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103 results on '"SARTIANI, CARLO"'

1. Validation of Modern JSON Schema: Formalization and Complexity

Author

Attouche, Lyes, Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, and Scherzinger, Stefanie

Subjects
Computer Science - Databases, Computer Science - Programming Languages
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 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 PTIME complexity of Classical JSON Schema validation, and we were surprised to discover a completely different truth: data validation, that 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 consider a central contribution of the work that we present here. We then prove that its data validation problem is PSPACE-complete. We prove that the origin of the problem lies in 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 PTIME 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.

Published
2023

2. Negation-Closure for JSON Schema

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, and Scherzinger, Stefanie

Subjects
Computer Science - Databases, Computer Science - Programming Languages
Abstract

JSON Schema is an evolving standard for describing families of JSON documents. It is a logical language, based on a set of assertions that describe features of the JSON value under analysis and on logical or structural combinators for these assertions, including a negation operator. Most logical languages with negation enjoy negation closure, that is, for every operator they have a negation dual that expresses its negation. We show that this is not the case for JSON Schema, we study how that changed with the latest versions of the Draft, and we discuss how the language may be enriched accordingly. In the process, we define an algebraic reformulation of JSON Schema, which we successfully employed in a prototype system for generating schema witnesses.

Published
2022

3. Witness Generation for JSON Schema

Author

Attouche, Lyes, Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, and Scherzinger, Stefanie

Subjects
Computer Science - Databases, Computer Science - Programming Languages
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 assertions, and features negation and recursion. The static analysis of JSON Schema documents comprises practically relevant problems, including schema satisfiability, inclusion, and equivalence. These three 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, 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, and on the ability of the algorithm to run in a reasonable time on a large set of real-world examples, despite the exponential complexity of the underlying problem.

Published
2022

4. An Empirical Study on the 'Usage of Not' in Real-World JSON Schema Documents (Long Version)

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, and Scherzinger, Stefanie

Subjects
Computer Science - Databases
Abstract

In this paper, we study the usage of negation in JSON Schema data modeling. Negation is a logical operator that is rarely present in type systems and schema description languages, since it complicates decision problems. As a consequence, many software tools, but also formal frameworks for working with JSON Schema, do not fully support negation. As of today, the question whether covering negation is practically relevant, or a mainly theoretical exercise (albeit challenging), is open. This motivates us to study whether negation is really used in practice, for which aims, and whether it could be - in principle - replaced by simpler operators. We have collected the most diverse corpus of JSON Schema documents analyzed so far, based on a crawl of 90k open source schemas hosted on GitHub. We perform a systematic analysis, quantify usage patterns of negation, and also qualitatively analyze schemas. We show that negation is indeed used, following a stable set of patterns, with the potential to mature into design patterns.

Published
2021

5. Not Elimination and Witness Generation for JSON Schema

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, and Scherzinger, Stefanie

Subjects
Computer Science - Databases
Abstract

JSON Schema is an evolving standard for the description of families of JSON documents. JSON Schema is a logical language, based on a set of assertions that describe features of the JSON value under analysis and on logical or structural combinators for these assertions. As for any logical language, problems like satisfaction, not-elimination, schema satisfiability, schema inclusion and equivalence, as well as witness generation, have both theoretical and practical interest. While satisfaction is trivial, all other problems are quite difficult, due to the combined presence of negation, recursion, and complex assertions in JSON Schema. To make things even more complex and interesting, JSON Schema is not algebraic, since we have both syntactic and semantic interactions between different keywords in the same schema object. With such motivations, we present in this paper an algebraic characterization of JSON Schema, obtained by adding opportune operators, and by mirroring existing ones. We present then algebra-based approaches for dealing with not-elimination and witness generation problems, which play a central role as they lead to solutions for the other mentioned complex problems.

Published
2021

7. An Empirical Study on the 'Usage of Not' in Real-World JSON Schema Documents

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Scherzinger, Stefanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ghose, Aditya, editor, Horkoff, Jennifer, editor, Silva Souza, Vítor E., editor, Parsons, Jeffrey, editor, and Evermann, Joerg, editor

Published
2021
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9. Scalable Saturation of Streaming RDF Triples

Author

Farvardin, Mohammad Amin, Colazzo, Dario, Belhajjame, Khalid, Sartiani, Carlo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hameurlain, Abdelkader, editor, Tjoa, A Min, editor, Lamarre, Philippe, editor, and Zeitouni, Karine, editor

Published
2020
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10. Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas

Author

Fruth, Michael, Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Scherzinger, Stefanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Grossmann, Georg, editor, and Ram, Sudha, editor

Published
2020
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11. Typing Regular Path Query Languages for Data Graphs

Author

Colazzo, Dario and Sartiani, Carlo

Subjects
Computer Science - Databases, Computer Science - Programming Languages
Abstract

Regular path query languages for data graphs are essentially \emph{untyped}. The lack of type information greatly limits the optimization opportunities for query engines and makes application development more complex. In this paper we discuss a simple, yet expressive, schema language for edge-labelled data graphs. This schema language is, then, used to define a query type inference approach with good precision properties.

Published
2015

17. Witness Generation for JSON Schema

Author

Attouche, Lyes, primary, Baazizi, Mohamed-Amine, additional, Colazzo, Dario, additional, Ghelli, Giorgio, additional, Sartiani, Carlo, additional, and Scherzinger, Stefanie, additional

Published
2022
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18. Efficient Inclusion for a Class of XML Types with Interleaving and Counting

Author

Ghelli, Giorgio, Colazzo, Dario, Sartiani, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Arenas, Marcelo, editor, and Schwartzbach, Michael I., editor

Published
2007
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19. A Query Algebra for XML P2P Databases

Author

Sartiani, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Grust, Torsten, editor, Höpfner, Hagen, editor, Illarramendi, Arantza, editor, Jablonski, Stefan, editor, Mesiti, Marco, editor, Müller, Sascha, editor, Patranjan, Paula-Lavinia, editor, Sattler, Kai-Uwe, editor, Spiliopoulou, Myra, editor, and Wijsen, Jef, editor

Published
2006
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20. Mapping Maintenance in XML P2P Databases

Author

Colazzo, Dario, Sartiani, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bierman, Gavin, editor, and Koch, Christoph, editor

Published
2005
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21. XPeer : A Self-Organizing XML P2P Database System

Author

Sartiani, Carlo, Manghi, Paolo, Ghelli, Giorgio, Conforti, Giovanni, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lindner, Wolfgang, editor, Mesiti, Marco, editor, Türker, Can, editor, Tzitzikas, Yannis, editor, and Vakali, Athena I., editor

Published
2005
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23. A Typed Text Retrieval Query Language for XML Documents.

Author

Colazzo, Dario, Sartiani, Carlo, Albano, Antonio, Manghi, Paolo, Ghelli, Giorgio, Lini, Luca, and Paoli, Michele

Abstract

Discussion of XML focuses on a description of Tequyla-TX, a typed text retrieval query language for XML documents that can search on both content and structures. Highlights include motivations; numerous examples; word-based and char-based searches; tag-dependent full-text searches; text normalization; query algebra; data models and term language; and grammar. (LRW)

Published
2002

25. A Tool for JSON Schema Witness Generation

Author

Attouche, Lyes, Baazizi, Mohamed-Amine, Colazzo, Dario, Falleni, Francesco, Ghelli, Giorgio, Landi, Cristiano, Sartiani, Carlo, Scherzinger, Stefanie, Bases de Données (BD), LIP6, and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
Abstract

International audience; JSON Schema is an evolving standard for the description of JSON documents. It is an extremely powerful language endowed with boolean operators and recursive definitions. Hence, classical problems like schema consistency and equivalence may be challenging without well-principled tools. Based on our recent effort for laying down an algebraic formal semantics of JSON Schema, we demonstrate an approach for generating valid witnesses of a userdefined schema. Our goal is not only to allow programmers to design schemas that meet their intentions, but also to guide them in their journey to understanding the semantics of existing schemas, in an interactive fashion. We thus aim to contribute to the adoption of the JSON Schema language by facilitating its use.

Published
2021

26. Not Elimination and Witness Generation for JSON Schema (short version)

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Scherzinger, Stefanie, Bases de Données (BD), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), and Baazizi, Mohamed-Amine

Subjects
negation, witness generation, JSON Schema, [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; JSON Schema is an evolving standard for the description of families of JSON documents. JSON Schema is a logical language, based on a set of assertions that describe features of the JSON value under analysis and on logical or structural combinators for these assertions. As for any logical language, problems like satisfaction, not-elimination, schema satisfiability, schema inclusion and equivalence, as well as witness generation, have both theoretical and practical interest. While satisfaction is trivial, all other problems are quite difficult, due to the combined presence of negation, recursion, and complex assertions in JSON Schema. To make things even more complex and interesting, JSON Schema is not algebraic, since we have both syntactic and semantic interactions between different keywords in the same schema object. With such motivations, we present in this paper an algebraic characterization of JSON Schema, obtained by adding opportune operators, and by mirroring existing ones. We present then algebrabased approaches for dealing with not-elimination and witness generation problems, which play a central role as they lead to solutions for the other mentioned complex problems.

Published
2020

27. Human-in-the-Loop Schema Inference for Massive JSON Datasets

Author

Baazizi, Mohamed-Amine, Berti, Clément, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Bases de Données (BD), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Informatica [Pisa] (DI), University of Pisa - Università di Pisa, Dipartimento di Matematica e Informatica (DMI), and Università degli Studi della Basilicata

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

International audience; JSON established itself as a popular data format for representing data whose structure is irregular or unknown a priori. JSON collections are usually massive and schema-less. Inferring a schema describing the structure of these collections is crucial for formulating meaningful queries and for adopting schema-based optimizations. In a recent work, we proposed a Map/Reduce schema inference approach that either infers a compact representation of the input collection or a precise description of every possible shape in the data. Since no level of precision is ideal, it is more appealing to give the analyst the freedom of choosing between different levels of precisions in an interactive fashion. In this paper we describe a schema inference system offering this important functionality.

Published
2020

33. A type system for interactive JSON schema inference (extended abstract)

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Bases de Données (BD), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Informatica [Pisa], University of Pisa - Università di Pisa, Dipartimento di Matematica Informatica ed Economia (DiMIE), Università degli studi della Basilicata [Potenza] (UNIBAS), Christel Baier, Ioannis Chatzigiannakis, Paola Flocchini, and Stefano Leonardi

Subjects
000 Computer science, knowledge, general works, JSON, JSON typing, interactive JSON typing, JSON typing, type systems, JSON, Computer Science, [INFO]Computer Science [cs], interactive inference, interactive JSON typing
Abstract

International audience; In this paper we present the first JSON type system that provides the possibility of inferring a schema by adopting different levels of precision/succinctness for different parts of the dataset, under user control. This feature gives the data analyst the possibility to have detailed schemas for parts of the data of greater interest, while more succinct schema is provided for other parts, and the decision can be changed as many times as needed, in order to explore the schema in a gradual fashion, moving the focus to different parts of the collection, without the need of reprocessing data and by only performing type rewriting operations on the most precise schema.

Published
2019

34. Proofs for Parametric Schema Inference for Massive JSON Datasets

Author

Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Bases de Données (BD), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Informatica [Pisa], University of Pisa - Università di Pisa, Dipartimento di Matematica e Informatica (DMI), Università degli Studi della Basilicata, and Baazizi, Mohamed-Amine

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Published
2018

35. A typed text retrieval query language for XML documents

Author

Colazzo, Dario, Sartiani, Carlo, Albano, Antonio, Manghi, Paolo, Ghelli, Giorgio, Lini, Luca, and Paoli, Michele

Subjects
XML (Document markup language) -- Research, Information storage and retrieval -- Research, Information science -- Research, Query languages -- Research, Computers, Library and information science, Mathematics
Abstract

A typed text-retrieval query language, called Tequyla-TX, is described. Tequyla-TX is able to perform word-based and char-based text searches, supporting the requirements of literary applications.

Published
2002

38. Schema Inference for Massive JSON Datasets

Author

Baazizi, Mohamed-Amine, Ben Lahmar, Houssem, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Bases de Données (BD), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université de stuttgart, Université de Stuttgart, Université Paris sciences et lettres (PSL), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Informatica [Pisa] (DI), University of Pisa - Università di Pisa, Dipartimento di Matematica e Informatica (DMI), Università degli Studi della Basilicata, and Università degli studi della Basilicata [Potenza] (UNIBAS)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], JSON, CCS Concepts •Information systems → Semi-structured data, Data model extensions, •Theory of computation → Type theory, Logic, schema inference, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience; Recent years have seen the widespread use of JSON as a data format to represent massive data collections. JSON data collections are usually schemaless. While this ensures several advantages, the absence of schema information has important negative consequences: the correctness of complex queries and programs cannot be statically checked, users cannot rely on schema information to quickly figure out structural properties that could speed up the formulation of correct queries, and many schema-based optimizations are not possible. In this paper we deal with the problem of inferring a schema from massive JSON data sets. We first identify a JSON type language which is simple and, at the same time, expressive enough to capture irregularities and to give complete structural information about input data. We then present our main contribution, which is the design of a schema inference algorithm, its theoretical study and its implementation based on Spark, enabling reasonable schema inference time for massive collections. Finally, we report about an experimental analysis showing the effectiveness of our approach in terms of execution time, precision and conciseness of inferred schemas, and scalability.

Published
2017

39. Inférence de Schémas pour Données JSON Massives

Author

Baazizi, Mohamed-Amine, Ben Lahmar, Houssem, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Bases de Données (BD), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), University of Stuttgart, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Dipartimento di Informatica [Pisa] (DI), University of Pisa - Università di Pisa, Dipartimento di Matematica e Informatica (DMI), Università degli Studi della Basilicata, Baazizi, Mohamed-Amine, and Università degli studi della Basilicata [Potenza] (UNIBAS)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

National audience; Ces dernières années ont connu une large adoption de JSON en tant que format de représentation de données massives. Les données JSON sont généralement dépourvues de schémas puisqu'elles sont produites et gérées de manière flexible. Malgré cet avantage, l'absence de schéma présente de nombreux inconvénients : la correction des requêtes et des programmes ne peut être vérifiée de manière statique comme c'est la cas traditionnellement, les utilisateurs ne disposent d'aucun moyen le permettant de découvrir la structure des données sous-jacentes et, de manière plus générale, les techniques d'optimisations basées sur les schémas ne peuvent être appliquées.Dans ce travail nous nous intéressons à l'inférence de schémas pour des données JSON massives. Notre première contribution consiste à proposer un langage de types pour JSON permettant de représenter la structure complexe des données analysées. Notre seconde contribution concerne le développement d'un algorithme d'inférence distribué et de son implantation dans Spark afin de garantir une exécution efficace sur des données volumineuses. Les résultats obtenus suite à une première étude expérimentale permettent de conclure que notre approche est satisfaisant en terme de temps d'exécution et de concision de schémas inférés.

Published
2016

40. PathGraph

Author

Colazzo, Dario, primary, Mecca, Vincenzo, additional, Nolé, Maurizio, additional, and Sartiani, Carlo, additional

Published
2018
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49. Evaluating Queries and Updates on Big XML Documents.

Author

Bidoit, Nicole, Colazzo, Dario, Malla, Noor, and Sartiani, Carlo

Subjects
XML (Extensible Markup Language), STATISTICS, QUERY languages (Computer science), ELECTRONIC data processing, COMPUTER systems
Abstract

In this paper we present Andromeda, a system for processing queries and updates on large XML documents. The system is based on the idea of statically and dynamically partitioning the input document, so as to distribute the computing load among the machines of a MapReduce cluster. [ABSTRACT FROM AUTHOR]

Published
2018
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50. Typing Massive JSON Datasets

Author

Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Database optimizations and architectures for complex large data (OAK), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Dipartimento di Informatica [Pisa], University of Pisa - Università di Pisa, Dipartimento di Matematica e Informatica (DMI), Università degli Studi della Basilicata, Colazzo, Dario, and Università degli studi della Basilicata [Potenza] (UNIBAS)

Subjects
[INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience; Cloud-specific languages are usually untyped, and no guarantees about the correctness of complex jobs can be statically obtained. Datasets too are usually untyped and no schema information is needed for their manipulation. In this paper we sketch a typing algorithm for JSON datasets. Our approach can be used to infer a succinct type from scratch for a collection of JSON objects, as well as to validate a dataset against a human-designed type and, if necessary, to adapt and improve this type.

Published
2012

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