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Big Data
Different Types of Analytics
Descriptive and Predictive Analytics:
◦ Descriptive analytics is reporting what happened and analyzing the data that contributed to figuring out
why it happened.
◦ Predictive Analytics is using statistics and data mining techniques to make predictions about the future.
Prescriptive Analytics:
◦ Analytics that recommends actions
Social Media Analytics:
◦ Doing analysis on public opinion (behavioral patterns, tastes, targeted marketing)
Entity Analytics:
◦ Analytics that groups/clusters data about entities (and learns from the raw data)
Cognitive Computing:
◦ Human/Computer Interaction that is targeted for information exchange
Big Data
Big data are datasets whose size
exceeds the typical reach of a
DBMS to capture, store, manage,
and analyze.
One way to categorize the
different types of big data is
according to the 4 V's.
◦
◦
◦
◦
Volume
Velocity
Variety
Veracity
http://scet.berkeley.edu/big-data-solutions-for-small-firms/
Big Volume
Volume - the size of the data managed by the
system
Often automatically collected information can
lead to huge amounts of data.
Examples:
◦ Sensor Data (environmental or
manufacturing/processing)
◦ Scanning Equipment (card readers)
◦ Industrial Internet of Things (heavily sensored
manufacturing processing / RFID)
◦ Multimedia Data (Video / Audio / Everything Else)
http://www.interfaces.com/blog/2013/05/longterm-strategy-versus-fast-success/
Velocity
Velocity - the speed at which data is created,
accumulated, ingested and processed.
Even if a database can handle the amount of data
that needs to be stored, it also needs to be fast
enough to process the information as quickly as
needed.
Examples:
◦ High Frequency Stock Trading
◦ Detection of Malicious Activity in Call Network
◦ Real-Time Processing of Trends on Facebook / Twitter
https://en.wikipedia.org/wiki/Velocity_(comics)
Variety
Big data includes structured, semi structured, and
unstructured data in different proportions based on context.
Structured data feature a formally structured data model,
such as the relational model (rows and columns) or
hierarchical (nested structures).
Unstructured data has no identifiable formal structure.
Examples:
◦ In MongoDB, we used semi structured document-oriented data.
◦ In Neo4j, we stored data as a graph.
◦ Other unstructured data:
◦ Emails, web content (blogs), pdfs, audio, video, images, clickstreams (cookie
tracking).
http://www.corporate-eye.com/main/digital-diversity-bottom-line/
Veracity
Veracity is composed of two components:
◦ Credibility of the source
◦ Suitability of the data for its target audience
Much of the data in big data stores has different
levels of trustworthiness and must go though
quality testing and credibility analysis before
being used.
Many sources generate data that is uncertain,
incomplete, and inaccurate.
Databases holding such information needs to be
able to manage such questionable data.
http://www.picturequotes.com/on-the-internetnobody-knows-youre-a-dog-nobody-quote-46682