جست‌وجوی استاد و پژوهشگر

Holger Gieseمشاهده پروفایل

استاد

• Model-Driven Engineering
• Self-Adaptive Systems
• Cyber-Physical Systems
• +۱۲ مورد دیگر

Prof. Dr. Holger Giese is a full Professor at the System Analysis and Modeling Group of the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. He leads research initiatives in model-driven engineering , self-adaptive systems , and cyber-physical systems , with a focus on causal representations , neuro-symbolic AI , and multi-agent reinforcement learning . His research explores the intersection of formal modeling and machine learning , addressing challenges in: Runtime verification and validation of dynamic systems Model transformation and synchronization Code generation for self-optimizing architectures Probabilistic decision-making under uncertainty Transfer learning for autonomic computing Recent work (2024-2025) emphasizes neuro-symbolic approaches for robust multi-agent systems , spatio-temporal graph modeling for cloud systems, and incremental query evaluation in dynamic environments. Collaborations span institutions like IBM Japan, Krems University, and Humboldt University. He actively teaches courses such as Advanced Topics in Software Engineering , Graph Neural Networks , and AI Ethics Engineering , and leads labs including the Model-Driven Engineering Laboratory (MDELab.de) and Software Engineering for Self-Adaptive Systems (self-adaptive.org) .

Tobias Wernerمشاهده پروفایل

پژوهشگر

• Geospatial Data
• GIS
• Spatio-Temporal Data Analysis
• +۳ مورد دیگر

Tobias Werner, M.Sc., is an active researcher at the IAPG Institute of the University of Oldenburg, specializing in geospatial data analysis and spatio-temporal systems. His work bridges theoretical research and practical applications in GIS, digital tachograph analysis, and 3D metrology, with significant contributions to projects funded by governmental bodies like the Federal Office for Cartography and Geodesy. Werner's research focuses on geospatial data management, spatio-temporal analytics, and privacy-aware route reconstruction. His expertise spans machine learning applications in sovereign cloud environments, participatory GIS development, and sustainable geospatial toolboxes for rural transformation. He collaborates extensively with institutions including Leibniz University Hannover on cutting-edge geospatial initiatives. Recent publications reveal a strong trajectory toward cloud-based geospatial processing, real-time data stream management for autonomous systems, and privacy-preserving methodologies in transportation data. His work consistently leverages open-source tools like OpenStreetMap and SpatiaLite while addressing critical challenges in data sovereignty and temporal analysis. Scientific Awards: No scientific awards listed in available documentation Werner contributes to multiple funded projects including "Evaluation of driving data from digital tachographs" (Central Criminal Investigation Department), "The temporal change of geodata" (Federal Office for Cartography and Geodesy), and "Northwest Lower Saxony Sustainable New (4N): Geo-Toolbox" (Lower Saxony Advance). No thesis supervision activities are documented. He operates within the Laboratory for optical 3D metrology, collaborating with senior researchers like Prof. Dr. Thomas Brinkhoff on geospatial data transformation frameworks. His technical leadership manifests in developing specialized applications for route reconstruction from tachograph data and creating the Geo-Toolbox platform for sustainable rural development, demonstrating applied research impact across law enforcement, cartography, and environmental planning domains.

• Semantic Modeling
• Geographic Information Systems
• Spatio-temporal Databases
• +۳ مورد دیگر

Stefano Spaccapietra is a professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) specializing in semantic modeling, geographic information systems, and spatio-temporal databases. A recipient of the 2012 Peter P. Chen Award, his research focuses on foundational aspects of data representation and knowledge engineering. His research explores: Semantic modeling for geospatial systems Multi-representation databases Trajectory data analysis and mobility patterns Ontology design for spatial information Temporal evolution in object-oriented systems Publication analysis reveals strong emphasis on: Fundamental theories of spatial and temporal representation Integration frameworks for heterogeneous systems Evolution of conceptual modeling approaches Spatio-temporal query processing techniques Awards & Honors: Peter P. Chen Award (2012) His work has significantly contributed to database integration methodologies and established foundations for modern geospatial data management systems through both theoretical frameworks and practical implementations.

Paras Mehtaمشاهده پروفایل

پژوهشگر

• Spatial Data Mining
• Geoinformatics
• Information Retrieval
• +۳ مورد دیگر

Dr. Paras Mehta is a Researcher at the Department of Databases and Information Systems, part of the Computer Science faculty at Freie Universität Berlin. His work focuses on spatial data mining, geoinformatics, and spatio-temporal analysis with applications in disaster response systems and social media analytics. He contributes to projects such as MoveSafe and CliniScale, addressing challenges in real-time data processing and location-based services. Research interests include geotagged post analysis, spatio-textual data retrieval, and distributed computing for large-scale spatial datasets. His recent work emphasizes continuous summarization of streaming data and hotspot detection using frameworks like Apache Spark. Notable projects involve developing systems like μTOP for trending topic detection in microblogs and LocXplore for urban region profiling. His publications span spatial keyword queries, trajectory aggregation, and disaster response frameworks. Paras Mehta holds a PhD and has contributed to both academic and applied research in database systems. Despite no listed awards or grants in the provided texts, his research portfolio demonstrates strong engagement with spatial computing challenges. He collaborates within the Databases and Information Systems group at Freie Universität Berlin, contributing to both theoretical advancements and practical software solutions.

• Spatial Databases
• Big Spatial Data
• Location-Based Services
• +۶ مورد دیگر

Mohamed F. Mokbel is a Professor at the University of Minnesota with a distinguished career in spatial databases and big spatial data management. His research spans over two decades with more than 260 publications in top-tier venues including ICDE, SIGMOD, VLDB, and GIS conferences. He has established himself as a leading researcher in trajectory data management, mobility data science, and scalable spatial processing systems. Dr. Mokbel's research interests focus on the intersection of spatial databases, big data, and machine learning. He has pioneered work in trajectory data management systems, developing frameworks like ST-Hadoop for processing spatio-temporal data at scale. His recent research explores the application of large language models to trajectory analysis, spatial data cleaning systems with spatial awareness, and innovative approaches to mobility data science. His work addresses fundamental challenges in handling massive spatial datasets while maintaining efficiency and accuracy. His publication record reveals significant trends toward integrating machine learning with spatial data management, particularly in trajectory imputation using BERT models (KAMEL system) and applying NLP techniques to trajectory analysis. Recent work shows increasing focus on urban mobility applications, privacy considerations in location data, and the development of specialized frameworks for processing different types of spatial data at scale. Dr. Mokbel has mentored numerous students who have become productive researchers in their own right, with many publications featuring students as first authors. His collaborative network is extensive, with frequent co-authorship with researchers like Walid G. Aref, Ahmed Eldawy, and Amr Magdy, indicating strong research group leadership. He has contributed significantly to the spatial database community through systems like RASED for monitoring OpenStreetMap updates, KAMEL for trajectory imputation, and Sparcle for spatial data cleaning. His work bridges theoretical database concepts with practical applications in transportation, urban planning, and location-based services.

Ludger Beckerمشاهده پروفایل

پژوهشگر

• Database Systems
• Geographic Information Systems
• Spatio-Temporal Data Management
• +۳ مورد دیگر

Ludger Becker is an Academic Director at the University of Münster, where he has been working since December 2003. He currently serves as Head of the Information Security Unit since June 2023 and as CISO of the University of Münster since April 2023. Previously, he was Head of IVV5 from January 1998 to December 2023 and held positions as Senior Academic Councillor and Academic Councillor at the Institute of Computer Science at the University of Münster. Dr. Becker received his Doctor of Engineering (Computer Science) from the University of Siegen in July 1992 and completed his Diploma in Computer Science from the University of Dortmund in April 1989. His research spans several key areas in computer science, with particular expertise in database systems , geographic information systems , and software engineering . Dr. Becker has made significant contributions to spatio-temporal data management, developing frameworks for representing moving objects and efficient index structures for spatial queries. More recently, his work has expanded into health informatics, particularly in clinical data generation and literature database analysis. His teaching focuses on software engineering principles, databases, and object-oriented programming, as evidenced by his leadership of the Software Internship course. Dr. Becker's publication record shows a clear evolution from foundational work in spatial databases and GIS in the 1990s and early 2000s, through educational technology applications in the early 2000s, to more recent contributions in health informatics. His research consistently demonstrates expertise in data management challenges across multiple domains. Dr. Becker regularly teaches Software Internship, Databases, and Advanced Database Concepts courses at the University of Münster. His teaching materials show particular attention to software design processes, object-oriented principles, and modern development tools and frameworks, including detailed documentation on implementing @pre, @post, and @inv tags in Javadoc for specifying software contracts. As Head of the Information Security Unit and CISO, Dr. Becker oversees critical information security infrastructure and policies for the University of Münster, applying his technical expertise to protect academic data and systems across the institution's extensive network.

Hannes Feilhauerمشاهده پروفایل

استاد

• Remote Sensing
• Biodiversity
• Species Distribution Patterns
• +۷ مورد دیگر

Hannes Feilhauer is a full Professor for Remote Sensing in Geo- and Ecosystem Research at the Remote Sensing Centre for Earth System Research (RSC4Earth), which is a joint initiative of the Faculty of Physics and Earth System Sciences at Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). His work focuses on the spatio-temporal analysis of species distribution patterns, functional ecosystem properties, ecosystem services, and biodiversity using optical remote sensing sensors, field measurements, and modeling techniques. His educational background includes a PhD in Geography from Bonn University (2009) and a Diploma in Geoecology from Bayreuth University (2006). His academic career path includes positions as Visiting Professor for Remote Sensing and Geoinformatics at the Free University of Berlin (2018), Senior Scientist at the Institute of Geography, Friedrich-Alexander Universität Erlangen-Nürnberg (2010-2018), and various research positions dating back to 2006. Feilhauer's research primarily investigates the application of remote sensing for ecological analysis, with particular emphasis on mapping fuzzy ecosystem characteristics that cannot be adequately represented in conventional maps. His work bridges the gap between spectral data and ecological patterns, developing methods to map continuous fields of biodiversity and ecosystem properties. He has made significant contributions to understanding the relationship between spectral variation and biodiversity, the application of optical trait indicators, and the mapping of habitat variability. His recent publications reveal a strong focus on advancing remote sensing applications for biodiversity monitoring, with increasing integration of machine learning approaches and analysis of high-resolution satellite data (particularly Sentinel-2). His work spans from fundamental methodological developments in spectral analysis to applied ecosystem monitoring, with particular attention to grassland ecosystems, species distribution patterns, and the impacts of environmental change. Science award for Physical Geography of the Prof. Dr. Frithjof Voss Stiftung für Geographie Feilhauer leads a research group that includes multiple PhD candidates and postdoctoral scientists working on projects related to vegetation monitoring, biodiversity assessment, and remote sensing methodology development. His group is involved in several major research initiatives including AIReSVeg (AI and Remote Sensing for Vegetation Monitoring), Jena Experiment, and DeepFeatures, reflecting his engagement with both fundamental ecological research and cutting-edge technical developments in remote sensing. The Remote Sensing in Geo- and Ecosystem Research group operates within the RSC4Earth framework, collaborating closely with researchers from the Earth System Data Science group and the Remote Sensing Department at UFZ. This collaborative environment enables integration of remote sensing data with ecological modeling and Earth system science approaches.

Michael Grossniklausمشاهده پروفایل

استاد

• Data Management
• Information Systems
• Stream Processing
• +۳ مورد دیگر

Michael Grossniklaus is Professor for Databases and Information Systems at University of Konstanz's Department of Computer and Information Science. His research develops data management and processing technologies supporting data science applications, with emphasis on stream processing, context-aware systems, and web engineering solutions. Key research areas: Continuous query processing for streaming data Context-aware mobile data management Machine learning for database optimization Multi-channel web information systems Semantic web and RDF stream processing Publications demonstrate sustained contributions to database query processing, with recent work focusing on cardinality estimation using geometric deep learning and cloud database optimization. Developed C-SPARQL language extension for continuous RDF stream queries.

• Database Systems
• Data Mining
• Machine Learning
• +۷ مورد دیگر

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

Felix Kiehnمشاهده پروفایل

پژوهشگر

• Polyglot Persistence
• Data Migration
• Distributed Systems
• +۲ مورد دیگر

Felix Kiehn is a Researcher in the Department of Informatics at the University of Hamburg, where he focuses on distributed database systems and polyglot persistence architectures. He is currently completing his dissertation titled Design and Implementation of a scalable polyglot database system for data migration and mediation under the supervision of Prof. Dr. Norbert Ritter. His research explores: Polyglot persistence frameworks for heterogeneous data systems Data migration and mediation techniques Scalable architectures for NoSQL databases Distributed query optimization Kiehn's publications primarily address challenges in distributed data management, with recent work focusing on constraint modeling in polyglot systems (2024) and operator placement for spatio-temporal data processing (2022). He actively supervises student research, advising both Bachelor's and Master's theses on database topics. Current projects include contributing to the HADeS (Heterogenous and Adaptive Database System) research initiative.

Johanna Geißمشاهده پروفایل

پژوهشگر

• Event Detection
• Information Extraction
• Information Retrieval
• +۵ مورد دیگر

Johanna Geiß is a postdoctoral researcher at the Institute of Computer Science , Heidelberg University, specializing in Natural Language Processing , Information Extraction , and Event Detection . She contributes to projects like SCIDATOS (sepsis diagnosis) and EventAE (event-based Linked Data exploration). Education: PhD from University of Cambridge (2011), Magister Artium in Computational Linguistics (Heidelberg, 2006) Her research integrates Geoparsing , Social Network Analysis , and Graph Theory for tasks such as toponym disambiguation and entity resolution. Recent work includes frameworks like HeidelPlace and tools for semantic word clouds. Key awards include the 2010 Lundgren Research Award and multiple grants from the EPSRC and Cambridge European Trust. She has taught courses on Information Networks and Data Mining at Heidelberg University. As a postdoc , she advises on scientific computing applications in healthcare and contributes to open-source tools like NECKAr and EventAE .

• Precision Horticulture
• Spectro-optical Measurement Methods
• LiDAR and Hyperspectral Sensing
• +۴ مورد دیگر

Manuela Zude-Sasse is a Research Professor at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) in Potsdam, Germany, and former Professor at Beuth University of Applied Sciences Berlin (2009–2017). As Working Group Leader for Precision Horticulture, she focuses on spectro-optical measurement methods (LiDAR, hyperspectral analysis) for fruit quality assessment, canopy modeling, and data science applications in bioeconomy. M.Sc. in Chemistry/International Agronomy (1996, TU Berlin) Ph.D. in Horticulture summa cum laude (1999, TU Berlin) Habilitation in Applied Plant Physiology (2004, Humboldt University) Her research spans Precision Fruit Production , Plant Phenotyping , and 3D Sensor Integration , with projects like CrackSense (fruit cracking prediction) and horDIGrow (resource-efficient horticulture). She employs LiDAR, hyperspectral imaging, and time-resolved fluorescence to analyze temperate and tropical fruits. Recent publications in Plant Phenomics and Horticulturae demonstrate her work on spatial chlorophyll estimation via LiDAR and fruit water stress indices. Her studies in the Journal of Food Engineering (2024) and Postharvest Biology and Technology (2023) highlight machine learning applications for ripeness detection and 3D canopy analysis. Technology Transfer Award (State of Brandenburg, 2003) Silver Medal for Innovation (Agritechnica, 2017) She leads the SMART Farming Technology Research Center (SFTRC) collaboration in Malaysia and serves on editorial boards for Plant Phenomics , International Agrophysics , and Biosystems Engineering . Her 3D point cloud methodologies enable non-contact fruit biomechanics and nutrient management studies.

• Spatial data management
• Spatio-temporal statistics
• Machine learning in GIS
• +۳ مورد دیگر

Felix Kunde is a Researcher at the Beuth University of Applied Sciences Berlin, contributing to the ExCELL and MAGDa projects. He holds a Bachelor's degree in Geography and a Master's degree in Geoinformation Science. His professional experience includes roles as a database developer for 3D city models and teaching geodatabase systems at universities. He actively participates in conferences such as FOSSGIS, FOSS4G, and WhereCamp, presenting work on topics like traffic forecasting, geospatial data management, and PostGIS extensions. Research interests focus on spatial data management (RDBMS/NoSQL/HDFS), spatio-temporal statistics, linear referencing, machine learning in GIS, and 3D city modeling. Current projects address short-term traffic forecasting with STARIMA/SVM/ANN methods, handling missing data in time series, and scalable geostatistical algorithms on Hadoop. He also works on open data models for traffic in OpenStreetMap and mobile databases using GeoPackage. Education: Bachelor of Geography Master of Geoinformation Science Key Contributions: Co-developer of pgMemento for spatial database auditing Contributor to the 3DCityDB project Lead researcher on ExCELL mobility platform Teaching: Lectures on geodatabases at multiple universities PostGIS workshops at conferences His publications span topics from geodatabase systems to traffic prediction algorithms, with notable work in spatial-temporal data analysis and geoinformatics applications. He advises multiple students on projects related to mobility analytics, data mining, and geospatial technology integration.

• Computer Science
• Database Systems
• Business Process Management
• +۵ مورد دیگر

Jianwen Su is a Professor at the University of California, Santa Barbara, USA, with a distinguished career spanning over three decades in Computer Science , particularly in Database Systems , Business Process Management , and Web Services . Their research bridges theoretical foundations with practical applications, focusing on data-centric process modeling , formal verification , and spatio-temporal data analysis . Key contributions include the design of artifact-centric workflow models , temporal constraint languages , and query systems for uncertain data . Recent work integrates machine learning with proteomic analysis for stress biomarker discovery and LLM-based extraction of structured data from clinical reports. Notable scientific recognition includes the 2019 ACM PODS Alberto O. Mendelzon Test-of-Time Award . Collaborations span institutions globally, with frequent co-authorship in journals like Information Systems , ACM Transactions on Management Information Systems , and conferences such as BIBM and TIME .