Welcome to the ADMT Lab website

The Advanced Data Management Technologies Laboratory at the University of Pittsburgh is co-directed by Panos K. Chrysanthis and Alexandros Labrinidis. Research projects are targeted towards network-centric data management applications (e.g., mobile data management, sensor networks, web-databases, etc) and the approach taken is user-centric: emphasis is given on Quality of Service (QoS) and Quality of Data (QoD) returned to the users, and on controlling the trade-off between QoS and QoD, in a way that is prescribed by the users.

Recent Publications: SACMAT'16MDM'16VLDBJ'16ICDE'16ICDE'16EDBT'16SIGMOD'15ExploreDB'15CIKM'15,

[News] ICDE 2017 Poster Accepted

Congratulations to Angen Zheng, for his poster: Skew-Resistant Graph Partitioning was recently accepted by ICDE2017

[News] HDMM 2017 Paper Accepted

Congratulations to Vineet Raghu and Xiaoyu Ge, for their paper: Integrated Theory- and Data-driven Dimensionality Reduction in High-throughput Gene Expression Data Analysis was recently accepted for oral presentation in the 2nd International Workshop on Health Data Management and Mining (HDMM 2017)  

[News] Prof. Panos K. Chrysanthis spoke at the Software Seminar at the University of Michigan

Prof. Panos K. Chrysanthis was a guest speaker at the Software Seminar of the University of Michigan's Computer Science Department on December 19, 2016. He discussed the issue of Data-driven Serendipity Navigation in Urban Places.

[News] Our Mobile Personal Guide (MPG) will be demonstrated in EDBT 2017

Congratulations to Xiaoyu and Samanvoy for their EDBT 2017 Demonstration: In Search for Relevant, Diverse and Crowd­screen Points of Interests  

[News] New NSF Award focusing on Big Data

Prof. Chrysanthis and Prof. Labrinidis, together with colleagues from the School of Engineering (Prof. Peyman Givi, PI) and the Math Department (Prof. William Layton) received new research funding from the National Science Foundation for their project entitled "Appraisal of Subgrid Scale Closures in Reacting Turbulence via DNS Big Data." The project will employ a range of strategies and computational tools for utilizing DNS data to appraise the performance of large eddy simulation (LES) predictions in turbulent combustion. The study will pave the way for LES to become the primary means of predictions for future design and manufacturing of combustion systems, while building a data sharing infrastructure, and providing educational and outreach programs to students at all levels. [Abstract@NSF]

[News] The CS Department awarded Xiaoyu Ge the Orrin E. and Margaret M. Taulbee Award as a runner-up for 2016

The CS Department awarded Xiaoyu Ge the Orrin E. and Margaret M. Taulbee Award as a runner-up for 2016! Congratulations Xiaoyu!

[News] Congratulations to Vineet Raghu for receiving the Graduate Student Mentor Award, NIH T32 Fellowship, and passing his comprehensive exams

Congratulations to Vineet Raghu for receiving the Dietrich School of Arts and Sciences Graduate Student Mentor Award, an NIH T32 Fellowship for 2016-2017, and passing his comprehensive exams!! Vineet is co-advised by Dr. Chrysanthis and Takis Benos, Professor of the Department of Computational and Systems Biology.