erp module ii

8/4/2019 Erp Module II

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Enterprise Resource

Planning

Module II


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ERP Module II Prepared By: Ms. Swapna Anildas 2

Topics Covered

Technical Architecture of ERP systems

Distributed computing

Client server systems

Concept of business objects

Distributed object computing architecture

Support for data mining and warehousing


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Technical Architectures of ERP

Two-tier Architecture

Three-Tier Architecture

Web-based Architecture



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Two-Tier Architecture

The server handles both application and

database duties The clients present the data and pass user

input back to the server.



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Two-tier Architecture


Server:

Application and

Database

Clients: Input and

Presentation

Presentation Layer

(GUI Applications)

Data

Applications/

Database Layer


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3-Tier Architecture

Database and application functions are

separated.

The three layers: Presentation Layer

Business Logic / Application Layer

Data Management Layer



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Three-tier Architecture


Database Server

Local / Remote

Clients

Application Server


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Presentation Layer

The top most layer of application in which

the user interacts with the system.

Screen layout, Navigation, language

integration, and other user based elements

are controlled in this layer.



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Business Logic Layer

This layer coordinates the application,

processes commands, makes logical

decisions and evaluations, and performs

calculations

It also moves and processes data between

two surrounding layers.



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Here information is stored and retrieved

from a database or file system. The

information is then passed back to the logic

layer for processing and then eventually

back to the user.



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Presentation Layer

Business Logic Layer


Get Sales

Total

Total

Sales

Get list of all sales

made last year

Add all sales

together

DB

QuerySales 1

Sales 2

Sales 3


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Web-based Architecture

The main reason for the migration of ERP systems into the

Web is to improve their competitiveness, companies need to

drive collaborative business. Implementing this collaborativebusiness requires integrating their existing heterogeneous IT

landscapes and extending this integration to their business

partners, customers and suppliers. Internet appears as the

solution for integrating different applications belonging to

different Information Systems. To answer to these newrequirements, vendors have developed a new generation of

ERP systems based on the Web technology: the Web-based

ERP.



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Web-based Architecture..contd.

The fundamental advantage of Internet is that it is a

standards-based environment with no owner, so nobody can

claim to have new and improved Internet. Any companycan access to a web-based ERP as long as it has an Internet

connection and the right authorization. The Web allows the

creation of an open platform that will permit different

applications to communicate easily through the standardsoffered by Internet.



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In essence, a web service is three specific things :

A way to find and register interest in a service (UDDI)

A transport mechanism to access a service (SOAP)

A way to define what the input and output parameters are for

such a service (WSDL)

Web services represent an independent platform, not

controlled by any one vendor that provides a way to allow

applications to talk to one another. To communicate using

web services, applications first have to find the service that

is located in a library called UDDI (Universal Discovery,

Description, and Integration).



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The UDDI sends the service to the client with the

defined interface, then the application can

communicate with the service through a

standardized protocol called SOAP (Simple Object

Access Protocol) built-up using an XML schema.

The following schema (see Figure 7) shows these

different actions:



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Web Based ERP Architecture


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Centralized vs. Distributed Computing

mainframe computerworkstation

network host

network link

terminal

centralized computing

distributed computing

Early computing was

performed on a single

processor.Uni-processor

computing can be

called Centralizedcomputing.

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Centralized vs. Distributed Computing

mainframe computerworkstation

network host

network link

terminal

centralized computing

distributed computing

A distributed system is a collection of

independent computers, interconnected

via a network, capable of collaborating

on a task.

Distributed computing is computing

performed in a distributed system.

Distributed computing has become

increasingly common due advances thathave made both machines and networks

cheaper and faster



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Example Distributed systems

Internet

ATM (bank) machines

Intranets/Workgroups



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Computers in a Distributed System

Workstations: computers used by end-users to

perform computing

Server machines: computers which provide

resources and services

Personal Assistance Devices: handheld

computers connected to the system via awireless communication link.

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Goals/Benefits

Resource sharing

Scalability

Fault tolerance and availability

Performance

Parallel computing can be considered a subsetof distributed computing

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Components of Distributed Software Systems

Distributed systems

Middleware

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Challenges(Differences from Local Computing)

Heterogeneity

Latency

Remote Memory vs. Local Memory

SynchronizationConcurrent interactions the norm

Partial failureApplications need to adapt gracefully in the face of partial

failure

Lamport once defined a distributed system as One onwhich I cannot get any work done because some machine Ihave never heard of has crashed

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Challenges.contd

Need for openness

Open standards: key interfaces in software andcommunication protocols need to be standardized

Security

Denial of service attacks

Mobile code

Scalability Transparency

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Distributed applications Applications that consist of a set of processes that

are distributed across a network of machines and

work together as an ensemble to solve a commonproblem

In the past, mostly client-server

Resource management centralized at the server

Peer to Peer computing represents a movement

towards more truly distributed applications

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Data MiningandData MiningandData WarehousingData Warehousing

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Introduction

Organizations getting larger and amassing ever

increasing amounts of data

Historic data encodes useful information aboutworking of an organization.

However, data scattered across multiple sources,

in multiple formats.

Data warehousing: process of consolidating datain a centralized location

Data mining: process of analyzing data to find

useful patterns and relationships



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What is Data Mining and Knowledge

Discovery ?

Data Mining is a tactical process that uses

mathematical algorithms to sift through large data-

stores to extract data patterns/models/rules The Knowledge Discovery is the process of

identifying and understanding potentially useful

hidden anomalies, trends and patterns. Data

mining is an integral part of knowledge discoveryprocess

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Typical data analysis tasks

Report the per-capita deposits broken down by

region and profession.

Are deposits from rural coastal areas increasingover last five years?

What percent of small business loans were cleared?

Why is it less than last years? How did similarbusinesses that did not take loans perform?

What should be the new rules for loan eligibility?



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Bombay branch Delhi branch Calcutta branch

Census

data

Operational data

Detailed

transactionaldata

Data warehouse

Merge

Clean

Summarize

Direct

Query

Reporting

tools

Mining

tools

Decision support tools

Oracle SASIMS

Crystal reportsIntelligent Miner

GIS

data

OLAP



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Data warehouse construction

Heterogeneous data integration merge from various sources, fuzzy matches

remove inconsistencies Data cleaning:

missing data, outliers, clean fields e.g. names/addresses

Data mining techniques

Data loading: summarize, create indices Products: Prism warehouse manager, Platinum info

refiner, info pump, QDB, Vality



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Warehouse maintenance

Data refresh

when to refresh, what form to send updates?

Materialized view maintenance with batch

updates.

Query evaluation using materialized views

Monitoring and reporting tools

HP intelligent warehouse advisor



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Mining market players

Major players:

Clementine, IBMs Intelligent Miner,

SGIs MineSet,

SASs Enterprise Miner.



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Business Data Mining Applications

Area Applications

Retailing Market basket analysis, cross-sell

Banking Customer relationship mgmt

Credit Card Mgmt Lift, churn

Insurance Fraud detection

Telecommunications Churn (customer turnover)

Telemarketing On-line caller information

Human Resource Mgmt Churn (employee turnover)

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Prepared By: Ms. Swapna

Anildas


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End of Module II

erp module ii

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