TY - GEN
T1 - Faster Than LASER - Towards Stream Reasoning with Deep Neural Networks
AU - Ferreira, João
AU - Lavado, Diogo
AU - Gonçalves, Ricardo
AU - Knorr, Matthias
AU - Krippahl, Ludwig
AU - Leite, João
N1 - Funding Information:
Acknowledgments. We thank the anonymous reviewers for their helpful comments and acknowledge support by FCT project RIVER (PTDC/CCI-COM/30952/2017) and by FCT project NOVA LINCS (UIDB/04516/2020).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to process and reason with such data in real time. Whereas reasoning over time-annotated data with background knowledge may be challenging, due to the volume and velocity in which such data is being produced, such complex reasoning is necessary in scenarios where agents need to discover potential problems and this cannot be done with simple stream processing techniques. Stream Reasoners aim at bridging this gap between reasoning and stream processing and LASER is such a stream reasoner designed to analyse and perform complex reasoning over streams of data. It is based on LARS, a rule-based logical language extending Answer Set Programming, and it has shown better runtime results than other state-of-the-art stream reasoning systems. Nevertheless, for high levels of data throughput even LASER may be unable to compute answers in a timely fashion. In this paper, we study whether Convolutional and Recurrent Neural Networks, which have shown to be particularly well-suited for time series forecasting and classification, can be trained to approximate reasoning with LASER, so that agents can benefit from their high processing speed.
AB - With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to process and reason with such data in real time. Whereas reasoning over time-annotated data with background knowledge may be challenging, due to the volume and velocity in which such data is being produced, such complex reasoning is necessary in scenarios where agents need to discover potential problems and this cannot be done with simple stream processing techniques. Stream Reasoners aim at bridging this gap between reasoning and stream processing and LASER is such a stream reasoner designed to analyse and perform complex reasoning over streams of data. It is based on LARS, a rule-based logical language extending Answer Set Programming, and it has shown better runtime results than other state-of-the-art stream reasoning systems. Nevertheless, for high levels of data throughput even LASER may be unable to compute answers in a timely fashion. In this paper, we study whether Convolutional and Recurrent Neural Networks, which have shown to be particularly well-suited for time series forecasting and classification, can be trained to approximate reasoning with LASER, so that agents can benefit from their high processing speed.
UR - http://www.scopus.com/inward/record.url?scp=85115443215&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86230-5_29
DO - 10.1007/978-3-030-86230-5_29
M3 - Conference contribution
AN - SCOPUS:85115443215
SN - 978-3-030-86229-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 363
EP - 375
BT - Progress in Artificial Intelligence - 20th EPIA Conference on Artificial Intelligence, EPIA 2021, Proceedings
A2 - Marreiros, Goreti
A2 - Melo, Francisco S.
A2 - Lau, Nuno
A2 - Lopes Cardoso, Henrique
A2 - Reis, Luís Paulo
PB - Springer
CY - Cham
T2 - 20th EPIA Conference on Artificial Intelligence, EPIA 2021
Y2 - 7 September 2021 through 9 September 2021
ER -
