Yi Chen, Ph.D. - US grants

This project is addressing a systemic problem in scientific research: although datasets collected through scientific protocols may be properly stored, the protocol itself is often only recorded on paper or stored electronically as the script developed to implement the protocol. Once the scientist who has implemented the protocol leaves the laboratory, this record may be lost. Collected datasets become meaningless without a description of the process used to produce them; furthermore, the experiment designed to produce the data is not reproducible.

This research is developing a database (ProtocolDB) to manage scientific protocols and the collected datasets obtained from their execution. The approach will allow scientists to query, compare and revise protocols, and express queries across protocols and data. The research is also addressing the issue of recording and querying the provenance (the why and where) of data. ProtocolDB will benefit scientists by providing a scientific portfolio for the laboratory which not only enables querying and reasoning about protocols, executions of protocols and collected datasets, but enables data sharing and collaborations between teams.

The intellectual merit of the research includes the design of a model for scientific workflows, and a query language to retrieve, transform, compare scientific workflows, integrate datasets, and reason about data provenance. This theoretical contribution will establish advances in the development of systems supporting the expression of scientific protocols. The ProtocolDB implementation will be evaluated by our scientific partners. The broader impact resulting from the project is the development of a general-purpose system for managing scientific protocols and their collected datasets. The established collaborations, involving academic, governmental, and private institutions, will contribute significantly to the breadth of its use.

Research on effectively searching scientific workflows is limited. Existing work assumes that once workflow data is stored in a database, standard query languages can be directly used to access the data. Several challenges remain to be addressed in order to provide effective access to scientific workflows. First, workflows have unique structure, and a user query should be able to specify the structure of target workflows. Second, search requirements can be diverse according to user needs. Sometimes a scientific user knows exactly what s/he is looking for, and prefers to issue a sophisticated structured query over complex workflow data in order to obtain precise search results. In other cases, scientists do not have a clear idea of what they are searching for, and/or enjoy a simple search mechanism without needing to understand complex data schemas and query languages. The work is planned to enable semantic keyword search on scientific workflows, where relevant search results
will be intelligently inferred and returned in ranked order and to design a user-friendly and expressive query language by which users can express precise and sophisticated queries to retrieve scientific workflows. Common types of searches will be used to identify the logical operators that are essential to express these searches. Various indexing and labeling schemes will be designed to speed up processing. The proposed project will benefit scientists by enabling effective and efficient access to scientific workflows in a repository, so that they can reuse existing workflow designs, compare and analyze several workflows, and design new workflows guided by existing ones. By providing effective access to a scientific workflow repository, this research facilitates data sharing and collaborations among teams. The proposed project will not only make significant contributions to fundamental techniques for managing scientific workflows, but also deliver a generic and scalable system for scientific users to search workflows, thereby benefiting scientific collaboration. There are also significant educational and training objectives.

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

The goal of this project is to provide a unique opportunity for students to present their research results, learn the cutting edge research, and interact with internationally recognized researchers from both academia and industry at the ACM SIGMOD 2012 (International Conference on Data Management). As one of the most prestigious conferences in data management research, ACM SIGMOD has contributed significantly to the advance of all aspects of data management technologies and applications since 1975. Today ACM SIGMOD is a dynamic and comprehensive program for publication, education, and interaction; and it is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results and to exchange techniques, tools, and experiences. This project provides partial support for students matriculated at U.S. institutions, especially female and minority students, to attend and present their research work at ACM SIGMOD 2012 in Scottsdale, Arizona. Besides meeting with researchers from academia and industry during regular program sessions, in SIGMOD 2012 students are able to participate in the following interactive activities as partially supported by this project: a new researcher symposium, an undergraduate research poster competition, vis-a-vis meetings with leading researchers, and a female student mentoring workshop. These opportunities will have a long lasting impact on the future career of the participants. The broader impact is to train the future generation of leaders and workforce in the critical field of data management. The project details are available via the website http://www.sigmod.org/2012/.