TY  - GEN
AB  - In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
AD  - Department of Computer Science, KU Leuven Cam- pus Kulak, Belgium
AD  - School of Management, HES-SO University of Applied Sciences and Arts Western Switzerland Valais
AD  - Linköping University, Sweden
AD  - Aalborg University, Denmark
AD  - DEIB - Politecnico di Milano, Italy
AD  - Technische Universität Wien, Austria
AD  - DEIB - Politecnico di Milano, Italy
AD  - Linköping University, Sweden
AD  - Alpen-Adria-Universität Klagenfurt, Austria
AD  - Technical University Berlin, Germany
AD  - Insight Centre for Data Analytics, Dublin City Uni- versity, Ireland
AD  - Technische Universität Wien, Austria ; Siemens AG, Chemnitz, Germany
AD  - INSA Lyon, CNRS LIRIS, France ; University of Tartu, Estonia
AD  - Vrije Universiteit Amsterdam, The Netherlands
AD  - DEIB - Politecnico di Milano, Italy
AU  - Bonte, Pieter
AU  - Calbimonte, Jean-Paul
AU  - de Leng, Daniel
AU  - Dell’Aglio, Daniele
AU  - Della Valle, Emanuele
AU  - Eiter, Thomas
AU  - Giannini, Federico
AU  - Heintz, Fredrik
AU  - Schekotihin, Konstantin
AU  - Le-Phuoc, Danh
AU  - Mileo, Alessandra
AU  - Schneider, Patrik
AU  - Tommasini, Riccardo
AU  - Urbani, Jacopo
AU  - Ziffer, Giacomo
CY  - Germany
DA  - 2024-05
DO  - 10.4230/tgdk.2.1.2
DO  - DOI
EP  - 2:1-2:47
ID  - 14348
JF  - Transactions on Graph Data and Knowledge (TGDK)
KW  - Economie/gestion
KW  - Informatique
KW  - stream reasoning
KW  - stream processing
KW  - RDF streams
KW  - streaming linked data
KW  - continuous query processing
KW  - temporal logics
KW  - high-performance computing
KW  - databases
L1  - https://arodes.hes-so.ch/record/14348/files/Calbimonte_2024_Grounding_stream.pdf
L2  - https://arodes.hes-so.ch/record/14348/files/Calbimonte_2024_Grounding_stream.pdf
L4  - https://arodes.hes-so.ch/record/14348/files/Calbimonte_2024_Grounding_stream.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/14348/files/Calbimonte_2024_Grounding_stream.pdf
N2  - In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
PB  - Schloss Dagstuhl – Leibniz-Zentrum für Informatik
PP  - Germany
PY  - 2024-05
SN  - 2942-7517
SP  - 2:1-2:47
T1  - Grounding stream reasoning research
TI  - Grounding stream reasoning research
UR  - https://arodes.hes-so.ch/record/14348/files/Calbimonte_2024_Grounding_stream.pdf
VL  - 2024, 2
Y1  - 2024-05
ER  -