(Georgia Institute of Technology, 2002)
Ruiz, Duncan Dubugras; Liu, Ling; Pu, Calton
Workflow systems have been one of the enabling technologies for automation
of business processes in corporate enterprises. Many modern production
workflows need to incorporate deadline control throughout the workflow
management system. However, the increasing volume and diversity of digital
information available online and the unpredictable amount of network delays
or server failures have led to a growing problem that conventional workflow
management systems do not have, namely how to reorganize existing workflow
activities in order to meet deadlines in the presence of unexpected delays.
We refer to this problem as the workflow-restructuring problem. This paper
describes the notation and issues of workflow restructuring, and discusses a
set of workflow activity restructuring operators. We illustrate the inherent
semantics of these restructuring operators using the Transactional Activity
Model (TAM). The paper contains two main contributions. First, we study
the environmental instabilities (e.g., resource shortages and network
delays) that cause workflows to perform sub-optimally and how workflow
restructuring can address this problem. Second, we evaluate the
effectiveness of workflow-restructuring operators through simulation. Our
initial experiments demonstrate that run-time workflow restructuring can
improve response time significantly for unstable environments.
(Georgia Institute of Technology, 2000)
Buttler, David John; Liu, Ling; Pu, Calton
This paper presents a fully automated object extraction system - Omini.A
distinct feature of Omini is the suite of algorithms and the automatically
learned information extraction rules for discovering and extracting objects
from dynamic Web pages or static Web pages that contain multiple object
instances. We evaluated the system using more than 2,000 Web pages over 40
sites. It achieves 100% precision (returns only correct objects) and
excellent recall (between 93% and 98%, with very few significant objects
left out). The object boundary identification algorithms are fast, about
0.1 second per page with a simple optimization.