The Web and Big Data Flashcards
Describe Web 1.0 (The Web of contents)
-Created 1989 by Time Berners-Lee
-Created as a way to facilitate information exchange among colleagues
-“Read only” web
-Limited user participation
-Static information only
Describe Web 2.0 (The “Read-write” web)
-Live journal and Blogger were created in 1999 and contributed to the development of a web based on interactions.
-The rise of social media
Describe Web 3.0 (The semantic web)
-Decentralisation: data stored in data-bases and protected by blockchain technology
-Permissionless: no more need to give info to 3rd parties (cookies)
-AI & Semantics: Improved capacity of elaborating and understanding data.
-Ubiquity: IOT and smart devices wil enhance connectivity.
Describe Web 4.0 & 5.0
- 4.0 web is the web of wireless connectivity (mobile). It will improve the applications of Web 2.0 and 3.0
-The web 5.0 is called the “symbotic Web” allowing human-computer interactions based on emotions
How is data important to companies?
- Better prediction of consumer needs
- More efficient production
- More value
- Improve decision-making
- Risk management
- Provides a competitive advantage
Define Big Data
data that contains greater variety, arriving in increasing volumes with more velocity. AKA the three V’s.
What are the 3 V’s?
-Volume
-Velocity
-Variety
Define Volume
High volume of low density, unstrucutred data
Define Velocity
The fast rate at which data is recevied & acted on.
Define Variety
The types of data that are available eg text, video audios and images etc.
What are the two more V’s?
-Value
-Veracity
Define Value
Data has intrinsic value. But it’s of no use until that value is discovered.
Define Veracity
Can you trust your data?
Define Data
The unstructured collection of facts
What is information?
Information puts data into context. When data is analysed and interpreted, it becomes information.
What are some characteristics of the Web 3.0?
-Validated by the community
-Personalised interaction
-Intelligent search (AI)
-Behavioural advertising
-Understanding of semantics
Define Big Data analytics
The collecting, processing, cleaning, and analysis of large data sets to help organisations operationalise their big data.
What are the steps to make sense of big data?
Collect, Process, Clean and analyse.
What does it mean to collect big data?
Collect unstructured data from a variety of sources—from cloud storage to mobile applications, etc.
What does it mean to process big data?
Data must be organised properly to get accurate results in analytical queries, especially when it is large and unstructured.
What does it mean to clean big data?
All data must be formatted correctly, and any duplicated or irrelevant data must be eliminated or accounted for.
What does it mean to analyse big data?
To find patterns, make predicitions and find insights.
What are the three methods of big data analytics?
-Data mining
-Predicitive analysis
-Deep learning
Define Data mining
Sorts through large data sets to identify patterns and relationships by identifying anomolies and creating data clusters.
Define Deep learning
Imitating human learning patterns by using AI and machine learning to layer algorithms and find patterns
Define predicitive analysis
Using historical data to make predictions about the future and identify upcoming risks and opportunities
