Demo Data from SIGIR 2026 / reviewers in the wild / expert
Lyes Attouche
dblp:287/7106
· DBLP profile ↗
5ranked-venue papers
5as first author
5since 2021 · last 2026
0009-0008-8454-6364ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 3 first-author · 3 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
3 papers |
Data models and query languages · 63% Query processing and optimization · 32% Data integration and cleaning · 5% | |
| Theoretical computer science
3 papers |
Computational complexity · 39% Automated reasoning and model checking · 30% Automata and formal languages · 16% | |
| Software engineering, system software, and programming languages
1 paper |
Program verification · 100% |
Topics — the 6 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages › schema languages
JSON Schema |
2.3 | 3 | 2026 | Witness Generation for Classical JSON Schema · ACM Trans. Database Syst. 2026 Validation of Modern JSON Schema: Formalization and Complexity · Proc. ACM Program. Lang. 2024 Witness Generation for JSON Schema · Proc. VLDB Endow. 2022 |
Query processing and optimization › query result explanation
witness generation |
1.6 | 2 | 2026 | Witness Generation for Classical JSON Schema · ACM Trans. Database Syst. 2026 Witness Generation for JSON Schema · Proc. VLDB Endow. 2022 |
Data models and query languages
schema languages |
0.8 | 1 | 2024 | Validation of Modern JSON Schema: Formalization and Complexity · Proc. ACM Program. Lang. 2024 |
Automated reasoning and model checking
satisfiability |
0.6 | 1 | 2022 | Witness Generation for JSON Schema · Proc. VLDB Endow. 2022 |
Automata and formal languages › tree automata
alternating tree automata |
0.3 | 1 | 2026 | Witness Generation for Classical JSON Schema · ACM Trans. Database Syst. 2026 |
Data integration and cleaning › data quality
data validation |
0.2 | 1 | 2024 | Validation of Modern JSON Schema: Formalization and Complexity · Proc. ACM Program. Lang. 2024 |
Methods — techniques the papers use, named apart from their topics
experimental evaluation · 3.0direct algorithm · 2.0formalization · 1.5complexity analysis · 1.5PSPACE-completeness proof · 1.5direct algorithms · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Elimination of annotation dependencies in validation for Modern JSON SchemaabstractInternational audience Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli, Stefan Klessinger, Carlo Sartiani, Stefanie Scherzinger |
Theor. Comput. Sci. | 1 |
| 2026 | Witness Generation for Classical JSON SchemaabstractJSON Schema is an important, evolving standard schema language for families of JSON documents. It is based on a complex combination of structural and Boolean operators, including negation, as well as mutually recursive variables. The static analysis of JSON Schema documents comprises practically relevant problems, including schema satisfiability, inclusion, and equivalence. These three can be reduced to witness generation: given a schema, generate an element of the schema — if it exists — otherwise report unsatisfiability. Schema satisfiability, inclusion, and equivalence have been shown to be decidable, by reduction to reachability in alternating tree automata. However, no witness generation algorithm has yet been formally described. We contribute a first, direct algorithm for JSON Schema witness generation. We study its effectiveness and efficiency, in experiments over several schema collections, including thousands of real-world schemas. Our focus is on the completeness of the language (where we only exclude the "uniqueItems" operator), on the ability of the algorithm to run in reasonable time on a large set of real-world examples, despite the exponential complexity of the problem, and on proving its correctness and completeness. Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli, Carlo Sartiani, Stefanie Scherzinger |
ACM Trans. Database Syst. | 1 |
| 2024 | Validation of Modern JSON Schema: Formalization and ComplexityabstractJSON Schema is the de-facto standard schema language for JSON data. The language went through many minor revisions, but the most recent versions of the language, starting from Draft 2019-09, added two novel features, dynamic references and annotation-dependent validation , that change the evaluation model. Modern JSON Schema is the name used to indicate all versions from Draft 2019-09, which are characterized by these new features, while Classical JSON Schema is used to indicate the previous versions. These new “modern” features make the schema language quite difficult to understand and have generated many discussions about the correct interpretation of their official specifications; for this reason, we undertook the task of their formalization. During this process, we also analyzed the complexity of data validation in Modern JSON Schema, with the idea of confirming the polynomial complexity of Classical JSON Schema validation, and we were surprised to discover a completely different truth: data validation, which is expected to be an extremely efficient process, acquires, with Modern JSON Schema features, a PSPACE complexity. In this paper, we give the first formal description of Modern JSON Schema, which we have discussed with the community of JSON Schema tool developers, and which we consider a central contribution of this work. We then prove that its data validation problem is PSPACE-complete. We prove that the origin of the problem lies in the Draft 2020-12 version of dynamic references, and not in annotation-dependent validation. We study the schema and data complexities, showing that the problem is PSPACE-complete with respect to the schema size even with a fixed instance but is in P when the schema is fixed and only the instance size is allowed to vary. Finally, we run experiments that show that there are families of schemas where the difference in asymptotic complexity between dynamic and static references is extremely visible, even with small schemas. Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli, Carlo Sartiani, Stefanie Scherzinger |
Proc. ACM Program. Lang. | 1 |
| 2022 | Witness Generation for JSON SchemaabstractJSON Schema is a schema language for JSON documents, based on a complex combination of structural operators, Boolean operators (negation included), and recursive variables. The static analysis of JSON Schema documents comprises practically relevant problems, including schema satisfiability, inclusion, and equivalence. These problems can be reduced to witness generation: given a schema, generate an element of the schema --- if it exists --- and report failure otherwise. Schema satisfiability, inclusion, and equivalence have been shown to be decidable. However, no witness generation algorithm has yet been formally described. We contribute a first, direct algorithm for JSON Schema witness generation, and study its effectiveness and efficiency in experiments over several schema collections, including thousands of real-world schemas. Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli, Carlo Sartiani, Stefanie Scherzinger |
Proc. VLDB Endow. | 1 |
| 2021 | A Tool for JSON Schema Witness GenerationabstractInternational audience Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Francesco Falleni, Giorgio Ghelli, Cristiano Landi, Carlo Sartiani, Stefanie Scherzinger |
EDBT | 1 |