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Lyes Attouche

dblp:287/7106 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Data models and query languages › schema languages
JSON Schema
2.332026
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
1.622026
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
0.812024
Validation of Modern JSON Schema: Formalization and Complexity · Proc. ACM Program. Lang. 2024
Automated reasoning and model checking
satisfiability
0.612022
Witness Generation for JSON Schema · Proc. VLDB Endow. 2022
Automata and formal languages › tree automata
alternating tree automata
0.312026
Witness Generation for Classical JSON Schema · ACM Trans. Database Syst. 2026
Data integration and cleaning › data quality
data validation
0.212024
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
YearPublicationVenuePosition
2026 Elimination of annotation dependencies in validation for Modern JSON Schema
abstract
International 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 Schema
abstract
JSON 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 Complexity
abstract
JSON 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 Schema
abstract
JSON 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 Generation
abstract
International audience
Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Francesco Falleni, Giorgio Ghelli, Cristiano Landi, Carlo Sartiani, Stefanie Scherzinger
EDBT1