Intelligent Project Approval Cycle for Local Government

Case-Based Reasoning Approach

M Kashif FarooqMalik Jahan Khan Shafay Shamail Mian M Awais

ICEGOV09 Partners

Intelligent workflow for multilevel project approval cycle

Application of CBR for approval of small projects in public sector

The CBR engine maintains a repository of past cases

On a new project approval request, the proposed inference system matches similar historical cases

Suggests a solution for the new project

Abstract

CCB Citizen Community Board –

Small CSO Civil Service Organization at local level

CCB proposes small local level projects related to social development or public service delivery

Projects may be small schools, health units, drinking water units, Roads and streets parks, vocational training centers, advocacy movements for society (public awareness)

Application Domain

Social Welfare Department (SWD) of local government receives these project proposals from local CCB

CCB has to fund raising up to 20% of total project cost to show public interest

If SWD approve the project, then SWD grants 80% amount of total cost of project

Application Domain

To provide support and automate the technical evaluation of the project by using CBR (Case Based Reasoning)

Scope of the Paper

Evaluation parameters may be grouped into the two clusters:

Class A: Objective Parameters

Can be evaluated by formula, rule or principle

Class B: Subjective Parameters

Can be assessed by experience

Evaluation Parameters

Nature of Project

Budget

Profile of CCB or CSO

Experience of CCB or CSO

Cost of Service

Class A: Objective Parameters

Need of Project

Socially Viable

Socio-economics

Political Support

Sustainability

Quality of Service

Class B: Subjective Parameters

CBR Based Proposed Approach for Project Appoval

Collection of n parameters as ith case of the case-base in the form of a vector as given in equation:

C represents a case and each parameter Pij represents defined parameters from the project approval dataset.

A case base CB containing m cases may be represented as given in equation:

Case Preparation

j

iniiiji PPPPC )..,....,( 21

k

mk CCCCCB )....,,( 21

There are many case retrieval methods to match the current case and a case in the case-base.

Some well known methods are Manhattan distance, Euclidean distance, Mahalanobis distance, Geometric similarity measures, and Probabilistic similarity measures

Case Retrieval and Reuse

Our empirical study suggests that Manhattan distance is the most suitable similarity measure for the domain of project approval cycle as our selected parameters to represent the relevant cases are of numeric nature.

It is used to retrieve matching cases from the case-base. It calculates the weighted sum of absolute differences

between the current case and any other case in the case-base.

This weight is set by the user or analyst. It is given by as

Where dij means distance between ith and jth cases with respect to all parameters

W represents weight. x is the current case while c is the historical case from CB

Manhattan or City distance

k

jkikkij cxWd

In first phase we implemented it on the projects related to the development of small health care units.

Experts finalized few critical parameters for the approval of projects.

We studied 50 cases and created a case library

Implementation

Table 1. Sample Case Data

# Parameters Health Care Projects

P1 P2 P3 P4

1 Need of Project (NP) 2.5 3 1.5 2.75

i Existing facility 3 2 0 2

ii Available Alternative 2 3 2 4

iii Capacity of existing and alternatives facility 2 4 3 3

iv Quality of existing and alternative service 3 3 1 2

2 Socially Viable (SV) 2.25 2.75

2.5 2.75

i Cultural conflict 2 3 2 2

ii Religious conflict 2 2 3 3

iii Awareness and literacy 3 2 3 4

iv Negative believes 2 4 2 2

3 Socio-Economics (SE) 3.3 3 4 3.6

i Affordability 3 3 5 4

ii Average income per person 4 3 4 4

iii Available low cost alternative 3 3 3 3

4 Technically Viable (TV) 3.25

3 3.5 3

i Availability of trained staff 3 3 2 3

ii Sustainability of trained staff 4 3 4 3

iii Availability of utilities (energy, supplies, communication, etc.)

3 2 4 2

iv Technical support for equipment 3 4 4 4

5 Political Support (PS) 2 3 3.3 3

i Political ownership 3 3 4 2

ii Political stability 2 4 3 4

iii Political conflicts 1 2 3 3

6 Sustainability (S) 3 3.6 4 3

i Financial sustainability 3 4 3 3

ii Legal sustainability 2 3 4 3

iii Institutionalization 4 4 5 3

7 Quality of Service (QS) 3 3.3 3 3

i Customer or citizen satisfaction 2 4 3 3

ii By social audit 4 4 3 2

iii By media trial 3 2 3 4

Seven macro parameters have been used to define project evaluation

One parameter Predicted Probability to predict the matching solution

Ci = (NP, SV, SE, TV, PS, S, QS, PP)

PS: Political Support S: Sustainability QS: Quality of Service PP: Predicted Probability

of project acceptance

RESULTS

NP: Need of Project SV: Socially Viable SE: Socio-

Economics TV: Technically

Viable

Leave-One-Out (LOO) has been applied as the cross validation method on a dataset of 50 projects

All of the data items were labeled, so it was supervised learning process

We used solution of one nearest neighbor for reuse phase

We adapted simplest revision mechanism which is suggested to pick the second nearest neighbor if the first one does not fit in

RESULTS

Metric Result

Accuracy 90%

AAE 0.011

RMSE 0.0376

RESULTS

We used three evaluation metrics to validate our results

50 iterations ? computed accuracy, root mean squared error

(RMSE) and average absolute error (AAE)

Our proposed approach is expected to provide benefits such as

quick and efficient decision making process with impartial, high quality and informed decisions

Current work involved pre-processing of data and did not deal with ambiguous input parameters, it would be very useful to deal with ambiguity and vagueness of the real data in future work

CONCLUSION AND FUTURE WORK