Big Data at UoM
In 09.12.2013 there was a meeting in The University of Manchester, with researchers, educators, executives and students, in order to launch of the Big Data Community.
The programme was comprised by presentations and round tables with around 6 members (each table had a number and members should debate about some interesting fields concerned to Big Data, opportunities and challenges, considering activities at the university).
During the meeting there was a screen showing Tweets from members and other participants. The hashtag was #UoMBigData .
At the end university leadears presented their view and possibilities for the future…
The Programme:
| 09.00 – 09.30 | Registration and Networking |
| 09.30 – 09.45 | Welcome from Manchester Informatics and Introduction to Rethink: Big |
| 09.45 – 10.00 | Magnus Rattray, Faculty of Life Sciences |
| 10.00 – 10.15 | Gemma Sattherwaithe, AstraZeneca |
| 10.15 – 10.30 | Mattia Prosperi, Centre for Health Informatics |
| 10.30 – 10.45 | Break |
| 10.45 – 11.45 | Workshop: Big Data- the Big Picture |
| 11.45 – 12.00 | Kshitij Kumar, Hitachi Ltd/European Big Data Lab |
| 12.00 – 12.15 | Michael Gleaves, Hartree Centre |
| 12.15 – 13.15 | Lunch |
| 13.15 – 14.15 | Workshop: Big Data, what would make a difference? |
| 14.15 – 14.30 | Carole Goble, Computer Science |
| 14.30 – 14.45 | Tim Harris, Oracle Labs (Cambridge) |
| 14.45 – 15.00 | Mark Elliot, School of Social Sciences/UK Administrative Data Liaison Service |
| 15.00 – 15.15 | Break |
| 15.15 – 16.45 | Plenary: How does Manchester establish itself as a Centre of Excellence in Big Data? |
| 16.45 – 17.00 | Closing Remarks |
Some personal insights from the event:
Software is important, hardware is important, as well. However, I think that Human–computer interaction (HCI) should be thought as a critical as other aspects. I experienced lots of examples of systems that did not work because of human-computer interaction. So Educational projects should be considered, the culture must be studied, monitored, activities should be assessed sistematically.
There are eight great technologies after carefully analysing UK scientific and business capabilities. Each technology:
- •is an area in which the UK has world-leading research
- •has a range of applications across a spectrum of industries
- •has the potential for the UK to be at the forefront of commercialisation
The eight great technologies are:
- •big data and energy-efficient computing
- •Satellites and commercial applications of space
- •robotics and autonomous systems
- •synthetic biology
- •regenerative medicine
- •agri-science
- •advanced materials and nanotechnology
- •energy and its storage
Big Data is one of the 8 great technologies that are driving funding over the next few years.
Some insights concerns to all V’s and can be explained at http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/ in the article:
Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity
this article was retrieved from http://inside-bigdata.com/ (by Kevin Normandeau, in September 2013)
Normandeau’s article starts like this…
“We have all heard of the the 3Vs of big data which are Volume, Variety and Velocity. Yet, Inderpal Bhandar, Chief Data Officer at Express Scripts noted in his presentation at the Big Data Innovation Summit in Boston that there are additional Vs that IT, business and data scientists need to be concerned with, most notably big data Veracity.”
I liked the exposition of the 6 Vs concerns for the Big Data:
Volume
Big data implies enormous volumes of data. It used to be employees created data. Now that data is generated by machines, networks and human interaction on systems like social media the volume of data to be analyzed is massive. Yet, Inderpal states that the volume of data is not as much the problem as other V’s like veracity.
Variety
Variety refers to the many sources and types of data both structured and unstructured. We used to store data from sources like spreadsheets and databases. Now data comes in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured data creates problems for storage, mining and analyzing data. Jeff Veis, VP Solutions at HP Autonomy presented how HP is helping organizations deal with big challenges including data variety.
Velocity
Big Data Velocity deals with the pace at which data flows in from sources like business processes, machines, networks and human interaction with things like social media sites, mobile devices, etc. The flow of data is massive and continuous. This real-time data can help researchers and businesses make valuable decisions that provide strategic competitive advantages and ROI if you are able to handle the velocity. Inderpal suggest that sampling data can help deal with issues like volume and velocity.
Veracity
Big Data Veracity refers to the biases, noise and abnormality in data. Is the data that is being stored, and mined meaningful to the problem being analyzed. Inderpal feel veracity in data analysis is the biggest challenge when compares to things like volume and velocity. In scoping out your big data strategy you need to have your team and partners work to help keep your data clean and processes to keep ‘dirty data’ from accumulating in your systems.
Validity
Like big data veracity is the issue of validity meaning is the data correct and accurate for the intended use. Clearly valid data is key to making the right decisions. Phil Francisco, VP of Product Management from IBM spoke about IBM’s big data strategy and tools they offer to help with data veracity and validity.
Volatility
Big data volatility refers to how long is data valid and how long should it be stored. In this world of real time data you need to determine at what point is data no longer relevant to the current analysis.
