Results 261 to 270 of about 123,183 (310)

StructuredRAG: JSON Response Formatting with Large Language Models

arXiv.org
The ability of Large Language Models (LLMs) to generate structured outputs, such as JSON, is crucial for their use in Compound AI Systems. However, evaluating and improving this capability remains challenging.
Connor Shorten, Charles Pierse, Thomas Benjamin Smith, Erika Cardenas, Akanksha Sharma, John Trengrove, Bob van Luijt   +6 more
semanticscholar   +1 more source

Beyond JSON: Evaluating Serialization Formats for Space-Efficient Communication

Advances in Cyber-Physical Systems
Distributed systems rely on efficient inter-service communication, heavily impacted by data transmission costs. This study investigates alternative serialization formats, like Avro and MessagePack, to reduce data size compared to the common JSON format ...
Eduard Maltsev, O. Muliarevych
semanticscholar   +1 more source

JSON document clustering based on schema embeddings

Journal of information science, 2022
The growing popularity of JSON as the data storage and interchange format increases the availability of massive multi-structured data collections. Clustering JSON documents has become a significant issue in organising large data collections.
D. Priya, P. S. Thilagam
semanticscholar   +1 more source

JsonCurer: Data Quality Management for JSON Based on an Aggregated Schema

IEEE Transactions on Visualization and Computer Graphics
High-quality data is critical to deriving useful and reliable information. However, real-world data often contains quality issues undermining the value of the derived information. Most existing research on data quality management focuses on tabular data,
Kai Xiong, Xinyi Xu, Siwei Fu, Di Weng, Yongheng Wang, Yingcai Wu   +5 more
semanticscholar   +1 more source

IDEA-DAC: Integrity-Driven Editing for Accountable Decentralized Anonymous Credentials via ZK-JSON

IACR Cryptology ePrint Archive
Decentralized Anonymous Credential (DAC) systems are increasingly relevant, especially when enhancing revocation mechanisms in the face of complex traceability challenges.
Shuhao Zheng, Zonglun Li, Junliang Luo, Ziyue Xin, Xue Liu   +4 more
semanticscholar   +1 more source

ReCG: Bottom-Up JSON Schema Discovery Using a Repetitive Cluster-and-Generalize Framework

Proceedings of the VLDB Endowment
The schemalessness, one of the major advantages of JSON representation format, comes with high penalties in querying and operations by denying various critical functions such as query optimizations, indexing, or data verification.
Joohyung Yun, Byungchul Tak, Wook-Shin Han   +2 more
semanticscholar   +1 more source

Large Language Models for JSON Schema Discovery

arXiv.org
Semi-structured data formats such as JSON have proved to be useful data models for applications that require flexibility in the format of data stored.
Michael J. Mior
semanticscholar   +1 more source

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

International Conference on Conceptual Modeling, 2021
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.
M. Baazizi, Dario Colazzo, G. Ghelli, C. Sartiani, Stefanie Scherzinger   +4 more
semanticscholar   +1 more source

JSON Numeric Values: Defining Numbers in JSON

2016
Chapter 7 looked at the string data type; now let’s examine the number data value in JSON. It lets you define a wide range of numeric data values that can be supported in your JSON data-definition structures.
openaire   +1 more source

JSON Character Values: Defining Strings in JSON

2016
Chapter 6 looked at the two different types of special data types, Boolean and null; Chapter 4 covered JSON object data structures, the primary structures used to define objects; and Chapter 5 discussed the secondary array data structure used to define data arrays.
openaire   +1 more source