Results 11 to 20 of about 772,854 (294)

Human-in-the-Loop Schema Inference for Massive JSON Datasets. [PDF]

International Conference on Extending Database Technology, 2020
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.
Baazizi, Mohamed-Amine, Berti, Clément, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo   +4 more
semanticscholar   +3 more sources

Schema-Agnostic Data Type Inference and Validation for Exchanging JSON-Encoded Construction Engineering Information

Buildings
Modern construction and infrastructure projects produce large volumes of heterogeneous data, including building information models, JSON sensor streams, and maintenance logs.
Seokjoon You, Hyon Wook Ji, Hyunseok Kwak, Taewon Chung, Moongyo Bae   +4 more
doaj   +4 more sources

A Type System for Interactive JSON Schema Inference (Extended Abstract). [PDF]

International Colloquium on Automata, Languages and Programming, 2019
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
Baazizi, Mohamed-Amine, Colazzo, Dario, Ghelli, Giorgio, Sartiani, Carlo   +3 more
semanticscholar   +5 more sources

Schema, Inference, and Memory

, 2022
The question of how knowledge structures, or schemas, are formed and how they influence memory and inference has posed long-standing challenges for cognitive scientists. Recent neuroscientific advances have improved our ability to quantify schemas as they are formed and during their expression in novel situations, thus improving our mechanistic ...
Nicole Varga, Neal Morton, Alison Preston   +2 more
openaire   +2 more sources

Schema Inference for Property Graphs.

, 2021
Graphs are pervasive in many applications in which interconnected data are used to represent, explore and predict digital and real-world phenomena. Oftentimes, graph data comes without a predefined structure and in a constraint-less fashion, thus leading to inconsistency and poor quality.
Lbath, Hanâ, Bonifati, Angela, Harmer, Russ   +2 more
openaire   +2 more sources

Graph schemas as abstractions for transfer learning, inference, and planning [PDF]

CoRR, 2023
Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neurobiological insights, we propose graph schemas as a mechanism of abstraction for transfer learning.
J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George   +8 more
openaire   +3 more sources

The effects of different sports activities on body scheme in preschoolers and primary school children: an experimental and theoretical analysis [PDF]

Journal of Physiological Anthropology
Background Children are now introduced to sports at an early age, often beginning to learn complex movements between the ages of 3 and 5, which coincides with the formation of a body schema.
Mikhail Shestakov, Elena Godina, Tamara Abramova, Alexander Korchagin   +3 more
doaj   +2 more sources

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology

The Open Journal of Astrophysics
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan   +14 more
doaj   +2 more sources

A causal discovery-based adaptive fusion algorithm for multi-source heterogeneous knowledge graphs [PDF]

Scientific Reports
Multi-source heterogeneous knowledge graph fusion faces significant challenges due to schema heterogeneity, entity conflicts, and relationship inconsistencies across different knowledge sources.
Ting Wang
doaj   +2 more sources

Inference-based schema discovery for RDF data

Data and Knowledge Engineering
The Semantic Web represents a huge information space where an increasing number of datasets, described in RDF, are made available to users and applications. In this context, the data is not constrained by a predefined schema. In RDF datasets, the schema may be incomplete or even missing.
Zoubida Kedad, Stéphane Lopes, Redouane Bouhamoum   +2 more
exaly   +3 more sources