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Towards a Task-Oriented Framework for Smart Spaces Chuong C. Vo, Torab Torabi, & Seng W. Loke Department of Computer Science & Computer Engineering La.

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Presentation on theme: "Towards a Task-Oriented Framework for Smart Spaces Chuong C. Vo, Torab Torabi, & Seng W. Loke Department of Computer Science & Computer Engineering La."— Presentation transcript:

1 Towards a Task-Oriented Framework for Smart Spaces Chuong C. Vo, Torab Torabi, & Seng W. Loke Department of Computer Science & Computer Engineering La Trobe University, Australia 1 Speaker: Seng W. Loke C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

2 Outlines Introduction – Smart spaces – Usability problems with smart spaces – Task-oriented scenarios Approach: a task-oriented framework – Concepts & components Current implementation Related work Conclusion & future work 2 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

3 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. 3 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

4 Smart space What is a smart space? – 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]. 4 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

5 Usability problems with smart spaces 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]. – Users need to understand how to map devices' functions to their tasks & sub-tasks. 5 Slideshow controller TV controller A future smart space Audio controller Blind controller TV Video-conference Display Temperature controller Projector DVD player Printer Computer Light controller Recorder Smart phone Door Coffeemaker C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

6 Usability problems (cont.) Invisibility & Overload of features – Technologies blend into environments. – Frequently adding/removing devices and services to/from the spaces. – One device  tens of features – Different combinations of devices  Hundreds of features 6 MediaCup [Beigl et al. 2000] C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

7 Usability problems (cont.) Inconsistency of user interfaces – Brand identification, product differentiation [Rich 2009; Oliveira 2008] Inconsistency of task executions – Same tasks but different operations/procedures when being executed in different smart spaces. 7 How to abate these usability problems? Our approach is based on task-oriented computing. C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

8 Task-oriented scenarios Pervasive city’s scenario – “It’s 7p.m., it’s raining, and you’re walking in the centre of 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…” Pervasive university campus’s 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.” 8

9 Task-oriented scenarios (cont.) 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.” 9 Our long term aim is to realize these scenarios! C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

10 Task-Oriented Computing Our approach is based on task-oriented 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’ functionality. – Users focus on the tasks at hand rather than on the means for achieving those tasks [Masuoka2003]. – Application function is modeled as tasks and sub- tasks. 10 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

11 Approach: a task-oriented framework ProblemProposed solution Complexity of useTask-based user interfaces Invisibility & overload of features Context-aware task recommendation Inconsistency of UIs & task executions Abstraction of task models 11 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

12 Overview of the task-oriented framework 12 Developer /Designer Task model Task models Task Repository Context-Aware Task Recommender Task Execution Engine Context Information Manager User(s) (1) Publishes (2a) Advertises task models (2b) Provides context (3) Recommends tasks (4a) selects tasks (4b) Provides context Devices Services (5) invoke C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

13 Task description language Syntax and semantics for describing tasks. Some requirements: – Abstraction – Interpretability – Support for inter-task dependencies – Support for proactive task guidance: What should I do next? How do I do ? When should I do ? Why did you do ? Did succeed? Why did the task failed? How to fix the problem?... C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University 13

14 Task description repository Manage and advertise task model descriptions Challenges: – Effective mechanisms for searching and matching task models – indexing techniques and task description queries 14 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

15 Context-aware task recommendation A combination of the following methods: – Location based – Pointing gesture based – Collaborative filtering using situation similarity 15 A set of all possible tasks Location-based recommendation Pointing gesture based recommendation Situation-based collaborative filtering

16 Location-based task recommendation 16 Location = La Trobe University Campus Find a path Find a place Find a parking spot … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

17 Location-based task recommendation 17 Location = La Trobe University Campus Find a path Find a place Find a parking spot … Tasks Enroll a subject Find a room … Tasks Location = Building PS1 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

18 Location-based task recommendation 18 Location = La Trobe University Campus Find a path Find a place Find a parking spot … Tasks Enroll a subject Find a room … Tasks Location = Building PS1 Location = Personal office PS1-219 Make coffee Dim lights Watch TV … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

19 Location-based task recommendation 19 Location = La Trobe University Campus Find a path Find a place Find a parking spot … Tasks Enroll a subject Find a room … Tasks Location = Building PS1 Location = Personal office PS1-219 Make coffee Dim lights Watch TV … Tasks Location = TV’s zone Make coffee Dim lights Watch TV … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

20 Pointing-based task recommendation 20 Location = the Agora Find a place Meet friend Coffee … Tasks Theatre Coffee shop … C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

21 Pointing-based task recommendation 21 Location = the Agora Find a place Meet friend Coffee … Tasks Theatre Coffee shop … Pointing at = the Theatre What is on? Special offer? Ticket booking … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

22 Pointing-based task recommendation 22 Location = the Agora Find a place Meet friend Coffee … Tasks Theatre Coffee shop … Pointing at = the Coffee shop What is on? Special offer? Ticket booking … Tasks Coffee Food Special offer? … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

23 Pointing-based task recommendation 23 Location = Personal office TV Air-conditioner Make coffee Dim lights Watch TV … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

24 Pointing-based task recommendation 24 Location = Personal office Make coffee Dim lights Watch TV … Tasks Pointing at = the Air-conditioner TV Air-conditioner Temperature up Temperature down Set fan speed … Tasks C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

25 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: 25 Find similar users Find similar situations Rate tasks done by the similar users in the similar situations The details are our previous work Vo et al. 2009).

26 Task execution engine Load and execute descriptions of the tasks selected by the user. Provide instructions to guide the user through the task accomplishment. Inform the user of the progress of task completion and failures. Manage sessions of task executions. 26 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

27 Current implementation Technologies for estimating locations 27 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

28 Current implementation 28 Location-based task recommendation Location = University campusLocation = Personal office

29 Television Current implementation 29 Indoor pointing: uses Cricket system Outdoor pointing: uses iPhones with compass & GPS built-in Air-conditioner Cricket Listener Cricket Beacon pointing at TV pointing at Air-conditioner tasking C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

30 Current implementation 30 Executing tasks Power line Task Execution Engine Wirelessly execute tasks We’ve implemented some simple tasks using X10 technology Kettle Light C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

31 Future work Design a comprehensive task description language Develop a graphical editor for authoring task descriptions Extend the task execution engine 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…. 31 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

32 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. 32 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

33 Conclusion Defined usability problems with smart spaces: Complex of use, Invisibility & overload of features, and Inconsistency of UIs & tasks. Presented the task-oriented framework with components: Task description language, task repository, context-aware task recommender, task execution engine… Given an early implementation & applications. Outlined the research agenda. 33 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University

34 References M. Weiser, “The computer for the 21st century,” Sci. American, 3(265), pp. 94–104, C. Vo, T. Torabi, and S. Loke. Context-aware task recommendation. In ICPCA-09, Taiwan, Z. Wang & D. Garlan, “Task-driven computing,” School of Computer Science, Carnegie Mellon University, Tech. Rep., D. Garlan et al. “Project Aura: toward distraction-free pervasive computing,” Pervasive Computing, IEEE, 1(2), pp. 22–31, R. Masuoka et al. “Task computing – the semantic web meets pervasive computing,” The SemanticWeb, pp. 866–881, 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, A. K. Dey, “Understanding and using context,” Per. and Ubi. Computing, 5(1), pp. 4–7, D. Cheng et al. “Mobile situation-aware task recommendation application,” in The 2 nd Int. Conf. on Next Generation Mobile App., Services, and Tech., A.Messer et al. “InterPlay: A middleware for seamless device integration and task orchestration in a networked home,” in PERCOM’ , pp. 296–307. H. Ni et al. “Context-dependent task computing in pervasive environment,” Ubi. Comp. Sys., pp. 119–128, 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,

35 Questions? Thank you! 35 C.C. Vo, T. Torabi, and S. W. Loke - La Trobe University


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