Welcome to the Quicktext Query Language

The Quicktext Query Language is a domain-specific language oriented to the corpus processing. It includes the Corpus Definition Language (CDL), the Corpus Manipulation Language (CML), the Corpus Control Language (CCL), the Corpus Query Language (CQL), and the Corpus Visualization Language (CVL).


Welcome to the Quicktext Query Language

Quicktext Query Language is called the QQL for short.

Quicktext Query Language is a new DSL language designing for the QuickCoprus.

QuickCoprus is a new NOSQL database.

We have added patent database: http://www.wepd.org/

We have added paper database: http://www.doi.ai/

We have added main website: http://www.quicktext.cn/

News

2019.5.5 We have added patent database: http://www.wepd.org/

2019.3.27 The data server is updating , it will be restarted on May 1, 2019.

2019.3.15 Version 0.0.4 Release!

Windows Terminal URL: https://github.com/quicktext/qql/releases/tag/v0.0.4

Web Terminal Demo URL: http://www.quicktext.cn/QQLTerminal/

0.0.3

DEBUG KEY Download: http://www.quicktext.cn/debug.qprivate

A simple demo:

select 	'title','author','abstract','url' from	'cssci','cscd'
where keyword=['media','AI','AI+media']
which schema=[
		'cssci'='http://www.quicktext.cn/ris?eeaeb365bb7a45cbb1f8773d63ead0fc'], 
	corpus=[
		'cssci'='http://cssci.doi.ai/json?q=']

A full demo:

select 
	'title','author','abstract','url'
from
	'cssci','cscd','patent'
where 
	keyword=['media','AI','AI+media'],
	limit 0,1000,
	update = local 
do 
	test=[
		'lucene1.sort.year' = 'desc',
		'lucene1.sort.name' = 'asc',
		'tensorflow2' = 'cscd',
		'caffe2' = 'cscd'
		],
	filter = [
		'name' = 'information',
		'year' = 'analytics'],
	black filter = [
		'name'='data',
		'year'='research']
with	
	visualize=[
		'mail'='genix@quicktext.cn',
		'sms'='+8600000000000',
		'csv'='1.csv',
		'ris'='2.ris']
which 
	schema=[
		'cssci'='http://www.quicktext.cn/ris?eeaeb365bb7a45cbb1f8773d63ead0fc',
		'cscd'='http://www.quicktext.cn/ris?bda02d1d34cd45fc9ce3f1d05e2dde57'], 
	corpus=[
		'cssci'='http://cssci.doi.ai/json?q=',
		'cscd'='http://cscd.doi.ai/json?q=',
		'patent'='http://username:password@corpus.quickcopus.cn/sci/token3'],
	testmodel=[
		'tensorflow1'='D:/google.model',
		'caffe2'='C:/berkery.model',
		'lucene3'='C:/index_dir1/'],
	trainmodel=[
		'tensorflow4'='D:/google.model',
		'caffe5'='C:/berkery.model',
		'lucene6'='C:/index_dir1/'],
	visualization=[
		'mail'='http://username:password@action.quickcorpus.cn/mail/token4',
		'sms'='http://username:password@action.quickcorpus.cn/sms/token5'],
	license=[
		'license'='http://www.quickcoprus.cn/debug.qprivate']

About Quicktext Query Language

The Quicktext Query Language is called QQL for short.

On the other hand, the Structure Query Language (SQL) is a widely used programming language in the database.

The QQL is a domain-specific language oriented to the corpus processing, while the SQL is oriented to the data processing.

Although the corpus processing belongs to the data processing, yet there are many differences in the processing.

The principle of the QQL is under the CAP theory while the principle of the SQL is under the ACID theory.

Copyrights

All the QQL, QuickCorpus and QuickVIZ were developed by Genix independently when he began working in Quicktext Infotech Co., Ltd. They are the products of Quicktext Infotech Co., Ltd.

For non-commercial use, please under the license of the MIT license.

For commercial use, please contact the Quicktext Infotech Co., Ltd to register a license.

The family of the Quicktext Query Language (QQL)

  • QQLCloud: The cloud storage for QQL users.
  • QQLHub: The hub for QQL development.
  • QQLstudio: The IDE for QQL developers.
  • QQLVIZ: The data visualization toolkits.
  • QQLModel: The data modelization toolkits
  • QuickCorups: A server implements of the QQL standard
  • QuickFS: The File System of the QQL
  • QQL: The specification of the Quicktext Query Language.

The comparison tables between the QQL and SQL.

Documents

Documents for beginners

Documents for designers of QQL compiler

About Genix

Genix is a Java coder. He began learning Java Programming since 2003.

Contact Genix

Only reply for technology questions, other questions please contact with the company!

Genix’s email: genix@quicktext.cn

Genix doesn’t use any social instant messages such as the Wechat and Telegram, only use the email!

Contact Company (For any questions)

Company website: http://www.quicktext.cn

Genix’s twitter (maintained by the company): @realGenix

China

Mr Ding

E-mail: alex@quicktext.cn

QQ: 635512001

Wechat: 18652029400

Mobile: 18652029400

For Other countries

Mr Li

Business manager

E-mail: you.li@quicktext.cn

QQ: 1169195645

Wechat: LY10184712

Mobile: 13125091018