Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: A Topology of Search Concepts
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > A Topology of Search Concepts
Data Mining

A Topology of Search Concepts

Daniel Tunkelang
Daniel Tunkelang
3 Min Read
SHARE

Vegard Sandvold has an interesting post entitled “Help Me Design a Topology of Search Concepts” in which he visualizes assorted search approaches in a two-dimensional space, the two dimensions being the degree of information accessibility and whether the approach is algorithm-powered or user-powered.

His four quadrants:

  • Low information accessibility + algorithm-powered = simple search (e.g., keyword search)
  • Low information accessibility + user-powered = superficial search (e.g., collaborative filtering)
  • High information accessibility + algorithm-powered = ingenious search (e.g., question answering)
  • High information accessibility + user-powered = diligent search (e.g., faceted search)

I’m not sure how I feel about the quadrant names (though I like how my employer and I are champions of diligence!), but I do like this attempt to lay out different approaches to supporting information seeking, and I like his choice of axes…

More Read

Cloud Computing Predictions for 2009
Privacy Policy Perspectives
I’m a Data Miner: T-Shirts, Mugs and Mousepads
Readability of Decision Trees
SAS adds support to R

Vegard Sandvold has an interesting post entitled “Help Me Design a Topology of Search Concepts” in which he visualizes assorted search approaches in a two-dimensional space, the two dimensions being the degree of information accessibility and whether the approach is algorithm-powered or user-powered.

His four quadrants:

  • Low information accessibility + algorithm-powered = simple search (e.g., keyword search)
  • Low information accessibility + user-powered = superficial search (e.g., collaborative filtering)
  • High information accessibility + algorithm-powered = ingenious search (e.g., question answering)
  • High information accessibility + user-powered = diligent search (e.g., faceted search)

I’m not sure how I feel about the quadrant names (though I like how my employer and I are champions of diligence!), but I do like this attempt to lay out different approaches to supporting information seeking, and I like his choice of axes.

More importantly, I hope this analysis helps advance our ability as technologists to match solutions to information seeking problems. Many of us have an intuitive sense of how to do so, but I rarely see principled arguments–particularly from vendors who may be reluctant to forgo any use case that could translate into revenue.

Of course, it would be nice to quantify these axes, or at least to formalize them a bit more rigorously. For example, how do we measure the amount of user input into the process–particuarly for applications that may involve human input at both indexing and query time? Or how do we measure information accessibility in a corpus that might include junk (e.g., spam)?

Still, this is a nice start as a framework, and I’d be delighted to see it evolve into a tool that helps people make technology decisions.

Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive
data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Program-Ace offers fully-functional custom Virtual 3D City…

1 Min Read

one in five people still lacks access to clean, safe drinking…

2 Min Read

NCAA Data Visualizer for March Madness Face-Offs

2 Min Read

Adding all the numbers

3 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?