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.
|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.http://www.nsf.gov/awardsearch/showAward?AWD_ID=1609120|
[News] The CS Department awarded Xiaoyu Ge the Orrin E. and Margaret M. Taulbee Award as a runner-up for 2016
[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!!
|Congratulations to Cory Thoma for passing his proposal defense: Towards Scalable, Cloud-Based, Confidential Data Stream Processing!|
|Back from the internships, three PhD students from our group: Nick, Angen, and Anatoli successfully passed their comprehensive exams.|
[News] Prof. Panos K. Chrysanthis was a keynote speaker at the 2016 Joint Conference on Medical Informatics (JCMIT)
|Prof. Panos K. Chrysanthis was a keynote speaker at the 2016 Joint Conference on Medical Informatics (JCMIT) in Taiwan on June 11, 2016 and briefly spoke at the graduation ceremony of the Graduate Institute of Biomedical Informatics at the Taiwan Medical University on June 13, 2016.|
[News] Prof. Panos K. Chrysanthis has been selected as the Computer Science's Honorary Visiting Professor at the University of Diaspora initiative of the University of Cyprus.
|The initiative invites distinguished Professors/Scientists from abroad to carry out research at the leading University of Cyprus. As part of this initiative, Prof. Chrysanthis gave a talk titled "Recommending Interesting Visualizations for Data Exploration" on June 1, 2016 and delivered a tutorial on "Graph Partitioning in Distributed Graph Computation" on June 2, 2016.|
[News] The group goes to Internships: three summer internships to HP Labs, HPE Vertica and MS Research
HPE Labs Intern: Angen Zheng|
HPE Vertica Intern: Anatoli Shein
MS Research Intern: Nick R. Katsipoulakis
|Congratulations to Xiaoyu for his MDM2016 paper: MPG: Not so Random Exploration of a City!|
Abstract: The proliferation of mobile, ubiquitous and spatial computing has led to a number of services aiming into facilitate the exploration of a city. Platforms such as Foursquare and Yelp curate information about establishments in an area that can then be used for recommendation purposes. Traditionally an approach followed by these systems is to rank places based on their popularity, proximity or any other feature that represents the quality of the venue and then return the top-k of them. However, this approach, while simple and intuitive, is not necessarily providing a diverse set of recommendations, since similar venues typically are ranked closely. Therefore, in this paper we design and introduce MPG (which stands for Mobile Personal Guide), a mobile service that provides a set of diverse venue recommendations better aligned with user preferences. MPG takes into consideration the user preferences (e.g., distance willing to cover, types of venues interested in exploring, etc.), the popularity of the establishments, as well as their distance from the current location of the user by combining them in a single composite score. We evaluate our approach using a large- scale dataset of approximately 14 million venues collected from Foursquare. Our results indicate that MPG can increase coverage of the result set compared to the baselines considered. It further achieves a significantly better Relevancy-Diversity trade-off ratio.