III-Core-Small: Collaborative Research: Mining and Optimizing Ad Hoc Workflows

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:

URL: http://www.cs.ucsb.edu/~xyan/smartflow.htm