Current focus on Big Data
− Emphasis on power of data and data mining
− Technology solutions to handle large volumes of
data; e.g. Hadoop, NoSQL, Graph Databases, …
− Trying to find patterns and trends from large
volumes of data…
Top 5 Myths About Big Data
− Big Data is only about massive data volume
− Big Data means Hadoop
− Big Data means unstructured data
− Big Data is for social media feeds and sentiment
− NoSQL means No SQL
Brain Gentile, http://mashable.com/2012/06/19/big-data-myths/
What happens if we only focus on data
− Number of burgers consumed per day.
− Number of cats outside.
− Amount of rain fall.
− What insight would you draw?
… but also Data Dynamicity:
Not just Volume…
How can we efficiently deal with:
- Large amounts of (heterogeneous/distributed) data?
- Both static and dynamic data?
- In a re-usable, modular, flexible way?
- Integrate different types of data
- Provide hypothesis and create more context-aware solutions
Adapted from: M. Hauswirth. A. Mileo, Insight, National University of Ireland, Galway.
Big Data for Smart Cities
−Big data should help:
−provide more business opportunities for companies
(and SMEs) and private sector services
−create better governance of our cities and better
−provide smarter monitoring and control
−improve energy efficiency, create greener
−create better healthcare, elderly-care…
Sensor devices are becoming widely available
- Programmable devices
- Off-the-shelf gadgets/tools
More “Things” are being connected
Business and Public
Source: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).
Data in smart cities
− Turn 12 terabytes of Tweets created each day into sentiment
analysis related to different events/occurrences or relate them to
products and services.
− Convert (billions of) smart meter readings to better predict and
balance power consumption.
− Analyze thousands of traffic, pollution, weather, congestion, public
transport and event sensory data to provide better traffic
− Monitor patients, elderly care and much more…
Adapted from: What is Bog Data?, IBM
“People want answers, not numbers”
(Steven Glaser, UC Berkley)
What is the temperature at home?Freezing!
Big Data is not we need, what we need is
* Amit Sheth, “Transforming Big Data into Smart Data”, Kno.e.sis, Wright State University, 2013.
− Data with the right semantics, annotations
− Provenance, quality of information
− Interpretable formats
− Links and interconnections
− Background knowledge, domain information
− Hypotheses, expert knowledge
− Adaptable and context-aware solutions
Smart Data is the starting point to create an
efficient set of Actions.
The goal is to create actionable knowledge.
Data alone is not enough
− Domain knowledge
− Machine interpretable meta-data
− Delivery, sharing and representation services
− Query, discovery, aggregation services
− Publish, subscribe, notification, and access
− More open solutions for innovation and citizen participation
− Efficient feedback and control mechanisms
− Social network and social system analysis
− In cities, interactions with people and social systems is the
Storing, handling and processing
Image courtesy: IEEE Spectrum
− Discovery: finding appropriate device and data sources
− Access: Availability and (open) access to data resources
− Search: querying for data
− Integration: dealing with heterogeneous devices, networks
− Large-scale data mining, adaptable learning and efficient
computing and processing
− Interpretation: translating data to knowledge that can be
used by people and applications
− Scalability: dealing with large numbers of devices and a
myriad of data and the computational complexity of
interpreting the data.
− Transforming traditional perceptions of physical
objects, online engagement and social
− Implications of the confluence of physical-cyber-
social systems on societies, including aspects
such as citizen participation, democracy, open
government, open government data and others.
− How to solve the real problems…
A. Sheth, P. Barnaghi, M. Strohmaier, R. Jain, S.Staab (editors), Physical-Cyber-Social Computing (Dagstuhl Reports 13402), Dagstuhl Reports, vol. 3, no.9,
pp. 245-263, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, January, 2014.
Open DataOpen Data
Challenges and opportunities
− Providing infrastructure
− Publishing, sharing, and accessing solutions on both local and global
− Indexing and discovery (data and resources)
− Aggregation, integration and fusion
− Trust, privacy, ownership and security
− Data mining and creating actionable knowledge
− Integration into services and applications in e-health, the public
sector, retail, manufacturing and personalized apps.
− Mobile apps, location-based services, monitoring control etc.
− Social aspects: cities are complex social systems
− New business models