1. Heterogeneous Information Management
prepared for CERN seminar, June 2000
Heterogeneous Information Management
June 2000
Gio Wiederhold
Stanford University
11/19/2018
Gio - CERN

2. Abstract
Information is created by applying knowledge (enoded as programs or rules) to collected data and message received.
Data and computation resources are provided by a variety of suppliers, public and private.
The autonomy of the suppliers causes heterogeneity and inconsistencies. The number of potential suppliers and their autonomy also creates information overload
To cope with these issues novel intermediate services are needed, opening up new opportunities. Many traditional relationships among consumers and vendors will change.
We will present the concepts and status of such services. Collaboration, security, and payment schemes are some of the considerations.
11/19/2018
Gio - CERN

3. Outline Resolving Semantic Heterogeneity
Background for Mediated Systems
Motivation and Functions needed
Architecture
Current Status
Resolving Semantic Heterogeneity
Research Directions
Background
Maintenance
Research Projects
Integration of Simulation Information
11/19/2018
Gio - CERN

4. Evolution of mediation
applications A3 A4 A2 A5 A1 A6
integrators a. I2 I1
mediators b. M1
network c. M2 d. e.
wrappers D1 W3 D6 W2 D5 D4 W1 D2 D3
datasources
11/19/2018
Gio - CERN

5. Transforming Data to Information
users at workstations
Application Layer
Mediation Layer
Foundation Layer
value-added services
data and simulation resources
11/19/2018
Gio - CERN

6. Data and Knowledge Information is created at the confluence of
Knowledge Loop
Data Loop
Education
Recording
Action
Storage
Selection
Integration
Summarization
Decision-making
State changes
Abstraction
Experience
Information is
created at the
confluence of
data -- the state
&
knowledge --
the ability to
select and
project the
state into
the future
11/19/2018
Gio - CERN

7. Definition*
A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications.
It should be small and simple, so that it can be maintained by one expert or, at most, a small and coherent group of experts.
* Wiederhold: IEEE Computer March 1992
11/19/2018
Gio - CERN

8. Information overload Data starvation
More databases
public & corporate
Faster communication
digital
packeting: TCP-IP, ATM
World-wide connectivity
Internet & Intranets
world-wide web
Disintermediation
ubiquitous publishing
11/19/2018
Gio - CERN

9. Change in Supply vs Demand
What information consumes is rather obvious, it consumes the attention of its recipients.
Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.
[Herbert Simon]
11/19/2018
Gio - CERN

10. Function of Mediation
Apply Domain-specific Specialist Knowledge to add value
to locate data sources
to convert for consistency
to integrate from diverse sources
to describe data for processing
to abstract for insight / models
to extrapolate to new situations
to summarize for presentation
INFORMATION
11/19/2018
Gio - CERN

11. Interfaces MEDIATION Human-computer Interaction User interface
Service
interface
Resource access
User interface
Real-world
Human-computer
Interaction
Application-
specific code
Domain-
specific
code
Source-
MEDIATION
11/19/2018
Gio - CERN

12. Making data relevant Data reduction Data abstraction
Level changing
Summarization
Exception search
Level change to integrate with
other data sources
Follow Customer Model: hierarchical, divide-and-conquer, a common paradigm
11/19/2018
Gio - CERN

13. Functions inside Mediation
Selection
Summarize
Transform
Inte gration
articulation
Hetero-
genous
resources
11/19/2018
Gio - CERN

14. Status of Mediation Technology
Today
Handcrafted
Expert consults with programmer
Programmer codes the knowledge needed
Resource changes require advise, program update
Future
Generated from models
Domain Expert maintains models
Specification determines functions
Resource changes trigger regeneration
11/19/2018
Gio - CERN

15. Coverage of Current DARPA I3 Efforts
Good progress / active research / related work / poor coverage ( ] | )
Databases / Web / Text / Simulation
Facilitation
(auto linking)
Maintenance
(rule technology?)
Discovery
(web,schema
searching)
Wrapping (syntactical heterogeneity)
Integration
over sources
Abstraction
for relevance
to customer
Mediators
for multiple domains
Caching /
History
:-[
:-(
:-)
Security
for cooperation
:-|
:-)
:-[
11/19/2018
Gio - CERN

16. Mediator Design Principle
Transform Data into Information
Match
Costumer Model
Hierarchical to
Resource Model
General network
(and maintain models)
11/19/2018
Gio - CERN

17. Heterogeneity among Domains
If interoperation involves distinct
domains mismatch ensues
Autonomy conflicts with consistency,
Local Needs have Priority,
Outside uses are a Byproduct
Heterogeneity must be addressed
Platform and Operating Systems 4 4
Representation and Access Conventions 4
Naming and Ontology :
11/19/2018

18. Unsolved problem in Interoperation
Common assumption in assembling and integrating distributed information resources
The language used by the resources is the same
Sublanguages used by the resources are subsets of a globally consistent language
This assumption is provably false.
Working towards the goal of global consistency is
1. naïve -- the goal cannot be achieved
2. inefficient -- languages are efficient in local contexts
11/19/2018
Gio - CERN

19. Ontology: components .
We represent the contents and structure of a languages by its ontology:
a set of well-defined terms, which delimit the domain of discourse
relationships among those terms, chosen from a limited set
a formalizable subset of expert knowledge
11/19/2018
Gio - CERN

20. SKC’s grounded definition .
Ontology:
a set of terms and their relationships
Term:
a reference to real-world and abstract objects
Relationship:
a named and typed set of links between objects
Reference:
a label that names objects
Real-world object:
an entity instance with a physical manifestation
Abstract object:
a concept which refers to other objects
11/19/2018
Gio - CERN

21. Where are Ontologies found?
Ontologies allow communication among partners in enterprises (rarely in machine-readable form)
Relationships determine meaning - parent, school, company
Variable and Class names in Software
Databases use ontologies during design in their E-R diagrams (implicitly) and to represent the leaf nodes in their schemas.
Knowledge-bases use term ontologies (often explicitely), add class definition (to hold instances), constraints, and operations among the terms.
11/19/2018
Gio - CERN

22. Establishing Ontologies
Top-down:
Commonly acceptable UPPER layers
Domain-specific
Analysis and Sharing tools
Model and Object-type based
Bottom-up
Wordlist creation from task-specific collections
Database models, schemas, and contents
11/19/2018
Gio - CERN

23. Large Ontologies: good or bad?
Have all the Knowledge together
simple for customers of KBs
hard for owners of KBs, must synchronize with many others
in the limit -- everybody must be globally consistent
Large KB will cover multiple / all domains
created by a committee -- slow
maintained by a committee -- costly
Differences in level of abstraction -- efficiency
homeowner: nail
carpenter: sinker, brad, boxnail, . . .
11/19/2018
Gio - CERN

24. Domain ontology assumption .
a domain will contain known objects
the object configuration is consistent
within a domain all terms are consistent &
relationships among objects are consistent
context is implicit in use
explicit context is needed
for external use
Domain Ontology
No committee is needed to forge compromises * within a domain
Compromises hide valuable details
11/19/2018
Gio - CERN

25. SKC Objective Provide for Maintainable Ontologies
devolve maintenance onto many domain-specific experts / authorities
provide an algebra to compute composed ontologies that are limited to their articulation terms
enable interpretation within the source contexts
SKC
11/19/2018
Gio - CERN

26. Conservative assumption !
When dealing with multiple ontologies one can never be sure that identically or similarly spelled words mean the same thing,
I.e, refer to exactly the same set of real-world objects under all current and future conditions
Common, optimistic assumption: Meaning is identical
Gets worse when terms are stemmed
SKC, conservative or pessimistic assumption: Meaning never matches, unless there is a match rule
number of matching rules is reduced by focusing on the articulation
11/19/2018
Gio - CERN

27. An Ontology Algebra A knowledge-based algebra for ontologies
The Articulation Ontology (AO) consists of matching rules that link domain ontologies
Intersection create a subset ontology
keep sharable entries
Union create a joint ontology
merge entries
Difference create a distinct ontology
remove shared entries
11/19/2018
Gio - CERN

28. Sample Operation: INTERSECTION
Result contains
shared terms
Terms useful
for purchasing
Source Domain 1:
Owned and maintained
by Store
Source Domain 2:
Owned and maintained
by Factory
11/19/2018
Gio - CERN

29. INTERSECTION support Articulation ontology Matching rules that use
terms from the
2 source domains
Terms useful
for purchasing
Store
Ontology
Factory
Ontology
11/19/2018
Gio - CERN

30. color =table(colcode)
Sample Intersections
Shoe Store
Shoes { }
Customers { }
Employees { }
size = size
color =table(colcode)
style = style
Ana-
tomy
{. . . }
Material inventory {...}
Machinery { }
Processes { }
Shoe Factory
Hard-
ware
Articulation
ontology
matching rules :
foot = foot
Employees Employees
Nail (toe, foot) Nail (fastener)
Department
Store
11/19/2018
Gio - CERN

31. Other Basic Operations
typically prior
intersections
UNION: merging
entire ontologies
DIFFERENCE: material
fully under local control
Arti-
culation
ontology
11/19/2018
Gio - CERN

32. Features of an algebra Operations can be composed
Operations can be rearranged
Alternate arrangements can be evaluated
Optimization is enabled
The record of past operations can be
kept and reused
11/19/2018
Gio - CERN

33. Knowledge Composition
Composed knowledge for
applications using A,B,C,E
Articulation
knowledge
for U (A B) (B C) (C E)
Legend:
U : union U
: intersection
Articulation knowledge
for U (C E)
Articulation
knowledge
for
Knowledge
resource E
Knowledge
resource C (A B) U U (B C) U (C D)
Knowledge
resource A
Knowledge
resource B
Knowledge
resource D
11/19/2018
Gio - CERN

34. Sample Processing in HPKB
What is the most recent year an OPEC member nation was on the UN security council?
Related to DARPA HPKB Challenge Problem
SKC resolves 3 Sources
CIA Factbook ‘96 (nation)
OPEC (members, dates)
UN (SC members, years)
SKC obtains the Correct Answer
1996 (Indonesia)
Other groups obtained more,
but factually wrong answers
Problems resolved by SKC
Factbook has out of date OPEC & UN SC lists
Indonesia not listed
Gabon (left OPEC 1994)
different country names
Gambia => The Gambia
historical country names
Yugoslavia
UN lists future security council members
Gabon 1999
intent of original question
Temporal variants
11/19/2018
Gio - CERN

35. Tools to create articulations
Graph matcher
for
Articulation-
creating
Expert
Vehicle
ontology
Transport
ontology
Suggestions
for articulations
11/19/2018
Gio - CERN

36. continue from initial point
Also suggest similar terms
for further articulation:
by spelling similarity,
by graph position
by term match repository
Expert response:
1. Okay
2. False
3. Irrelevant
to this articulation
All results are recorded
Okay’s are converted into articulation rules
11/19/2018
Gio - CERN

37. Candidate Match Repository
Term linkages automatically extracted from 1912 Webster’s dictionary *
* free, other sources have been processed.
Based on processing headwords ý definitions using algebra primitives
Notice presence of 2 domains: chemistry, transport
11/19/2018
Gio - CERN

38. Using the match repository
11/19/2018
Gio - CERN

39. Navigating the match repository
11/19/2018
Gio - CERN

40. Primitive Operations Model and Instance Unary Constructors
Summarize -- structure up
Glossarize - list terms
Filter - reduce instances
Extract - circumscription
Binary
Match - data corrobaration
Difference - distance measure
Intersect - schem discovery
Blend - schema extension
Constructors
create object
create set
Connectors
match object
match set
Editors
insert value
edit value
move value
delete value
Converters
object - value
object indirection
reference indirection
11/19/2018
Gio - CERN

41. Future: exploiting the result
Avoid n2 problem of interpreter
mapping as stated by Swartout
as an issue in HPKB year 1
Result has links
to source
Processing & query evaluation is best performed within Source Domains & by their engines
11/19/2018
Gio - CERN

42. SKC Synopsis
Research: Reliable query answers from heterogeneous, imperfect data sources
Sources:
General: CIA World Factbook ‘96, UN www, OPEC www
Webster’s Dictionary, Thesaurus, Oxford English Dictionary
Topical: OPEC, BattleSpace Sensors, Logistics Servers
Client: DARPA High Performance Knowledge Base
(HPKB) project
Theory: Rule-based algebra
Translation & Composition primitives
11/19/2018
Gio - CERN

43. Innovation in SKC No need to harmonize full ontologies
Focus on what is critical for interoperation
Rules specific for articulation
Potentially many sets of articulation rules
Maintenance is distributed
to n sources
to m articulation agents
is m < n2 , depending on architecture density a research question
11/19/2018
Gio - CERN

44. Domain Specialization
Knowledge Acquisition (20% effort) &
Knowledge Maintenance (80% effort *)
to be performed by
Domain specialists
Professional organizations
Field teams of modest size
automously
maintainable
Empowerment
* based on experience with software
11/19/2018
Gio - CERN

45. SKC Summary . Algebra enables Interoperation by
dealing explicitly with differences by knowledge
identifying maintenance domains
keeping sources autonomous
Assumes domain has a common ontology
composing domain ontologies requires the algebra to manage the linkages where articulation occurs
processes are best executed within the domains
Knowledge about articulation is disjoint
allows integration specialists to work independently
supports multiple intersections and views
Maintenance is structured and partitioned
11/19/2018
Gio - CERN

46. Current SKC Directions
Experience with real world (imperfect) data confirms validity of our approach
Expert sources are better maintained than general sources
Rules applied to multiple sources provide more reliable and accurate query results
Component architecture enables scalable, maintainable knowledge base development
Porting the concepts to the DARPA Markup Language (DAML) setting
11/19/2018
Gio - CERN

47. Mediation Research Topics
Mediator management and maintenance
Representation of knowledge and customer models
Balancing dynamic and warehouse solutions
Formalization of semantic heterogneities
many levels and types
roles for wrappers vs. mediators vs. applications
scalability by partitioning -- make it simple!
Domain Ontologies --- tools, validation, . . .
Effect of object paradigm and method-based access
Service and business models
New types of information systems
11/19/2018
Gio - CERN

48. Long Range Science Vision
Artificial
Intelligence
knowledge mgmt
domain expertise
uncertainty
Databases
access
storage
algebras
Systems
Engineering
analysis
documentation
costing
Integration Methods
GIS
Integration
Science
Spatial is special.
11/19/2018
Gio - CERN

49. Background Material: Technology Sources Maintenance Projects
Information about the Future
11/19/2018
Gio - CERN

50. Interfaces Human « Computer Application « Mediator
{x-widgets, HTML}
Application « Mediator
{OQL, KQML, ...}
Mediator « Data sources
{SQL, TQL, XML, … }
Data ¬ real world
{sensors, clerks, … }
11/19/2018
Gio - CERN

51. Support for KB-Algebra
Ontolingua [Gruber, Stanford KSL]:
Repository for Domain Terminologies
Used for mechanical design, bibliographies, catalogs
LOOM USC ISI]:
Classification-based Expert System
Helps in structuring and processing ontologies
PROTÉGÉ Stanford MIS] Reuse
Penguin [Barsalou, Stanford MIS, CIFE]:
Object manipulation based on Relational Algebra
Used for genetics laboratory, building design
11/19/2018
Gio - CERN

52. Getting there: Available Technology/Science
Web Search Tools
Multimedia Interfaces
Agents
Database Models
Security Filters
Domain Ontologies
Object Bases
Temporal Algebras
Uncertainty algebras
Customer Models
Constraint Management
Circumscription
Communication Standards
GIS
GIS
Case-based Reasoning
Active Databases
Internet Billing
Knobots
Simulation Access
DB Views
Wrappers
Caching
Text & Speech Processing
Public Databases
Distributed Storage Systems
High Perf.Comm.
11/19/2018
Gio - CERN

53. Fat versus thin mediators
too thin: insufficient added value
Too fat: hard to
compose
Too narrow: few costumers
Just right
service
scope
too broad:
hard to maintain, needs a committee
domain scope
11/19/2018
Gio - CERN

54. Maintenance is good for you?
years 10 4 2 7 3 1 8 9 6 5 13 11 12
100% 40 20 70 30 10 80 90 60 50
depreciation = 1 / lifetime
maintenance cost
relative annual
lifetime
automobile hardware software
11/19/2018
Gio - CERN

55. Client-Server Architecture
Client system
data and simulation resources
Fast build of clients
by resource reuse s X
Changes (x) are difficult,
can affect many clients
11/19/2018
Gio - CERN

56. Systems with Mediators
Applications
Gio Wiederhold
Mediators
Data Resources . . .
11/19/2018
Gio - CERN

57. Growth through Reuse New Application Prior & Revised Mediators
Gio Wiederhold
Prior & Revised Mediators
Extended Data Resources
11/19/2018
Gio - CERN

58. Linear O(n) Cost of Growth-- now O(n2)7 2
Data changes only affect some mediators; only in their domain
Mediators can
1. supply old information to n-1 prior applications
2. provide better information to the new application
3. be partially or completely reused
New applications, using the new data, can be developed and inserted dynamically
11/19/2018
Gio - CERN

59. A mediator is not just static software: Knowledge ages
Application
Interface
Changes of
user needs
Software & People
Domain
changes
Owner / Creator
Maintainer
Lessor - Seller
Advertisor
Models, programs,
rules, caches,
Resource Interfaces
Resource
changes
11/19/2018
Gio - CERN

60. Roles Computer Scientists Domain Experts Provide tools
adapatation
integration
matching
composing
Assess Standards
Assure scalability
Domain Experts
Learn to use the tools
Select resources
Assess their value
Rank their quality
Resolve semantics
Get client feedback
Give provide feedback
11/19/2018
Gio - CERN

61. Assigning maintenance responsibility
a. Source data quality –
supplier database, files, or web pages
b. Interface to the source –
wrapper, supplier or vendor for supplier
c. Source selection –
expert specialist in mediator
d. Source quality assessment –
customer input to mediator
e. Semantic interoperation –
specialist group providing input to the mediator
f. Consistency and metadata information –
mediator service operation or warehouse
g. Informal, pragmatic integration –
client services with customer input
h. User presentation formats –
Sources
Services
Customers
11/19/2018
Gio - CERN

62. Sample projects Tsimmis at Stanford E-Commerce in Digital Libraries
INEEL: information integration for environmental restoration
MIFT: feedback for training
Civil Engineering and Architecture
F-22
SimQL
Security
11/19/2018
Gio - CERN

63. Projects at Stanford DB group
Data Mining.
Mediator & Wrapper
Generation.
Warehousing.
Security Mediators.
Megaprogramming.
Simulation Access.
Changes, Consistency,
and Configurations.
MIDAS
WHIPS
TSIMMIS
TIHI
SimQL
CHAIMS C3
11/19/2018
Gio - CERN

64. The TSIMMIS Project Ramana Yerneni, Yannis Papakonstantinou, ...
Objective: Support mediation technology
integrated access to distributed, autonomous, heterogeneous data sources, using object fusion
wrapper toolkit to rapidly create wrappers, based on source specification, a uniform interface to heterogeneous sources
mediator toolkit to rapidly construct mediators, based on a mediator specification, to integrate data from a set of wrappers
11/19/2018
Gio - CERN

65. Investors Need to Fuse Information from Multiple Sources .
Network
WWW
Ticker Tape
Personal
database
group together information about
the same real-world entity
remove redundancies
resolve conflicts
11/19/2018
Gio - CERN

66. An Integration Architecture
Client
Application
portfolios for each company
Mediator
stock market prices
business reports
Wrapper
Wrapper
Dialog
Ticker
Tape
11/19/2018
Gio - CERN

67. Additional Challenge: Sources Without a Well-Structured Schema
Examples
semistructured
irregular
deeply nested
incomplete schema knowledge
autonomous
dynamic
World Wide Web
SGML documents
genome, chemical structures
bibliographic information
files
11/19/2018
Gio - CERN

68. Wrappers & Mediators from High-Level Specifications
DeclarativeMediator
Specification
Client
Mediator Specification
Interpreter
Mediator
Wrapper Specification
Interpreter
Wrapper
Wrapper
Declarative
Source
Specifications
Source
Source
11/19/2018
Gio - CERN

69. yes E-money Services must be paid for
Incentive for creation and improvement
price proportional to value added, often small
profit f (cost, market, price, overhead )
price low per item, so overhead must be low
Simple payment (no credit accounts, checks)
Enabled through secure signatures
yes
11/19/2018
Gio - CERN

70. E-Commerce in the Digital Library
Steven Ketchpel & DL Economics Group
Delivery
Cryptolope
DigiBox
HTTP
Payment
CyberCash
DigiCash
First Virtual
SET
Major
Integration
Problem
Shopping Models: Pay-per-view, Subscription, Session, Shareware, Auctions, Site License,
Gift Certificate, Layaway, Pre-paid vouchers, … .
11/19/2018
Gio - CERN

71. Shopping model: merchant-independent logic controlling flow of business model
Example shopping models:
Order, Pay, (Deliver 52 times)
(1 month; Order, Deliver) Pay
State
Information
Event Handlers
Event Handlers
Bill 2
Merchant
Customer 1
Order
Complete
Event Handlers 3 4
Payment
Complete
Start Transfer $
Abstract API
allows application to
interact with many
different services
in a consistent way
Event Handlers
Proxy event handlers
translate from
native applications
to shopping model
defined protocols
Payment/Delivery/
Other Services
11/19/2018
Gio - CERN

72. TSIMMIS Status
Mediator Specification Interpreter running on Ultrix, AIX, OSF.
9000 lines of C/C++ code
4000 C++ lines of Server/Client Support Libraries
Integration of three disparate bibliographic sources
legacy system
flat BibTeX files
relational DB
wwWeb files
11/19/2018
Gio - CERN

73. Mediator Specification Interpreter Architecture
Result
Query
Mediator
Specification
Query Rewriter
logical datamerge
program
Cost-Based Optimizer
plan
Datamerge Engine
Queries to
Wrappers
Results
11/19/2018
Gio - CERN

74. Environmental Restoration at INEL Undoing 50 years of messes ….
MSL [Stanford]
OQL [ODMG]
MQL [ISX]
QEM
OEM
OEM
QEM
OEM
QEM
OEM
OEM
QEM
QEM
other
mediators
mediator
CORBA
wrapper
OEM
OEM
QEM
QEM
QEM
wrapper
wrapper
ERIS
wrapper
IEDMS
Many projects
many sources
Idaho National
Engineering Laboratory
LOCKHEED MARTIN
ISX - Stanford Univ.
11/19/2018
11/19/2018
Gio - CERN

75. Mediation to Implement Feedback in Training David Maluf, Priya Panchapagesan, Ted Linden
Another task of mediators, prior to integration
MIFT
Abstraction
Abstraction to match levels of granularity
11/19/2018
Gio - CERN

76. Mediation Feedback: Playback or Graph Wrapped Simulation Resources
User Interface
Commanders
Training
Developers
Trainees
UI in
Java
Analysts
Observers
Application
Layer
Standards
in KQML
Objectives
Mediation
Layers
Stanford
Mediators with
rules in CLIPS
Tasks
I.D.A
Wrappers
in C/C++
Wrapped Simulation Resources
Janus
SimNet
11/19/2018
Gio - CERN

77. MIFT . Result . Analyses: Force ratio Losses Area gain Exercise
Simulator
Type
11/19/2018
Gio - CERN

78. Control Valve Sizing, Future
From Andrew Arnold: Civ. Eng. Qualification Exam
Interpretation
Programmatic
Analysis
Integrated
Evaluation
Transformation
Automated
11/19/2018
Gio - CERN

79. F-22 IWSDB Phase 6 User Interfaces Integration Services Databases
Wrappers PD DS
Appli-
cation
PRIDE
Change
Notification
Sy-
base
Index
Suppliers
Provi-
sioner
Query Re-
formulation
WAIS
server
Match
maker
Domain
Model
Engi-
neer
IWSDB
client S Q L
Domain
Matching
GUI
11/19/2018
Gio - CERN 19

80. Simulation services 1. Continously executing: weather prediction
SimQL result reports best match samples
2. Execution specific to query: what-if assessment, spreadsheets
may require HPC power for adequate response
3. Complement base data: materials data, assembly
performs inter- or extra-polations to match query parameters
4. Combinations of 2. and 3.: top layer simulation using stored partial lower level results: weapon performance in setting
5. Human-in-the-loop (mediated by an agent program): SAFs
Note
A simulation service program can be written in any language
A simulation service must be compliant to the interface
11/19/2018
Gio - CERN

81. SimQL: Simulation Access Service
Information Systems should also deal
with the Future
past SQL now SimQL future
time
Decision-making requires dealing with the future, as well the past
Databases deal well with the past
Sensors can provide current status
Spreadsheets, simulations deal with the likely futures
Information systems should be able to combine all three
11/19/2018
Gio - CERN