Iterative Waterfall Case Study: Network Information Data On-line Analysis Alessandro Zorer alessandro.zorer@create-net.it - PowerPoint PPT Presentation

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Iterative Waterfall Case Study: Network Information Data On-line Analysis Alessandro Zorer alessandro.zorer@create-net.it

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Alessandro Zorer alessandro.zorer_at_create-net.it Agenda Iterative Waterfall methodology (based on Sodalia SIMEP) General approach DWH tailoring Case Study ... – PowerPoint PPT presentation

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Title: Iterative Waterfall Case Study: Network Information Data On-line Analysis Alessandro Zorer alessandro.zorer@create-net.it


1
Iterative Waterfall Case Study Network
Information Data On-line Analysis Alessandro
Zorer alessandro.zorer_at_create-net.it
2
Agenda
  • Iterative Waterfall methodology (based on Sodalia
    SIMEP)
  • General approach
  • DWH tailoring
  • Case Study Network Information Data On-line
    Analysis
  • Needs
  • Approach
  • Focus on System Architecture
  • Functional View
  • Process View
  • Development View
  • Physical View
  • Fifth View
  • Summary
  • QA

3
Iterative Waterfall Methodology Process
  • Iterative approach
  • Multilayer
  • Multiperspective

System Development Strategy
Methodology
Iteration 1
Iteration 2
Iteration n
System Architecture.
Component Design.
Component Design.
Component Design.
Component Design.
Component Design.
Component Design.
System Requirements
System Test.
Component Design.
Component Development.
Component Test.
4
Adaptation to DWH
Methodology
5
Context Analysis
Methodology
6
Requirement Definition and System Architecture
Analysis
System Architecture
Requirements Analysis
Business Analysis
System Architecture Design
Architectural Qualities
Methodology
Tools Assessment Evaluation
  • Use case identification
  • Data Sourcces identification
  • Data Consumers identification

Performance
Capacity
Scalability
UML
DESMET
Concept Exploration
7
Architectural Views
  • Logical View
  • A logical abstract view of system elements and
    of the services to be provided to the end user
  • Process View
  • Analysis the dynamic aspect of the system
    through scenarios and other diagrams (e.g.,
    sequence, collaboration and activity diagrams).
    The elements focused are tasks with their
    workflow, processes with their dependency,
    synchronization and concurrence aspects.
  • Development View
  • Organization of the actual software modules in
    the software-development environment. The modules
    may be packaged in components or subsystems
    (component diagram) which may be organized in a
    hierarchy of layers, each layer providing a
    narrow well-defined interface to other layers.
  • Physical View
  • Provide the deployment configurations in terms
    of Hardware and Software Components. This view
    shows the System Topology, a network of
    processing nodes with the software running on
    them. Capacity issues are addressed.
  • Fifth View
  • Orthogonal view. Issues addressed are Potential
    Software Reuse Analysis, Requirements allocation
    on Components, Performance Analysis,
    Functionality Categorization and Ranking.

8
DESMET
  • Methodology for evaluating COTS based on
  • Functional Qualities
  • Architectural qualities (i.e. adaptability,
    Scalability)
  • Performance analysis
  • Business aspects
  • Time to market
  • System lifecycle
  • Contractual constraints
  • Support organization

Methodology
9
DW Design
MD Schema
Data Marts
Input Design
Output Design
Methodology
Data Flow Design
Metadata Management Design
UML
E-R
System Architecture Design
10
DM Design
Data Mart Construction
Testing
Training
Methodology
Customization
Unit Testing
Data Flow
Hardware
Database
11
Support tools infrastructure
Methodology
12
Case Study Network Information Data On-line
Analysis
  • Business needs
  • Definition and development of a DataWarehouse
    Framework for Multidimensional Analysis based on
  • Call Data (Network Management)
  • Fault Data (Problem Management)
  • Performance Data (End-to-End Analysis)
  • Optimization of network performances through
    gathering and analysis
  • High integrability of new data sources
  • Optimization and extension of on-line analysis
    functionalities
  • Quick creation of reports and flexibility for the
    end user (through custom Data Marts)
  • Extension of capabilities in term of historical
    data management.

Case Study Intro.
13
Solution
  • A specialized and adaptable Data Warehouse
    solution to support Network Traffic Management
    and Call Behavior Analysis through a smart data
    correlation among CDR and configuration,
    performance and trouble tickets
  • Highly scalable to adapt from small to large
    business needs
  • Based an a mix of COTS and developed components
  • Flexible to accomadate a variety of different
    sources and Call Data Record formats
  • Detailed data analysis capabilities to support
    different DSS customer organizations
  • Predefined good example analysis library to
    quikly develop and deliver QoS monitors and
    Service Level Management functions

Case Study Intro.
14
System Framework Approach
  • Simplify the design, implementation, and
    management of data warehousing solutions
  • An open architecture that allows easy integration
    with and extended by third party vendors
  • Heterogeneous data import, export, validation and
    cleansing services with optional data lineage
  • Integrated metadata for warehouse design, data
    extraction/transformation, server management, and
    end-user analysis tools
  • Core management services for scheduling, storage
    management, performance monitoring,
    alerts/events, and notification

Case Study Intro.
15
Logical View
  • UML Domain and System Modeling
  • describes system concepts in a formal way
  • drives data modeling
  • drives components design
  • drives dynamic modeling
  • Standard-based Object Information Model (OIM)
    from Microsoft and Metadata Coalition

C.S. Logical View
16
Layered Modeling Organization
Data Analysis Layer
C.S. Logical View
Data Warehouse Layer
MetaData Management
WorkFlow Management
Data Transformation Layer
17
Generic Record-oriented Model
Element
SummaryInformation
TransformableObject
Column
Classifier
RecordItem
C.S. Logical View
ModelElement
Attribute
0..
88Level
DeployedCatalogs
Field
Record
Group
GroupDef
Type
RecordFormat
DeployedRecord
LogicalRecord
DeployedGroup
LogicalGroup
DeployedField
LogicalField
18
Generic Call Data Record Model
DeployedRecord
NEType
C.S. Logical View
CallDataRecord
NetworkElement
ServiceType
SourceID
DestinationID
Elapsed
Measure
19
Generic OLAP Model
Package
Connection
DataSource
Catalog
Store
C.S. Logical View
Data Sources
OLAPDatabase
Connection
OLAP Server
Cube
Cubes
Dimension
Dimensions
DeployedCatalogs
0..
ModelElement
DeployedOLAPDatabase
LogicalOLAPDatabase
DimHierarchies
1..
DimHierarchy
20
OLAP and DSS
  • Fast
  • five seconds or less.
  • Analysis
  • Performs basic numerical and statistical analysis
    of the data, predefined or ad hoc
  • Shared
  • Implements the security requirements across a
    large user population
  • Multidimensional
  • Is the essential characteristic of OLAP
  • Information
  • Accesses all the data and information wherever it
    may reside and not limited by volume.

C.S. Logical View
21
Metadata Management
  1. The link between the DSS system and the
    business analysts.
  2. Critical for maintaining, controlling, and
    expanding the DSS system. Reduces the cost and
    cycle time of problem resolution.

C.S. Logical View
22
Metadata Consumer
  • Business Users
  • Less technical
  • Use predefined queries reports
  • DSS navigation and definition

C.S. Logical View
  • Power Business Users
  • More technical
  • Ad-hoc
  • Technical Users
  • Acquisition access developers, analysts, data
    modelers, architects
  • Need users access patterns frequency
  • Transformation rules

23
Metadata Management
Business Meta Data
Technical Meta Data
Transformation Rules Attribute Names Domain
Values Access Patterns Entity Relationships
Attribute Business Definitions Entity Business
Definitions Aggregation Rules Report Business
Descriptions List of Available Reports
C.S. Logical View
Technical Users (Developers Analysts)
Business Users (Executives Business Analysts)
Power Business Users Data Administrator
24
Data transformation
  • Finding the right data to satisfy end users
    needs
  • Moving the right data to the target
  • Scheduling and monitoring
  • Providing visual access
  • Linking transformations and movement metadata
    with all other metadata activity

C.S. Logical View
25
Workflow Management
C.S. Logical View
26
Sequence Diagram
C.S. Process View
27
Functional Architecture
CASE Modeling Tools
Meta Data Management
Meta Data Movement Replication Tools
Meta Data Access Tools
Meta Data Administration Utilities
Project Deliverables Generator
Change Management Tools
Operational Systems Data
Data Mining Simulation Tools
C.S. Development View
OLAP Data Query, Reporting and
Visualization Tools
Query
Source Data Extract Tools
Database Utilities
Data Quality Assessment Tools
DW
Data Marts
Data Transformation Tools
Load Validation Tools
Data Cleansing Tools
Operational DB Applications Meta Data Sources
Warehouse Management Tools
Data Warehouse
Trasformation
28
Layered Architecture
Network Admin
Operations Manager
Database Admin
Data Analyst
Applic. Developer
IT Users
Data Capture
Source Data (Internal and/or External)
Data Transformation
C.S. Development View
Enterprise Warehouse
Data Management
Support Infrastructure
Replication Propagation
Workflow Management
Data Warehouse Middleware
Network Management Database Management Systems
Management
Metadata Logical Data Model Physical
Data base Design Data Dictionaries
Dependent Data Mart
Knowledge Discovery / Data Mining
Information Access / Applications
Data Analysis
Business Users
Power Analyst
Knowledge Worker
Executive/ Manager
Customer Contact
Application Server
29
Components Integration
Data Management

C.S. Development View
Integrated Support Infrastructure
Data Capture

Data Analysis
30
Components Integration
Data Management
WEB Services
Data Browser
Schedule-driven


Summarization
acquisition
C.S. Development View
Communication



System Management
Service Infrastructure

Query
Data capture


Report Sched
Workflow

Change Management


Integrated Support Infrastructure
Data Analysis DSS
31
Physical View
Server Platform
Directory Services
DW
OLAP
C.S. Physical View
Windows NT
Intranet
Unix WS
Windows 95/98/NT
Client Platform
32
System Scalability
  • System Sizing
  • Small Size ( lt 10 M CDR/day )
  • Medium Size ( gt 10 M lt 50 M CDR /day )
  • Large Size ( gt 50 M lt 200 M CDR / day )
  • Solutions
  • Process distribution (divide et impera)
  • Different COTS choice (performance and TCO)
  • Hardware platform

C.S. Physical View
33
Architectural Qualities
  • Performance (Canned queries, MD Analysis, Ad hoc,
    Min. Impact on Operational System)
  • Flexibility (MD Flex, Ad hoc, Change data
    structure)
  • Scalability (No. of Users, Volume of Data)
  • Ease of Use (Location, Formulation, Navigation,
    Manipulation)
  • Data Quality (Consistent, Correct, Timely,
    Integrated)
  • Connection to the Detail Business Transactions

C.S. Fifth View
34
Summary
  • Iterative waterfall approach for large projects
  • Architecture as a CENTRAL activity for the
    success of projects
  • Scalability as a driving factor in this case
  • Standard adoption (Metadata Coalition OIM Model)
  • COTS developed components to meets Time to
    market and Best-in-class solution
  • Flexibility in data capturing and high modularity
    to improve the level of integration with already
    in place systems
  • QA
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