Collaborative Thinking

A Look At Twitter, Big Data, And Analytics

Worth scanning

NoSQL at Twitter (NoSQL EU 2010)

NoSQL at Twitter (NoSQL EU 2010)

Big Data at Twitter, Chirp 2010

Big Data at Twitter, Chirp 2010

Spatial Analytics, Where 2.0 2010

Spatial Analytics, Where 2.0 2010

Collaborative Thinking: New Policy, New Voice

Since the acquisition of Burton Group by Gartner, I have been pretty busy becoming acquainted with new colleagues and reconnecting with former Meta Group co-workers. It has been a hectic past couple of months and my blogging activity has unfortunately taken a back seat. As we integrate the research teams, I had a normal workload of documents and preparations for our Catalyst Prague event on top of the orientation and on-boarding activities. February-April has been more than just a little busy. As things return to a more normal pace, there are a few updates I wanted to share:

• Gartner has a strict policy on personal analyst blogs when it comes to the topic of IT. Analysts are encouraged to join the Gartner Blog Network (GBN) community as the preferred outlet for sharing our thoughts and interacting with the public. For more information on GBN, see Gartner Blog Network FAQ and Gartner public web participation guidelines.
• Gartner also has policies on analyst participation in external events. There is a strong emphasis on maintaining objectivity. To adhere to that policy, I've discontinued my participation on the Enterprise 2.0 Advisory board. I will however still be participating in the Enterprise 2.0 conference but in a role as moderator or speaker.

Once I am setup to blog on GBN (no date established yet), I'll cross-post some links here so readers/subscribers can continue to follow my thoughts on IT. I will also post on the Burton Group Collaboration and Content blog (again, sharing the links to those posts here) during the transition phase since that blog is still an acceptable platform. As for Collaborative Thinking - the Gartner blog policy pertains to IT so I will continue to share my thoughts on the non-IT aspects of social computing and collaboration (perhaps more academic, touching on the non-IT aspects of social structures, group formation, communities, networks, etc). I believe that sharing links to other articles can also continue. And of course, you can still follow me on Twitter @MikeGotta.

Connected By Default

Social network site providers continue to make social data more public and visible to third-parties.

Facebook Further Reduces Your Control Over Personal Information

Commentary by Kurt Opsahl

Once upon a time, Facebook could be used simply to share your interests and information with a select small community of your own choosing. As Facebook's privacy policy once promised, "No personal information that you submit to Facebook will be available to any user of the Web Site who does not belong to at least one of the groups specified by you in your privacy settings."

How times have changed.

Today, Facebook removed its users' ability to control who can see their own interests and personal information. Certain parts of users' profiles, "including your current city, hometown, education and work, and likes and interests" will now be transformed into "connections," meaning that they will be shared publicly. If you don't want these parts of your profile to be made public, your only option is to delete them.

Facebook Further Reduces Your Control Over Personal Information | Electronic Frontier Foundation

X-RIME: Hadoop based large scale social network analysis

Earlier this year I added to this post: Social Graph Engines (Updated). Below are some other interesting efforts:

X-RIME: Hadoop based large scale social network analysis

Motivation

Today's Internet-based social network sites possess huge user communities. They hold large amount of data about their users and want to generate core competency from the data. A key enabler for this is a cost efficient solution for social data management and social network analysis (SNA).

Such a solution faces a few challenges. The most important one is that the solution should be able to handle massive and heterogeneous data sets. Facing this challenge, the traditional data warehouse based solutions are usually not cost efficient enough. On the other hand, existing SNA tools are mostly used in single workstation mode, and not scalable enough. To this end, low cost and highly scalable data management and processing technologies from cloud computing society should be brought in to help.
However, most of existing cloud based data analysis solutions are trying to provide SQL-like general purpose query languages, and do not directly support social network analysis. This makes them hard to optimize and hard to use for SNA users. So, we came up with X-RIME to fix this gap.
So, briefly speaking, X-RIME wants to provide a few value-added layers on top of existing cloud infrastructure, to support smart decision loops based on massive data sets and SNA. To end users, X-RIME is a library consists of Map-Reduce programs, which are used to do raw data pre-processing, transformation, SNA metrics and structures calculation, and graph / network visualization. The library could be integrated with other Hadoop based data warehouses (e.g., HIVE) to build more comprehensive solutions.

X-RIME: Hadoop based large scale social network analysis

HW09 Social network analysis with Hadoop

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