Demo Data from EDBT 2026 / reviewers in the wild / expert
Paula Elaine Stretz
dblp:41/8421
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › epidemiology
computational epidemiology |
0.2 | 1 | 2014 | ISIS: a networked-epidemiology based pervasive web app for infectious disease pandemic planning and response · KDD 2014 |
High-performance computing
scientific computing systems |
0.1 | 1 | 2014 | ISIS: a networked-epidemiology based pervasive web app for infectious disease pandemic planning and response · KDD 2014 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | ISIS: a networked-epidemiology based pervasive web app for infectious disease pandemic planning and responseabstractWe describe ISIS, a high-performance-computing-based application to support computational epidemiology of infectious diseases. ISIS has been developed over the last seven years in close coordination with public health and policy experts. It has been used in a number of important federal planning and response exercises. ISIS grew out of years of experience in developing and using HPC-oriented models of complex socially coupled systems. This identified the guiding principle that complex models will be used by domain experts only if they can do realistic analysis without becoming computing experts. Richard J. Beckman, Keith R. Bisset, Jiangzhuo Chen, Bryan L. Lewis, Madhav V. Marathe, Paula Elaine Stretz |
KDD | 6 |
| 2013 | Simfrastructure: A Flexible and Adaptable Middleware Platform for Modeling and Analysis of Socially Coupled SystemsabstractSocially coupled systems are comprised of interdependent social, organizational, economic, infrastructure and physical networks. Today's urban regions serve as an excellent example of such systems. People and institutions confront the implications of the increasing scale of information becoming available due to a combination of advances in pervasive computing, data acquisition systems as well as high performance computing. Integrated modeling and decision making environments are necessary to support planning, analysis and counter factual experiments to study these complex systems. Here, we describe SIMFRASTRUCTURE - a flexible coordination middleware that supports high performance computing oriented decision and analytics environments to study socially coupled systems. Simfrastructure provides a multiplexing mechanism by which simple and intuitive user-interfaces can be plugged in as front-end systems, and high-end computing resources can be plugged in as back-end systems for execution. This makes the computational complexity of the simulations completely transparent to the users. The decoupling of user interfaces and data repository from simulation execution allows users to access simulation results asynchronously and enables them to add new datasets and simulation models dynamically. Simfrastructure enables implementation of a simple yet powerful modeling environment with built-in analytics-as-a service platform, which provides seamless access to high end computational resources, through an intuitive interface for studying socially coupled systems. We illustrate the applicability of Simfrastructure in the context of an integrated modeling environment to study public health epidemiology. Keith R. Bisset, Suruchi Deodhar, Hemanth Makkapati, Madhav V. Marathe, Paula Elaine Stretz, Christopher L. Barrett |
CCGRID | 5 |