Negation-Closure for JSON Schema

arXiv (Cornell University), 2020

We propose a logical framework, based on Datalog, to study the foundations of querying JSON data. The main feature of our approach, which we call J-Logic, is the emphasis on paths. Paths are sequences of keys and are used to access the tree structure of nested JSON objects. J-Logic also features "packing" as a means to generate a new key from a path or subpath. J-Logic with recursion is computationally complete, but many queries can be expressed without recursion, such as deep equality. We give a necessary condition for queries to be expressible without recursion. Most of our results focus on the deterministic nature of JSON objects as partial functions from keys to values. Predicates defined by J-Logic programs may not properly describe objects, however. Nevertheless we show that every object-to-object transformation in J-Logic can be defined using only objects in intermediate results. Moreover we show that it is decidable whether a positive, nonrecursive J-Logic program always returns an object when given objects as inputs. Regarding packing, we show that packing is unnecessary if the output does not require new keys. Finally, we show the decidability of query containment for positive, nonrecursive J-Logic programs. This papter is the extended version of an earlier version published in the proceedings of SIGMOD/PODS 2017 [23].

Lecture Notes in Computer Science, 2020

JSON Schema is maturing into the de-facto schema language for JSON documents. When JSON Schema declarations evolve, the question arises how the new schema will deal with JSON documents that still adhere to the legacy schema. This is particularly crucial in the maintenance of software APIs. In this paper, we present the results of our empirical study of the first generation of tools for checking JSON Schema containment which we apply to a diverse collection of over 230 realworld schemas and their altogether 1k historic versions. We assess two such special-purpose tools w.r.t. their applicability to real-world schemas and identify weak spots. Based on this analysis, we enumerate specific open research challenges that are based on real-world problems.

2021

In recent years knowledge graphs gained much attention. Many big companies, like Amazon, Facebook, or Google, are building a knowledge graph. This movement also affected smaller companies, as a growing number of those ask for knowledge graph solutions or start to build them themselves. The Semantic Web provides a long list of knowledge representation formalisms and ontology languages to build knowledge graphs. Those formalisms and languages cover a broad range of computational complexity, from simple to even undecidable. Focusing on the manifoldness of companies building knowledge graphs, a language that many people understand and accept is intended. Therefore, we present an alternative approach to existing knowledge representation formalisms and ontology languages based on JSON. JSON is the most popular format for exchanging information between systems. Also, JSON comes with its schema language called JSON Schema, allowing to define the structure of a given JSON document. Our paper...

2019

JSON Schema (application/schema+json) has several purposes, one of which is JSON instance validation. This document specifies a vocabulary for JSON Schema to describe the meaning of JSON documents, provide hints for user interfaces working with JSON data, and to make assertions about what a valid document must look like.

2021

A test suite for tools that check JSON Schema containment. The code is being maintained at https://github.com/sdbs-uni-p/json-schema-containment-testsuite.

Advances in Databases and Information Systems, 2020

Document databases use JSON (JavaScript Object Notation) for data representation. In the category of NoSQL databases they support an ability to handle large volumes of data at the absence of an explicit data schema. On the other hand, schema information is sometimes essential for applications during data retrieval. Consequently, there are approaches to schema construction in the JSON community. We will suppose JSON collections equipped by a schema compatible in its expressivity to the JSON Schema recommendation. We use a formal approach based on typed functional data objects. We accommodate a classical approach to functional databases based on a typed λ-calculus to obtain a powerful nonprocedural query language over JSON data with sound semantics.

2019

When a JSON collection is obtained from any source, it is often the case that its schema is not perfectly known, hence a tool that can infer that schema its extremely useful. JSON collections are often irregular, and, in this case, different schemas may be inferred, characterized by a different degree of succinctness and precision, going from an extremely succinct schema that only specifies which fields appear, to a very detailed, and long, list of all possible field combinations. While one may try and define an 'optimal' trade-off between succinctness and precision, we believe that such an absolute optimum does not actually exist, since the relative value of succinctness and detail depends on the specific task at hand. 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 poss...

2019

JSON Schema defines the media type "application/schema+json", a JSON- based format for describing the structure of JSON data. JSON Schema asserts what a JSON document must look like, ways to extract information from it, and how to interact with it. The "application/ schema-instance+json" media type provides additional feature-rich integration with "application/schema+json" beyond what can be offered for "application/json" documents.

Proceedings of the 2019 International Conference on Management of Data, 2019

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, or on the type abstractions offered by modern programming languages like Swift or TypeScript. The main aim of this tutorial is to provide the audience with the basic notions for enjoying all the benefits that schemas 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 review how modern programming languages support JSON data as first-class citizens; (3) we analyze tools that can infer schemas from data, or that exploit schema information for improving data parsing and management; and (4) we discuss some open research challenges and opportunities related to JSON data.

2017

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...