Archive for the ‘Context-aware’ Category

Understanding Context

Sunday, January 25th, 2015

Understanding Context by Andrew Hinton.

From the post:

Technology is destabilizing the way we understand our surroundings. From social identity to ubiquitous mobility, digital information keeps changing what here means, how to get there, and even who we are. Why does software so easily confound our perception and scramble meaning? And how can we make all this complexity still make sense to our users?

Understanding Context — written by Andrew Hinton of The Understanding Group — offers a powerful toolset for grasping and solving the challenges of contextual ambiguity. By starting with the foundation of how people perceive the world around them, it shows how users touch, navigate, and comprehend environments made of language and pixels, and how we can make those places better.

Understanding Context is ideal for information architects, user experience professionals, and designers of digital products and services of any scope. If what you create connects one context to another, you need this book.

Final_Final_forWeb_250_withMorville

Amazon summarizes in part:

You’ll discover not only how to design for a given context, but also how design participates in making context.

  • Learn how people perceive context when touching and navigating digital environments
  • See how labels, relationships, and rules work as building blocks for context
  • Find out how to make better sense of cross-channel, multi-device products or services
  • Discover how language creates infrastructure in organizations, software, and the Internet of Things
  • Learn models for figuring out the contextual angles of any user experience

This book is definitely going on my birthday wish list at Amazon. (There done!)

Looking forward to a slow read and in the meantime, will start looking for items from the bibliography.

My question, of course, is that after expending all the effort to discover and/or design a context, how do I pass that context onto another?

To someone coming from a slightly different context? (Assuming always that the designer is “in” a context.)

From a topic map perspective, what subjects do I need to represent to capture a visual context? Even more difficult, what properties of those subjects do I need to capture to enable their discovery by others? Or to facilitate mapping those subjects to another context/domain?

Definitely a volume I would assign as reading for a course on topic maps.

I first saw this in a tweet by subjectcentric.

Coeffects: The next big programming challenge

Sunday, January 12th, 2014

Coeffects: The next big programming challenge by Tomas Petricek.

From the post:

Context-aware programming matters

The phrase context in which programs are executed sounds quite abstract and generic. What are some concrete examples of such context? For example:

  • When writing a cross-platform application, different platforms (and even different versions of the same platform) provide different contexts – the API functions that are available.
  • When creating a mobile app, the different capabilities that you may (or may not) have access to are context (GPS sensor, accelerometer, battery status).
  • When working with data (be it sensitive database or social network data from Facebook), you have permissions to access only some of the data (depending on your identity) and you may want to track provenance information. This is another example of a context.

These are all fairly standard problems that developers deal with today. As the number of devices where programs need to run increases, dealing with diverse contexts will be becoming more and more important (and I’m not even talking about ubiquitous computing where you need to compile your code to a coffee machine).

We do not preceive the above things as problems (at best, annoyances that we just have to deal with), because we do not realize that there should be a better way. Let me digg into four examples in a bit more detail.

This post is a good introduction to Tomas’ academic work.

A bit further on Tomas explains what he means by “coeffects:”

Coeffects: Towards context-aware languages

The above examples cover a couple of different scenarios, but they share a common theme – they all talk about some context in which an expression is evaluated. The context has essentially two aspects:

  • Flat context represents additional data, resources and meta-data that are available in the execution environment (regardless of where in the program you access them). Examples include resources like GPS sensors or databases, battery status, framework version and similar.
  • Structural context contains additional meta-data related to variables. This can include provenance (source of the variable value), usage information (how often is the value accessed) or security information (does it contain sensitive data).

As a proponent of statically typed functional languages I believe that a context-aware programming language should capture such context information in the type system and make sure that basic errors (like the ones demonstrated in the four examples above) are ruled out at compile time.

This is essentially the idea behind coeffects. Let’s look at an example showing the idea in (a very simplified) practice and then I’ll say a few words about the theory (which is the main topic of my upcoming PhD thesis).

I don’t know that Tomas would agree but I see his “coeffects,” particularly “meta-data related to variables,” as keying off the subject identity of variables.

Think of it this way: What is the meaning of any value with no express or implied context?

My answer would be that a value without context is meaningless.

Be example, how would you process the value “1” Is it a boolean? Integer? A string?

Imbuing data with “meta-data” (or explicit identity as I prefer) is a first step towards transparent data.

PS: See Petricek and Skeet’s Real-World Functional Programming.

Context-Aware Recommender Systems 2012 [Identity and Context?]

Tuesday, September 11th, 2012

Context-Aware Recommender Systems 2012 (In conjunction with the 6th ACM Conference on Recommender Systems (RecSys 2012))

I usually think of recommender systems as attempts to deliver content based on clues about my interests or context. If I dial 911, the location of the nearest pizza vendor probably isn’t high on my lists of interests, etc.

As I looked over these proceedings, it occurred to me that subject identity, for merging purposes, isn’t limited to the context of the subject in question.

That is some merging tests could depend upon my context as a user.

Take my 911 call for instance. For many purposes, a police substation, fire station, 24 hour medical clinic and a hospital are different subjects.

In a medical emergency situation, for which a 911 call might be a clue, all of those could be treated as a single subject – places for immediate medical attention.

What other subjects do you think might merge (or not) depending upon your context?

Table of Contents

  1. Optimal Feature Selection for Context-Aware Recommendation Using Differential Relaxation
    Yong Zheng, Robin Burke, Bamshad Mobasher.
  2. Relevant Context in a Movie Recommender System: Users’ Opinion vs. Statistical Detection
    Ante Odic, Marko Tkalcic, Jurij Franc Tasic, Andrej Kosir.
  3. Improving Novelty in Streaming Recommendation Using a Context Model
    Doina Alexandra Dumitrescu, Simone Santini.
  4. Towards a Context-Aware Photo Recommender System
    Fabricio Lemos, Rafael Carmo, Windson Viana, Rossana Andrade.
  5. Context and Intention-Awareness in POIs Recommender Systems
    Hernani Costa, Barbara Furtado, Durval Pires, Luis Macedo, F. Amilcar Cardoso.
  6. Evaluation and User Acceptance Issues of a Bayesian-Classifier-Based TV Recommendation System
    Benedikt Engelbert, Karsten Morisse, Kai-Christoph Hamborg.
  7. From Online Browsing to Offline Purchases: Analyzing Contextual Information in the Retail Business
    Simon Chan, Licia Capra.

Sarcastic Computers?

Thursday, May 31st, 2012

You may have seen the headline: Could Sarcastic Computers Be in Our Future? New Math Model Can Help Computers Understand Inference.

And the lead for the article sounds promising:

In a new paper, the researchers describe a mathematical model they created that helps predict pragmatic reasoning and may eventually lead to the manufacture of machines that can better understand inference, context and social rules.

Language is so much more than a string of words. To understand what someone means, you need context.

Consider the phrase, “Man on first.” It doesn’t make much sense unless you’re at a baseball game. Or imagine a sign outside a children’s boutique that reads, “Baby sale — One week only!” You easily infer from the situation that the store isn’t selling babies but advertising bargains on gear for them.

Present these widely quoted scenarios to a computer, however, and there would likely be a communication breakdown. Computers aren’t very good at pragmatics — how language is used in social situations.

But a pair of Stanford psychologists has taken the first steps toward changing that.

Context being one of those things you can use semantic mapping techniques to capture, I was interested.

Jack Park pointed me to a public PDF of the article: Predicting pragmatic reasoning in language games

Be sure to read the entire file.

A blue square, a blue circle, a green square.

Not exactly a general model for context and inference.

Managing context data for diverse operating spaces

Wednesday, May 16th, 2012

Managing context data for diverse operating spaces by Wenwei Xuea, Hung Keng Pungb, and Shubhabrata Senb.

Abstract:

Context-aware computing is an exciting paradigm in which applications perceive and react to changing environments in an unattended manner. To enable behavioral adaptation, a context-aware application must dynamically acquire context data from different operating spaces in the real world, such as homes, shops and persons. Motivated by the sheer number and diversity of operating spaces, we propose a scalable context data management system in this paper to facilitate data acquisition from these spaces. In our system, we design a gateway framework for all operating spaces and develop matching algorithms to integrate the local context schemas of operating spaces into a global set of domain schemas upon which SQL-based context queries can be issued from applications. The system organizes the operating space gateways as peers in semantic overlay networks and employs distributed query processing techniques over these overlays. Evaluation results on a prototype implementation demonstrate the effectiveness of our system design.

This article came up in a sweep for “semantic overlay networks.”

Encouraging recognition that results may need to vary based on physical context. Who knows? Perhaps recognition that the terminology for one domain and its journals/authors/monographs has different semantics than other domains.

Imagine that, a system that manages queries across semantic domains for users, as opposed to users having to understand all the possible semantic domains in advance to have useful query results (or better query results).

Perhaps the “context” metaphor may be a useful one in marketing topic maps. Less aggressive than “silo.” Let the client come up with that to characterize competing agencies or information sources.

“Context” in the sense of physical space is popular among the smart phone crowd so don’t neglect that as an avenue for topic maps as well. (Looking at your surroundings would mean breaking eye contact with your phone. Might miss an ad or something.)

Context-aware intelligent recommender system

Tuesday, October 5th, 2010

Context-aware intelligent recommender system Authors: Mehdi Elahi Keywords: active learning, classification, context-aware, fuzzy logic, recommendation systems, recommenders

Abstract:

This demo paper presents a context-aware recommendation system. The system mines data from user’s web searches and other sources to improve the presentation of content on visited web pages. While user is browsing the internet, a memory resident agent records and analyzes the content of the webpages that were either searched for or visited in order to identify topic preferences. Then, based on such information, the content of requested web page is ranked and classified with different styles. The demo shows how a music weblog can be modified automatically based on user’s affinities.

Context-aware recommendation systems help present relevant information in large topic maps but I am more interested in their use for authoring systems.

Automatic construction of topics/roles/associations based on prior choices (for user approval) comes to mind.

Not a tool for a casual author but certainly a power tool for professional information explorers. (librarians?)