I am a Node on the Edge because I cannot claim to have all the answers or a decade long history of experience but I can help to define questions to be solved. Hopefully their is a potential finite set of questions that allow most requirements be solved and my aim is define these with collaboration of the community.
It would be amazing if these questions developed into some kind of NP Complete space where they all referred to one question such in NP Complete Theory or Monty Pythons Holy Grail but that is/maybe asking too much?

Thursday 28 January 2016

Node on Effective Graph Layout: Key Factors

in Node and Edge Graph Layouts


        "Getting the message across in the simplest layout without confusing the user."

                  Most Intuitive Layout Vs Most Semantic Meaning -> a Ink : Data ratio
METRIC: Ink : Data ratio  ->  Amount of Ink used  : to : Amount of Data Visualised

                         Edge Type,
                         Node Grouping
                         Node/Edge Position
  These are 4 main variable in a graph which can be changed to improve this ink : data ratio

KF: Complexity - Sometime the nodes are so connected that it seem every node is connected with one edge to every other node. This sometimes masks those nodes which are less connected i.e have less edge attached. This complexity of connections makes the graph layout look messy and it becomes difficult to interpret.
SOLUTION: Use groups so that every node in that group is connected by an edge to every node in another group. Then it easy to see that the less connected node are those with less edges attached.
EXAMPLE: The 1st Graph look's like all node are connected to every other node but the 2nd Graph shows 3 is not connected to 5 or 1 so not well connected in fact not all nodes are connected to every other node.
VARIABLES: Reduce Amount of Edges and Add more Groups
Reduce Complexity by Grouping Node on their Edge Connections

TECHNICAL SOLUTION: cola.js using d3.js  Simple (0) -- Difficult(5) : to USE 0 to DEVELOP 4     see example

Sunday 10 January 2016

Node finding the Edge of History: Long History of Big Data

Long History of Big Data 

Big Data is a buzzword to define the Explosion of Data available from Start to now

Here is what catalysized it, proceed it, progress its made and the main events.  


More  sensors  More  data stored  More analysis  More access

1 Information Overload

The explosion of information available

Defined in Future Shock by Alvin Toffler in 1970 
This is both information and data without any explanation of its larger context. The larger context is where the most meaning is. Without processing the information and data this meaning cannot be seen Problem: Not seeing this meaning because of too much information or information overload is a Catalyst.
Solution: It is a Catalyst to finding how to process it to summarise the meaning so can understand more information quicker.      
  • A rapid increase in the production rate of new information
  • The ease of duplication and transmission of data across the Internet
  • An increase in the available channels of incoming information (e.g. telephone, e-mail, instant messaging, rss)
  • Large amounts of historical information
  • Contradictions and inaccuracies in available information
  • A low signal-to-noise ratio (informally, the ratio of useful information to false or irrelevant data)
  • lack of a method for comparing and processing different kinds of information

Key Timelines:

1 The information overload from US Census in 1880 took 8 year to complete 
   A key realisation for governments of the task of population management.   

2 The information overload 



Saturday 9 January 2016

Node on the Edge of a bookshelf: Reinventing Discovery M Nielsen

Node on a Bookshelf

Book Review:

Reinventing Discovery The New Era of Networked Science by Michael Nielsen

Reviewed: 01/2016

Reinventing Discovery

Part 1 Amplifying Collective Intelligence

Online Tools Make Us Smarter

Restructuring Expert Attention

Patterns of Online Collaboration

The Limits and the Potential of Collective Intelligence

Part 2 Networked Science

All the World's Knowledge

Democratizing Science

The Challenge of Doing Science in the Open

The Open Science Imperative

Selected Sources and Suggestions for Further Reading