A Fair Assignment Algorithm for Multiple Preference Queries

Consider an internship assignment system, where at the end of each academic year, interested university students search and apply for available positions, based on their preferences (e.g., nature of the job, salary, office location, etc). In a variety of facility, task or position assignment context...

Full description

Saved in:
Bibliographic Details
Main Authors: U, Leong Hou, Mamoulis, Nikos, Mouratidis, Kyriakos
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2009
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/873
https://ink.library.smu.edu.sg/context/sis_research/article/1872/viewcontent/VLDB09_20__20Preference_20Assignment.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary:Consider an internship assignment system, where at the end of each academic year, interested university students search and apply for available positions, based on their preferences (e.g., nature of the job, salary, office location, etc). In a variety of facility, task or position assignment contexts, users have personal preferences expressed by different weights on the attributes of the searched objects. Although individual preference queries can be evaluated by selecting the object in the database with the highest aggregate score, in the case of multiple simultaneous requests, a single object cannot be assigned to more than one users. The challenge is to compute a fair 1-1 matching between the queries and the objects. We model this as a stable-marriage problem and propose an efficient method for its processing. Our algorithm iteratively finds stable query-object pairs and removes them from the problem. At its core lies a novel skyline maintenance technique, which we prove to be I/O optimal. We conduct an extensive experimental evaluation using real and synthetic data, which demonstrates that our approach outperforms adaptations of previous methods by several orders of magnitude.