Iterative Waterfall Case Study:
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Iterative Waterfall Case Study: Network Information Data On-line Analysis Alessandro Zorer [email protected] Agenda. Iterative Waterfall methodology (based on Sodalia SIMEP) General approach DWH ‘tailoring’ Case Study: Network Information Data On-line Analysis Needs Approach

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Agenda

Iterative Waterfall Case Study: Network Information Data On-line AnalysisAlessandro [email protected]


Agenda

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

  • Q&A


Iterative waterfall methodology process

Concept Exploration and Context Analysis

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.


Adaptation to dwh

Expressed as a UML activity diagram

Use Case and Actor

identification

Use Case model

Structuring

Context Analyst

Product impact Analysis

Use Case Prioritization

Integration Analysis

Requirement Manager

Data Warehouse design

Architect

DW Designer

DW Analysis Interface

Prototyping

DW may be tailored to each specific project and domain

Data Mart Designer

Adaptation to DWH

Methodology


Context analysis

Manage

Dependencies

Develop

Business Model

Elicit Stakeholder

Needs

Find Actors

and Use Cases

Structure the

Use-Case

Model

Capture a

Common

Vocabulary

Context

Analyst

Business Model

Activity

Diagrams

Use-Case

Model

Requirement

Attributes

Stakeholder

Identification

Responsible for

Glossary

Context Analysis

Methodology


Requirement definition and system architecture

Requirement Definition and System Architecture

Analysis

System Architecture

Requirements Analysis

Business Analysis

Architectural Qualities

System Architecture Design

Methodology

Tools Assessment & Evaluation

  • Use case identification

  • Data Sourcces identification

  • Data Consumers identification

Capacity

Performance

Scalability

UML

DESMET

Concept Exploration


Architectural views

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.


Desmet

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


Dw design

Physical Architecture Design

Data Modeling

DW Design

Input Design

Data Marts

MD Schema

Output Design

Methodology

Data Flow Design

Metadata Management Design

UML

E-R

System Architecture Design


Dm design

Deployment Iteration

DM Design

Data Mart Construction

Testing

Training

Methodology

Customization

Unit Testing

Hardware

Data Flow

Database


Support tools infrastructure

Business

Process

Modeling

Object /

Component

Modeling

Logical / Physical

Database

Modeling

Enterprise Integrated DW Modeling

Support tools infrastructure

Methodology


Case study network information data on line analysis

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.


Solution

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.


System framework approach

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.


Logical view

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


Layered modeling organization

Layered Modeling Organization

Data Analysis Layer

C.S.: Logical View

Data Warehouse Layer

MetaData Management

WorkFlow Management

Data Transformation Layer


Generic record oriented model

Generic Record-oriented Model

Element

SummaryInformation

TransformableObject

Column

Classifier

RecordItem

C.S.: Logical View

ModelElement

Attribute

0..*

88Level

+DeployedCatalogs

Record

GroupDef

Group

Field

+Type

RecordFormat

DeployedRecord

LogicalRecord

DeployedGroup

LogicalGroup

DeployedField

LogicalField


Generic call data record model

Generic Call Data Record Model

DeployedRecord

NEType

C.S.: Logical View

CallDataRecord

ServiceType

NetworkElement

SourceID

DestinationID

Elapsed

Measure


Generic olap model

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


Olap and dss

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


    Metadata management

    METADATA

    Technical Users

    (Developers & Analysts)

    Business Users

    (Executives & Business Analysts)

    Data Administrator

    Metadata Management

    The link between the DSS system and the business analysts.

    Critical for maintaining, controlling, and expanding the DSS system. Reduces the cost and cycle time of problem resolution.

    C.S.: Logical View


    Metadata consumer

    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


    Metadata management1

    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)

    Power Business Users

    Data Administrator

    Business Users

    (Executives & Business Analysts)


    Data transformation

    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


    Workflow management

    Process integration

    Data

    integration

    DW

    Metadata

    Operational

    Data

    Workflow Management

    C.S.: Logical View


    Sequence diagram

    Sequence Diagram

    C.S. : Process View


    Functional architecture

    Metadata Repository

    Enterprise

    Reference

    Data

    Functional Architecture

    CASE &

    Modeling

    Tools

    Meta Data Management

    Meta Data

    Administration

    Utilities

    Meta Data

    Access

    Tools

    Meta Data

    Movement &

    Replication Tools

    Change

    Management

    Tools

    Project

    Deliverables

    Generator

    Operational Systems Data

    Data Mining &

    Simulation Tools

    C.S.: Development View

    OLAP Data

    Query, Reporting

    and Visualization

    Tools

    Query

    Data Quality

    Assessment

    Tools

    Source

    Data Extract

    Tools

    Database

    Utilities

    DW

    Data Marts

    Data

    Cleansing

    Tools

    Data

    Transformation

    Tools

    Load

    Validation

    Tools

    Operational DB

    Applications

    Meta Data

    Sources

    Warehouse Management

    Tools

    Data Warehouse

    Trasformation


    Layered architecture

    Staging Area

    Layered Architecture

    Data

    Analyst

    Database

    Admin

    Operations

    Manager

    Network

    Admin

    Applic.

    Developer

    IT Users

    Data Capture

    Source Data

    (Internal and/or External)

    Data

    Transformation

    C.S.: Development View

    Enterprise Warehouse

    Data Management

    SupportInfrastructure

    Replication &

    Propagation

    Workflow Management

    Data Warehouse Middleware

    Network ManagementDatabase ManagementSystems 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


    Components integration

    Components Integration

    Data

    Management

    C.S.: Development View

    Integrated

    Support

    Infrastructure

    Data Capture

    Data

    Analysis


    Components integration1

    Transformation

    chain

    Asynchronous

    Data Cleaning

    acquisition

    & Maintenance

    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


    Physical view

    Corporate Data

    Unix

    MVS

    Physical View

    Server Platform

    Directory Services

    DW

    OLAP

    C.S.: Physical View

    Windows NT

    Intranet

    Unix WS

    Windows

    95/98/NT

    Client Platform


    System scalability

    System Scalability

    System Sizing

    • Small Size ( <= 10 M CDR/day )

    • Medium Size ( >= 10 M <= 50 M CDR /day )

    • Large Size ( >= 50 M <= 200 M CDR / day )

      Solutions:

    • Process distribution (divide et impera)

    • Different COTS choice (performance and TCO)

    • Hardware platform

    C.S.: Physical View


    Architectural qualities

    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


    Summary

    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

        Q&A


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