Transcript SWArch PPT - University of Connecticut

Software and Enterprise Architectures
CSE
5810
Prof. Steven A. Demurjian, Sr.
Computer Science & Engineering Department
The University of Connecticut
371 Fairfield Road, Box U-255
Storrs, CT 06269-2155
[email protected]
http://www.engr.uconn.edu/~steve
(860) 486 - 4818
Copyright © 2008 by S. Demurjian, Storrs, CT.
SWEA1
Software Architectures
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Emerging Discipline in Mid-1990s
Software as Collection of Interacting Components
What are Local Interactions (within Component)?
What are Global Interactions (between Components)?
Advantages of SW Architectural Design
 Understand Communication/Synchronization
 Definition of Database Requirements
 Identification of Performance/Scaling Issues
 Detailing of Security Needs and Constraints
Towards Large-Scale Software Development
For Biomedical Informatics:
 What are Architectures for Data Sharing?
 How is Interoperability Facilitated?
SWEA2
Concepts of Software Architectures
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Exceed Traditional Algorithm/Data Structure
Perspective
Emphasize Componentwise Organization and System
Functionality
Focus on Global and Local Interactions
Identify Communication/Synchronization
Requirements
Define Database Needs and Dependencies
Consider Performance/Scaling Issues
Understand Potential Evolution Dimensions
SWEA3
Software Design Levels
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Architecturally:
 Modules
 Interconnections Among Modules
 Decomposition into Subsystems
Code:
 Algorithms/Data Structures
 Tasking/Control Threads
Executable:
 Memory Management
 Runtime Environment
Is this a Realistic/Accurate View?
 Yes for a Single “Application”
 What about Application of Applications?
 System of Systems?
SWEA4
Software Engineering - an Oxymoron?
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Is there any Engineering?
Is there any Science?
Collection of Disparate Techniques:
 Data-Flow Diagrams
 E-R Diagrams
 Finite State Machines
 Petri Nets
 UML Class, Object, Sequence, Etc.
 Design Patterns
 Model Drive Architectures
What is being “Engineered”?
How do we Know we are Done?
 E.g. Does Artifact Match Specification?
SWEA5
What's Available for Engineering Software?
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Specification (Abstract Models, Algebraic Semantics)
Software Structure (Bundling Representation with
Algorithms)
Languages Issues (Models, Scope, User-Defined
Types)
Information Hiding (Protect Integrity of Information)
Integrity Constraints (Invariants of Data Structures)
Is this up to date?
What else can be Added to List?
 Design Patters
 Model Driven Architectures
 XML –Data Modeling and Dependencies
 Others?
SWEA6
Engineering Success in Computing
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Compilers Have Had Great Success
 Originally by Hand
 Then Compiler Compilers
 Parser Generators - Lex/Yacc
Solid Science Behind Compilers
 Regular, Context Free, Context Sensitive
Languages
 FSAs, PDAs, CFGs, etc.
Science has Provided Engineering Success re. Ease
and Accuracy of Modern Compiler Writing
SWEA7
History of Programming
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C - Still Remains Industry Stronghorse
 Separate Compilation
 Decomposition of System into Subsystems, etc.
 Shared Declarations
 ADTs in C, But Compiler won't Enforce Them
Modula-II and Ada 83 Had
 Information Hiding
 Public/Private Paradigm
 Module/Package Concepts
 Import/Export Paradigm
Rigor Enforced by Compiler – but Can’t
 Bind/Group Modules into Subsystems
 Precisely Specify Interconnections and Interactions
Among Subsystems and Components
SWEA8
‘Recent-Past’ Generation?
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C++ and Ada95
 Considered “Legacy” Languages - Old
Java, C# - Are they Headed Toward Legacy?
 How do they Rate?
 What Do they Offer that Hasn't been Offered
Before?
 What are Unique Benefits and Potential of Java?
What about new Web Technologies?
 Javascript, Perl, PhP, Phython, Ruby
 XML and SOAP
 Mobile Computing
 How do all of these fit into this process?
 Particularly in Regards to C/S Solutions!
SWEA9
What's Next Step?
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Architectural Description Languages
 Provide Tools to Describe Architectures
 Definition and Communication
Codification of Architectural Expertise
Frameworks for Specific Domains
DB vs. GUI vs. Embedded vs. C/S
Formal Underpinning for Engineering Rigor
What has Appeared for Each of these?
 Struts for GUI
 Open Source Frameworks (mediawiki)
 Wide-Ranging Standards (XML)
 Model-Driven Architectures
 What Else???
SWEA10
Architectural Styles
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What are Popular Architectural Styles?
 How are they Characterized?
 Example in Practice
Explore a Taxonomy of Styles
Focus on “Micro-Architectures”
 Components
 Flow Among Components
 Represents “Single” Application
Forms Basis for “Macro-Architectures”
 System of Systems
 Application of Applications
 Significantly Scaling Up
SWEA11
Taxonomy of Architectural Styles
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Data Flow Systems
 Batch Sequential
 Pipes and Filters
Call & Return Systems
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 Main/Subroutines
(C, Pascal)
 Object Oriented
 Implicit Invocation
 Hierarchical Systems
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Virtual Machines
 Interpreters
 Rule Based Systems
Data Centered Systems
 DBS
 Hypertext
 Blackboards
Independent
Components
 Communicating
Processes/Event
Systems
Client/Server
 Two-Tier
 Multi-Tier
SWEA12
Taxonomy of Architectural Styles
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Establish Framework of …
 Components
 Building Blocks for Constructing Systems
 A Major Unit of Functionality
 Examples Include: Client, Server, Filter, Layer, DB
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Connectors
 Defining the Ways that Components Interact
 What are the Protocols that Mandate the Allowable
Interactions Among Components?
 How are Protocols Enforced at Run/Design Time?
 Examples Include: Procedure Call, Event Broadcast,
DB Protocol, Pipe
SWEA13
Overall Framework
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What Is the Design Vocabulary?
 Connectors and Components
What Are Allowable Structural Patterns?
 Constraints on Combining Components &
Connectors
What Is the Underlying Conceptual Model?
 Von Newman, Parallel, Agent, Message-Passing…
 Are their New Emerging Models?
 Collaborative Environments/Shareware?
What Are Essential Invariants of a Style?
 Limits on Allowable Components & Connectors
Common Examples of Usage
Advantages and Disadvantages of a Style
Common Specializations of a Style
SWEA14
Pipes and Filters
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Components are Independent
Entities. No Shared State!
Components with
Input and Output
Sort
Sort
Merge
Connectors for Flow Streams of I/O
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Filters:
 Invariant: Unaware of up and Down Stream
Behavior
 Streamed Behavior: Output Could Go From
One Filter to the Next One Allowing Multiple
Filters to Run in Parallel.
SWEA15
Pipes and Filters
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Possible Specializations:
 Pipelines - Linear Sequence
 Bounded - Limits on Data Amounts
 Typed Pipes - Known Data Format
What is a Classic Example?
Other Examples:
 Compilers
 Sequential Processes
 Parallel Processes
SWEA16
Pipes and Filters - Another Example
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Text Information Retrieval Systems
 Scanning Newspapers for Key Words, Etc.
 Also, Boolean Search Expressions
Where is Such an Architecture Utilized Today?
What is Potential Usage in BMI?
User
Commands
Search
Disk
Controller
Controller
Programming
Result
Query
Resolver
Control
Term
Search
Comparator Data DB
SWEA17
Pipes and Filters – In BMI
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Can be Structured to Model Medical Workflows
Series of Actions taken by Stakeholders on Patient
SWEA18
Patterns for Ontologies
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Extension of Rishi’s work …
Linear Ontology Architectural Pattern (LOAP)
 Model Knowledge in a Process
 Continue with Examples from Prior PPT
http://www.engr.uconn.edu/~steve/Cse5810/Attaining-SemanticEnterprise-Interoperability-through-Ontology-ArchitecturalPatterns.pdf
SWEA19
Patterns for Ontologies
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Linear Ontology Architectural Pattern (LOAP)
 Diagnosis, Test, and Anatomy Ontologies
SWEA20
Extending the Example
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SWEA21
What has OO Evolved Into?
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What has Classic OO Solution Evolved into Today?
 Client (Browser + Struts)
 Server (Many Variants of OO Languages)
 Database Server (typically Relational)
Different Style (e.g., Design Pattern)
 Does Pattern Capture All Aspects of Style?
 Do we Need to Couple Technology with Pattern?
Dr. D, Jan 01, 08
Fever, Flu, Bed Rest
No Scripts
No Tests
Item(Phy_Name*, Date*,
Visit_Flag, Symptom, Diagnosis, Treatment,
Presc_Flag, Pre_No, Pharm_Name, Medication,
Test_Flag, Test_Code, Spec_No, Status, Tech)
SWEA22
Design Patterns as Software Architectures
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Emerged as the Recognition that in Object-Oriented
Systems Repetitions in Design Occurred
Gained Prominence in 1995 with Publication of
“Design Patterns: Elements of Reusable ObjectOriented Software”, Addison-Wesley
 “… descriptions of communicating objects and
classes that are customized to solve a general
design problem in a particular context…”
 Akin to Complicated Generic
Usage of Patterns Requires
 Consistent Format and Abstraction
 Common Vocabulary and Descriptions
Simple to Complex Patterns – Wide Range
SWEA23
The Observer Pattern
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Utilized to Define a One-to-Many Relationship
Between Objects
When Object Changes State – all Dependents are
Notified and Automatically Updated
Loosely Coupled Objects
 When one Object (Subject – an Active Object)
Changes State than Multiple Objects (Observers –
Passive Objects) Notified
 Observer Object Implements Interface to Specify
the Way that Changes are to Occur
 Two Interfaces and Two Concrete Classes
SWEA24
The Observer Pattern
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SWEA25
Model View Controller
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http://java.sun.com/blueprints/patterns/MVC-detailed.html
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SWEA26
Model View Controller
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Three Parts of the Pattern:
 Model
 Enterprise Data and Business Rules for Accessing and
Updating Data
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View
 Renders the Contents (or Portion) of Model
 Deals with Presentation of Stored Data
 Pull or Push Model Possible
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Controller
 Translates Interactions with View into Actions on
Model
 Actions could be Button Clicks (GUI), Get/Post http
(Web), etc.
SWEA27
Model View Controller
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http://java.sun.com/blueprints/patterns/MVC-detailed.html
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SWEA28
The Façade Design Pattern
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Unified higher-level global interface/system
developed from
 a set of complex heterogeneous source
interfaces/subsystems
 makes local sources easier to utilize for the clients
Composition of Pattern
 Subsytems
 System Composed of Subsytems
 Clients
SWEA29
Facade
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SWEA30
Other Ontology Architectural Patterns
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Leverage Façade Pattern for
 Local As View (LAV) Methodology
 MApping FRAmework (MAFRA) provides a
conceptual framework for building semantic
mappings between heterogeneous ontology models
using semantics bridges
 High Level Centralized Ontology Architectural
Patterns (COAP)
Extend Façade Concept
 Subsystems are Local Schemas
 System is Global Schema
SWEA31
LAV Ontology and Example
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SWEA32
MAFRA Ontology and Example
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SWEA33
COAP Ontology and Example
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COAP Allows us to Define and Integrate Ontologies
at a Much Higher Level
Integrating Multiple Ontologies
Global Ontology Model (OG)
OM1
Local
Ontology
Model (LO1)
OM2
Local
Ontology
Model (LO2)
OMN
OM3
Local
Ontology
Model (LO3)
….
Local
Ontology
Model (LON)
SWEA34
COAP Ontology and Example
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Example Unifies ICS, DSM, SNOMED etc.
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UMLS
SNOMED-CT
OMIM
Disease
Gene
Ontology
Gene
Symptoms, Procedure,
Findings, etc.
Mental
Disorders
ICD
DSM
SWEA35
COAP Ontology and Example
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Example Unifies ICS, DSM, SNOMED etc.
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UMLS Metathesaurus
MICD
ICD Codes
MNCBI
MSNOMED
SNOMED
…………
NCBI
MLOINC
LONIC
SWEA36
Layered Systems
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Useful Systems
Base Utility
Core
level
Users
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Components - Virtual Machine at Each Layer
Connectors - Protocols That Specify How Layers
Interact
Interaction Is Restricted to Adjacent Layers
SWEA37
Layered Systems
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Advantages:
 Increasing Levels of Abstraction
 Support Enhancement - New Layers
 Support for Reuse
Drawbacks:
 Not Feasible for All Systems
 Performance Issues With Multiple Layers
 Defining Abstractions Is Difficult.
SWEA38
Layered Systems in BMI
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One Approach to Constructing Access to Patient Data
for Clinical Research and Clinical Practice
Construct Layered Data Repositories as Below
 Each Layer Targets Different User Group
 Need to Fine Tune Access Even within Layers
Aggregated
De-identified
Patient
Data
Provider
Cl. Researchers
Public Health Researchers
SWEA39
ISO as Layered Architecture
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ISO Open Systems Interconnect (OSI) Model
 Now Widely Used as a Reference Architecture
 7-layer Model
 Provides Framework for Specific Protocols (Such
as IP, TCP, FTP, RPC, UDP, RSVP, …)
Application
Presentation
Session
Transport
Network
Data Link
Physical
Application
Presentation
Session
Transport
Network
Data Link
Physical
SWEA40
ISO OSI Model
Application
Presentation
Session
Transport
Network
Data Link
Physical
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Application
Presentation
Session
Transport
Network
Data Link
Physical
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Physical (Hardware)/Data Link Layer Networks:
Ethernet, Token Ring, ATM
Network Layer Net: The Internet
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Transport Layer Net: Tcp-based Network
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Presentation/Session Layer Net: Http/html, RPC,
PVM, MPI
Applications, E.g., WWW, Window System,
Algorithm
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SWEA41
Layered Ontology Architectural Pattern (LaOAP)
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Consider a set of Domain Models
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id
Name
Disease
Id:Integer
Name:String
Disease
Disease
hasSymptom
0…*
0…*
Symptom
Name
Symptom
id
(a): Clinical ERD Model
cId
cEmail
Id:Integer
Name:String
(b): Clinical UML Model
Customer
cId:Integer
cEmail:String
Customer
Laboratory
Tests
owl:Class
∩
Disease
owl:Class
Laboratory Tests
(c): Clinical OWL representation
Customer
hasCloudSpace
0…*
0…*
Cloud Space
Cloud Space
Cloud Space
Space
Space:Integer
Location:String
Location
Content Allowed
Content
Allowed
(d): Business ERD Model
cloudAllows
Content
Allowed
types:Enum
owl:Class
owl:Class
Customer ∩ CloudSpace
(e): Business UML Model
(f): Business OWL representation
SWEA42
LaOAP and Example
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Query and Web Service
Disease Queries
Query and Web Service
Terminology
Heart Attack, Fever, Cold
Model Terminology
Mapping
Axioms & Rules
Mapping
Disease(id) ~ Disease(uid)
Axiom
Disease ∩ Symtom
Ontology
Conceptual
Model
(a): Layered Ontology Architectural Pattern
(LaOAP).
Disease Ontology
Model
(b): Instance of LaOAP.
SWEA43
Implementation from Model to Code
Query and Web Service Layer
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PREFIX laoap: <http://xmlns.com/Laoap/>
Select ?disease ?symp {?disease laoap:hasSymptom ?symp}
Terminology Layer
High Fever, Asthma, Heart Attack, John Smith, 50GB,
Mapping Layer
Disease
Illness
severity
uid
id
severity
name
commonName
Axiom & Rules Layer
owl:Class
Disease
∩
owl:Class
Symptom
owl:Class
CloudSpace
∩
owl:Class
Customer
Conceptual Model Layer
Disease
hasSymptom
Symptom
cloudAllows
ContectAllowed
CloudSpace
hasSpace
Space
SWEA44
id
severity
Implementation from Model to Code
name
commonName
Axiom & Rules Layer
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owl:Class
Disease
owl:Class
CloudSpace
∩
owl:Class
Symptom
∩
owl:Class
Customer
Conceptual Model Layer
Disease
hasSymptom
Symptom
cloudAllows
ContectAllowed
CloudSpace
hasSpace
Space
SWEA45
Implementation from Model to Code
Query and Web Service Layer
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PREFIX laoap: <http://xmlns.com/Laoap/>
Select ?disease ?symp {?disease laoap:hasSymptom ?symp}
Terminology Layer
High Fever, Asthma, Heart Attack, John Smith, 50GB,
Mapping Layer
Disease
Illness
severity
id
uid
severity
commonName
name
Axiom & Rules Layer
owl:Class
owl:Class
SWEA46
Other Ontology Patterns
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Ontology Pattern
(OP)
Content
OP
Structural
OP
Architectural
OP
Lexico-Syntactic
OP
Logical
OP
Logical Macro
OP
Naming
OP
Transformation
OP
Reasoning
OP
Annotation
OP
Presentation
OP
Correspondence
OP
Reengineering
OP
SchemaReengineering
OP
Mapping
OP
Refactoring
OP
Gangemi, A., & Presutti, V. (2009). Ontology
Design Patterns. In Handbook on Ontologies:
International Handbooks on Information Systems
(pp. 221-243). IOS Press.
SWEA47
Other Ontology Patterns
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1
Setting-for
Setting-for
Object
1
1
1
Space-Location
Space-Location
Event
Object
1
Setting-for
SpaceRegion
SpaceRegion
Time-IndexedParticipation
1
Object
temporal-part-of
1…*
temporal-location
TimeInterval
Participant-in
Constant-Participantin
(a): CODeP - Time Indexed Participation Pattern.
1…*
1…*
defines
Role
Task
Modal
Target
1…*
Description
defines
Temporal-location
TimeInterval
1…*
satisfies
Part-of
classifies
classifies
Object
Event
1…*
Temporal-location
1…*
participant
Event
Setting-for
Situation
1…*
Event
1…*
TimeInterval
(c): CODeP - Participation Pattern.
(b): CODeP - Task Role Pattern.
Gangemi, A. (2006). Ontology Patterns for Semantic
Web Content. Proceeding of 4th International Semantic
Web Conference, (pp. 262-276).
SWEA48
Repositories
ks8
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ks1
Blackboard
(shared data)
ks2
ks3
ks6
ks4
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ks7
ks5
Knowledge Sources Interact With the Blackboard.
Blackboard Contains the Problem Solving State Data.
Control Is Driven by the State of the Blackboard.
DB Systems Are a Form of Repository With a Layer
Between the BB and the KSs - Supports
 Concurrent Access, Security, Integrity, Recovery
SWEA49
Database System as a Repository
c8
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c1
Database
(shared data)
c2
c3
c6
c4
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c7
c5
Clients Interact With the DBMS
Database Contains the Problem Solving State Data
Control is Driven by the State of the Database
 Concurrent Access, Security, Integrity, Recovery
 Single Layer System: Clients have Direct Access
 Control of Access to Information must be
Carefully Defined within DB Security/Integrity
SWEA50
Team Project as a Repository
c8
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c1
Web Portal
Shared
c2
c3
c6
c4
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c7
c5
Clients are Providers, Patients, Clinical Researchers
Database Underlies Web Portal
Simply a Portion of Architecture
 Interactions with PHR (Patients)
 Interactions with EMR (Providers)
 Interactions with Database/Warehouse (Researchers)
SWEA51
Virtual Chart as a Repository
c8
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c1
Virtual Chart
c2
c3
c6
c4
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c7
c5
Clients are Providers, Patients, Clinical Researchers
SWEA52
Interpreters
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Inputs
Outputs
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Program being
interpreted
Data
(program state)
Simulated
interpretation
engine
Selected
instruction
Selected
data
Internal
interpreter
state
What Are Components and Connectors?
Where Have Interpreters Been Used in CS&E?
 LISP, ML, Java, Other Languages, OS
Command Line
SWEA53
Java as Interpreter
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SWEA54
Process Control Paradigms
Input variables
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Set
point
Ds to
manipulated
variables
Controller
Input variables
Set
point
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Controller
Ds to
manipulated
variables
With Feedback
Process
Controlled
variable
Without Feedback
Process
Controlled
variable
Also:
 Open vs. Close Loop Systems
 Well Defined Control and Computational
Characters
 Heavily Used in Engineering Fields.
SWEA55
Process Architecture: Statechart Diagram?
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SWEA56
Process Architecture: Activity Diagram?
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Clear Applicability to Medical Processes that have
Underlying BMI – Low Level Processes
Waiting for
Heart Signal
timeout
irregular beat
Heartbeat
Heart Signal
Waiting for
Resp. Signal
Breath
Trigger
Local
Alarm
Trigger
Remote
Alarm
Resp Signal
Alarm Reset
SWEA57
Single and Multi-Tier Architectures
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Widespread use in Practice for All Types of
Distributed Systems and Applications
Two Kinds of Components
 Servers: Provide Services - May be Unaware of
Clients
 Web Servers (unaware?)
 Database Servers and Functional Servers (aware?)

Clients: Request Services from Servers
 Must Identify Servers
 May Need to Identify Self
 A Server Can be Client of Another Server
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Expanding from Micro-Architectures (Single
Computer/One Application) to Macro-Architecture
SWEA58
Single and Multi-Tier Architectures
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Normally, Clients and Servers are Independent
Processes Running in Parallel
Connectors Provide Means for Service Requests and
Answers to be Passes Among Clients/Servers
Connectors May be RPC, RMI, etc.
Advantages
 Parallelism, Independence
 Separation of Concerns, Abstraction
 Others?
Disadvantages
 Complex Implementation Mechanisms
 Scalability, Correctness, Real-Time Limits
 Others?
SWEA59
Example: Software Architectural Structure
CSE
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Initial Data
Entry Operator
(Scanning &
Posting)
Advanced Data
Entry
Operators
Analyst
Manager
10-100MB Network
Document
Server
Stored
Images/CD
Database
Server
Running
Oracle
RMI Registry
RMI Act.
Obj/Server
RMI Act.
Obj/Server
Functional Server
SWEA60
Business Process Model
CSE
5810
DB
DB
Historical Completed
Records Applications
Licensing
DB
Supervisor
Review
Scanner
DB
Licensing
Division
Scanning
Operator
Stored
Images
Licensing Division Printer
Data Entry Operator
DB
Basic
Information
Entered
New Licenses
New Appointments
FOI
Letters (Request
Information, etc.)
SWEA61
Two-Tier Architecture

CSE
5810 



Small Manufacturer Previously on C++
New Order Entry, Inventory, and Invoicing
Applications in Java Programming Language
Existing Customer and Order Database
Most of Business Logic in Stored Procedures
Tool-generated GUI Forms for Java Objects
SWEA62
Three-Tier Architecture

CSE
5810 




Passenger Check-in for Regional Airline
Local Database for Seating on Today's Flights
Clients Invoke EJBs at Local Site Through RMI
EJBs Update Database and Queue Updates
JMS Queues Updates to Legacy System
DBC API Used to Access Local Database
SWEA63
Four-Tier Architecture

CSE

5810



Web Access to Brokerage Accounts
Only HTML Browser Required on Front End
"Brokerbean" EJB Provides Business Logic
Login, Query, Trade Servlets Call Brokerbean
Use JNDI to Find EJBs, RMI to Invoke Them
SWEA64
Architecture Comparisons

CSE
5810 



Two-tier Through JDBC API is Simplest
Multi-tier: Separate Business Logic, Protect Database
Integrity, More Scaleable
JMS Queues vs. Synchronous (RMI or IDL):
 Availability, Response Time, Decoupling
JMS Publish & Subscribe: Off-line Notification RMI
IIOP vs. JRMP vs. Java IDL:
 Standard Cross-language Calls or Full Java
Functionality
JTS: Distributed Integrity, Lockstep Actions
SWEA65
Comments on Architectural Styles

CSE
5810



Architectural Styles Provide Patterns
 Suppose Designing a New System
 During Requirements Discovery, Behavior and
Structure of System Will Emerge
 Attempt to Match to Architectural Style
 Modify, Extend Style as Needed
By Choosing Existing Architectural Style
 Know Advantages and Disadvantages
 Ability to Focus in on Problem Areas and
Bottlenecks
 Can Adjust Architecture Accordingly
Architectures Range from Large Scale to Small Scale
in their Applicability
We’ll see Examples for BMI Shortly …
SWEA66
The Next Big Challenge

CSE
5810

Macro-Architectures
 System of Systems
 Application of Applications
 Particularly for HIT and HIE!
Involves Two Key Issues
 Interoperability
 Heterogeneous Distributed Databases
 Heterogeneous Distributed Systems
 Autonomous Applications

Scalability




Rapid and Continuous Growth
Amount of Data
Variety of Data Types
Different Privacy Levels or Ownerships of Data
SWEA67
Interoperability: A Classic View
CSE
5810
Local
Schema
Simple Federation
Multiple Nested Federation
FDB Global
Schema
FDB Global
Schema 4
Federated
Integration
Federated
Integration
Local
Schema
Local
Schema
FDB 1
Local
Schema
Federation
FDB3
Federation
SWEA68
Database Interoperability in the Internet

CSE
5810

Technology
 Web/HTTP, JDBC/ODBC, CORBA (ORBs +
IIOP), XML
Architecture
Information Broker
•Mediator-Based Systems
•Agent-Based Systems
SWEA69
Connecting a DB to the Web

CSE
5810
DBMS

CGI Script Invocation
or JDBC Invocation
Web Server

Web Server are
Stateless
DB Interactions Tend
to be Stateful
Invoking a CGI
Script on Each DB
Interaction is Very
Expensive, Mainly
Due to the Cost of
DB Open
Internet
Browser
SWEA70
Connecting More Efficiently

CSE
5810
DBMS
Helper
Processes
CGI Script
or JDBC
Invocation

Web Server
Internet

To Avoid Cost of
Opening Database, One
can Use Helper
Processes that Always
Keep Database Open
and Outlive Web
Connection
Newly Invoked CGI
Scripts Connect to a
Preexisting Helper
Process
System is Still Stateless
Browser
SWEA71
DB-Internet Architecture
CSE
5810
WWW Client
(Netscape)
WWW client
(Info. Explore)
WWW Client
(HotJava)
Internet
HTTP Server
DBWeb Gateway
DBWeb Gateway
DBWeb Gateway
DBWeb
Dispatcher
DBWeb Gateway
SWEA72
Biomedical Architectures

CSE
5810 

Transcend Normal Two, Three, and Four Tier Solutions –
Macro-Architecture
Emerging Standards
 FHIR, SMART, open mHealth
An Architecture of Architectures!
 Need to Integrate Systems that are Themselves Multi-Tier
and Distributed
 Need to Resolve Data Ownership Issues
 State of Connecticut Agencies Don’t Share
 Competing Hospitals Seek to Protect Market Share

T1, T2, and Clinical Research Requires
 Interoperating Genomic Databases/Supercomputers
 Integration of De-identified Patient Data from Multiple Sources to
Allow Sufficient Study Samples
 De-identified Data Repositories or Data Marts

Dealing with Ownership Issues (DNA Research)
SWEA73
Internet and the Web

CSE
5810
A Major Opportunity for Business
 A Global Marketplace
 Business Across State and Country Boundaries

A Way of Extending Services
 Online Payment vs. VISA, Mastercard

A Medium for Creation of New Services
 Publishers, Travel Agents, Teller, Virtual Yellow
Pages, Online Auctions …


A Boon for Academia
 Research Interactions and Collaborations
 Free Software for Classroom/Research Usage
 Opportunities for Exploration of Technologies in
Student Projects
What are Implications for BMI, HIE?
SWEA74
WWW: Three Market Segments
Server
CSE
5810
Business to Business
Corporate
Network



Server
Intranet




Decision
support
Mfg.. System
monitoring
corporate
repositories
Workgroups
Information sharing
Ordering info./status
Targeted electronic
commerce
Internet
Corporate
Server Network
Internet




Sales
Marketing
Information
Services
Provider Network
Server
Provider Network
Exposure to Outside
SWEA75
Information Delivery Problems on the Net

CSE
5810



Everyone can Publish Information on the Web
Independently at Any Time
 Consequently, there is an Information Explosion
 Identifying Information Content More Difficult
There are too Many Search Engines but too Few
Capable of Returning High Quality Data
Most Search Engines are Useful for Ad-hoc Searches
but Awkward for Tracking Changes
What are Information Delivery Issues for BMI?
 Publishing of Patient Education Materials
 Publishing of Provider Education Materials
 How Can Patients/Providers find what Need?
 How do they Know if its Relevant? Reputable?
SWEA76
Example Web Applications

CSE
5810


Scenario 1: World Wide Wait
 A Major Event is Underway and the Latest, Up-tothe Minute Results are Being Posted on the Web
 You Want to Monitor the Results for this
Important Event, so you Fire up your Trusty Web
Browser, Pointing at the Result Posting Site, and
Wait, and Wait, and Wait …
What is the Problem?
 The Scalability Problems are the Result of a
Mismatch Between the Data Access Characteristics
of the Application and the Technology Used to
Implement the Application
May not be Relevant to BMI: Hard to Apply Scenario
SWEA77
Example Web Applications

CSE
5810


Scenario 2:
 Many Applications Today have the Need for
Tracking Changes in Local and Remote Data
Sources and Notifying Changes If Some Condition
Over the Data Source(s) is Met
 To Monitor Changes on Web, You Need to Fire
Your Trusty Web Browser from Time to Time,
Cache the Most Recent Result, and Difference
Manually Each Time You Poll the Data Source(s)
Issue: Pure Pull is Not the Answer to All Problems
BMI: If a Patient Enters Data that Sets off a Chain
Reaction, how Can Provider be Notified and in Turn
the Provider Notify the Patient (Bad Health Event)
SWEA78
What is the Problem?

CSE
5810

Applications are Asymmetric but the Web is Not
 Computation Centric vs. Information Flow Centric
Type of Asymmetry
 Network Asymmetry
 Satellite, CATV, Mobile Clients, Etc.

Client to Server Ratio
 Too Many Clients can Swamp Servers

Data Volume
 Mouse and Key Click vs. Content Delivery

Update and Information Creation
 Clients Need to be Informed or Must Poll


Clearly, for BMI, Simple Web Environment/Browser
is Not Sufficient – No Auto-Notification
FHIR and moving to Mobile Dominated World
SWEA79
What are Information Delivery Styles?

CSE
5810


Pull-Based System
 Transfer of Data from Server to Client is Initiated
by a Client Pull
 Clients Determine when to Get Information
 Potential for Information to be Old Unless Client
Periodically Pulls
Push-Based System
 Transfer of Data from Server to Client is Initiated
by a Server Push
 Clients may get Overloaded if Push is Too
Frequent
Hybrid
 Pull and Push Combined
 Pull First and then Push Continually
SWEA80
Publish/Subscribe

CSE
5810


Semantics: Servers Publish/Clients Subscribe
 Servers Publish Information Online
 Clients Subscribe to the Information of Interest
(Subscription-based Information Delivery)
 Data Flow is Initiated by the Data Sources
(Servers) and is Aperiodic
 Danger: Subscriptions can Lead to Other
Unwanted Subscriptions
Applications
 Unicast: Database Triggers and Active Databases
 1-to-n: Online News Groups
May work for Clinical Researcher to Provider Push
SWEA81
Design Options for Nodes

CSE
5810
Three Types of Nodes:
 Data Sources
 Provide Base Data which is to be Disseminated

Clients
 Who are the Net Consumers of the Information

Information Brokers
 Acquire Information from Other Data Sources, Add
Value to that Information and then Distribute this
Information to Other Consumers
 By Creating a Hierarchy of Brokers, Information
Delivery can be Tailored to the Need of Many Users

Brokers may be Ideal Intermediaries for BMI!
 Act on Behalf of Patients, Providers
 Incorporate Secure Access
SWEA82
Research Challenges

CSE
5810
Ubiquitous/Pervasive
Many computers and information
appliances everywhere,
networked together

Inherent Complexity:
 Coping with Latency (Sometimes
Unpredictable)
 Failure Detection and Recovery
(Partial Failure)
 Concurrency, Load Balancing,
Availability, Scale
 Service Partitioning
 Ordering of Distributed Events
“Accidental” Complexity:
 Heterogeneity: Beyond the Local
Case: Platform, Protocol, Plus All
Local Heterogeneity in Spades.
 Autonomy: Change and Evolve
Autonomously
 Tool Deficiencies: Language
Support (Sockets,rpc),
Debugging, Etc.
SWEA83
Infosphere
Problem: too many sources,too much information
CSE
5810
Internet:
Information Jungle
Infopipes
Clean, Reliable,
Timely Information,
Anywhere
Digital
Earth
Personalized
Filtering &
Info. Delivery
Sensors
SWEA84
Current State-of-Art
CSE
5810
Web
Server
Mainframe
Database
Server
Thin
Client
SWEA85
Infosphere Scenario – for BMI
CSE
5810
Infotaps &
Fat Clients
Sensors
Variety
of Servers
Many sources
Database
Server
SWEA86
Heterogeneity and Autonomy

CSE
5810
Heterogeneity:
 How Much can we Really Integrate?
 Syntactic Integration
 Different Formats and Models
 Web/SQL Query Languages

Semantic Interoperability
 Basic Research on Ontology, Etc

Autonomy
 No Central DBA on the Net
 Independent Evolution of Schema and Content
 Interoperation is Voluntary
 Interface Technology (Support for Isvs)
 DCOM: Microsoft Standard
 CORBA, Etc...
SWEA87
Security and Data Quality

CSE
5810
Security
 System Security in the Broad Sense
 Attacks: Penetrations, Denial of Service
 System (and Information) Survivability
 Security Fault Tolerance
 Replication for Performance, Availability, and
Survivability

Data Quality
 Web Data Quality Problems




Local Updates with Global Effects
Unchecked Redundancy (Mutual Copying)
Registration of Unchecked Information
Spam on the Rise
SWEA88
Data Warehousing and Data Mining

CSE
5810

Data Warehousing
 Provide Access to Data for Complex Analysis,
Knowledge Discovery, and Decision Making
 Underlying Infrastructure in Support of Mining
 Provides Means to Interact with Multiple DBs
 OLAP (on-Line Analytical Processing) vs. OLTP
Data Mining – Role in BMI and Healthcare?
 Discovery of Information in a Vast Data Sets
 Search for Patterns and Common Features based
 Discover Information not Previously Known
 Medical Records Accessible Nationwide
 Research/Discover Cures for Rare Diseases

Relies on Knowledge Discovery in DBs (KDD)
SWEA89
Data Warehousing and OLAP

CSE
5810


A Data Warehouse
 Database is Maintained Separately from an
Operational Database
 “A Subject-Oriented, Integrated, Time-Variant,
and Non-Volatile Collection of Data in Support for
Management’s Decision Making Process
[W.H.Inmon]”
OLAP (on-Line Analytical Processing)
 Analysis of Complex Data in the Warehouse
 Attempt to Attain “Value” through Analysis
 Relies on Trained and Adept Skilled Knowledge
Workers who Discover Information
Data Mart
 Organized Data for a Subset of an Organization
 Establish De-Identified Marts for BMI Research
SWEA90
Building a Data Warehouse

CSE
5810
Option 1
 Leverage Existing
Repositories
 Collate and Collect
 May Not Capture All
Relevant Data

Option 2
 Start from Scratch
 Utilize Underlying
Corporate Data
Corporate
data warehouse
Option 1:
Consolidate Data Marts
Option 2:
Build from
scratch
Data Mart
...
Data Mart
Data Mart
Data Mart
Corporate data
SWEA91
BMI – Partition/Excerpt Data Warehouse

CSE
5810

Clinical and Epidemiological Research (and for T2 and T1)
Each Study Submitted to Institutional Review Board (IRB)
 For Human Subjects (Assess Risks, Protect Privacy)
 See: http://resadm.uchc.edu/hspo/irb/
To Satisfy IRB (and Privacy, Security, etc.), Reverse Process to
Create a Data Mart for each Approved Study
 Export/Excerpt Study Data from Warehouse
 May be Single or Multiple Sources
BMI
data warehouse
Data Mart
...
Data Mart
Data Mart
Data Mart
SWEA92
Data Warehouse Characteristics

CSE

5810


Utilizes a “Multi-Dimensional” Data Model
Warehouse Comprised of
 Store of Integrated Data from Multiple Sources
 Processed into Multi-Dimensional Model
Warehouse Supports of
 Times Series and Trend Analysis
 “Super-Excel” Integrated with DB Technologies
Data is Less Volatile than Regular DB
 Doesn’t Dramatically Change Over Time
 Updates at Regular Intervals
 Specific Refresh Policy Regarding Some Data
SWEA93
Three Tier Architecture
CSE
5810
monitor
External data sources
OLAP Server
integrator
Summarization
report
Operational databases
Extraxt
Transform
Load
Refresh
serve
Data Warehouse
Query report
Data mining
metadata
Data marts
SWEA94
Data Warehouse Design

CSE
5810


Most of Data Warehouses use a Start Schema to
Represent Multi-Dimensional Data Model
Each Dimension is Represented by a Dimension
Table that Provides its Multidimensional Coordinates
and Stores Measures for those Coordinates
A Fact Table Connects All Dimension Tables with a
Multiple Join
 Each Tuple in Fact Table Represents the Content
of One Dimension
 Each Tuple in the Fact Table Consists of a Pointer
to Each of the Dimensional Tables
 Links Between the Fact Table and the Dimensional
Tables for a Shape Like a Star
SWEA95
What is a Multi-Dimensional Data Cube?

CSE
5810



Representation of Information in Two or More
Dimensions
Typical Two-Dimensional - Spreadsheet
In Practice, to Track Trends or Conduct Analysis,
Three or More Dimensions are Useful
For BMI – Axes for Diagnosis, Drug, Subject Age
SWEA96
Multi-Dimensional Schemas

CSE
5810



Supporting Multi-Dimensional Schemas Requires
Two Types of Tables:
 Dimension Table: Tuples of Attributes for Each
Dimension
 Fact Table: Measured/Observed Variables with
Pointers into Dimension Table
Star Schema
 Characterizes Data Cubes by having a Single Fact
Table for Each Dimension
Snowflake Schema
 Dimension Tables from Star Schema are
Organized into Hierarchy via Normalization
Both Represent Storage Structures for Cubes
SWEA97
Example of Star Schema
CSE
5810
Product
Date
Date
Month
Year
Sale Fact Table
Date
ProductNo
ProdName
ProdDesc
Categoryu
Product
Store
Customer
Unit_Sales
Store
StoreID
City
State
Country
Region
Dollar_Sales
Customer
CustID
CustName
CustCity
CustCountry
SWEA98
Example of Star Schema for BMI
CSE
5810
Vitals
Date
Date
Month
Year
Patient Fact Table
Visit Date
BP
Temp
Resp
HR (Pulse)
Vitals
Symptoms
Patient
Medications
Symptoms
Pulmonary
Heart
Mus-Skel
Skin
Digestive
Etc.
Patient
PatientID
PatientName
PatientCity
PatientCountry
Reference another Star
Schema for all Meds
SWEA99
A Second Example of Star Schema …
CSE
5810
SWEA100
and Corresponding Snowflake Schema
CSE
5810
SWEA101
Data Warehouse Issues

CSE
5810

Data Acquisition
 Extraction from Heterogeneous Sources
 Reformatted into Warehouse Context - Names,
Meanings, Data Domains Must be Consistent
 Data Cleaning for Validity and Quality
is the Data as Expected w.r.t. Content? Value?
 Transition of Data into Data Model of Warehouse
 Loading of Data into the Warehouse
Other Issues Include:
 How Current is the Data? Frequency of Update?
 Availability of Warehouse? Dependencies of Data?
 Distribution, Replication, and Partitioning Needs?
 Loading Time (Clean, Format, Copy, Transmit,
Index Creation, etc.)?
 For CTSA – Data Ownership (Competing Hosps).
SWEA102
Knowledge Discovery

CSE
5810


Data Warehousing Requires Knowledge Discovery to
Organize/Extract Information Meaningfully
Knowledge Discovery
 Technology to Extract Interesting Knowledge
(Rules, Patterns, Regularities, Constraints) from a
Vast Data Set
 Process of Non-trivial Extraction of Implicit,
Previously Unknown, and Potentially Useful
Information from Large Collection of Data
Data Mining
 A Critical Step in the Knowledge Discovery
Process
 Extracts Implicit Information from Large Data Set
SWEA103
Steps in a KDD Process

CSE

5810







Learning the Application Domain (goals)
Gathering and Integrating Data
Data Cleaning
Data Integration
Data Transformation/Consolidation
Data Mining
 Choosing the Mining Method(s) and Algorithm(s)
 Mining: Search for Patterns or Rules of Interest
Analysis and Evaluation of the Mining Results
Use of Discovered Knowledge in Decision Making
Important Caveats
 This is Not an Automated Process!
 Requires Significant Human Interaction!
SWEA104
OLAP Strategies

CSE
5810

OLAP Strategies
 Roll-Up: Summarization of Data
 Drill-Down: from the General to Specific (Details)
 Pivot: Cross Tabulate the Data Cubes
 Slide and Dice: Projection Operations Across
Dimensions
 Sorting: Ordering Result Sets
 Selection: Access by Value or Value Range
Implementation Issues
 Persistent with Infrequent Updates (Loading)
 Optimization for Performance on Queries is More
Complex - Across Multi-Dimensional Cubes
 Recovery Less Critical - Mostly Read Only
 Temporal Aspects of Data (Versions) Important
SWEA105
On-Line Analytical Processing

CSE
5810

Data Cube
 A Multidimensonal Array
 Each Attribute is a Dimension
In Example Below, the Data Must be Interpreted so
that it Can be Aggregated by Region/Product/Date
Product
Product
Store
Date
Sale
acron
Rolla,MO 7/3/99 325.24
budwiser LA,CA
5/22/99 833.92
large pants NY,NY
2/12/99 771.24
Pants
Diapers
Beer
Nuts
West
East
3’ diaper Cuba,MO 7/30/99 81.99
Region
Central
Mountain
South
Jan
Feb March April
Date
SWEA106
On-Line Analytical Processing

CSE
5810
For BMI – Imagine a Data Table with Patient Data
 Define Axis
 Summarize Data
 Create Perspective to Match Research Goal
 Essentially De-identified Data Mart
Medication
Patient
Med
BirthDat Dosage
Steve
Lipitor
1/1/45 10mg
John
Zocor
2/2/55
Harry
Crestor
3/3/65 5mg
Lois
Lipitor
4/4/66 20mg
Charles Crestor
7/1/59
Lescol
Crestor
Zocor
Lipitor
80mg
10mg
5
10
Dosage
20
40
80
1940s 1950s 1960s 1970s
Decade
SWEA107
Examples of Data Mining

CSE
5810
The Slicing Action
 A Vertical or Horizontal Slice Across Entire Cube
Months
Slice
on city Atlanta
Products Sales
Products Sales
Months
Multi-Dimensional Data Cube
SWEA108
Examples of Data Mining

CSE
5810
The Dicing Action
 A Slide First Identifies on Dimension
 A Selection of Any Cube within the Slice which
Essentially Constrains All Three Dimensions
Months
Products Sales
Products Sales
Months
March 2000
Electronics
Atlanta
Dice on Electronics and Atlanta
SWEA109
Examples of Data Mining
Drill Down - Takes a Facet (e.g.,
Q1)
and Decomposes into Finer Detail
Jan Feb March
Products Sales
CSE
5810
Drill down
on Q1
Roll Up
on Location
(State, USA)
Roll Up: Combines Multiple Dimensions
From Individual Cities to State
Q1 Q2 Q3 Q4
Products Sales
Products Sales
Q1 Q2 Q3 Q4
SWEA110
Mining Other Types of Data

CSE

5810
Analysis and Access Dramatically More Complicated!
Time Series Data for Glucose, BP, Peak Flow, etc.
Spatial databases
Multimedia databases
World Wide Web
Time series data
Geographical and Satellite Data
SWEA111
Advantages/Objectives of Data Mining

CSE
5810


Descriptive Mining
 Discover and Describe General Properties
 60% People who buy Beer on Friday also have
Bought Nuts or Chips in the Past Three Months
Predictive Mining
 Infer Interesting Properties based on Available
Data
 People who Buy Beer on Friday usually also Buy
Nuts or Chips
Result of Mining
 Order from Chaos
 Mining Large Data Sets in Multiple Dimensions
Allows Businesses, Individuals, etc. to Learn about
Trends, Behavior, etc.
 Impact on Marketing Strateg
SWEA112
Data Mining Methods (1)

CSE
5810
Association
 Discover the Frequency of Items Occurring
Together in a Transaction or an Event
 Example
 80% Customers who Buy Milk also Buy Bread
Hence - Bread and Milk Adjacent in Supermarket
 50% of Customers Forget to Buy Milk/Soda/Drinks
Hence - Available at Register

Prediction
 Predicts Some Unknown or Missing Information
based on Available Data
 Example
 Forecast Sale Value of Electronic Products for Next
Quarter via Available Data from Past Three Quarters
SWEA113
Association Rules

CSE

5810


Motivated by Market Analysis
Rules of the Form
 Item1^Item2^…^ ItemkItemk+1 ^ … ^ Itemn
Example
 “Beer ^ Soft Drink  Pop Corn”
Problem: Discovering All Interesting Association
Rules in a Large Database is Difficult!
 Issues
 Interestingness
 Completeness
 Efficiency

Basic Measurement for Association Rules
 Support of the Rule
 Confidence of the Rule
SWEA114
Data Mining Methods (2)
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CSE
5810
Classification
 Determine the Class or Category of an Object
based on its Properties
 Example
 Classify Companies based on the Final Sale Results in
the Past Quarter

Clustering
 Organize a Set of Multi-dimensional Data Objects
in Groups to Minimize Inter-group Similarity is
and Maximize Intra-group Similarity
 Example
 Group Crime Locations to Find Distribution Patterns
SWEA115
Classification

CSE
5810
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
Two Stages
 Learning Stage: Construction of a Classification
Function or Model
 Classification Stage: Predication of Classes of
Objects Using the Function or Model
Tools for Classification
 Decision Tree
 Bayesian Network
 Neural Network
 Regression
Problem
 Given a Set of Objects whose Classes are Known
(Training Set), Derive a Classification Model
which can Correctly Classify Future Objects
SWEA116
An Example
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CSE
5810


Attributes
Attribute
Possible Values
outlook
sunny, overcast, rain
temperature continuous
humidity
continuous
windy
true, false
Class Attribute - Play/Don’t Play the Game
Training Set
 Values that Set the Condition for the Classification
 What are the Pattern Below?
Outlook Temperature Humidity
sunny
85
85
overcast 83
78
sunny
80
90
sunny
72
95
sunny
72
70
…
…
…
Windy
false
false
true
false
false
…
Play
No
Yes
No
No
Yes
...
SWEA117
Data Mining Methods (3)
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CSE
5810
Summarization
 Characterization (Summarization) of General
Features of Objects in the Target Class
 Example
 Characterize People’s Buying Patterns on the Weekend
 Potential Impact on “Sale Items” & “When Sales Start”
 Department Stores with Bonus Coupons

Discrimination
 Comparison of General Features of Objects
Between a Target Class and a Contrasting Class
 Example
 Comparing Students in Engineering and in Art
 Attempt to Arrive at Commonalities/Differences
SWEA118
Summarization Technique

CSE

5810
Attribute-Oriented Induction
Generalization using Concert hierarchy (Taxonomy)
barcode category
14998
milk
brand
diaryland
content
size
Skim
2L
food
12998 mechanical MotorCraft valve 23a 12in
…
…
…
…
...
Milk
…
Skim milk … 2% milk
Category
milk
milk
…
Content Count
skim
2%
…
280
98
...
bread
White
whole
bread … wheat
Lucern … Dairyland
Wonder … Safeway
SWEA119
Why is Data Mining Popular?
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CSE
5810
Technology Push
 Technology for Collecting Large Quantity of Data
 Bar Code, Scanners, Satellites, Cameras

Technology for Storing Large Collection of Data
 Databases, Data Warehouses
 Variety of Data Repositories, such as Virtual Worlds,
Digital Media, World Wide Web


Corporations want to Improve Direct Marketing and
Promotions - Driving Technology Advances
 Targeted Marketing by Age, Region, Income, etc.
 Exploiting User Preferences/Customized Shopping
What is Potential for BMI?
 How do you see Data Mining Utilized?
 What are Key Issues to Worry About?
SWEA120
Requirements & Challenges in Data Mining
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CSE
5810
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Security and Social
 What Information is Available to Mine?
 Preferences via Store Cards/Web Purchases
 What is Your Comfort Level with Trends?
User Interfaces and Visualization
 What Tools Must be Provided for End Users of
Data Mining Systems?
 How are Results for Multi-Dimensional Data
Displayed?
Performance Guarantees
 Range from Real-Time for Some Queries to LongTerm for Other Queries
Data Sources of Complex Data Types or Unstructured
Data - Ability to Format, Clean, and Load Data Sets
SWEA121
CSE
5810
An Initiative of the University of Connecticut
Center for Public Health and Health Policy
Robert H. Aseltine, Jr., Ph.D.
Cal Collins
January 16, 2008
SWEA122
What is CHIN?

CSE
5810

State of Connecticut Agencies Collect and Maintain
Data in Separate Databases such as:
 Vital Statistics: Birth, Death (DPH)
 Surveillance data: Lead Screening and
Immunization Registries (DPH)
 Administrative services: LINK system (DCF),
CAMRIS (DMR)
 Benefit programs: WIC (DPH), Medicaid (DSS)
 Educational achievement: (PSIS)
Such Data is Un-Integrated
 Impossible to Track Assess Target Populations
 Difficult to Develop Evidence-Based Practices
 Limits Meaningful Interactions Among State
Agencies
SWEA123
What Do We Mean by “Integration?”
UCONN Health Center
Low Birth Weight Infant Registry
Dept. of Mental Retardation
Birth to Three System
CT Dept. of Education
PSIS System
CSE
5810
Last Name
First Name
DOB
SSN
Birth Wt.
(kg)
Last Name
First Name
DOB
Street
Town
Appel
April
01/01/1
999
016-000-9876
2.8
Allen
Gwen
01/01/19
99
Apple
Enfie
Berry
John
02/02/1
997
216-000-4576
2.9
Buck
Jerome
07/01/19
99
Burbank
West
Carat
Colleen
03/03/1
993
119-000-1234
1.9
Cleary
Jane
03/03/19
93
Cedar
Tolla
Ernst
Max
04/04/1
994
116-000-3456
2.7
Dory
Daniel
03/03/19
93
Dogfish
Hartf
Gomez
Gloria
05/05/1
995
036-000-9999
2.6
Ernst
Max
04/04/19
94
Elm
Enfie
Hurst
William
06/06/1
996
016-000-5599
3.1
Friday
Joe
11/03/19
99
Fruit
Wind
Keller
Helene
07/07/1
997
017-000-2340
2.5
Glenn
Valerie
03/23/19
98
Glen
Branf
Pedro
08/08/1
998
018-000-9886
Martinez
Pedro
08/08/19
98
High
Hartf
Felix
09/09/1
999
029-000-9111
Riley
Lily
03/03/19
96
Ipswich
Bridg
Sanchez
Ramon
New
Peggy
016-000-8787
03/03/19
93
Juniper
10/10/2
000
Martinez
Rodriguez
Smith
3.0
2.8
2.5
Last Name
First Name
CMT
Math
Polio Vac
Date
Days in
Attendance
Appel
April
134
01/05/
1999
179
Carat
Colleen
256
05/01/
1998
122
Cleary
Jane
268
01/28/
2000
178
Ernst
Max
152
01/09/
1999
145
Gomez
Gloria
289
01/01/
1999
168
Friday
Joe
265
10/01/
1999
170
Keller
Helene
309
11/01/
2001
180
Martinez
Pedro
248
12/01/
2003
180
Riley
Lily
201
01/01/
1999
122
Sanchez
Ramon
249
01/01/
1999
159
Last Name
First Name
DOB
SSN
Birth Wt.
Street
Town
CMT Math
Grade 3
Polio
Vaccination
Date
Days in
Attendance
Ernst
Max
04/04/1994
116-000-3456
2.7
Elm
Enfield
152
01/09/1999
145
Martinez
Pedro
08/08/1998
018-000-9886
3.0
High
Hartford
248
12/01/2003
180
SWEA124
Key Challenges to Integrating Data

CSE
5810




Security and Privacy
 HIPAA
 FERPA
 WIC, Social Security (Medicaid/Medicare)
regulations
 State statutes
Alteration/disruption of business practices
Unique identification of individuals/cases
Accuracy and reliability of data
Disparate hardware/software platforms
SWEA125
Key Challenges to Integrating Data

CSE
5810




Security and Privacy
 HIPAA
 FERPA
 WIC, Social Security (Medicaid/Medicare)
regulations
 State statutes
Alteration/disruption of business practices
Unique identification of individuals/cases
Accuracy and reliability of data
Disparate hardware/software platforms
SWEA126
The Solution: CHIN

CSE

5810

Connecticut Health Information Network
A Federated Network That:
 Allows Shared Access to “Health”-related Data
From Heterogeneous Databases
 Allows Agencies to Retain Complete Control Over
Access to Data
 Has Minimal Impact on Business Practices
 Complies with Security and Privacy Statutes
 Incorporates Cutting-edge Approaches to Case
Matching
Partnership of:
 Early Partners: DPH, DCF, DDS, DoE, DOIT,
UConn, Akaza Research
SWEA127
Current CHIN Architecture
CSE
5810
SWEA128
Path – Modular Data Integration

CSE
5810




Produce relational, record-level datasets by merging
data from multiple agencies to support research into
health, education, and social services, licensing
De-identify or anonymize that data to the level
necessary for a particular application
Utilized internally within an agency to integrate data
that does not need to be anonymized.
Supports Integraiton with legacy systems that hold
data in incompatible formats
http://www.publichealth.uconn.edu/pathproduct.html
SWEA129
Path – Capabilities

CSE
5810







integrates data from diverse sources that may or may
not share a universal record identifier
handles data in a HIPAA and FERPA compliant
manner
utilizes a highly secure architecture
maintains the autonomy of agency data - exposure,
location, and schema
provides an extremely easy to learn and flexible user
interface
requires no changes to agency database schemas
needs minimal upgrade to departmental computer
hardware and software
once installed, it can quickly and efficiently produce
integrated datasets
SWEA130
Concluding Remarks

CSE
5810

Only Scratched Surface on Architectures
 Micro Architectures
 Macro Architectures
 Super-Macro Architectures (We’ll see …)
What’s are Key Facets in the Discussion?
 Role and Impact of Standards
 Open Solutions
 Architectural Variants – Reuse “Architecture”
 Can we Reuse CHIN for Clinical Practice?
 Are All Contributors Simply Each Hospital and EHR?
 How do we Connect all of the Pieces?

What are Next Steps?
 Let’s Review Some other Work
 Source: Wide Range of Presentations on Web
SWEA131