2008–, with S. Ceri and F. van Harmelen.
This is where Stream Reasoning started. In 2007 I proposed treating data streams as first-class citizens of the Semantic Web: if knowledge representation and reasoning could work over data that never stops arriving, a whole class of problems — urban mobility, telecommunications, social media, industrial monitoring — would become tractable in a principled way rather than through ad-hoc pipelines.
The concrete contributions were RDF streams and Continuous SPARQL, extensions to the Semantic Web stack that were brought to the W3C RDF Stream Processing community group, and later RSP-QL as a reference model that made different engines comparable. Around them grew the C-SPARQL Engine, RSP4J, a benchmark tradition, and the Stream Reasoning Workshop series, which I have organized at every edition. The line also covers Graph Stream Processing and Complex Event Processing.
Selected works
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Grounding Stream Reasoning Research
Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell’Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, Giacomo Ziffer
TGDK, 2024Written by the organisers of the Stream Reasoning Workshop series and other community members, it summarises the results discussed in its first six editions along four areas (large data streams, semantic technologies for streams, deductive and inductive reasoning) and lists use cases and open challenges to attract new researchers.
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Streaming Linked Data: From Vision to Practice
Springer, 2023A book, with R. Tommasini, P. Bonte, and F. Spiga, on the continuous engineering of Web streams: the motivations for processing data streams with Web technologies, background models, processing RDF streams with RSP-QL, the life cycle of streaming linked data from publishing streams on the Web onwards, benchmarks and systems, and a set of examples and exercises. It also introduces RSP4J. Written mainly for graduate students and researchers in Web and stream data management.
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RSP4J: An API for RDF Stream Processing — Best Paper Award, ESWC 2021
Riccardo Tommasini, Pieter Bonte, Femke Ongenae, Emanuele Della Valle
The Semantic Web – 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, 2021RSP4J, a modular API to build RDF Stream Processing engines according to the RSP-QL reference model, designed to make RSP research reproducible and comparable. Best Paper Award at ESWC 2021.
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Stream reasoning: A survey and outlook
Daniele Dell’Aglio, Emanuele Della Valle, Frank van Harmelen, Abraham Bernstein
Data Sci., 2017Written when Stream Reasoning turned ten: it analyses how the field grew, what it achieved, and which open challenges it will face in the next decade.
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RSP-QL Semantics: A Unifying Query Model to Explain Heterogeneity of RDF Stream Processing Systems
Daniele Dell’Aglio, Emanuele Della Valle, Jean-Paul Calbimonte, Óscar Corcho
Int. J. Semantic Web Inf. Syst., 2014Different RDF Stream Processing engines return different answers to the same query on the same stream. RSP-QL is a unifying formal model that extends SPARQL with concepts from CQL and SECRET, makes the engines’ hidden assumptions explicit, and defines correctness for continuous query answers; it informed the work of the W3C RSP Community Group.
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Incremental Reasoning on Streams and Rich Background Knowledge — Nominated for the ESWC 7-year most influential paper award (2017)
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus
The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30 – June 3, 2010, Proceedings, Part I, 2010A technique for Stream Reasoning that incrementally maintains the materialization of ontological entailments over RDF streams: by attaching an expiration time to each triple, it computes a correct materialization at every window change much faster than recomputing it from scratch. Nominated for the ESWC 7-year most influential paper award in 2017.
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C-SPARQL: a Continuous Query Language for RDF Data Streams
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus
Int. J. Semantic Comput., 2010The journal definition of C-SPARQL: RDF streams, windows, continuous query registration, and aggregates as orthogonal extensions of SPARQL, so that reasoners can work on knowledge that evolves over time.
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Querying RDF streams with C-SPARQL
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus
SIGMOD Rec., 2010Presents the C-SPARQL language to the database community: syntax and examples of continuous queries over windows of RDF streams, the application domains that already used it (sensors, urban computing, social semantic data), and an outlook on future research.
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C-SPARQL: SPARQL for continuous querying
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus
Proceedings of the 18th International Conference on World Wide Web, WWW 2009, Madrid, Spain, April 20-24, 2009, 2009The first presentation of C-SPARQL, the extension of SPARQL for continuous queries registered over windows of RDF data streams, illustrated with Urban Computing examples. It is one of my most cited works.
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It’s a Streaming World! Reasoning upon Rapidly Changing Information
Emanuele Della Valle, Stefano Ceri, Frank van Harmelen, Dieter Fensel
IEEE Intell. Syst., 2009With S. Ceri, F. van Harmelen, and D. Fensel, it presents Stream Reasoning as a new multidisciplinary research area that integrates data streams, the Semantic Web, and reasoning systems, and sketches its research agenda, with traffic monitoring and mobile applications as motivating examples.
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A First Step Towards Stream Reasoning
Emanuele Della Valle, Stefano Ceri, Davide Francesco Barbieri, Daniele Braga, Alessandro Campi
Future Internet – FIS 2008, First Future Internet Symposium, FIS 2008, Vienna, Austria, September 29-30, 2008, Revised Selected Papers, 2008The paper that introduced the concept of Stream Reasoning. It observes that reasoners scale on static ontological knowledge but ignore rapidly changing information, while stream management systems cannot reason, and proposes to couple the two to reason in real time.