Big Data for Better Innovation. Professor Yike Guo Director, Data Science Institute Imperial College London

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1 Big Data for Better Innovation Professor Yike Guo Director, Data Science Institute Imperial College London

2 Interaction between participants Digital landscape: towards a digital service economy Big Data Wireless Cloud Anytime Service Availability Anything Anywhere Anywhere Anytime Anytime Service Accessibility Service Richness

3 Digital landscape: structure Cloud: computing infrastructure Big data : contents Wireless : interaction platform

4 Cloud Computing: cheaper and better Technology _ Virtualisation, big data management (NoSQLDB), WebService and PAYG support Impact _ Revolution in IT resource provision _ Unlimited, centralised computing capacity Future trends _ Cloud computing: the main IT provision mechanism _ Green data centre: equipped with power computing resource _ In memory data management and analysis: providing crucial support

5 Wireless technology: ubiquitous Technology _ High throughput mobile networks; new mobile devices; new mobile Internet services Impact _ Revolution in social media and Internet computing; sensor + wireless technology enables real-time datafication; mobile devices as the platform of interaction (human <-> services, data <-> physical) Future trends _ 5G wireless network _ Context aware service provision _ Ambient intelligence _ Big data integration

6 Big data: dataficating everything Quantifying physical objects through measuring their features Technology _ Pervasive sensing technology, big data analytics, wireless communication, cloud computing Impact _ On scientific research; real-time decision making; building a valuable data asset Future trends _ A new ecosystem: sensor fusion, software agents and community sensing _ Building composed data products: interaction and integration of quantification _ Real-time decision support services on the mobile net

7 How are innovation processes changing? I-o-T Cloud Mobile Data Distributed Digital Global Anytime Anywhere Anything

8 Business model innovation New patterns of innovation: Using data to drive growth [Parmar, Mackenzie, Cohn and Gann, Harvard Business Review, January/February 2014]

9 Data-driven innovation How to use data to drive growth 1. Augmenting products to generate data Which data relates to your products and their use? 4. Trading data How could data be structured and analysed to yield high-value insights? 2. Digitizing assets Which assets are wholly or essentially digital? 5. Codifying a distinctive service capability Do you have distinctive capabilities that others would value? 3. Combining data within and across industries How might data be combined with that held by others to create new value? [Parmar, Mackenzie, Cohn and Gann, Harvard Business Review, January/February 2014]

10 Augmenting media with viewing feedback Neuro-iBroadcasting

11 Digitizing life: Body Sensor Informatics Technology _ Wearable sensors: capturing personal physiological and behavioural information _ Cloud: data analysis Impact _ Enabling real-time health monitoring and behaviour characterisation _ The foundation: personalisation of products and services Future trends _ Ecosystem: personalised services _ Integrated data products: combined with personal biological data for personalised medicine _ Real-time decision support: mobile health monitoring

12 Body Sensor Informatics for Homecare of mental diseases 3-Axis accelerometer s Using body sensors to assess gait in progressive multiple sclerosis patients in their home environments. Access of MS Disability Continuous and dense measures in home environment Calibration with personalized clinical model

13 Digital City Exchange : Combining data within and across city sectors Traffic flow, car emission data and weather condition will generate a dynamic map for the air quality of a city.

14 Wikisensing: Combining data within and across city sectors Data storage platform for community sensing: Collecting the sensor data in a wiki way: everyone can store any data if it s describable (by ontology) Data storage and management infrastructure with high performance, low cost, and good security using the Big Data architecture Universal Query Language (UQL) for retrieving data from various sources, and acquiring data on demand UQL WikiSensing Data Storage health weather social network pollution traffic energy

15 Pay-by-Data Model: enable data trading Four components: data collection service data pricing agreement authentication service interface to app and app marketplace When an application is submitted to the application marketplace, the agreement of data usage is also submitted. Here is an example of such agreement (json): { app_id : ios , url : scope :[ precise-location ], frequency : , amount : 10, valid_time : , operation : read_only }

16 Codifying our multidisciplinary capacity for better science Imperial College Data Science Institute: A Focal Point FACULTIES STRATEGIC APPLICATIONS Faculty of Engineering Faculty of Medicine Faculty of Natural Science Imperial College Business School Health, Wellbeing & Personalised Medicine Data Science Institute Discovery Science Sustainable Development Energy & Environment of Future Cities

17 Thank you Data Science Institute Imperial College London Professor Yike Guo