Research Article
Social Network Supported Process Recommender System
Yanming Ye,
1,2
Jianwei Yin,
1
and Yueshen Xu
1
1
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
2
College of Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Correspondence should be addressed to Yanming Ye; yanmingye
com@sina.com
Received October ; Accepted November ; Published January
Academic Editors: W. Sun and J. Zhou
Copyright © Yanming Ye et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Process recommendation technologies have gained more and more attention in the eld of intelligent business process modeling
to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take
the social features of processes into account, while the process modeling is complex and comprehensive in most situations. is
paper studies the feasibility of social network research technologies on process recommendation and builds a social network system
of processes based on the features similarities. en, three process matching degree measurements are presented and the system
implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.
1. Introduction
Workow technology has gained further application and
development with the rapid growth of modern business
environment. And process models are being widely used in
the development of organizational structures [], informa-
tion systems [], service-oriented architectures [], and web
services []. However, business process modeling is either
complex or time-consuming, which oen involves in select-
ing concrete activities to be performed, determining their
execution order, dealing with the exceptions that may occur,
and so forth. Besides, in modern commerce, both frequent
changes of custom demands and the specialization of the
business process necessitate the ability of modeling business
processes in an eective and ecient way for enterprises.
us, many business intelligence (BI) based techniques have
been adopted to improve the business process modeling
work, such as process mining and process retrieval [–].
Both process mining and process retrieval are complex or
need more manual works. For improving the eciency, some
process modeling technologies so-called business process
recommendation are proposed recently. However, most of
the existing process recommendation technologies are only
based on the static structure analysis of the process and
the other properties of a process, such as the performer
behaviors and the editor intensions, are not considered. In
practice, when creating a new process, a modeler is inclined
to refer to the modeling conduct of the familiar users or the
processes with the same modeling intension or the process
fragments that are used more frequently by certain users.
is paper presents a social network supported method that
can recommend the processes or process parts using the
social features of process modeling, such as intension, activity
performers, usage frequency, and modeling history.
is paper is organized as follows. Aer this introduction
and the related works in Section , Section gives some
denitions with related basic instructions, and at the end
of this section how to calculate process matching degree is
highlighted. In Section , we discuss the implementation of
the social network based process recommender system. In
Section , some experiments on the system are discussed.
Finally, Section is devoted to research perspective.
2. Related Work
A process model is oen in form of some graphical notations
and describes how a certain process is composed out of
dierent tasks, in which resources are involved in carrying
out these tasks and objects are manipulated [, ]. erefore,
most of the existing process mining methods recur to graph
minings, especially graphic structure mining. As for the
Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 349065, 8 pages
http://dx.doi.org/10.1155/2014/349065