arXiv subjects

Stefan Klessinger

Publications and source records attributed to Stefan Klessinger.

5 recordsLinked to original sources

arXiv2026

JSON Schema Inclusion through Refutational Normalization: Reconciling Efficiency and Completeness

JSON Schema is the de facto standard for describing the structure of JSON documents. Deciding JSON Schema inclusion - whether every instance of a schema $S$ is also an instance of a schema $S^{\prime}$ - is a building block for version and API compatibility checks, schema refactoring, and large-scale corpus analysis. The problem is EXPTIME-complete, and the two existing families of algorithms fail in practice in complementary ways: rule-based algorithms are fast but incomplete by design, while witness-generation algorithms are complete but so slow that, under any reasonable timeout, they are incomplete in practice as well. We show that this specific problem admits a way out. We redesign the normalization core of the complete algorithm, so that, on the easy cases, it mimics the behavior of the fast rule-based algorithm, without losing completeness. We prove that it is exactly as efficient as the rule-based approach on every inclusion that approach can prove, while remaining complete on all others. We validate this on a vast collection of real-world schemas and hand-crafted schemas. We show that our algorithm advances the state of the art, making tractable a range of use cases that were out of reach for existing tools.

cs.DB↗

arXiv2025

Elimination of annotation dependencies in validation for Modern JSON Schema

JSON Schema is a logical language used to define the structure of JSON values. JSON Schema syntax is based on nested schema objects. In all versions of JSON Schema until Draft-07, collectively known as Classical JSON Schema, the semantics of a schema was entirely described by the set of JSON values that it validates. This semantics was the basis for a thorough theoretical study and for the development of tools to decide satisfiability and equivalence of schemas. Unfortunately, Classical JSON Schema suffered a severe limitation in its ability to express extensions of object schemas, which caused the introduction, with Draft 2019-09, of two disruptive features: annotation dependency and dynamic references. These new features undermine the previously developed semantic theory, and the algorithms used to decide satisfiability for Classical JSON Schema are not easy to extend. One possible solution is rewriting a schema written in Modern JSON Schema into an equivalent schema in Classical JSON Schema. In this paper we prove that the elimination of annotation dependent keywords cannot, in general, avoid an exponential increase of the schema dimension. We provide an algorithm to eliminate these keywords that, despite the theoretical lower bound, behaves quite well in practice, as we verify with an extensive set of experiments.

cs.DB↗

arXiv2023

Extracting JSON Schemas with Tagged Unions

With data lakes and schema-free NoSQL document stores, extracting a descriptive schema from JSON data collections is an acute challenge. In this paper, we target the discovery of tagged unions, a JSON Schema design pattern where the value of one property of an object (the tag) conditionally implies subschemas for sibling properties. We formalize these implications as conditional functional dependencies and capture them using the JSON Schema operators if-then-else. We further motivate our heuristics to avoid overfitting. Experiments with our prototype implementation are promising, and show that this form of tagged unions can successfully be detected in real-world GeoJSON and TopoJSON datasets. In discussing future work, we outline how our approach can be extended further.

cs.DB↗

arXiv2023

A Plaque Test for Redundancies in Relational Data [Extendend Version]

Inspired by the visualization of dental plaque at the dentist's office, this article proposes a novel visualization of redundancies in relational data. Our approach is based on a well-principled information-theoretic framework that has so far seen limited practical application in systems and tools. In this framework, we quantify the information content (or entropy) of each cell in a relation instance given a set of functional dependencies. The entropy value signifies the likelihood of recovering the cell value based on the dependencies and the remaining tuples. By highlighting cells with lower entropy, we effectively visualize redundancies in the data. We present an initial prototype implementation and demonstrate that a straightforward approach is insufficient to handle practical problem sizes. To address this limitation, we propose several optimizations which we prove to be correct. In addition, we present a Monte Carlo approximation with a known error, enabling a computationally tractable analysis. By applying our visualization technique to real-world datasets, we showcase its potential. Our vision is to empower data analysts by directing their focus in data profiling toward pertinent redundancies, analogous to the diagnostic role of a plaque test at the dentist's office.

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arXiv2022

Beyond the Badge: Reproducibility Engineering as a Lifetime Skill

Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science. SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that even at flagship conferences like ICSE, only slightly more than one-third of the technical papers (at the 2021 edition) receive recognition for artefact reusability. Knowledge and capabilities in setting up engineering environments that allow for reproducing artefacts and results over decades (a standard requirement in many traditional engineering disciplines), writing semi-literate commit messages that document crucial steps of a decision-making process and that are tightly coupled with code, or sustainably taming dynamic, quickly changing software dependencies, to name a few: They all contribute to solving the scientific reproducibility crisis, and enable software engineers to build sustainable, long-term maintainable, software-intensive, industrial systems. We propose to teach these skills at the undergraduate level, on par with traditional SWE topics.

cs.SE↗