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Web-service Based Human Resource Recruitment by UsingMatchmaking Decision Support
Dickson K. W. CHIUSenior Member, IEEE
Dickson Computer SystemsHong Kong
[email protected], [email protected]
Ho-fung LEUNGSenior Member, IEEE
Dept. of Computer Science & Engineering
Chinese University of Hong Kong
Gilbert H.L. LUKDept. of Computer Science & Engineering
Hong Kong University of Science & Technology
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Introduction Effective decision making often requires disparate
information from heterogeneous sources Current ineffective platform support for information
integration Case Study: Human resource recruitment
Increasing cost (esp. for professionals) => important decision to management
Different job natures => Multi-domain knowledge Different sources of applicants – intranet, websites,
agencies Different information format – application form, letter, CV
(paper and electronic) Tedious and time-consuming process Studies how the application of Web service technologies
helps
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Typical Hiring Process
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e-HR System Overview
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Dataflow of e-HR
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e-HR System Architecture
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UML Concept Model for e-HR
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Web Service Operations
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Profile Request and Response
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XML DTD for Profile
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Example Weight XML
N
j jj=1
Score = W * V
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Ranking
Profile Components Offer A Offer B Expected
Years of experiences 3 5 4
Salary 10000 13000 12000
Availability 1 0.5 1
Profile Components
Offer A Offer B Weight
Years of experiences
(3 – 4)/4 = -0.25 (5 - 4)/4 = 0.25 8
Salary (12000 – 10000)/12000 = 0.166
(12000-13000)/12=-0.083
5
Availability (1 -1) /1 = 0 (1 – 0.5) / 1 = 0.5 3
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Ranking - Result
Profile Components
A B Weighting
Years of experiences
-0.25*8 = -2
0.25*8=2
8
Salary 0.166*5= 0.833
-0.083*5= -0.415
5
Availability 0*3=0 0.5*3= 1.5 3
Total Score -1.166 3.085
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Summary Web services and agents integrates disparate
information to facilitate decision support Attempts to address real-life problems: complexity
of contracts and decision Value added service from end-users’ perspective Web Service Architecture (WSA)
provides an efficient channel to communicate among parties in the business processes
extend the value of current legacy systems less paper exchange more automated and accelerated processes
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Future Work
Further enhancement and automotion of the mortgage application workflow
Reach a state of paperless and all-in-one stop for recuitment
Enhance the current built-in raking function: pattern recognition and data mining techniques
Abstracting the experience gathered from our case study to a higher level methodology and meta-model
Application of ontologies
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Question and Answer
Thank you!
