making sense of (big) data with visual analytics

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Making Sense of (Big) Data with Visual Analytics Dr Kai Xu Associate Professor in Data Analytics Middlesex University, London, UK [email protected] https://kaixu.me

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Page 1: Making Sense of (Big) Data with Visual Analytics

Making Sense of (Big) Data

with Visual AnalyticsDr Kai Xu

Associate Professor in Data AnalyticsMiddlesex University, London, UK

[email protected] https://kaixu.me

Page 2: Making Sense of (Big) Data with Visual Analytics

Outline• What is Sensemaking• Why do we need Visual Analytics• Demo – SAVI: Social Analytics Visualisation• Demo – SenseMap: A ‘Map’ for Sensemaking

Page 3: Making Sense of (Big) Data with Visual Analytics

What is Sensemaking?• Making sense of data• Collecting, understanding, analysing, reasoning, and

making decisions

• It is something we do everyday:• Plan a holiday, buy a house, understand an illness, …• Defence, policing, investment, medical diagnosis, …

• How is it different from data analysis?• The task is usually not well defined

Page 4: Making Sense of (Big) Data with Visual Analytics

Example: what is the best camera for about £500?

What is the best camera for £500?

Pixel number

Sensor size

Image qualitychromatic aberration?!

Noise reduction

What does experts say?

Online reviews

What does my friend say? Smart phone

Compact

Full frame?

Micro 4/3?Sony RX100

Nikon D750Samsung Galaxy S7

What are the price?

How do I compare? Panasonic

LX100

Form factorModels

Camera LensAperture

Page 5: Making Sense of (Big) Data with Visual Analytics

This is usually what it looks like after one hour

• What is relevant and what is not?• Where is the information about image quality?• How to compare the models?• Where did I left off two days ago?• How do I explain to my wife?

Page 6: Making Sense of (Big) Data with Visual Analytics

Not just in browser

Page 7: Making Sense of (Big) Data with Visual Analytics

Making Sense of (Big) Data

Page 8: Making Sense of (Big) Data with Visual Analytics

Why IBM Watson or AlphaGo can’t do it• Watson is good at:

• Natural language processing, e.g., understand the Jeopardy! Questions• Find the (relevant) fact quickly

• However, the £500 camera task is• Every personal, Watson need all the information about me and understand it• No ‘best’ answer, so can’t just search it

• For AlphaGo, the Go game is very complex and difficult, but• The goal and rules are very well defined, and the results are easily measurable

• However, the £500 camera task is ill defined and not easily measurable• How many people have the knowledge and resource to build a deep neural network,

collect all the training data, and then train and tune it, just to find a camera?

Page 9: Making Sense of (Big) Data with Visual Analytics

Who is the best chess player in the world?• Deep Blue, was in 1997• Currently, probably a human-machine

team• And the two people on the team are not

even professional chess players

• The power of integrating the complementary strength of human and machine

Page 10: Making Sense of (Big) Data with Visual Analytics

Visual Analytics = Human + Computing Intelligence

Visualisation

Data Analysis Interaction

Information RetrievalMachine Learning

Data Mining

Information VisualisationScientific Visualisation

Computer Graphics

Human-Computer InteractionCognitive Psychology

Perception

Page 11: Making Sense of (Big) Data with Visual Analytics

Some work in the last five years

Page 12: Making Sense of (Big) Data with Visual Analytics

Example - SAVI: Social Analytics Visualisation• IEEE Visual Analytics Science & Technology (VAST) Challenge• Provide dataset and analysis tasks• Entry: visual analytics systems• Leading research groups and companies

• VAST Challenge 2014 – Mini Challenge 3• Data: tweets• Task: detect and describe a crime

Page 13: Making Sense of (Big) Data with Visual Analytics

The Data

Page 14: Making Sense of (Big) Data with Visual Analytics

SAVI: Social Analytics Visualisation

Page 16: Making Sense of (Big) Data with Visual Analytics

Map Visualisation and Sensemaking Support

Page 17: Making Sense of (Big) Data with Visual Analytics

The Final Findings

• Still a long time before AI can do such sensemaking• Difficult for human, too: almost impossible

without the tool

• Human leads, the tool supports• The tool does not provide answer, • Reveal pattern, help with organisation and

reasoning, and many more

Page 18: Making Sense of (Big) Data with Visual Analytics

A ‘Map’ of Sensemaking• Sensemaking is kind of like exploring a maze …• What may be helpful is something like this …

Page 19: Making Sense of (Big) Data with Visual Analytics

SenseMap – A ‘Map’ for Online Sensemaking

Browser enhancement

History Map

Knowledge Map

Page 22: Making Sense of (Big) Data with Visual Analytics

Takeaway Messages• Sensemaking is how people understand, reason, and make decisions

with data• It is important to Big Data, but there is limited support available• Visual Analytics combines data visualisation with analytics• A promising approach for sensemaking support

More details about SAVI and SenseMap: http://vis4sense.github.io/