Modelling Data Processing for Interactive Scores Using Coloured Petri Nets

Transcript

1. Modelling Data Processing for Interactive Scores
   Using Coloured Petri Nets
   Jaime Arias
   Joint work with Myriam Desainte-Catherine and Camilo Rueda
   Laboratoire Bordelais de Recherche en Informatique (LaBRI)
   Université de Bordeaux
   14th International Conference on Application of Concurrency to
   System Design
   Tunis, June 2014
   1
   
   

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2. Motivation
   Interactive Scores
   • Formalism for writing and executing interactive multimedia
   scenarios [Desainte-Catherine et al., 2013] (e.g., art installations).
   • An interactive score is composed of
   ◦ Multimedia objects.
   ◦ Hierarchical boxes.
   ◦ Temporal relations (Allen’s relations [Allen]).
   • Rigid relation
   • Flexible relation
   ◦ Interaction points.
   • i-score1 [Marczak et al., 2011] is a software that implements the
   above formalism.
   ◦ Authoring side : Constraint Satisfaction Problem (Gecode).
   ◦ Performance side : Hierarchical Time Stream Petri Net [Sénac et al.,
   1995] (HTSPN).
   1http://i-score.org
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 1/22
   1/22
   

   View Slide

3. Motivation
   Interactive Scores
   • Formalism for writing and executing interactive multimedia
   scenarios [Desainte-Catherine et al., 2013] (e.g., art installations).
   • An interactive score is composed of
   ◦ Multimedia objects.
   ◦ Hierarchical boxes.
   ◦ Temporal relations (Allen’s relations [Allen]).
   • Rigid relation
   • Flexible relation
   ◦ Interaction points.
   • i-score1 [Marczak et al., 2011] is a software that implements the
   above formalism.
   ◦ Authoring side : Constraint Satisfaction Problem (Gecode).
   ◦ Performance side : Hierarchical Time Stream Petri Net [Sénac et al.,
   1995] (HTSPN).
   1http://i-score.org
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 1/22
   1/22
   

   View Slide

4. Motivation
   Interactive Scores
   • Formalism for writing and executing interactive multimedia
   scenarios [Desainte-Catherine et al., 2013] (e.g., art installations).
   • An interactive score is composed of
   ◦ Multimedia objects.
   ◦ Hierarchical boxes.
   ◦ Temporal relations (Allen’s relations [Allen]).
   • Rigid relation
   • Flexible relation
   ◦ Interaction points.
   • i-score1 [Marczak et al., 2011] is a software that implements the
   above formalism.
   ◦ Authoring side : Constraint Satisfaction Problem (Gecode).
   ◦ Performance side : Hierarchical Time Stream Petri Net [Sénac et al.,
   1995] (HTSPN).
   1http://i-score.org
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 1/22
   1/22
   

   View Slide

5. Motivation
   Interactive Scores
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 2/22
   2/22
   

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6. Motivation
   Interactive Scores
   temporal relation
   interaction point
   hierarchical box
   process box
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 2/22
   2/22
   

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7. Motivation
   Interactive Scores
   • Temporal relations are preserved during the authoring and
   performance sides ; a scenario can be interpreted in a limited set of
   possibilities.
   • The execution of multimedia processes is carried out by external
   applications (e.g., Pure Data, Max/MSP).
   • A wide range of applications such as the games and museum
   installations.
   ◦ Linear execution (i.e., without branching and loops).
   • Composers have increasingly needed to represent and manipulate
   complex data in their multimedia scenarios.
   ◦ i-score does not handle complex data (e.g., audio streams).
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 3/22
   3/22
   

   View Slide

8. Motivation
   Interactive Scores
   • Temporal relations are preserved during the authoring and
   performance sides ; a scenario can be interpreted in a limited set of
   possibilities.
   • The execution of multimedia processes is carried out by external
   applications (e.g., Pure Data, Max/MSP).
   • A wide range of applications such as the games and museum
   installations.
   ◦ Linear execution (i.e., without branching and loops).
   • Composers have increasingly needed to represent and manipulate
   complex data in their multimedia scenarios.
   ◦ i-score does not handle complex data (e.g., audio streams).
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 3/22
   3/22
   

   View Slide

9. Motivation
   Interactive Scores
   • Temporal relations are preserved during the authoring and
   performance sides ; a scenario can be interpreted in a limited set of
   possibilities.
   • The execution of multimedia processes is carried out by external
   applications (e.g., Pure Data, Max/MSP).
   • A wide range of applications such as the games and museum
   installations.
   ◦ Linear execution (i.e., without branching and loops).
   • Composers have increasingly needed to represent and manipulate
   complex data in their multimedia scenarios.
   ◦ i-score does not handle complex data (e.g., audio streams).
   Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 3/22
   3/22
   

   View Slide

10. Motivation
    Interactive Scores
    • Temporal relations are preserved during the authoring and
    performance sides ; a scenario can be interpreted in a limited set of
    possibilities.
    • The execution of multimedia processes is carried out by external
    applications (e.g., Pure Data, Max/MSP).
    • A wide range of applications such as the games and museum
    installations.
    ◦ Linear execution (i.e., without branching and loops).
    • Composers have increasingly needed to represent and manipulate
    complex data in their multimedia scenarios.
    ◦ i-score does not handle complex data (e.g., audio streams).
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 3/22
    3/22
    

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11. This talk is about …
    Extension of the execution model of i-score using Coloured Petri Nets
    (CPNs) in order to handle data streams.
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 4/22
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12. Outline
    What is essential to know?
    The behaviour of interactive scores
    Execution model of interactive scores
    Extending the model for handling data
    Summary
    Future work
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13. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
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14. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 6/22
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15. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 6/22
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16. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 6/22
    6/22
    

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17. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 6/22
    6/22
    

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18. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 6/22
    6/22
    

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19. What is essential to know?
    The behaviour of interactive scores
    2
    4
    3
    5
    1
    6
    0
    r2
    r1 r3
    r4
    r5
    r6
    r7
    time
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
    r1
    r2
    Box 1
    Box 2
    Box 6
    min max
    r3
    min max
    max
    min
    r5
    r6
    Box 5
    Box 4
    min max
    Box 3
    min
    r7
    24 25 26 27 28
    r4
    merge (r3,r4)
    Boxes behave like rela-
    tions with an attached
    process!
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 6/22
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20. Execution model of interactive scores
    Modelling a rigid interval
    time
    fixed interval
    interactive interval
    duration
    minimum duration
    maximum duration
    interaction point
    enabled
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    colset UT = UNIT timed;
    var dur : TIME;
    start
    duration
    TIME
    ()@+dur
    dur
    wait
    UT
    end
    ()
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 7/22
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21. Execution model of interactive scores
    Modelling a static box
    start
    box
    start
    process
    end
    box
    stop
    process
    dur dur
    duration
    TIME
    start
    process
    start
    interval
    end
    interval
    fixed
    interval
    duration
    TIME
    end
    process
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    var dur : TIME;
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 8/22
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22. Execution model of interactive scores
    Modelling a flexible interval
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    var dmin : TIME;
    var dmax : TIME;
    start
    interval end
    interval
    1
    duration
    1
    TIME
    start
    interval
    2
    end
    max.
    interval
    duration
    2
    TIME
    dmin
    dmax-dmin
    dmin
    dmax
    end
    min.
    interval
    min.
    duration
    TIME
    max.
    duration
    TIME
    fixed
    interval
    fixed
    interval
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 9/22
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23. Execution model of interactive scores
    Listening events
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    colset UT = UNIT timed;
    var ip_t : TIME;
    start
    stop
    end
    interact.
    point
    enabled
    ip_t
    ip_t
    TIME
    UT
    t3
    ()@+1
    time()
    t1
    [ip_t < time()]
    t2
    [ip_t = time()]
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 10/22
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24. Execution model of interactive scores
    Handling interaction points
    time
    fixed interval
    interactive interval
    duration
    minimum duration
    maximum duration
    interaction point
    enabled
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    colset BOOL = bool;
    var e : BOOL;
    interact.
    point
    start
    get
    stop
    get
    end
    interval
    get
    IP
    ss
    se
    flexible
    interval
    start
    interval
    TIME
    max.
    duration
    TIME
    min.
    duration
    end
    max.
    interval
    end
    min.
    interval
    enable
    e
    false
    BOOL
    true
    if (e=true)
    then 1`()
    else empty
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25. Execution model of interactive scores
    Modelling an interactive box
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    var dmin : TIME;
    var dmax : TIME;
    start
    box
    start
    process end
    box
    dmin
    min.
    duration
    TIME
    start
    process
    end
    process
    stop
    process
    TIME
    max.
    duration
    start
    interval
    TIME
    min.
    duration
    interact.
    point
    end
    interval
    interactive
    interval
    max.
    duration
    TIME
    dmax
    dmin
    dmax
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26. Execution model of interactive scores
    Synchronization of fixed intervals
    time
    fixed interval 1
    fixed interval 2
    merge fixed (1,2)
    end
    interval
    r5
    fixed
    interval
    end
    interval
    r6
    fixed
    interval
    TIME
    duration
    box
    D
    start
    box
    D
    fixed
    box
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27. Execution model of interactive scores
    Synchronization of interactive intervals
    interactive interval 1
    interactive interval 2
    merge interactive (1,2)
    min max
    min max
    min max
    interaction point
    enabled
    time
    TIME
    duration
    box
    C
    start
    box
    C
    fixed
    box
    end
    max.
    interval
    r3
    end
    min.
    interval
    r3
    flexible
    interval
    end
    max.
    interval
    r4
    end
    min.
    interval
    r4
    flexible
    interval
    get
    IP
    interact.
    point
    C
    start
    get
    stop
    get
    end
    get
    ss
    se
    enable
    e
    false
    BOOL
    true
    if (e=true)
    then 1`()
    else empty
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28. Extending the model for handling data
    Main idea
    • Multimedia streams are often cut into temporal frames to be carried
    from one process to another ; we model frames as coloured tokens.
    • Asynchronous functional composition (i.e., buffering the output of
    processes).
    • Modules for reading, appending, reversing audio files.
    • Implementation and simulation in CPN Tools2.
    2http://cpntools.org
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 15/22
    15/22
    

    View Slide

29. Extending the model for handling data
    Main idea
    • Multimedia streams are often cut into temporal frames to be carried
    from one process to another ; we model frames as coloured tokens.
    • Asynchronous functional composition (i.e., buffering the output of
    processes).
    • Modules for reading, appending, reversing audio files.
    • Implementation and simulation in CPN Tools2.
    2http://cpntools.org
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 15/22
    15/22
    

    View Slide

30. Extending the model for handling data
    Main idea
    • Multimedia streams are often cut into temporal frames to be carried
    from one process to another ; we model frames as coloured tokens.
    • Asynchronous functional composition (i.e., buffering the output of
    processes).
    • Modules for reading, appending, reversing audio files.
    • Implementation and simulation in CPN Tools2.
    2http://cpntools.org
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 15/22
    15/22
    

    View Slide

31. Extending the model for handling data
    Main idea
    • Multimedia streams are often cut into temporal frames to be carried
    from one process to another ; we model frames as coloured tokens.
    • Asynchronous functional composition (i.e., buffering the output of
    processes).
    • Modules for reading, appending, reversing audio files.
    • Implementation and simulation in CPN Tools2.
    2http://cpntools.org
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 15/22
    15/22
    

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32. Extending the model for handling data
    Reading an audio file
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    colset INT = int;
    colset BOOL = bool;
    colset DATA = INT;
    colset DURATION = TIME timed;
    colset FILE = product INT*DATA;
    var f_dur : TIME;
    var e : BOOL;
    var n_max : INT;
    var n : INT;
    var f : DATA;
    TIME
    frame
    duration
    start
    INT
    max
    number
    frames
    f_dur
    n_max
    f_dur get
    frame
    DURATION
    f_dur
    f_dur@+f_dur
    max
    num
    INT
    n_max
    n_max
    n_max
    file
    FILE (n,f)
    continue
    stop
    next
    frame
    INT
    n+1
    n
    receive
    frame
    FILE
    [n <= n_max]
    (n,f)
    (n,f)
    n_max
    n_max
    1
    if (n = n_max)
    then 1`()
    else empty
    (n,f)
    end
    EOF
    wait
    sync
    next
    frame
    f_dur
    R
    E
    A
    D
    limit
    if (n = n_max)
    then 1`()
    else empty
    if (e = true)
    then 1`()
    else empty
    2`()
    n
    frames
    read
    n-1
    INT
    stop
    enabled
    BOOL
    output
    FILE
    e
    1`false
    e
    1`false
    true
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33. Extending the model for handling data
    Appending two audio files
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    colset INT = int;
    colset DATA = INT;
    colset FILE = product INT*DATA;
    var n : INT;
    var n1 : INT;
    var n2 : INT;
    var i : INT;
    var f : DATA;
    TIME
    frame
    duration
    file 1
    start
    file 1
    INT
    max
    number
    frames
    file 1
    stop
    file 1
    file 1
    output
    file 1
    FILE
    end
    file 1
    EOF
    file 1
    next
    frame
    file 1
    TIME
    frame
    duration
    file 2
    start
    file 2
    INT
    max
    number
    frames
    file 2
    stop
    file 2
    file
    2
    output
    file 2
    FILE
    end
    file 2
    EOF
    file 2
    next
    frame
    file 2
    FILE FILE
    output
    FILE
    index
    (n,f)
    1
    INT
    i
    i+1
    i
    i+1
    (i,f)
    (n,f)
    (i,f)
    next
    1
    INT
    n
    n+1
    next 2
    1
    INT
    n
    n+1
    frames
    read 1
    INT
    frames
    read 2
    INT
    read
    file
    read
    file
    total
    frames
    read
    INT
    n2
    n1
    n1+n2
    stop
    append
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34. Extending the model for handling data
    Reversing an audio file
    Declarations
    colset TIME = time;
    colset UNIT = unit;
    colset INT = int;
    colset DATA = INT;
    colset FILE = product INT*DATA;
    var n : INT;
    var n_max : INT;
    var i : INT;
    var f : DATA;
    TIME
    frame
    duration
    start
    reverse
    INT
    max
    frames
    stop
    file
    temp
    output
    FILE
    end
    EOF
    next
    frame
    TIME
    frame
    duration
    start
    INT
    max
    number
    stop
    reverse
    file
    reverse
    output
    reverse
    FILE
    end
    reverse
    EOF
    reverse
    next
    frame
    reverse
    FILE
    FILE
    (n,f)
    index
    INT
    i
    i-1
    next
    1
    INT
    n
    n+1
    0
    INT
    max
    number
    frames
    [i=0]
    (i,f)
    [i>0]
    i
    n_max
    R
    E
    V
    n_max
    n_max
    n_max
    frames
    read
    INT
    frames
    reversed
    INT
    read
    file
    read
    file
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 18/22
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35. Extending the model for handling data
    Example
    Append
    Files
    Read
    File 1
    Read
    File 2
    time (ms)
    max
    min
    min max
    Reverse
    File
    r1
    (values)
    (values)
    (values)
    (param)
    (param)
    r2
    r3
    r4
    r5
    time (ms)
    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
    f1 f2 f3 f4 f5
    f1 f2 f3 f4 f5
    28 29 30 31 32
    min max
    f1 f2 f3
    33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
    f4 f5 f6 f7 f8
    54 55 56 57 58
    f1 f2 f3
    min max
    Read File 1 (process)
    Read File 1 (box)
    Read File 2 (process)
    Read File 2 (box)
    r3
    Reverse File (process)
    Reverse File (box)
    r5
    Append Files (process)
    Append Files (box)
    r4
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 19/22
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36. Summary
    • Modular model in CPNs for executing interactive multimedia scores
    with the capability to handle complex data (i.e., data audio streams).
    • Representation of audio frames as coloured tokens.
    • Asynchronous functional composition of processes by adding
    inputs/outputs to boxes and passing values through intervals.
    • Modules for processing audio files such as reading, appending and
    reversing files.
    • Implementation and simulation of our model in CPN Tools.
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 20/22
    20/22
    

    View Slide

37. Summary
    • Modular model in CPNs for executing interactive multimedia scores
    with the capability to handle complex data (i.e., data audio streams).
    • Representation of audio frames as coloured tokens.
    • Asynchronous functional composition of processes by adding
    inputs/outputs to boxes and passing values through intervals.
    • Modules for processing audio files such as reading, appending and
    reversing files.
    • Implementation and simulation of our model in CPN Tools.
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 20/22
    20/22
    

    View Slide

38. Summary
    • Modular model in CPNs for executing interactive multimedia scores
    with the capability to handle complex data (i.e., data audio streams).
    • Representation of audio frames as coloured tokens.
    • Asynchronous functional composition of processes by adding
    inputs/outputs to boxes and passing values through intervals.
    • Modules for processing audio files such as reading, appending and
    reversing files.
    • Implementation and simulation of our model in CPN Tools.
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 20/22
    20/22
    

    View Slide

39. Summary
    • Modular model in CPNs for executing interactive multimedia scores
    with the capability to handle complex data (i.e., data audio streams).
    • Representation of audio frames as coloured tokens.
    • Asynchronous functional composition of processes by adding
    inputs/outputs to boxes and passing values through intervals.
    • Modules for processing audio files such as reading, appending and
    reversing files.
    • Implementation and simulation of our model in CPN Tools.
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 20/22
    20/22
    

    View Slide

40. Summary
    • Modular model in CPNs for executing interactive multimedia scores
    with the capability to handle complex data (i.e., data audio streams).
    • Representation of audio frames as coloured tokens.
    • Asynchronous functional composition of processes by adding
    inputs/outputs to boxes and passing values through intervals.
    • Modules for processing audio files such as reading, appending and
    reversing files.
    • Implementation and simulation of our model in CPN Tools.
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 20/22
    20/22
    

    View Slide

41. Future work
    • We are working on extending our model with conditionals and loops.
    • We plan to model new modules for audio processing (e.g., change
    the speed for reading audio files).
    • We plan to explore the verification of properties in scenarios (e.g.,
    maximum number of processes that can be executed at the same
    time).
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 21/22
    21/22
    

    View Slide

42. Future work
    • We are working on extending our model with conditionals and loops.
    • We plan to model new modules for audio processing (e.g., change
    the speed for reading audio files).
    • We plan to explore the verification of properties in scenarios (e.g.,
    maximum number of processes that can be executed at the same
    time).
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 21/22
    21/22
    

    View Slide

43. Future work
    • We are working on extending our model with conditionals and loops.
    • We plan to model new modules for audio processing (e.g., change
    the speed for reading audio files).
    • We plan to explore the verification of properties in scenarios (e.g.,
    maximum number of processes that can be executed at the same
    time).
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 21/22
    21/22
    

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44. Thank you for your attention
    E-mail: jaime.arias@labri.fr
    Jaime Arias et al. (LaBRI) Modelling Data Processing for Interactive Scores Using Coloured Petri Nets 22/22
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45. Modelling Data Processing for Interactive Scores
    Using Coloured Petri Nets
    Jaime Arias
    Joint work with Myriam Desainte-Catherine and Camilo Rueda
    Laboratoire Bordelais de Recherche en Informatique (LaBRI)
    Université de Bordeaux
    14th International Conference on Application of Concurrency to
    System Design
    Tunis, June 2014
    1
    
    

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46. References
    Myriam Desainte-Catherine, Antoine Allombert, and Gérard Assayag.
    Towards a hybrid temporal paradigm for musical composition and
    performance: The case of musical interpretation. Computer Music
    Journal, 37(2):61–72, 2013.
    James F. Allen. Maintaining knowledge about temporal intervals. 26:
    832–843. ISSN 0001-0782. doi: 10.1145/182.358434.
    Raphaël Marczak, Myriam Desainte-Catherine, and Antoine Allombert.
    Real-time temporal control of musical processes. In The Third
    International Conferences on Advances in Multimedia, MMEDIA 2011,
    pages 12–17, 2011. ISBN 978-1-61208-129-8.
    

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47. References
    Patrick Sénac, Pierre de Saqui-Sannes, and Roberto Willrich.
    Hierarchical time stream petri net: A model for hypermedia systems.
    In Giorgio De Michelis and Michel Diaz, editors, Application and Theory
    of Petri Nets 1995, volume 935 of Lecture Notes in Computer Science,
    pages 451–470. Springer Berlin Heidelberg, 1995. ISBN
    978-3-540-60029-9. doi: 10.1007/3-540-60029-9_54. URL
    http://dx.doi.org/10.1007/3-540-60029-9_54.
    

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