International audience

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.

International audience

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.

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.

International audience

International audience

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.

The last few years have seen the fast and ubiquitous diffusion of JSON as one of the most widely used formats for publishing and interchanging data, as it combines the flexibility of semistructured data models with well-known data structures like records and arrays. The user willing to effectively manage JSON data collections can rely on several schema languages, like JSON Schema, JSound, and Joi, as well as on the type abstractions offered by modern programming and scripting languages like Swift or TypeScript. The main aim of this tutorial is to provide the audience (both researchers and practitioners) with the basic notions for enjoying all the benefits that schema and types can offer while processing and manipulating JSON data. This tutorial focuses on four main aspects of the relation between JSON and schemas: (1) we survey existing schema language proposals and discuss their prominent features; (2) we analyze tools that can infer schemas from data, or that exploit schema information for improving data parsing and management; and (3) we discuss some open research challenges and opportunities related to JSON data.

In the recent years JSON affirmed as a very popular data format for representing massive data collections. JSON data collections are usually schemaless. While this ensures sev- eral advantages, the absence of schema information has im- portant negative consequences: the correctness of complex queries and programs cannot be statically checked, users cannot rely on schema information to quickly figure out the 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 datasets. We first identify a JSON type language which is simple and, at the same time, expressive enough to capture irregularities and to give com- plete 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 implemen- tation based on Spark, enabling reasonable schema infer- ence time for massive collections. Finally, we report about an experimental analysis showing the effectiveness of our ap- proach in terms of execution time, precision, and conciseness of inferred schemas, and scalability.

While projection techniques have been extensively investigated for XML querying, we are not aware of applications to XML updating. This paper investigates a projection based optimization mechanism for XQuery Update Facility expressions in the presence of a schema. This paper includes a formal development and study of the method as well as experiments testifying its effectiveness.

The management of temporal data is a crucial issue in many applications. Recently, XML has become the standard for data exchange and representation. Consequently, important efforts have been made on the development of temporal extensions for XML. This paper investigates how to generate or maintain space-efficient time-stamped documents. We formally define a notion of compactness which allows for comparing documents. Then, we present two methods. For the first one, called general method, no restriction is made on the evolution of the XML documents whereas for the second one, called update-based method, changes are assumed to be specified by updates. For both methods, the issue is to enable processing very large documents, to use existing engines and to comply to Xquery Update Facility. The two methods are compared in terms of space-efficiency. The update-based method produces time-stamped XML documents that are more satisfactory wrt space-efficiency than the general method. This goes to show that the update-based method effectively takes advantage of the updates.