Current Status and Future Trends
in Data Centric Science
Prof. Yike Guo
Director， Data Science Institute
Imperial College London

Datafication Turns Sciences into Data Science
Natural Sciences

MedicalSystems oncology

Imaging
Particle physics

Realtime metabolic
profilingComplexsystems &network data
analytics

Infection/epidemiology

Cardiovascularscience
Neuroscience

System biology

Highthroughput
screening

Engineering
Business
Socialmedia &new databusiness

Algorithmic
trading© Imperial CollegeDigital cities& urban life

Public healthmanagement

Digitalisation vs Datafication
Digitalisation: is a process that has been active within society since the late 1950s, with thebirth of the emiconductor industry. It refers to the conversion of pieces of information into
digital formats, for example text into HTML pages, music into MP3s, images into JPEG orsimilar. As the process of digitalisation has progressed, the amount of data that could beprocessed has increased exponentially. Digitalisation, therefore, from a simplistic perspective
may be viewed as the embodiment of idea creation – it is capturing human ideas in digitalform for transmission, re-use and manipulation

Datafication relates to the use of digital technologies to unembed the knowledge associatedwith physical objects by decoupling them from the data associated with them. Datafication is
manifesting itself in society in a variety of forms and is often – but not always – associated withsensors/actuators and the emerging Internet of Things (IoT). Datafication may take many forms
and in many cases a mobile device is enough to create unembedded knowledge of a person, athing or a piece of infrastructure.

Ericssion White Paper : Impact of Datafication on the Strategic Level

Example: Dataficating LifeTechnology_ Wearable sensors: capturing personalphysiological and behavioural information_ Cloud: data analysis
Impact_ Enabling real-time health monitoring andbehaviour characterisation_ The foundation: personalisation of products
and services
Future trends_ Ecosystem: personalised services_ Integrated data products: combined withpersonal biological data for personalised
medicine_ Real-time decision support: mobile healthmonitoringTechnologies for Data CentricScience
Modelling andSimulation

Big data
Management

Data Assimilation

Experimental
DesignAnalysis &Machine
Learning

The most critical choice for a scientist is what problems to work on.
– Judea Pearl (Turing Award 2011)4I of Data Centric Science
Integration: Integrating data for system analysis
Intelligence: Machining learning for deep
understanding and prediction
Interaction: Integrating data with models and physicalsystems for adaptive analysis
Inter-discipline: Understanding complexity by crossdisciplinary study with data as glue

Being Data Centric => Integrative Analysis• Data centric research requires to collecting datameasuring the various aspects of a physical system• Collected measurements are required to becalibrated and meaningfully integrated.
• The meaningful integration explores the inherent
relationships of different modalities of data• Exploration the relationship requires deep analysis
and curation• Data integration is the core of “ Web Science”

BIOLOGY AS DATA SCIENCE•••••Genomics
Proteomics
Metabolomics
Phenome······

01/03/2015

Yike Guo (y.guo@imperial.ac.uk)

9Biology is now a data science
Biology

Biotechnology

Bioinformatics

Genomics

Data storage

Biologist

cellDNA

Experiments

Data handling
RNA

Transcriptomics
Data preprocessing
ResultsproteinProteomics

Data analysis

metabolite

Metabolomics

Data integration
Data interpretation

Integrative biology
or Systems biology

Informatics
ICT infrastructureKnowledge

Medicine is now a data scienceTranslational Research :Computing Correlations

Data Driven Medicine:
Searching associations

Being Data Centric => Intelligent Analysis• Data Centric means discovery and predictive• Discovery requires machine to discover patterns
and trends beyond statistical analysis• Predictive requires machine to build models
exploiting the insight from data
• The era of “Machine Science” is coming(king et al, “The Automation of Science”, Science 3 April 2009, Schmidt
M and Lipson H, “Distilling Free-Form Natural Laws from Experimental
Data”, Science 3 April 2009)

Big Algorithm + Small Data:
Exploring the Local Causality
Sophisticated algorithms and small data:• Representative sampling (N=k)• High quality data
• Sophisticated multivariate learningalgorithms• Exploring causality relationships

Small Algorithm + Big Data：
Exploring Global Correlation
Simple algorithms and bigdata:• Taking all the data (N=all)• Messy data tolerance• Simple/scalable learningalgorithms• Exploring correlation
relationships

fMRI : Datafication of Brain Function• ~150,000 locations ( voxels) in 2s/time• >100 times• Many experimental conditions• Many participants• Millions of reads and billions of pairwise
relationsfMRI Analysis

Small DataBig Data

Big Algorithms + Big Data ：Cognition

Deep Learning: From Vision toCognition

Machine Intelligence Drives the Analytical
Technology towards Cognition
Technology_ Deep learning, Secure learning and NLU
Impact_ Unlimited, centralised analytics/computing
capacity- Knowledge discovery in real time with bigdata
Future trends_ Machine cognition_ Machine Science-- Real time discovery-- Model based knowledge economy

Being Data Centric => Let Data Speak• Data Centric requires data to be interactive withother entities in the research ( models, physicalworld and human)• Such interaction enables adaptive decision making• The adaptions include : sampling strategies, model
parameters, visual understanding• Interaction suggests a “ Data Chemistry” !

WIKIHEALTH : AN INTEGRATED PLATFORM FOR
WEARABLE SENSOR INFORMATICSWeb Interface

01/03/2015

Mobile Interface

Yike Guo (y.guo@imperial.ac.uk)

23

Wiki-Health: Assimilation withhuman physiology models• Example of simulating full body temperatures and energy transformation• Core body temperature & Safety• Too high: heat exhaustion, heatstroke• Too low: hypothermia

Age: 27, Weight：80kg



1- 60 min running at a speed of 6mph with airtemperature 30°C



60-120 min sit still with air temperature 42°C



120-180 min sit still with air temperature 10°C

180-240 min running at a speed of 6mph with airtemperature 10°C

Chemistry and Data Chemistryn.
The study of the composition,structure, properties, andreactions of matter data
(everything that makes up theuniverse knowledge ).

25

The Basic Concept of DataChemistry••Element types : data, model
Properties:––•

Structures:–––•

Representation
Relation
Distributionn (Statistics)

Reaction:–––•

Semantics
Provenance

Data-data reaction : integration
Data-model reaction : assimilation
Model-model reaction : knowledge network

Derivatives :––Data exhausts
Models26

Data/Model Interaction Is Essential inData Product Innovation

Being Data Centric => Data as Glue
• Datafication made the interaction and integrationof scientific disciplines easier• Data enables a systematic research of a complex
system through integrative analysis• System to system level integration can be achieved
via data/model interaction• Inter-disciplinary research is changing theresearch organisation structure.

Digital City Exchange : Exploring
Data Economy of Smart City

City Air Quality Research
Traffic flow, car emission data and weather condition will
generate a dynamic map for the air quality of a city.

Building Data Products with Urban
Informatics structure

Concinnity: The Digital City
Exchange platform• A generic sensor data management
platform “Concinnity” [1] built on
Wikisensing [2] datastore andWikimodelling [3] model
integration workflow engine• 3 layers targeting different stages ofthe data lifecycle• Data products built using theapplication development
environment[1] Lee, C-H, David Birch, Chao Wu, Dilshan Silva, Orestis Tsinalis, Yang Li, Shulin Yan, Moustafa Ghanem, and Yike Guo. “Building a Generic Platform
for Big Sensor Data Applications.” Proceedings of the IEEE Big Data conference (2013), Santa Clara, CA, USA.
[2] Silva, Dilshan, Moustafa Ghanem, and Yike Guo. "WikiSensing: An Online Collaborative Approach for Sensor Data Management." Sensors, 12,no. 10 (2012): 13295-13332.
[3] Birch, David, Paul HJ Kelly, Anthony J. Field, and Alvise Simondetti. "Computationally unifying urban masterplanning." Proceedings of the ACM
International Conference on Computing Frontiers, p. 32. 2013.

Semantics Engine : Model and Data
integration
Allows workflows to reflect thehierarchies– Geographically in the city
– Temporally in data streams
Multi-scale analysis and data
integration is enabledConclusion• Datafication drives the era of data centric science• Data centric science has the 4I characters :Integrative, interactive, intelligent and interdisciplinary• Efforts are being made on the development of the 4Itechnology• 4I of data centric science are not only technical issues
but impact to the research organisation structure• Data Science Institute of IC aims to develop 4I toexplore Big Data for Better Science