JEDI: These aren't the JSON documents you're looking for...

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00365165" target="_blank" >RIV/68407700:21240/22:00365165 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3514221.3517850" target="_blank" >https://doi.org/10.1145/3514221.3517850</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3514221.3517850" target="_blank" >10.1145/3514221.3517850</a>

Jazyk výsledku

angličtina

Název v původním jazyce

JEDI: These aren't the JSON documents you're looking for...

Popis výsledku v původním jazyce

The JavaScript Object Notation (JSON) is a popular data format used in document stores to natively support semi-structured data. In this paper, we address the problem of JSON similarity lookup queries: given a query document and a distance threshold ????, retrieve all documents that are within ???? from the query document. Different from other hierarchical formats such as XML, JSON supports both ordered and unordered sibling collections within a single document which poses a new challenge to the tree model and distance computation. We propose JSON tree, a lossless tree representation of JSON documents, and define the JSON Edit Distance (JEDI), the first edit-based distance measure for JSON. We develop QuickJEDI, an algorithm that computes JEDI by leveraging a new technique to prune expensive sibling matchings. It outperforms a baseline algorithm by an order of magnitude in runtime. To boost the performance of JSON similarity queries, we introduce an index called JSIM and an effective upper bound based on tree sorting. Our upper bound algorithm runs in ???? (????????) time and ???? (???? +???? log ????) space, which substantially improves the previous best bound of ???? (????2) time and ???? (???? log ????) space (where ???? is the tree size). Our experimental evaluation shows that our solution scales to databases with millions of documents and JSON trees with tens of thousands of nodes.

Název v anglickém jazyce

JEDI: These aren't the JSON documents you're looking for...

Popis výsledku anglicky

The JavaScript Object Notation (JSON) is a popular data format used in document stores to natively support semi-structured data. In this paper, we address the problem of JSON similarity lookup queries: given a query document and a distance threshold ????, retrieve all documents that are within ???? from the query document. Different from other hierarchical formats such as XML, JSON supports both ordered and unordered sibling collections within a single document which poses a new challenge to the tree model and distance computation. We propose JSON tree, a lossless tree representation of JSON documents, and define the JSON Edit Distance (JEDI), the first edit-based distance measure for JSON. We develop QuickJEDI, an algorithm that computes JEDI by leveraging a new technique to prune expensive sibling matchings. It outperforms a baseline algorithm by an order of magnitude in runtime. To boost the performance of JSON similarity queries, we introduce an index called JSIM and an effective upper bound based on tree sorting. Our upper bound algorithm runs in ???? (????????) time and ???? (???? +???? log ????) space, which substantially improves the previous best bound of ???? (????2) time and ???? (???? log ????) space (where ???? is the tree size). Our experimental evaluation shows that our solution scales to databases with millions of documents and JSON trees with tens of thousands of nodes.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000421" target="_blank" >EF15_003/0000421: Big Code: Škálovatelná analýza rozsáhlých bází programů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

ISBN

978-1-4503-9249-5

ISSN

0730-8078

e-ISSN

—

Počet stran výsledku

14

Strana od-do

1584-1597

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Philadelphia

Datum konání akce

12. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000852705400115