Frontend Resource Intergration and Sharing - Modern Web 2016 review

•

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This document provides an outline and information about the Modern Web 2016 conference that took place from August 24-25. It discusses some of the topics that were covered at the conference, including Angular, React, Nginx, UI, UX and workflows. It also mentions growth hacking with Google AdWords and analyzing data with Google Analytics. The document references resources for Slack, Gitter, GitHub and the official website for the Modern Web 2016 conference.

Software

Modern Web 2016 review
CSBDBG FED
Laura Lee

Modern Web 2016
• 08/24 08/25  
•
• Angular React NGINX engine x
• UI UX Working Flow
•

Modern Web 2016
•
•
•
• /  
• http://modernweb.tw/
•

•
• [AdWords] Audience Marketing, Growth
Hacking
• [Google Analytics] Dance with i13n (
instrumentation )
• [Big Data]

• trivial
• ios app,  
•
• line / jira / slack / forum / grunt 
/ email / hangout……
•
• - -

Gitlab
• Base on coding
•
•
• code review
•
•

https://github.com/amfe/article/issues/1

Reference
• Modern Web 2016 ( / / / ) 
http://modernweb.tw/index.html
• Slack 
https://slack.com/
• Gitter 
https://gitter.im

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Steffen Staab

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Modeling languages are generally applied for developing systems and software – both internally, with domain-specific languages, and with standardized languages targeting a general purpose and a wide audience. Way too often these languages are weakly created and defined leading to poor quality: Language definitions tend to contain errors and inconsistencies; notations do not recognize the communication and problem-solving needs of humans; standardization organizations push exchange formats that do not fully work and offer certificates that do not measure mastery of the language. We describe common problems in language development and point them out with examples from known cases. To overcome these problems, we suggest several solutions to improve language development, including using modeling languages specifically designed to define modeling languages, continuous testing and prototyping, and keeping language users in the loop.

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