Analytics Centres – Advancing the Commercialization of Analytics Applications by Vasu Netrakanti

Alberta Advantage

  • Amongst the most prosperous jurisdictions in the world
  • Quality post-secondary educational institutions
  • Big data provincial and national industrial sectors
  • Data analytics is a sunshine industry

Clustering Effect of Analytics Centre

  • Infrastructure
  • Collaborative efforts
  • Forum for opportunities and solutions
  • Funding
  • Human resource development
  • Commercialization

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The Alberta Center for Analytics Products by Stuart Lomas

A Proposal to Build the Alberta Analytics Industry

Alberta educates some of the world’s top experts in Artificial Intelligence and Analytics. When they graduate, every one of them leaves the province. - A senior U of Alberta researcher, 2010

For the past year, the Alberta Council of Technologies has promoted the idea of an Alberta Center for Analytics Products.

The goal is to encourage an Advanced Analytics Industry in Alberta.

  • Keep our world-leading graduates in Alberta
  • Benefit Alberta companies as suppliers and customers
  • Encourage new Analytics companies based in Alberta and serving the world.

 Taking Action

  • ACAP has been well publicized in Alberta’s innovation community and Alberta government.
  • ACAP could be part of a wider strategy to encourage Advanced Analytics in Alberta, similar
    to the Alberta Nano Strategy.
  • The next step is to develop a specific business plan for ACAP, in cooperation with government
    and in a strategic framework.

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The Application of Interactive Data Exploration and Visualization by Fred Popowich

What is Visual Analytics?

Visual analytics is…

  • the science of analytical reasoning
    facilitated by interactive visual

Using technology for human
analysis & decision-making

  • Explore and synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data.!
  • Provide timely, defensible and understandable assessments.!
  • Communicate assessments effectively for action.

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Big Data and the Square Kilometre Array by Russ Taylor

SKA Technology Innovations

  • Large collecting Area
  • Large field-of-view technology
  • Large bandwidth
  • Power consumption
  • Massive Data

The beginning of a distributed global SKA  big data network

  • data management and processing
  •  data mining, visualiza-on and analytics

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CyberSKA – A Collaborative Platform by Rob Simmonds

CyberSKA – A Collaborative Platform for Data Intensive Radio Astronomy

CyberSKA started by establishing cyberinfrastructure to support current largescale astrophysical data needs generated by GALFACTS, PALFA and other high data volume SKA Pathfinder projects


  •  Motivation / Overview
  •  Participants / Industry Partners
  •  Architecture
  •  Current Status and Services
  •  Usage
  •  Current Work and Next Steps
  •  Summary


  • CyberSKA environment could support a range of projects collaborating on big data
    • Social networking tools, distributed data management, server side sharable visualization, data processing/analytics engine and 3ed party app support
  • CyberSKA portal tailored to the needs of radio astronomy
    • Specific tools to support surveys and image cube visualization
  • Portal currently supporting SKA pathfinder projects

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Enabling Remote Visualization of Big Data with PureWeb® by Cameron Kiddle

PureWeb ties into your software so it can be accessed on new interfaces on any device

Challenges in Data Visualization

    • Growing volume of data
    • Movement of data
    • Security
    • Limitations of end user hardware
    • Application portability
    • Accessibility
    • Collaboration

Features and Benefits

  • Web Enablement
    • Remote access to your application over the Internet
  • Mobility
    • Access to your application from multiple platforms including mobile devices
  • Collaboration
    • Multiple users in different locations can view and interact with the same application instance at the same time
  • Preserve Business Logic
    • PureWeb works with your existing application. No need to rewrite it.
  • Security
    • No direct client access to data
    • No persistence of data on client devices
    • Data stays where it is today
  • High Performance
    • Minimize latency to preserve interactive feel of remoted applications
  • High Fidelity Image Transfer
    • Suitable for high-end data visualization including medical review, diagnosis, gaming, engineering, virtual retail, life sciences, video, etc.
    • FDA cleared compression techniques (JPEG) natively supported by all client platforms
  • High Degree of Abstraction
    • Shield programmer from communication and network concepts
    • Shield application from differences in client capabilities
    • Lightweight transformation APIs minimize impact on existing applications


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Yield Management as A Process Governed by Data Mining in the auto Industry by Ayman Ammoura

Yield management is the process of understanding, anticipating and influencing consumer behavior in order to maximize yield or profits. -Wikipedia


  • Introducing main concepts
  • Applying our science and technology to a Canadian small business
  • Mining on The Revenue Side – Rates
  • Mining on The Expense Side – Insurance
  • Sharing success stories
Data mining is a process of extracting previously unknown, valid, and actionable information.

The presentation is viewable below:

Offer Targeting in Financial Services by Tom Peters

Leveraging A Big Data Clustering Strategy

Pursuing five analytics domains that are most relevant to the industries and clients we serve.

  • Advisory Analytics
  • Transformation Analytics
  • Managed Analytics
  • Subscription Analytics

Our delivery model across domains

  1. Customer Analytics – Reveue and and Margin Managemet
  2. Supply Chain Analytics – Reducing Cost and improving efficiency
  3. Financial Analytics- Corporate health and performance
  4. Workforce Analytics – Employee loyalty and productivity enhancement
  5. Risk & Regulatory Analytics – Risk reduction and regulatory compliance

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Migrating to the Hadoop Ecosystem by Eleni Srtoulia


  • Background
    – Why?
  • PaaS with “the Hadoop Ecosystem”:
  • HDFS,Hadoop,and HBase
    – What?
  • The TAPoR Migration
    – How?
  • Closing Remarks


Big Data… Cheap Hardware…

  • Data is growing at an unprecedented rate
    • More people use the web and publish data
      • The Internet Usage around the world:   in 2000: 360m; in 2011: 2billion (1/3 of earth population)
      • Facebook, in 2009 uploading 60 TB images every week
    • Things are on the Internet
      •  A jet engine produces 10TB data every 30 flight mins
  • Commodity hardware is cheap
  • Owning and maintaining hardware is expensive

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Is Your Data Cloud-Ready? by Dale Oldford

The Web evolution is enabling technologies and tools to facilitate information sharing and collaboration in virtual communities, where members are active participants as “prosumers” of content, instead of passive consumers of data.


• Finding meaningful data in the “Cloud”
• Example 1: Prime Time Blogs
• Open Data
• Example 2: “Unlocking” Your Data
• The “ABC” Formula
• Some of the Challenges
• Shaping your data for “Cloud” readiness
• Conclusions
• Q&A



  •  Web 2.0 technologies and analytics provide feasible options to cope with challenging characteristics of Big Data (i.e., volume, velocity, and variety).
  • Analytics enables enterprise search solutions to perform complex tasks requiring machine learning and automation.
  •  Cloud readiness goes beyond data and available technology.

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Finding Relevant Data in the Cloud for Actionable Decisions by Andres Dorado

An Information Need* is the topic about which the user desires to know more, and is differentiated from a query, which is what the user conveys to the computer in an attempt to communicate the information need.”


• Information Retrieval
• The “ABC” Formula
• Some of the Challenges
• Example 1: The Right Profile
• Example 2: Like it 
• Example 3: Promote it
• Conclusions
• Q&A


  • Analytics add capabilities to information retrieval systems that facilitate finding relevant data in the “cloud”.
  • Analytics enables information retrieval systems to deal with  large-scale data sets and therefore is recommendable for working with Big Data.
  • Analytics provides advanced techniques for more effective browsing and filtering of Big data.

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Big Data – Life Cycle Management & Challenges by Graham Irving

Big Data is a term applied to data sets whose size is beyond the ability of commonly used SW & HW to capture, manage, and process the data within a tolerable elapsed time.  – Wikipedia


Big Data sizes are a constantly moving target currently ranging from a few dozen TB’s to many PB’s in a single data set. – Wikipedia



  •  Introductions
  •  Big Data?
  •  Background
  • Life Cycle Management
  • 5/6/2012 (c) 2012, Storage Clarity 2
  •   Life Cycle Challenges
  •  Recommendations
  •  3-2-1 Archive

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Search Appliances for Big Data by Davood Rafiei

Atlas Detector

Atlas detector at CERN – 23 PB per second raw data – 10 PB per year filtered data, – Used by more than 150 universities and labs


Our Four Appliances

  • ReputaPon gauge (TOPIC,2000)
  • Network visualizaPon (ALVIN,2005)
  • Data extracPon (DeWild, 2007)
  • Result diversificaPon (Diver,2010)


  • Covered
  • Big (everyone’s) data
    • Four tools
    • TOPIC
    • ALVIN
    • DeWild
    • Diver
  • Surge in interest in big data
    • Obama’s big data initiative
    • This conference!
  • Search is far from being solved
    • And our quest for holy grail continues
  • No shortage of problems
  • Want to make sense of your big data
    • We sure can help!

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