A5101434315- Advanced Database Systems and Queries
- Semantic Web and Ontologies
- Data Management and Algorithms
- Graph Theory and Algorithms
- Scientific Computing and Data Management
- Data Quality and Management
- Logic, Reasoning, and Knowledge
- Cloud Computing and Resource Management
- Advanced Graph Neural Networks
- Advanced Data Storage Technologies
- Service-Oriented Architecture and Web Services
- Web Data Mining and Analysis
- Big Data and Business Intelligence
- Distributed and Parallel Computing Systems
- Multi-Agent Systems and Negotiation
- Peer-to-Peer Network Technologies
- Data Stream Mining Techniques
- Big Data Technologies and Applications
- Business Process Modeling and Analysis
- Biomedical Text Mining and Ontologies
- Hate Speech and Cyberbullying Detection
- Misinformation and Its Impacts
- Time Series Analysis and Forecasting
- IoT and Edge/Fog Computing
- Sensor Technology and Measurement Systems
University of Tartu
2020-2024
Institut National des Sciences Appliquées de Lyon
2022-2024
Laboratoire d'Informatique en Images et Systèmes d'Information
2022-2024
Institut National des Sciences Appliquées de Rennes
2023-2024
Centre National de la Recherche Scientifique
2023
Politecnico di Milano
2015-2020
Ghent University
2017-2018
Elettra-Sincrotrone Trieste S.C.p.A.
1994
Ensuring the success of big graph processing for next decade and beyond.
In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams data. To perform meaningful analysis over these streams, stream processing needs to support expressive reasoning capabilities infer implicit facts temporal capture dependencies. However, current approaches cannot required expressivity while detecting time dependencies high frequency data streams. There is still a mismatch between complexity rate produced in volatile domains. Therefore, we...
The ability to process large volumes of data on the fly, as soon they become available, is a fundamental requirement in today's information systems. Modern distributed stream processing engines (SPEs) address this and provide low-latency high-throughput cluster platforms, offering high-level programming interfaces that abstract from low-level details such distribution hardware failures. last decade saw rapid increase number available SPEs. However, each SPE defines its own model standardized...
Modern systems and applications generate consume an enormous amount of data from different sources, including mobile edge computing IoT systems. Our ability to locate analyze these massive amounts will shape the future, building next-generation Big Data Analytics (BDA) artificial intelligence in critical domains. Traditionally, big materialize a centralized repository (e.g., cloud) for running sophisticated analytics using decent computation. Nevertheless, many modern domains require...
Leveraging Big Data (BD) processing frameworks to process large-scale Resource Description Framework (RDF) datasets holds a great interest in optimizing query performance. Modern BD services are complicated data systems, where tuning the configurations notably affects Benchmarking different and provides community with best practices towards selecting most suitable configurations. However, of these benchmarking efforts classified as descriptive or diagnostic analytics. Moreover, there is no...
Many domains, such as the Internet of Things and Social Media, demand to combine data streams with background knowledge enable meaningful analysis in real-time. When takes form taxonomies class hierarchies, Semantic Web technologies are valuable tools their extension streams, namely RDF Stream processing (RSP), offers opportunity integrate streams. In particular, RSP Engines can continuously answer SPARQL queries while performing reasoning. However, current engines at risk failing perform...
Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks’ performance. In practice, when processing large (RDF) graphs on top relational BD systems, several design decisions emerge cannot decided automatically, e.g., the choice schema, partitioning technique, storage formats. PPA, in particular ranking functions, helps enable actionable insights performance data, leading practitioners...
Continuous queries, also known as standing or streaming are a class of queries that continuously monitor data sources over time, remaining active until explicitly terminated. This concept was introduced in 1992 by Terry and colleagues to address the need access changes time. Since then, domain continuous has seen significant development, research, application various systems, including multiple Database Management Systems (DBMS). Notably, past five years have marked rise Streaming Databases...
Streaming Linked Data represents a domain within the Semantic Web dedicated to incorporating Stream Reasoning capabilities into stack address dynamic data challenges. Such applied endeavours typically necessitate robust modelling process. To this end, RDF Processing (RSP) engines frequently utilize OWL 2 ontologies facilitate requirement. Despite rich body of research on Knowledge Representation (KR), even concerning time-sensitive data, notable gap exists in literature regarding...