جست‌وجوی استاد و پژوهشگر

Cindy Chenمشاهده پروفایل

دانشیار

• Spatio-temporal Databases
• Data Mining
• Cloud Computing
• +۴ مورد دیگر

Cindy Chen serves as Department Chair and Associate Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell. Her academic journey includes a Ph.D. in Computer Science with specialization in Database and Knowledge Base Management from UCLA (2001), an MS in Computer Science from UCLA, and a BS in Space Physics from Peking University. Ph.D.: Computer Science (Database and Knowledge Base Management), University of California at Los Angeles (2001) MS: Computer Science, University of California at Los Angeles BS: Space Physics, Peking University - Beijing, China Her research spans spatio-temporal databases, data mining, cloud computing, and social network analysis. Early work focused on spatio-temporal data models and query languages , evolving into cloud data warehouse optimization and social network mining (particularly online dating behavior), then advancing to high-dimensional indexing , knowledge graph querying , and recent innovations in temporal database granulation using Allan Variance techniques for CAV friction modeling. Current work integrates database systems with autonomous vehicle infrastructure and real-time urban analytics. Her publication timeline reveals a transition from foundational spatio-temporal database systems (1998-2008) to cloud/social applications (2008-2015), then to granulation techniques and CAV systems (2015-present). Key domains include XML databases, skyline queries, social trend discovery, and friction data modeling for autonomous vehicles. Dean's Fellowship (1998) Dean's Fellowship (1999) Dr. Chen has secured significant research funding including the UMASS Transportation Center grant for an Interactive TMC Decision Support Tool (2008) and a UML Healey Grant Award for Spatio-Temporal Knowledge Discovery in Nuclear Physics (2008). Her collaborative projects span transportation systems, nuclear physics applications, and autonomous vehicle infrastructure. She actively advises on database curriculum development and serves as department chair overseeing academic operations.

Peter Bunemanمشاهده پروفایل

استاد

• Database Systems
• Data Provenance
• Semistructured Data
• +۹ مورد دیگر

Peter Buneman is a Professor in the School of Informatics at the University of Edinburgh, affiliated with the Laboratory for Foundations of Computer Science (LFCS). He is a leading researcher in database systems, with a focus on data provenance, semistructured data, XML, and digital curation. He has been instrumental in advancing the theoretical foundations of database query languages and data integration. His research interests include database theory, data modeling, type systems, and the management of scientific data. He has made significant contributions to the understanding of data provenance, XML constraints, and the archiving of scientific datasets. His work bridges theoretical computer science with practical applications in bioinformatics and digital libraries. The recent publications highlight a consistent focus on data provenance, XML query processing, integrity constraints, and database archiving. His work spans both theoretical investigations and practical system design, often addressing challenges in data integration, curation, and long-term preservation. Peter Buneman has been actively involved in academic service, including editing proceedings for major conferences such as PODS and ICDT, and contributing to journals like the Journal of Digital Libraries. His leadership in the database community is evident through his editorial roles and collaborations. He advises and collaborates with researchers in database systems and related fields. His work has been supported by significant research initiatives, including involvement with the Digital Curation Centre, which underscores his commitment to sustainable data management practices. He is a key member of the Database Group at LFCS and has contributed to shaping research directions in data management. His past affiliation with the University of Pennsylvania’s Department of Computer and Information Science further reflects his long-standing impact on the field.

Soon M. Chungمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Text Mining
• +۶ مورد دیگر

Soon M. Chung is a tenured Professor at the Department of Computer Science and Engineering , Wright State University , with research expertise spanning Database Systems , Data Mining , Text Mining , and Parallel/Distributed Processing . He has taught courses like Advanced Database Systems, Computer Architecture, and Distributed Computing. Education Ph.D. in Computer Engineering, 1990 (Syracuse University) M.S. in Electrical Engineering, 1981 (KAIST) B.S. in Electronic Engineering, 1979 (Seoul National University) Research Interests Dr. Chung's work focuses on: Time-series anomaly detection using multi-model verification 3D point cloud analysis for action recognition Semantic/privacy-preserving access control in grid systems Parallel data mining algorithms for text/transaction databases Geotagged text data visualization for crisis management Scientific Awards IEEE Lifetime Member (2022) Who's Who Lifetime Achievement Award (2019) Multiple Who's Who listings (2006, 2002) Grants & Collaborations Principal investigator on AFRL/DAGSI grants for projects like: Anomaly detection in multi-seasonal time series (2023-2025) GPU-accelerated crisis management systems (2018-2022) Parallel text data mining (2003-2005) Collaborated with institutions including Purdue University , LexisNexis , and NCR Corporation .

• Data Management and Analysis
• Machine Learning
• Knowledge Representation
• +۵ مورد دیگر

Assoc Prof John Wang is an Associate Professor at the School of Information and Communication Technology , Griffith University. His work spans Data Management and Analysis (graph, text, spatial-temporal data), Machine Learning , Knowledge Representation , and Algorithm Design . He has published extensively in top venues like ACM Transactions on Database Systems , IEEE Transactions on Knowledge and Data Engineering , and conferences including SIGMOD , VLDB , and ICDE . Appointments : Member of the Institute for Integrated and Intelligent Systems (2003–2024). Education : PhD in Computer Science (Griffith University, 2003). His research has led to real-world implementations in Graph Data Processing , including techniques for shortest distance queries and temporal reachability. He actively contributes to program committees of conferences like VLDB and ICDE , and his funded projects include "Developing Soil Knowledge..." (ACIAR, 2024–2029) and "Harnessing Social Media..." (QLD Inspector-General, 2020). Recent publications focus on Graph Indexing , Causality Detection , and Spatial-temporal Querying . Examples include optimizing road network queries (2023), 2-hop labeling for shortest paths (2021), and geospatial skyline algorithms (2020). Co-authored works appear in venues such as IEEE Transactions on Knowledge and Data Engineering and Neurocomputing . Consultancy : Delivered commercial research on pandemic-related data analytics (2020). Supervision : Mentored 15+ PhD/Master’s students, including projects on deep learning, data leakage prevention, and XML querying. Teaching : Directed programs like Graduate Certificate in Information Technology and Diploma of Information Technology .

Tibor Kovácsمشاهده پروفایل

دانشیار

• Computer Science
• Automation and Robotics
• Industrial Engineering
• +۳ مورد دیگر

Tibor Kovács is an Associate Professor at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics. He has contributed extensively to applied informatics, focusing on graph database systems, 3D scanning technologies, industrial process optimization, and noise-resistant algorithms. His work bridges theoretical research with practical implementations in manufacturing, sensor design, and data management. Research Interests: Graph database benchmarking and optimization 3D geometry scanning and curvature adaptation Industrial production network performance analysis Energy efficiency in manufacturing systems Robust line/edge detection algorithms XML tree search methodologies Publication Trends: His recent work emphasizes scalable data systems (2019) and geometric sensor optimization (2018), while earlier studies focus on industrial energy efficiency (2016), technical advisory networks (2010), and foundational computer vision algorithms (2004–1994).

Holger Meyerمشاهده پروفایل

استاد

• Databases
• Digital Libraries
• Information Retrieval
• +۵ مورد دیگر

Dr. Holger Meyer is a faculty member at the University of Rostock's Faculty of Computer Science and Electrical Engineering, specifically in the Institute of Computer Science. He is based at the Konrad Zuse House on Albert-Einstein-Straße 22, Room 332, and can be reached at phone number 0381-498 7597 or via email at hme@informatik.uni-rostock.de. Dr. Meyer teaches a variety of database-related courses including Database Application Programming (Bachelor), Databases for Users (Bachelor), Databases III (Master), and Digital Libraries and Multimedia Information Retrieval (Master). He also supervises projects under KSWS/Project/NEidI. His research focuses on database systems, digital libraries, information retrieval, data science, and XML database technologies. Dr. Meyer has made significant contributions to the development of digital archive systems, particularly through the Hydra.PowerGraph System, which enables building digital archives with directed and typed hypergraphs. His work also extends to applying crowdsourcing techniques in cultural heritage projects like the Mecklenburg Field Name Archive and developing SQL-based approaches for machine learning and signal processing tasks. Dr. Meyer's recent publications (2017-2019) demonstrate a strong emphasis on practical applications of database technologies across diverse domains including environmental monitoring (particulate matter analysis), automotive engineering, digital humanities, and historical geography. His work bridges theoretical database concepts with real-world applications, particularly through SQL-based implementations of machine learning operations and signal processing techniques. Throughout his career, Dr. Meyer has collaborated extensively with colleagues at the University of Rostock, particularly with Professor Andreas Heuer, Meike Klettke, and other researchers in the database and information systems group. These collaborations have resulted in numerous publications in prestigious venues including Datenbank-Spektrum, LNI proceedings, and international conferences.

Veronique Benzakenمشاهده پروفایل

استاد

• Database Systems
• XML Processing
• Type Systems
• +۳ مورد دیگر

Veronique Benzaken is a Full Professor (Professeur de classe exceptionnelle) at University of Paris Sud 11, where she is a member of the LRI (Laboratoire de Recherche en Informatique), UMR 8623 - CNRS. She is currently a member of the VALS (Verification of Algorithms Languages and Systems) research group, a joint team between LRI and the Toccata group at INRIA - Saclay. Her research focuses on data-centric programming languages and systems, with particular expertise in XML processing, type systems, and formal verification of database systems. Her academic background includes: Dec 1996: Habilitation à diriger des recherches, University Paris Sud 11 (UFR des Sciences - Orsay) Jan 1990: PhD in Computer Science, University Paris Sud 11 (UFR des Sciences - Orsay) Sep 1986: DEA d'Informatique fondamentale, University Denis Diderot Paris 7 (Master in Theoretical Computer Science) June 1983: Diplomée de Chant et d'Art-Lyrique, Conservatoire National de Région de Grenoble Professor Benzaken's primary research interests lie at the intersection of database systems, programming languages, and formal methods. She has made significant contributions to XML-centric programming through the design and development of ℂDuce, an XML-centric general purpose functional programming language developed under an MIT license. Her work emphasizes type-safe and fast query and transformation of XML documents. More recently, she has focused on the formalization of data intensive management systems using the Coq proof assistant, particularly in the context of the Datacert project (2016-2021) which aims to certify and verify data intensive systems such as RDBMS's and XML processing engines. Her research spans several interconnected areas including type systems for data languages, formal semantics of query languages, verification of database systems, and language-integrated query processing. She has led significant research projects such as the ANR project Blanc SIMI2 Typex (Typeful certified XML) and has collaborated with Oracle Labs on developing intermediate representations for multi-lingual querying interfaces. Her publication record shows a clear trajectory from XML processing and type systems toward increasingly rigorous formal verification of database technologies. Professor Benzaken has been actively involved in the academic community through service on program committees for major conferences including ESOP, ICDE, VLDB, and others. She has also been an invited speaker at workshops such as the Coq workshop (CoqWS@FLOC) in 2018. Her research is supported by significant grants including the ANR project Datacert (2016-2021) and the ANR project Blanc SIMI2 Typex. She has collaborated extensively with researchers such as Évelyne Contejean, Chantal Keller, and Stefania Dumbrava on formal verification projects, producing notable publications at ITP 2017 and ITP 2018 on Datalog and SQL formalization. Professor Benzaken is a member of the PCRI research group within LRI, focusing on programming, systems, and their applications. Her work bridges theoretical computer science with practical database system implementation, contributing to both the academic understanding and industrial application of data management technologies, particularly in the areas of XML processing, query languages, and formal verification of database systems.

• Web Data Management
• Probabilistic Databases
• Provenance in Databases
• +۴ مورد دیگر

Pierre Senellart is a Professor in the Computer Science Department at École normale supérieure (ENS, Université PSL) and deputy director of the DI ENS laboratory, a joint CNRS/Inria/ENS unit. He leads the Valda team at Inria Paris and holds a chair in the PRAIRIE Paris School of AI. University: École normale supérieure (Université PSL) School: Department of Computer Science (DI ENS) Academic Rank: Professor Research Interests: His work bridges theoretical and practical aspects of web data management, including web crawling, archiving, information extraction, uncertainty management, and intensional data systems. He contributes to probabilistic XML data models, provenance tracking, and knowledge base construction. Recent Article Trends: His Google Scholar publications (2025–2023) focus on provability in probabilistic databases, theorem extraction from PDFs, and AI applications in uncertain data analysis. Topics span game theory, knowledge compilation, and multi-modal learning for scientific documents. Scientific Awards: Junior member of Institut Universitaire de France (2020–2025) Distinguished SIGMOD 2017 Program Committee Member ACM HyperText 2014 Douglas Engelbart Best Paper Award Adviser of Clément Genzmer (SIGMOD Programming Contest 2009 winner) Grants & Projects: He secures major funding (e.g., PRAIRIE Paris School of AI: €575k, DesCartes CNRS@CREATE: SGD50M), with roles in research projects like Dissemin (open science platforms) and ARCOMEM (social web archiving).

• Databases
• Data Science
• Information Systems
• +۴ مورد دیگر

María Nieves Rodríguez Brisaboa is a Professor in the Computer Science and Information Technology Department at the University of A Coruña. She is affiliated with the SUXI research group and has been active in teaching and research since 1994. 2025/2026: Final Degree Project (Computer Science) 2024/2025: Introduction to Databases (Bioinformatics) 2023/2024: Database Modeling (Data Science) Her research focuses on Databases , Data Science , Information Systems , and GIS Applications . She has contributed to the development of spatial and temporal databases, efficient data structures, and web-based GIS solutions. Recent publications include work in Information Sciences , Geo-spatial Information Science , and International Journal of Geographical Information Science , covering topics like spatial query optimization, data compression, and bioinformatics databases. Scientific contributions include: Patent: Eduardo Rodriguez López et al. (2013) Software Registration: Universidad da Coruña (2009) Software Registration: Gándara Portela et al. (2003) She has supervised multiple final degree and master's theses since 2013, including projects on film dubbing industry systems, elderly care organization management, and public transport analysis.

• Unified Processing of Multi-Model Data
• Influence Maximization in Social Networks
• Efficient Graph Data Processing
• +۳ مورد دیگر

Martin Svoboda is a Lecturer at the Department of Software Engineering , Faculty of Mathematics and Physics, Charles University, Prague. His work focuses on multi-model data management, social network analysis, and XML/linked data processing. University: Charles University School: Faculty of Mathematics and Physics Department: Department of Software Engineering Role: Lecturer Education: Doctoral Thesis: Correction of Invalid Trees with Respect to Regular Tree Grammars (2015) Master's Thesis: Processing of Incorrect XML Data (2010) Bachelor's Thesis: Information System for Small User Group Collaboration (2007) Research Interests encompass: Unified multi-model data processing Influence maximization in social networks Graph data efficiency Big Data and NoSQL systems Querying/indexing linked data XML document correction Publications highlight trends in multi-model data unification, XML correction techniques, and social network analysis, with a strong emphasis on category theory and graph-based approaches. Scientific Recognition: Dean’s Award for Best Master Thesis (2010) Best Poster Award at Reasoning Web Summer School (2012) Projects include grants from the Czech Science Foundation (20-22276S, 2010–2012), Technology Agency of the Czech Republic (TH03010276, 2018–2020), and international collaborations like NoSQL-Net (Germany, 2014).

Stefan Manegoldمشاهده پروفایل

استاد

• Database Systems
• Column-store Database Architectures
• Data Management for Sciences
• +۷ مورد دیگر

Stefan Manegold is a Professor for Data Management (0.2 fte) at Leiden University's Faculty of Science within the Leiden Institute of Advanced Computer Science (LIACS), while also serving as a Senior Researcher (0.8 fte) and former Head (2011-2024) of the Database Architectures Research Group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His career spans over 27 years at CWI and 11 years as a professor at Leiden University, with prior experience at Humboldt-Universität zu Berlin. Professor Manegold's research focuses on innovative database architectures, particularly column-store systems and hardware-aware database technologies. His expertise spans database query optimization, parallel and distributed information systems, XML storage and processing, and scientific data management. He has pioneered work in adaptive indexing, progressive query processing, and main-memory database systems that leverage modern hardware capabilities. His research bridges theoretical database concepts with practical implementations, as evidenced by his involvement in the MonetDB open-source database system. His work shows a clear evolution from foundational database research toward addressing modern challenges in big data management, scientific data processing, and interactive analytics. Recent publications demonstrate his continued leadership in database indexing techniques, GPU-accelerated database operations, and geospatial data management. Professor Manegold has received significant recognition for his contributions to the database community, including the prestigious 2020 ACM SIGMOD Contributions Award, the VLDB'2011 Challenges & Visions Track Best Paper Award, and the VLDB'2009 10-year Best Paper Award. His work has had substantial impact on both academic research and practical database system design. He has been actively involved in the academic community through conference organization, particularly with SIGMOD and VLDB events, and has contributed to numerous workshops including the Data Management on New Hardware (DaMoN) series. His leadership extends to collaborative research projects such as SciLens, PROMIMOOC, and DAMIOSO, which address data management challenges in scientific domains. Professor Manegold leads the Database Architectures Research Group at CWI, which has been at the forefront of database system research for decades. The group's work on MonetDB has influenced modern column-store database systems and continues to push boundaries in areas like progressive query processing and hardware-aware database design.

• Pattern Recognition
• Artificial Neural Networks
• Expert Systems
• +۷ مورد دیگر

Maher Ahmed is an Associate Professor at the Faculty of Science, Wilfrid Laurier University. His research focuses on pattern recognition, artificial neural networks, and expert systems. He has contributed to diverse fields including robotics, medical informatics, network security, and computer vision. Key research interests include developing algorithms for shape representation, improving network anomaly detection, and applying machine learning to financial fraud prevention. His work bridges theoretical computer science with practical applications in healthcare and autonomous systems. Publications span robotics localization, medical literature reviews, encryption techniques, and sign language recognition, reflecting a cross-disciplinary approach. He currently holds no listed awards but maintains an active research agenda with a focus on applied artificial intelligence.

Philip Bernhardمشاهده پروفایل

استاد

• Database Systems
• Spatial Databases
• Database Benchmarking
• +۶ مورد دیگر

Philip Bernhard is a Professor in the Department of Electrical Engineering and Computer Science at Florida Institute of Technology (FIT), holding concurrent roles as Associate/Assistant Dean and Program Chair for Cybersecurity. He specializes in Database Systems, Spatial Databases, and Computational Complexity. His research focuses on database performance tuning, NoSQL systems, big data, and theoretical aspects like computational complexity. Education: B.A. in Computer Science, SUNY Oswego M.S. and Ph.D. in Computer Science, SUNY Albany Research Interests: Spatial databases, database benchmarking, performance optimization, NoSQL technologies, and theoretical computer science topics such as computation theory. His work emphasizes practical applications in education and industry, including classroom database benchmarks and collaboration with organizations like Harris Corporation and the U.S. military. Grants & Projects: Recent projects include NoSQL database research ($114k from Harris Corp, 2012) and cloud-based database evaluations ($80k, 2011). He has led initiatives in coral database systems (USGS, 2004) and Air Force-funded database optimization tools (2006). Labs/Teams: Works on spatial database systems, performance analysis, and collaborative projects with industry partners. Teaches advanced courses like Spatial Databases (CSE 5800) and Computational Complexity (CSE 5610).

Guy De Tréمشاهده پروفایل

دانشیار

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +۷ مورد دیگر

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

• Database Systems
• NoSQL Databases
• Relational Databases
• +۴ مورد دیگر

Emanuela Mitreva is an Adjunct Professor at the Department of Computer Science, Sofia University St. Kliment Ohridski. She holds an M.Sc. in IT Services (2011) and a B.Sc. in Informatics (2009) from the same institution. Her research focuses on database systems, including NoSQL and relational databases, with applications in healthcare informatics, big data, and 3D visualization for education. She also explores digital storytelling for children's development and cultural heritage preservation through virtual collections. Her teaching interests include programming in Python and relational databases. Notable research contributions address XML data extraction in healthcare compliance, performance analysis of SQL/NoSQL systems, and leveraging Unity for 3D medical education tools. Her work bridges theoretical database research with practical implementations in medical and cultural domains. Mitreva has collaborated extensively on projects involving 3D visualization technologies and NoSQL solutions for analytical loads. Though no formal awards are listed, her publications reflect sustained engagement with interdisciplinary challenges in computer science applications.