Crowdsourcing Spatial Relationship Expressions
INITITATIVE: DATALAB @ COS, GMU
DataScience, Steering Committee
Duration: from 2016
DataLab is an initiative of the College of Science at George Mason University conducting multidisciplinary research and developing solutions on large-scale Data Analytics. Our faculty also provides education and training in Data Science and Data Analytics.
DataLab engages researchers across George Mason working on Computational and Data Sciences, as well on data-intensive application areas, such as Medical and Life Sciences, Computational Social Sciences, Earth Systems, GeoIntelligence and Transportation, and Evidence-based Policy.
We aim to partner with Academia, Industry, and Government in a collaborative, diverse, cross-disciplinary environment, to seek and try new ideas for innovation, education, and best practices.
PROJECT (MDR - GMU INTERNAL)
Data Science Tools for Discovering Indicators for Interactions in Finance and Government Sector
Interdisciplinary project with the School of Public Policy (GMU)
– Identifying keyplayers (influencers)
– Study their behavior in the finance and government sector
– Fusing diverse sources of facts (People, Roles in gov., Activities in financial sector)
– Heterogeneous Information Network Analysis
– Retrieving rich relationship patterns between diverse entities
– Visualizing relationship network to explore potential keyplayers by domain experts
Text Mining on Litigation Documents
Interdisciplinary project with an consultant company that works with law firms
– litigation document categorization
– cost-effective, reasonable and accurate
– lawyers in the document analysis loop
Data Science for Health Analytics
This project (PI Nektaria Tryphona) is conducted by a PhD Student who works in a data analytic team at the INOVA Fairfax Hospital
Efficient query processing and mining in Heterogeneous Information Networks
This industrial project is in collaboration with the Siemens AG (Siemens CT) in Munich.
High throughput spatial data stream management and analysis
This project is in the context of a collaboration with BMW-CarIT in Munich
ProQUeST: Probabilistic Query Processing in Uncertain Spatio-Temporal Data
If you are interested in working in one of these active projects, send me your interest (see contact below)
List of current and past projects
WELCOME TO THE WEBSITE OF
PROF. DR. MATTHIAS RENZ
Professor, Computer Science
Head of Archaeoinformatics - Data Science Group, CAU Kiel
Affiliate Faculty, Computational and Data Science, GMU, U.S.A.
Prof. Dr. Matthias Renz (Dr. rer. nat., habil)
Dr. Renz is Professor at the Department of Computer Science at the Christian-Albrechts-Universität zu Kiel (CAU, University of Kiel). Before he joined CAU in Summer 2018, Dr. Renz was Associate Professor in the Computational and Data Science Department at the George Mason University (GMU), Fairfax, VA, USA. He received his Ph.D. in Computer Science at the Ludwig-Maximilians University (LMU) Munich 2006, where he served as lecturer after finishing his habilitation (venia legendi) 2011. Before Dr. Renz moved to GMU 2016, he was acting chair of the database systems group at the LMU. Dr. Renz was co-founder and co-director of the Data Science Lab at LMU Munich which has been founded in cooperation with Siemens AG. Dr. Renz also co-founded and co-directed the Mason’s DataLab at the College of Science at the George Mason University, where he headed the DataLab's DataScience division.
Dr. Renz’s main research interest is Data Science with focus on scalable methods for searching and mining in very large, heterogeneous, dynamic and potentially uncertain data. He has published more than 120 papers on peer-reviewed international conferences and in international journals. His work has received considerable attention by the corresponding community with more than 2500 citations achieving an H-index of 23 and an i10-index of 51. He gave several invited tutorials, seminars, and keynotes on international conferences and in 2016, his work received the 10-Year Best Paper Award at the International Conference on Database Systems for Advanced Applications. His work has been supported by governmental research grants and industrial funding including Siemens AG, Volkswagen Group, Audi AG, and BMW AG. He has been a program committee member for several international conferences and workshops, including SIGMOD, VLDB, ICDE, and KDD. Furthermore, Dr. Renz co-organized and chaired several international conferences and workshops, including SSTD, ACM SIGSPATIAL, DASFAA, ACM SIGSPATIAL QUeST, ACM SIGMOD GeoRich, and ACM SIGSPATIAL LocalRec.
Areas and Statement
My research interests mainly address Data Science, Data Mining and Management and Knowledge Discovery with a focus on scalable methods for searching and mining in very large, heterogeneous, dynamic and potentially uncertain data=, as well as Spatial and Spatiotemporal Databases.
In my early studies, I have addressed spatial databases and shifted later toward spatiotemporal databases with a concentration on moving object management and query processing in traffic network data. One of my main research interest has been on managing and mining uncertain data with a focus on probabilistic query processing and mining in spatial and spatiotemporal data.
My current research activities are going toward data science and are highly interdisciplinary addressing areas such as Engineering, Medicine (Health-Analytics), Transportation (Smart Transportation), Urban Analytics, and areas of political, legal, and social sciences. In addition to my continuing work on concepts for management and analysis of user-generated spatial and spatiotemporal data and data with spatial context information, I have recently started studies on Heterogeneous Information network (HIN) analysis. For example, in an interdisciplinary collaboration with the team from the Schar School of Policy and Government, we study methods for identifying patterns of interaction of key players in the finance and government sector.
EFFCIENT INFORMATION FLOW MAXIMIZATION
IN PROBABILISTIC GRAPHS.
IEEE TKDE 2017
Frey C., Z¨ufle A., Emrich T., Renz M.: E cient Information Flow Maximization
in Probabilistic Graphs. IEEE Transactions on Knowledge and
Data Engineering (TKDE), 2017.
PATTERN SEARCH IN TEMPORAL
Franzke M., Emrich T., Z¨ufle A., Renz M.: Pattern Search in Temporal
Social Networks. In proc. of the 21st International Conference on
Extending Database Technology (EDBT 2018), Vienna, Austria, 2018.
SCENIC ROUTES NOW: EFFICIENTLY SOLVING THE TIME-DEPENDENT ARC
Lu Y., Josse G., Emrich T., Demiryurek U., Renz M., Shahabi C., Schubert
M.: Scenic Routes Now: E ciently Solving the Time-Dependent Arc
Orienteering Problem. To appear in Proc. of the 26th ACM Conference
on Information and Knowledge Management (CIKM 2017), Singapore,
KNOWLEDGE EXTRACTION FROM CROWDSOURCED DATA FOR THE ENRICHMENT OF ROAD NETWORKS
MY CURRENT TEAM
Research Area: Involved in all Data Science projects, Special focus on Heterogeneous Information Network Analysis
PhD student (GMU)
Research Area: Data Science Tools for Discovering Indicators for Interactions in Finance and Government Sector
LIU, XINYUE (JERRY)
PhD Student (GMU, INOVA)
Research Area: Big data related genomic/personalized medicine research
PhD Student (LMU)
Research Area: Efficient query processing and mining in Heterogeneous Information Networks
PhD Student (LMU)
Research Area: Querying and Mining Heterogeneous Spatial, Social, and Temporal Data
PhD Student (BMW-CarIT, LMU)
Research Area: High throughput data stream management and analysis
PhD Student (GMU)
Research Area: Cross-domain urban knowledge discovery and fusion based on user generated contents
PhD Student (GMU)
Research Area: Machine learning for analysis on large time sequence data
This is the status of my current Team. Last update Feb 22, 2018
FORMER MEMBERS OF MY TEAM
Assistant Professor at GMU
Former Postdoc in my Team
KLAUS ARTHUR SCHMID
(Ph.D.) Siemens CT
Former PhD student, finished his PhD in 2016