basis of their geometry. To deal with several spatial hierarchies at once in a uniform manner, objects in them are mapped to one or more of the M user-defined description granularity levels so that frequency of patterns as well as strength of rules depend on the level lof granularity with which patterns/rules describe data. More precisely, a pattern P (s%) at level l is frequent if s>= minsup [l] and all ancestors of P with respectto Hk are frequent at their corresponding levels. An association rule Q -> R (s%, c%) at level l is strong if the pattern Q --> R (s%) is frequent and c>= minconf[l].

 

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Architecture of the system

ARES' architecture is three-tiered:

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Experiment: “Highway caracterization (Stockport)”

The problem is to caracterize the highway Stockport's ED that are crossedby the highway.
There are 3 experiments, with different types of inputs:

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Experiment: “Stepping Hill hospital”

The problem is to determine the accessibility of Stepping Hill's hospital.

Input File for training
Output File of training

See the following reference for further details on this problem:

A. Appice, M. Ceci, A. Lanza, F.A. Lisi, & D. Malerba (2003). Discovery of Spatial Association Rules in Georeferenced Census Data: A Relational Mining Approach, Intelligent Data Analysis, 7,(in press).  
D. Malerba, F.A. Lisi, A. Appice & F. Sblendorio (2003). Mining Census and Geographic Data in Urban Planning Environments. In L. Santini& D. Zotta (Eds.), Terza Conferenza Nazionale su Informatica e Pianificazione Urbana e Territoriale, Alinea Editrice, Florence, Italy.

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The distribution package

There are five distribution packages:

First of all, download “Featex” zipfile: it contains three files:

  1. Open Oracle DBA Studio;

  2. Connect to a database (e.g., the database of Stockport Maps);

  3. Open Schema;

  4. Open Package;

  5. Create new empty package and name it FEATEX;

  6. Copy Featex.txt into FEATEX;

  7. Open Package Body;

  8. Create new empty package body and name it FEATEX;

  9. Copy Featex_body.txt in FEATEX package body;

  10. Open SQL Plus;

  11. Connect to the database (e.g., Stockport Maps);

  12. Execute line command:

  13. @ <directory>\ Create_Table.sql
    to create the tables necessary to execute the RELATE function;

  14. Execute commit command;

  15. Add the parameter: 

    UTL_FILE_DIR = <dir_name>
    to the 
    init.ora file in order to specify a directory where the file output by the GENERATE function is stored. The jolly character “*” is used for a generic directory.

  16. Restart the Oracle Server to make changes operational.

 

 

Download “Spada EXE” and “ARES Server” on the server machine, then unzip “Spada EXE”, putting the content ofthe zip file into “ C:\SPADA” folder on the server machine (this path is hard-coded). Then, it is possible to launch:

java -jar AresServer.jar [port-number]

on the server machine. Notice: the server machine must have a Windows [95/98/ME/NT/2000/XP] as OS.

Download “Autoclass DLL” on the client machine and copy the DLL into the SYSTEM folder of your Windows system (\WINDOWS\SYSTEM for Win95,98,ME \WINNT\SYSTEM32 for WinNT,2000, \WINDOWS\SYSTEM32 for WinXP).
Note. If you have a Linux machine as a client, do not use Autoclass as clustering algorithm. You can use only EM as clustering algorithm.

Finally, download “ARES Client” on the client machine, and launch:

java -jar AresClient.jar

The client will ask you for connection parameters (dependent from database); a click on “RUN ARES” will make the application start.

Warning: The system ARES is free for evaluation, research and teaching purposes, but not for commercial purposes. 
Please Acknowledge

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Project team

Prof. Donato Malerba

Dr. Annalisa Appice, Dr. Michelangelo Ceci, Prof. Floriana Esposito, Prof. Antonietta Lanza, Dr. Francesca A. Lisi, Dr. Francesco Sblendorio

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Related publications

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Acknowledgments

Modules of ARES have been implemented within the context of the following projects:

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Send all requests/comments to: Donato Malerba. 

Last update: July 9th, 2003