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DigitalCommons@USU
Other literature type . 2019
Data sources: DigitalCommons@USU
https://dx.doi.org/10.26076/fa...
Other literature type . 2019
Data sources: Datacite
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Temporal JSON

Authors: Goyal, Aayush;
Abstract

JavaScript Object Notation (JSON) is a format for representing data. In this thesis we show how to capture the history of changes to a JSON document. Capturing the history is important in many applications, where not only the current version of a document is required, but all the previous versions. Conceptually the history can be thought of as a sequence of non-temporal JSON documents, one for each instant of time. Each document in the sequence is called a snapshot. Since changes to a document are few and infrequent, the sequence of snapshots largely duplicates a document across many time instants, so the snapshot model is (wildly) inefficient in terms of space needed to represent the history and time taken to navigate within it. A more efficient representation can be achieved by “gluing" the snapshots together to form a temporal model. Data that remains unchanged across snapshots is represented only once in a temporal model. But we show that the temporal model is not a JSON document, and it is important to represent a history as JSON to ensure compatibility with web services and scripting languages that use JSON. So we describe a representational model that captures the information in a temporal model. We implement the representational model in Python and extensively experiment with the model. Our experiments show that the model is efficient.

Country
United States
Related Organizations
Keywords

web services, Computer Sciences, JSON, versioning, 005, temporal databases

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green