This document discusses simulation tools for print production automation. It introduces print production workflows and challenges in modeling complex, heterogeneous print service provider systems. The key challenges are to study emergent behaviors through simulation and discover patterns in print job data using machine learning techniques.

1. Introduction
Tools
Towards Print Production Simulation
Giordano B. Beretta
Print Production Automation Lab
Hewlett-Packard Laboratories
Palo Alto, California
27 May 2010
G. Beretta PPAL Commercial Print Automation Project

2. A Paradigm Shift in Printing
Introduction
Main Point
Tools
Agenda
Conventional Printing
Printing is an industrial manufacturing process
Key to success is an optimized workflow
Workflows have been been perfected over centuries
Employ highly skilled workers
Interface: purchasers and sales representatives
client art director graphic artist purchasing
contract
plant
fulfillment press operator sales rep
manager
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3. A Paradigm Shift in Printing
Introduction
Main Point
Tools
Agenda
Print Services
Paradigm shift enabled by digital printing
Combination with e-commerce and services (cloud
computing); run length can be as low as 1
Instead of saving work, labor-saving devices permit
everybody to do their own work (McLuhan)
Artwork creators are no longer highly skilled
office worker
print service
print service
consumer service license
provider
aggregator agreement
pupil
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4. A Paradigm Shift in Printing
Introduction
Main Point
Tools
Agenda
Conventional vs. PSP
metric conventional PSP
creator designer laymen + web service
customer direct indirect contact
run length > 500 >1
scheduling simple complex
motivation business vanity, hobby
marketing CRM social network
expectations realistic high
challenge cost manage expectations
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5. A Paradigm Shift in Printing
Introduction
Main Point
Tools
Agenda
Challenges
Known:
Workflow optimization
Mass customization
Finishing
New:
Complex heterogeneous system
Emergent model behavior
behavior caused by the interaction
between characteristics of different
models and not intended or foreseen
by the model
example: priority inversion between
threads in a real-time OS
leads to the invention of new
scheduling mechanisms
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6. A Paradigm Shift in Printing
Introduction
Main Point
Tools
Agenda
Main Point
To a print job a print plant looks like a pipeline
To a print plant a stream of print jobs is a collection of
processing steps that must be performed in parallel while
avoiding idle times on resources scheduling
The key challenge is to study emergent behavior
Emergent behavior is discovered through simulation — the
execution of models
Definition (amorphous modeling)
The style of handling heterogeneous models that allows
various interaction mechanisms to be specified between a
group of components at the same time is called amorphous
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7. A Paradigm Shift in Printing
Introduction
Main Point
Tools
Agenda
Agenda
Build formal amorphous models of
print service providers (PSP)
Discover emergent behavior through
simulation
Study emergent behavior through
trace and debug of real PSP data
Study PSP data through data mining
and classification using machine
learning
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8. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Typesetter Trade Union
Kristen Nygaard (1957) and Ole-Johan Dahl (1962)
First (1962) there was Simula I, a superset of Algol 60, for
simulating typesetting (discrete event networks)
Then there was Simula 67 with objects, classes,
subclasses, virtual methods, co-routines, discrete event
simulation, and garbage collection
From Oslo to Palo Alto: Smalltalk, Interlisp, Cedar,
Modula-2, Oberon — adding threads (true concurrency,
not just co-routines)
Side-trip to C++ and Standard Template Library
Finally: Java and C#
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9. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Actor-Oriented Design
Hewitt & co-worker’s 1973 extension to exploit massively
parallel computers
Concurrent execution of objects
Communication by message passing
Definition
An actor is a computational entity that, in response to a
message it receives, can concurrently:
send a finite number of messages to other actors
create a finite number of new actors
designate the behavior to be used for the next message it
receives
Communication is decoupled from sender
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10. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
What is Different?
First there were interrupt handlers and co-routines
Basic idea of threads: shared memory and global state
semaphores (Dijkstra), monitors (Hoare), serializer (Hewitt
& Atkinson)
In the actor model, messages are simply sent
no buffering, no synchronous handshaking
no ordering, FIFO requires explicit queue actor
everything is local
influenced by packet switched networks, PUP
Message can contain another actor (e.g., resumption,
a.k.a. continuation, stack frame) to which recipient sends
response variable topology
Behavior: mathematical function to express what an actor
does when it processes a message
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11. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Ptolemy II — What is it?
A laboratory for experimenting with
design techniques of concurrent
embedded systems that mix
technologies, for example
electronics and mechanical
devices
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12. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Modeling
Definition
Modeling is the act of representing a system or subsystem
formally
mathematical a set of assertions about properties of the
system such as its functionality or physical
dimensions
constructive defines a computational procedure that mimics a
set of properties of the system; also called
executable model or simulation
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13. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Design
Definition
Design is the act of defining a system or subsystem
Usually this involves defining one or more models of the system
and refining the models until the desired functionality is
obtained within a set of constraints
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14. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Embedded Software
Definition
Embedded software is software that resides in devices that are
not first-and-foremost computers
A key feature of embedded software is that it engages the
physical world, and hence has temporal constraints that
desktop software does not share
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15. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
MoC and Semantics
Definition (MoC)
Executable models are constructed under a model of
computation, which is the set of “laws of physics” that govern
the interaction of components in the model
Definition (Semantics)
A set of rules that govern the interaction of components is
called the semantics of the model of computation
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16. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Programming Paradigm
Most (but not all) Ptolemy II MoCs support actor-oriented
design (see slide 9)
The Ptolemy II abstract syntax is described by models,
actors, ports, parameters, and channels
A Ptolemy II model is a set of actors, ports, attributes, and
connections
Programming is done graphically, via XML documents, or
in a conventional programming language
Ptolemy II features:
large family of models of concurrency
not all actors are threads
communication needs not be strictly asynchronous
modularity (inner classes, multiple inheritance)
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17. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Data Mining
Definition
Data mining is the process of extracting patterns from data
Difference between data mining and statistics:
Data mining uses machine learning to analyze patterns in
large data sets, typically stored in databases
data mining aims at uncovering hidden patterns
Statistics refers to the collection, analysis, interpretation or
explanation, and presentation of smaller data sets, typically
stored in arrays
statistics aims at testing hypotheses
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18. Basics Concepts
Introduction
Ptolemy II Concepts
Tools
Data Mining Concepts
Data Mining Tasks
Data mining commonly involves four classes of tasks:
Clustering is the task of discovering groups and structures in
the data that are in some way or another “similar,”
without using known structures in the data
Classification is the task of generalizing known structure to
apply to new data (common algorithms include
decision tree learning, nearest neighbor, naive
Bayesian classification and neural networks)
Regression Attempts to find a function which models the data
with the least error
Association rule learning Searches for relationships between
variables
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