| Autor: |
Elena Mastria, Francesco Pacenza, Jessica Zangari, Francesco Calimeri, Simona Perri, Giorgio Terracina |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Big Data and Cognitive Computing, Vol 7, Iss 3, p 135 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2504-2289 |
| DOI: |
10.3390/bdcc7030135 |
| Popis: |
Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart Cities, Emergency Management, and Healthcare, despite being a relatively new field of research. The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. We proposed a new benchmark, called EnviroStream, for evaluating SR systems on weather and environmental data. The benchmark includes queries and datasets of different sizes. We adopted I-DLV-sr, a recently released SR system based on Answer Set Programming, as a baseline for query modelling and experimentation. We also showcased continuous online reasoning via a web application. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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