Towards Task-Oriented Computing for Pervasive Computing Environments Presenter: Chuong C. Vo...

Towards Task-Oriented Computing for Pervasive Computing

EnvironmentsPresenter: Chuong C. Vo

Supervisors: Dr Torab Torabi & Dr Seng W. Loke

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La Trobe University, 4/2010

Outlines

• Introduction to pervasive computing• Usability problems with pervasive computing

environments• Approach• Implementation• Evaluation• Research agenda• Conclusion

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Introduction

• The first era of computer (past, many-one)– Mainframe: central, heavy, and expensive

• The second (now, one-many)– Personal computers: personal, light, inexpensive

• The third (now & future, many-many)– Post-PC: pervasive (everywhere, every time),

computers blend into environments .– Whole environments are computers.

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Introduction (cont. 1)

• What is pervasive computing (or ubiquitous

computing)?– Seamlessly integrating computational elements

into the fabric of everyday life…” [Weiser 1991],– Everyday objects and environments are aware of

their surroundings & peers and behave smartly.

• The aims:– Support our activities, complement our skills, add

to our pleasure, convenience, accomplishments [Norman 2007].

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• What is a pervasive computing environment?– E.g., smart spaces, smart environments, intelligent

environments

• Pervasive city’s scenario– “It’s 7p.m., it’s raining, and you’re walking in

Melbourne. You consult your phone and it suggests ‘Dinner?’, ‘Taxi?’, ‘Bus?’. Selecting ‘Dinner?’ will present restaurants you’re apt to like and even dishes that you may want…”

5[cited from other work]


• Pervasive campus’ scenario– “You’re driving approaching La Trobe Uni. Campus,

the LCD on your car suggests ‘Campus map?’, ‘Find a place?’, ‘Parking spot?’. Selecting ‘Parking spot’ will guide you to find a parking spot.”

• Pervasive personal office’s scenario– “You enter your office. The lighting, heating, and

cooling levels are automatically adjusted based on you electronic profile. The coffeemaker works to give you a cup of hot white coffee.”

6[cited from other work]

Outlines



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Usability Problems with Pervasive Environments

• Complexity of use: Variety of devices, UIs, remote controllers– Requires too many buttons and menus on UIs, exceeds capacity of UIs

for users to operate them intuitively [Rich 2009].– 1/2 of all reportedly malfunctioning consumer electronics products

returned to stores are in full working order—customers just couldn’t figure out how to operate them. [Ouden 2006]

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Slideshowcontroller

TV controller

A future pervasive computing environment

Audio controller

Blind controller

TV

Video-conferenceDisplay

Temperaturecontroller

Projector

DVD player

Printer

ComputerLightcontroller

Recorder

Smart phone

Door

Coffeemaker

Usability problems (cont. 1)

• Invisibility & Overload of features– Technologies blend into environments [Pinto 2008].– Frequently adding/removing devices and services

to/from the environments.– Overload of features [Garlan et al. 2002].

• One device tens of features• Different combinations of devices Hundreds of features

9MediaCup [Beigl et al. 2000]

Usability problems (cont. 2)

• Inconsistency of user interfaces– Brand identification, product differentiation [Rich

2009; Oliveira 2008]

• Inconsistency of task executions– Same tasks but different operations/procedures

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Outlines



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Research hypothesis

• Our approach is based on task-driven computing [Wang

et al. 2000]:– A task is a user’s goal or objective [Loke 2009].– Users interact with/think of the computing in terms of

tasks instead of applications/devices.– Users focus on the tasks at hand rather than on the means

for achieving those tasks [Masuoka2003].– Application function is modeled as tasks and subtasks.

• Our research investigates the FEASIBILITY, USABILITY, and EFFECTIVENESS of the task-oriented approach to the mentioned problems.

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Approach: task-oriented framework

Problem Proposed approachComplexity of use Task-based user interfacesInvisibility & overload of features

Context-aware task recommendation

Inconsistency of UIs & task executions

Abstraction of task models

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Overview of Task-oriented framework

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Developer(s)/Designer(s)

Task modelTask modelTask models

Task Repository

Context-Aware Task Recommender

Task Execution Engine

Context Information Manager

User(s)

(1) P

ublis

hes

(2a) Advertises

models (2

b) P

rovi

des

cont

ext

(3) Recomm

ends

tasks

(4a)

Exe

cute

s

task

s

(4b) Provides

context

Focus of this talk

Context-aware task recommendation• A combination of the following methods:

– Location based– Pointing gesture based– Collaborative filtering using situation similarity

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Location-based recommendation

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Location = La Trobe University Campus

Find a pathFind a placeFind a parking spot…

Tasks


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Tasks

Enroll a subjectFind a room…

Tasks

Location = Building PS1


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Tasks


Tasks


Location = Personal office PS1-219

Make coffeeDim lightsWatch TV…

Tasks


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Tasks


Tasks


Location = Personal office PS1-219


Tasks Location = TV’s zone


Tasks

Pointing gesture-based recommendation

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Location = the Agora

Find a placeMeet friendCoffee…

Tasks

Theatre Coffee shop

…


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Tasks

Theatre Coffee shop

…

Pointing at = the Theatre

What is on?Special offer?Ticket booking…

Tasks


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Tasks

Theatre Coffee shop

…

Pointing at = the Coffee shop

What is on?Special offer?Ticket booking…

Tasks

CoffeeFoodSpecial offer?…

Tasks


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Location = Personal office

TV

Air-conditioner


Tasks


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Location = Personal office


Tasks

Pointing at = the Air-conditioner

TV

Air-conditioner

Temperature upTemperature downSet fan speed …

Tasks

Situation-based collaborative filtering

• Situation-based collaborative filtering– Assumption: “People tend to accomplish similar

tasks in similar situations”.– The tasks previously accomplished by similar

users in similar situations are recommended.

• Steps:

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Find similar users

Find similar situations

Rate tasks done by the similar users in the similar situations


• Situation similarity– A situation is defined as a vector of context

attributes: s = (c1, c2,…, cn).• E.g., s = (role=‘Student’, time=‘Monday’, location=‘Library’).

– Similarity between two situations, s and s’:

• is the number of common tasks and

is the number of all tasks typically accomplished in these situations.

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• User similarity– Similarity between two users u, u’ is the sum of

the similarity of their attributes such as age, gender, role, and occupation:

• Where denotes the significance weight assigned to attribute ai; simi(ai,a’i) is the per-attribute similarity between two values ai and a’i for the attribute i.

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Outlines



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Implementation

• Technologies for estimating locations

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Implementation

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• Location-based task recommendation

Location = University campus Location = Personal office

Television

Implementation

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• Indoor pointing: uses Cricket system• Outdoor pointing: uses iPhones with compass & GPS built-in

Air-conditioner

Cricket Listener

Cricket Listener

Cricket Beacon

pointing at TV

pointing at Air-conditioner

tasking

tasking

Implementation

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• Executing tasks

Power line

Task Execution EngineW

irele

ssly

exe

cute

task

s

We’ve implemented some simple device-

based tasks using X10 technology

Kettle

Light

Outlines

• Introduction to pervasive computing• Usability problems with pervasive


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How to evaluate our approach

• Metrics and methods:– Feasibility

• Implementing a prototype and evaluate performance

– Usability• Simulation & User evaluation (questionnaires)

– Effectiveness• Observe what tasks being selected by users and

compare them with the recommended tasks in a number of situations.

• Record recall and precision of task recommendation.

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Work to be done next

• Implementing the Task Execution Engine to support more complex tasks,

• Evaluating the framework, the techniques used, and the design decisions made.

• Extend task models to incorporate context information into task execution.

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Future work

• Extend the task model description standard,• Build a graphical tool for authoring task

models,• Develop mechanisms for effectively publishing

and retrieving task models,• Indexing, matching, searching, composing,

recognizing task models• Address conflicts of task executions in multi-

user environments.

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Publications

• C. Vo, T. Torabi, and S. Loke. Towards context-aware task recommendation. In ICPCA-09, Taiwan, 2009.

• C. Vo, T. Torabi, and S. Loke. TASKREC: A Task-Based User Interface for Smart Spaces. Submitted for review to UBICOMP 2010.

• C. Vo, T. Torabi, and S. Loke. Towards a Task-Oriented Framework for Smart Spaces. Submitted for review to SOOW 2010.

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Conclusion

• Defined usability problems with pervasive computing systems:– Complex of use– Invisibility & overload of features– Inconsistency of UI

• Presented the task-oriented approach, its implementation, and evaluation methodology,

• Outlined the research agenda.

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Outlines

• Introduction to pervasive computing• Usability problems with pervasive


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Questions?

Thank you!

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Related work• Situation-aware application recommendation [Cheng et al. 2008]

– They recommend applications <> We recommend tasks (multi-apps)– They use pure situation similarity <> We use task based similarity

• Homebird system [Rantapuska et al. 2008]– It recommends tasks based on features of devices discovered– However, because this approach does not consider user situation, it can recommend feasible

tasks which may be not relevant.• InterPlay [Messer et al. 2006]

– For device integration and task orchestration in a networked home.– It asks user to express their intended tasks and assumes that the users have knowledge about

feasible tasks.– In contrast, our approach can recommend relevant, feasible tasks without these

requirements.• Context-dependent task discovery [Ni et al. 2006]

– Discovering active tasks by matching current context with required context of tasks.– This can discover feasible tasks but potentially irrelevant tasks.

• Task retrieval [Fukazawa et al. 2005]– Ask user to specify target names (e.g., cafe shop, theatre) for retrieving tasks which are

associated with these names.– Our system has integrated this knowledge into place/devices-related task repository.

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ReferencesM. Weiser, “The computer for the 21st century,” Sci. American, 3(265), pp. 94–104, 1991.D. A. Norman, The Design of Future Things. Basic Books, 2007.Z. Wang & D. Garlan, “Task-driven computing,” School of Computer Science, Carnegie Mellon University, Tech.

Rep., 2000.D. Garlan et al. “Project Aura: toward distraction-free pervasive computing,” Pervasive Computing, IEEE, 1(2), pp.

22–31, 2002.R. Masuoka et al. “Task computing – the semantic web meets pervasive computing,” The SemanticWeb, pp. 866–

881, 2003.D. Magnusson & B. Ekehammar, “Similar situations–similar behaviors? a study of the intraindividual congruence

between situation perception and situation reactions,” J. of Research in Personality, 12, pp. 41–48, 1978.A. K. Dey, “Understanding and using context,” Per. and Ubi. Computing, 5(1), pp. 4–7, 2001.D. Cheng et al. “Mobile situation-aware task recommendation application,” in The 2nd Int. Conf. on Next

Generation Mobile App., Services, and Tech., 2008.A.Messer et al. “InterPlay: A middleware for seamless device integration and task orchestration in a networked

home,” in PERCOM’06. 2006, pp. 296–307.H. Ni et al. “Context-dependent task computing in pervasive environment,” Ubi. Comp. Sys., pp. 119–128, 2006.Y. Fukazawa et al. “A framework for task retrieval in task-oriented service navigation system,” in OTM Workshops

2005, pp. 876–885.O. Rantapuska and M. Lahteenmaki, “Homebird–task-based user experience for home networks and smart

spaces,” in PERMID 2008, 2008.

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