Ad hoc workflows are everywhere in service industry, scientific
research, as well as daily life, such as workflows of customer
service, trouble shooting, information search, etc. Optimizing ad
hoc workflows thus has significant benefits to the society.
Currently the execution of ad hoc workflows is based on human
decisions, where misinterpretation, inexperience, and ineffective
processing are not uncommon, leading to operation inefficiency.
The goal of this research project is to design and develop
fundamental models, concepts, and algorithms to mine and optimize ad
hoc workflows. The project includes novel research on the following
key areas: (1) Network Modeling and Structure Mining. A network model
is built that statistically captures the execution characteristics
of ad hoc workflows, and is optimized to improve the execution of
new workflows with respect to different optimization objectives.
(2) Workflow Artifact Mining. The network model built on workflow
executions is then extended with workflow artifact mining to realize
an optimization system that is able to take advantage of both
executions and text contents. (3) Role Discovery and Relation
Assessment. A computational framework is built to analyze the roles
and relationships of agents involved in ad hoc workflow executions
in order to further optimize workflows.
Advances from this project include models to represent ad hoc
workflows, algorithms for mining hidden collaborative models, and
techniques that optimize ad hoc workflow processing. The project
bridges two emerging research areas: service science and network
science, and enriches the principles and technologies of data mining.
It also enhances research infrastructure through the collaboration of
team members from different areas (data mining, database, and
network). This research is tightly integrated with education through
student mentoring and curriculum development.
Publications, software and course materials that arise
from this project will be disseminated on the project website:
|Effective start/end date||9/1/09 → 2/28/13|
- National Science Foundation: $249,817.00