Graduate Information Science and Technology Program

    School of Information Sciences

    University of Pittsburgh

Network-Aware Data Management Group








Hi-DFusion: Historical Data Fusion

Data Integration Systems provide users with uniform data access and efficient information sharing.  The ability to share information is particularly important for interdisciplinary research, where a comprehensive picture of the subject requires large amounts of historical data from disparate data sources from a variety of disciplines. For example, epidemiological data analysis often relies upon knowledge of population dynamics, climate change, migration of biological species, drug development, etc.  As another example, consider the task of exploring long-term and short-term social changes which requires consolidation of a comprehensive set of data on social-scientific, health, and environmental dynamics.  In this project, we address the challenges in developing a global integrated repository of historical data to support a wide range of interdisciplinary research.


Co-Adapt: Complex Adaptive Information Systems

Complex Adaptive System (CAS) is a large-scale and highly distributed environment, which can tune itself via simple rule-based interactions between its components. One of the most intriguing features of CAS is the ability of such simple interactions to form complex and “rational” system behavior. CASs manifested themselves in various disciplines ranging from life sciences  (e.g., organizational behavior of ants and patterns of neuronal activation) to social sciences and economics (e.g., formation of social networks and market regulations).  In this project we explore application of the CAS concepts in the context of information science and technology. Rapid evolution of Web and networked information systems strongly stimulates this research. Meanwhile, building and deploying industrial-strength Complex Adaptive Information Systems (CAIS) require more interdisciplinary research efforts.


DIP: Data-Intensive Process Monitoring

In this project we explore novel technologies for efficient summarization and sensemaking based on dynamic data from complex processes. This research is motivated by emerging advanced infrastructures that facilitate rapid operational data collection (e.g., bedside medical devices, energy monitoring hardware, data acquisition products based on wireless sensor networks, etc.).


Previous Projects


SCriM Project

Wireless sensor networks naturally apply to a wide range of system monitoring tasks. Meanwhile, there are obvious performance deficiencies in applying existing sensornets in mission-critical monitoring applications, where a system failure is often catastrophic. In this project we explore a database-driven approach to optimize monitoring queries in wireless sensor networks. We develop novel cross-layer optimization techniques that utilize information about how the lower network layers operate while processing the queries in critical sensor environments. Our framework enables both qualitative analysis and quantitative cost-based optimization of sensor queries.


Nebula Project

Wide Area Applications utilize a Wide-Area-Network (WAN) infrastructure e.g., the Internet, to connect a federation of hundreds of servers servicing tens of thousands of clients. The use of a public network for data delivery causes deterioration of service reliability (e.g., in the form of broken links) and performance (e.g., increased delivery time due to points of congestion in the network). Therefore, mediators over wide area applications need to provide, in addition to traditional services such as data integration, services that aim at improved reliability and reduced latency. In this project we aim at constructing profiles, information regarding servers, network, and client performance. With such profiles at hand, mediators can optimize query performance. Also, such information improves Web caching utilization, thus reducing retrieval latency, a common deficiency in contemporary wide area applications.