1. Visual Specification and Design of Component-based Slow Intelligence Systems

2. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Applications

3. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Applications

4. Why Slow Intelligence Systems
The slow intelligence system (SIS) technology is a novel technology for the design and/or improvement of complex information systems.

5. Characteristics of Complex Information Systems
Connected
Multiple sourced
Knowledge-based
Personalized
Hybrid
Prodigious

6. Smarter Planet
We are all now connected - economically, technically and socially. Our planet is becoming smarter via integration of information scattered in many different data sources: from the sensors, on the web, in our personal devices, in documents and in databases, or hidden within application programs. Often we need to get information from several of these sources to complete a task. Examples include healthcare, science, the business world and our personal lives. (Quoted from Josephine M. Cheng, IBM Fellow and Vice President of IBM Research)

7. (courtesy of IBM)

8. Hybrid Intelligence
While processor speed and storage capacity have grown remarkably, the geometric growth in user communities, online computer usage, and the availability of data is in some ways even more remarkable. Hybrid Intelligence offers great opportunities. We have to harness this data availability to build systems of immense potential. While today s large scale systems are evolutionarily based on the distributed computing technologies envisioned in the 70 s and 80 s, sheer scaling has led to many unanticipated challenges. (quoted from Alfred Z. Spector, Vice President, Research and Special Initiatives, Google, USA)

9. Prodigious Hybrid Intelligence Systems
Users and computers doing more than either could individually (quoted from Alfred Z. Spector, Google).

10. Characteristics of Complex Information Systems
Connected
Multiple sourced
Knowledge-based
Personalized
Hybrid
Prodigious
=> CONSTANTLY CHANGING

11. Challenges in the Design of Complex Information Systems
The operating environment, individual/collective user behavior and underlying technology base of such complex information systems are constantly changing.
There is never a stable and static solution for an “optimal” complex information system.
There are no general techniques for the design of a complex information system that can gradually improve and/or optimize its performance over time in a changing environment.

12. What is a Slow Intelligence System
A Slow Intelligence System (SIS) is a general-purpose system characterized by being able to improve performance over time through a process involving enumeration, propagation, adaptation, elimination and concentration. A SIS is characterized by employing super components, i.e., multiple components that can be activated either sequentially or in parallel to search for better solutions. A SIS continuously learns, searches for new solutions and propagates and shares its experience with peers. 12

13. The SIS Technology
This SIS technology consists of the visual specification of SIS as a system of super components, design principles of the timing controller, techniques for incremental application system design, SIS development framework and the SIS experimental test bed.

14. How can SIS technology help?
The SIS technology can be applied to design and/or modify a complex information system capable of improving its performance over time in a changing environment. In this presentation we will concentrate on visual specification, incremental design, development framework, user interface, and application to social influence analysis.

15. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Application to Social Influence Analysis

16. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time.
A slow intelligence system is
a system that (i) solves
problems by trying different
solutions, (ii) is context-
aware to adapt to different
situations and to propagate
knowledge, and (iii) may
not perform well in the
short run but continuously
learns to improve its
performance over time.

17. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving
Enumeration

18. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving
Enumeration
Propagation

19. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving
Enumeration
Propagation
Adaptation

20. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving
Enumeration
Propagation
Adaptation
Elimination

21. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving
Enumeration
Propagation
Adaptation
Elimination
Concentration

22. Slow Intelligence Systems
Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving
Enumeration
Propagation
Adaptation
Elimination
Concentration
Slow Decision Cycle
to complement Fast
Decision Cycle

23. Slow Intelligence Systems
A SIS continuously learns, searches for new solutions and propagates and shares its experience with other peers.
From the structural point of view, a SIS is a system with multiple decision cycles such that actions of slow decision cycle(s) may override actions of quick decision cycle(s), resulting in poorer performance in the short run but better performance in the long-run.
Timing Controller: What decision cycle to take is determined by the timing controller.

24. Slow Intelligence Systems
A SIS continuously learns, searches for new solutions and propagates and shares its experience with other peers.
From the structural point of view, a SIS is a system with multiple decision cycles such that actions of slow decision cycle(s) may override actions of quick decision cycle(s), resulting in poorer performance in the short run but better performance in the long-run.
Timing Controller: What decision cycle to take is determined by the timing controller.

25. Slow Intelligence Systems
A SIS continuously learns, searches for new solutions and propagates and shares its experience with other peers.
From the structural point of view, a SIS is a system with multiple decision cycles such that actions of slow decision cycle(s) may override actions of quick decision cycle(s), resulting in poorer performance in the short run but better performance in the long-run.
Timing Controller: What decision cycle to take is determined by the timing controller.

26. Slow Intelligence Systems
. A Slow Intelligence System is constructed from super components, which are the building blocks of SIS. Therefore SIS is an advancement over component based software systems: while a component-based software system is constructed from software components, in SIS some or all of its components are replaced by super components capable of improving their performances over time in a changing environment.
There are two types of super components: the basic building block and advanced building block.

27. Basic Building Block (BBB)

28. Advanced Building Block (ABB)

29. SIS is a component-based system built from BBBs and ABBs

30. Comparison with Other Approaches30 30

31. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Applications

32. Visual Specification of SIS

33. Dual visual representations by components and Petri net

34. Dual visual representations by class diagrams and sequence diagram

35. Super Components

36. Expanded Petri net based upon super components

37. A component generator for super components

38. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Applications

39. Partial dual visual representation (I-card1, C-card1) for Product & Service Customization (PSC) system

40. Partial dual visual representation (I-card2, C-card2) and (I-card3, C-card3)

41. Synthesis of partial visual representations (I-card1, C-card1), (I-card2, C-card2) and (I-card3, C-card3) into (I-card1-2-3,C-card1-2-3)

42. Outline Why Slow Intelligence Systems Introduction to SIS
Visual Specification of SIS
Incremental Design
Timing Controller
User Interface
Application to Topic/Trend Detection
Application to High Dimensional Feature Selection 42

43. Timing Controller Three Types of Timing Controllers:
Basic Timing Controller (using switching circuit)
Advanced Timing Controller (using associative memory)
Recursive Timing Controller (for super components)
Fast Cycle and Slow Cycle:
Any cycle without super component is a fast cycle
Any cycle with super component is a slow cycle 43 43

44. Basic Timing Controller
The basic timing controller consists of a latch register and a switching circuit
The latch register has as many cells as there are software components. Each cell stores a binary number indicating whether to invoke the corresponding software component (1) or not (0)
The latch register outputs this binary vector to the switching circuit, which computes another binary vector as new input to the latch register, to control the next round of software components invocation
This computation cycle repeats itself, until the binary vector stored in the latch register becomes (0,…,0) 44 44

45. Advanced Timing Controller
The associative memory also receives the binary vector from the latch register and uses it to search and access an associated binary vector (or vectors) as output
The timing controller with non-deterministic associative memory determines the invocation of software components by performing additional computations on Petri net structure, attributes, probability, degree of certainty, fuzzy measures or some other means
In SIS some or all of the software components may be super-components. In which case, certain cells in the latch register may be associated with super-components. 45 45

46. Recursive Timing Controller
Each cell associated with a super-component can be expanded into another latch register. The secondary latch register is part of another timing controller. In other words, timing controllers can be recursively defined when super components are present in a system
The ‘T’ icon adjacent to a cell associated with a super component indicates it can be expanded into another timing controller. An equivalent, but simpler, notation is to write a ‘T’ inside this cell, which can then be expanded into another latch register and its associative memory 46 46

47. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Application to Social Influence Analysis

48. SIS Test Bed
Initially, to experiment with an application system under development, the SIS test bed can be employed.

49. SIS Test Bed for Healthcare Systems

50. SIS Framework
The SIS framework is to test and develop the optimized application system based upon the SIS technology.

51. SIS Framework

52. SIS Framework
The Enumerator reads in the specification of functional blocks and creates multiple candidate components for each functional block. The Tester tests all the functional blocks and records their performance in the DB. The Eliminator selects the best candidate components based upon their performance. The Concentrator packs the selected candidate components based on dependency specifications and generates a generic software package. The Transformer is used to transform the Concentrator-generated software package to target software package that serves specific purpose. The Timing Controller is the system manager that controls all the above mentioned components, telling them when to perform what actions.

53. Requirements for SIS FrameworkID
Description R1
Java support R2
Dynamic lifecycle management of building blocks during runtime R3
XML message based communications R4
Code frame generation out of meta-models in a form of class diagram R5
Scalability of components upto 1,000 – 10,000 components R6
Integrated Development Environment (IDE) support

54. Related technologies for SIS Framework
Eclipse has a big ecosystem for Java based software development both the IDE and runtime framework
OSGi is a universal middleware which abstract heterogeneous communication protocols, support life-cycle management of software component during runtime which can be a basis for the SIS framework.
Eclipse EMF provides code generation from the meta-model
Eclipse ECF provides communication for point-to-point and publish-and-subscribe for the distributed systems
Semantic self-organization, self-similarity and autonomic component model concepts from CASCADAS project can be useful to augment the SIS approaches.

55. Relationships of related technologies to SIS

56. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Application to Social Influence Analysis

57. SIS User Interface
The user interface was developed in part based upon PIPE tool
In C-card, places specify messages and transitions specify components
In I-card, components can be simple components or super components
T-card is used to specify test data
Spec can be saved as PNML (Petri Net Markup Language) and XML documents

58. Screen Shot for C-card Design

59. Screen Shot for place editor and message editor design

60. Screen Shot for I-card Design

61. Screen Shot for T-card Design

62. Outline
Why Slow Intelligence Systems
Introduction to SIS
Visual Specification of SIS
Incremental Design
SIS Framework and Test Bed
User Interface
Applications

63. Applications Social Influence Analysis
Product and service customization
Topic/Trend Detection
High Dimensional Feature Selection
Personal healthcare
Emergency Management
Legacy Systems

64. Discussion A framework for knowledge-based software engineering.
Since time is relative, “slow” intelligence systems for some can also be “fast” for others.
A “slow intelligence system” can evolve into a “fast intelligence system”.

65. Further Work
How to check and maintain consistency in incremental application system design
How to find the critical components in a legacy system for replacement by super components

66. Q&A