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www.rclit.com

Activity Sequence Prediction

for Better Scheduling in BPM

Activity Sequence Prediction

for Better Scheduling in BPM

Project 0-0

Presenter : Amna Shifia Nisafani

Date : 2011. 7. 15

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In this project, we demonstrate how to predict the activity sequencein BPM.

In classical scheduling :

all resources and all activities are given.

There is no uncertainty in the behavior of resources and activities.

However, in BPM scheduling : Information about resources and activities are not always provided

There is uncertainty in the behavior of the resources and activities.

Therefore, it is necessary to predict the incoming activities that

highly to be executed in order to improve BPM scheduling

performance.

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This study proposes a modeling approach to predict highly incurred

activities for better scheduling in BPM.

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A sequence is the order or path of activities to be performed in a process.

Activity Execution Sequence (AES)

an ordered list of activities that are likely to be executed, and changes

dynamically while a process is being carried out.

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Markov Chain is a model that generate sequences in which the probability of symbol depends only on the previous symbol

In a Markov Chain model, the model is defined by

a set ofstates, Q, which emit symbols, and

a set oftransitions between states

Each transition has an associated probability,pkl, which represents the conditional probability of going to activity lin the next activity, given that the

current activity is k.

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The transition probabilities can also be written as a transition matri

x, P={pkl}. The value ofpklcan be obtained aspkl= Pr(al|ak).

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Example of sequence prediction

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We apply a simple BP. We use Java to develop simulation environment.

We have considered some simple dispatching rules such as First In First Out(FIFO), Shortest Processing Time (SPT), Earliest Due Date (EDD) andOperation Due Date (ODD) in order to capture the scheduling process in a

system. Mean flow time : FIFO vs. SPT Mean lateness : EDD vs. ODD Without sequence prediction: FIFO and EDD With sequence : SPT and ODD

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0

10

20

30

40

50

60

70

Mean Flow Time

w/o uncertainty reduction

w/ uncertainty reduction

Replication #

(Thousand)w/o sequence prediction

w sequence prediction

330

350

370

390

410

430

450

1 2 3 4 5 6 7 8 9 10

Mean Lateness

w/o uncertainty reduction

w/ uncertainty reduction

Replication #

w/o sequence prediction

w sequence prediction

Comparison of Scheduling with and without sequence prediction

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In this study, we propose a method to forecast the occurrence of activit

ies in BPM.

We demonstrate out method in two scheduling environment : with an

d without activity sequence prediction.

It is evident that the better forecast lead the better scheduling performance.

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Some research gaps remain to be filled, for instance, the means by which the accuracy of our sequence prediction algorithm

, which has a direct influence on the process completion time

, can be increased.

Other pending research includes the study of approaches for

dealing with large data logs that can diminish the performance of the mining processes.

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THANK YOU