It's a cliché that modern enterprise applications are simply web applications. But is that the whole truth? And if it isn't, what are all the pieces of an enterprise application and how do they fit together? How can we continue to use older technologies within these applications and how might we exploit new technologies in the future? What new challenges do enterprises face in the 21st Century and how might they affect the design of applications and programming systems?

1. The Landscape of Enterprise
Applications
- a personal view
Geoff Sharman
© Copyright geoffrey.sharman@bcs.org.uk
www.dcs.bbk.ac.uk/~geoff/

2. Typical Web Application?
Web servers Application Database
Servers Servers
Requests
from web
browsers Member
DB
Auction DB
 Static pages served from web server/content management
system
 Dynamic pages assembled by applications on application servers
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3. Did you do any of these today?
Buy something in a supermarket?
Buy a ticket for travel or entertainment?
Make a telephone call (mobile or fixed)?
Use a cash machine or debit card?
Pay for something with a credit card?
Use electricity, gas or water?
The chances are you used a traditional online
transaction system (running on a mainframe?)
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4. That's Cloud Power?
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5. What's an Enterprise, or an
Enterprise System?
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6. What is a System?
Outputs Inputs
information information
control function
energy energy
internal organisation,
possibly including
material sub-systems material
Physical, chemical, biological, social systems - real-time dynamic behaviour
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7. An Enterprise is a System
for delivering economic & social outputs
Outputs Inputs
products, information,
services finance
Control function
information labour,
energy
internal organisation,
possibly including
employment, sub-systems materials,
financial services
returns
Related pairs of inputs & outputs are often referred to as transactions
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8. Interesting
What's an Enterprise?

Large numbers of customers

Large numbers of transactions

Large financial throughputs

Complex behaviour/operations

Sustainable operation
➔
High scale for an extended period
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9. An Enterprise System
is the automated part of an enterprise
= a real-time model of the enterprise
Outputs Inputs
products, information,
services finance
Control function
information labour,
energy
hardware/software
employment, Enterprise System materials,
financial services
returns
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10. Where would you find an ES?
 Probably not here: (primary industry, 5% of economy)
 Agriculture, fisheries, forestry, water extraction
 Mining, oil & gas extraction
 Possibly here: (secondary industry, 40% of economy)
 Construction, utilities
 Transport, distribution, communications
 Manufacturing (discrete & continuous)
 Probably here: (tertiary industry, 55% of the economy)
 Financial & business services, media, retail
 Education, healthcare, tourism, entertainment
 Public admin
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11. Brief History of Enterprise
Systems
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12. Pioneers of Enterprise Systems

1952 – Joe Lyons & Co. Leo system
- batch accounting & payroll operations

1965 – American Airlines Sabre system
- online flight reservations & check in

1993 – Amazon.com
- direct customer service, just-in-time delivery

2001 – Google.com
- customised search using large amounts of data

2008 – Apple iPhone
- mobile applications
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13. Getting Closer to the Customer
6
5
4
System Type
3 1=Batch, 2=Online,
3=Network, 4=Web,
5=Social/mobile
2
1
0
1960s 1970s 1980s 1990s 2000s
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14. What were the Key Innovations?

1950s – main storage (delay lines, ferrite cores),
secondary storage (magnetic tape), batch job
scheduler

1960s – operating system, direct access storage,
database management, time sharing terminals

1970s – TP monitor, relational database

1980s – personal computer, networking

1990s – World Wide Web

2000s – search engine, social networking, mobile
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15. Programming Paradigms
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16. Time Sharing/Conversational
●
At logon time, operating system allocates:
●
Memory address space for application
●
Operating system process
●
Files, communications channels, etc.
●
These remain dedicated to user until logoff
●
Paradigm is widely used, but:
●
No sharing of resources
●
Not scalable beyond few hundred users
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17. TP Monitor PseudoConversations
●
TP Monitor acquires & retains shared resources
●
Applications, memory, processes, threads, files,
databases, communication channels, etc.
●
On receipt of user transaction request, provides
concurrent access to resources for application
●
Frees resources as soon as output message sent
●
Highly scalable to 10s of thousands of users
●
Requires stateless application programming
●
Conversation state held in “scratchpad” files
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18. Representational State Transfer
●
Underlying paradigm for Web hypertext transfers
●
Commonly abbreviated as REST
●
Web servers manage network & provide
concurrent access
●
Defines stateless clients for rendering data
●
Highly scalable to 10s of thousands of users
●
Does not define how to build update applications
on the Web
●
Disallows “cookies” - no scratchpad
●
Does not define server application model
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19. Google Applications
●
Underlying paradigm for Google search and
other applications
●
Uses GAE (Google App engine)
●
Requires stateless clients
●
Concurrent access to “scratchpad” storage via
GFS/BigTable
●
Highly scalable to 10s of thousands of users
●
Especially suitable for applications using read-
only data, e.g. search data, maps, etc.
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20. Why do these Paradigms Work?

All these paradigms embody the many-to-one
relationship between customers and the
enterprise

The TP, Restful, & Google paradigms provide
scalable concurrency & enable the enterprise to
exploit economies of scale

None of them is a complete description of
what modern enterprise systems need
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21. What Paradigm is Needed?
●
Stateless applications provide the highest
scalability and work well for read only requests
●
But commercial applications, e.g. web shopping,
need conversation state & concurrent update
●
Use HTTP because it supports any-client-to-any
server, unlike object-based protocols
●
Hold state on client or replicated server file system
●
Collect updates that form part of a transaction
●
Permanently save data at end of conversation
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22. Current Enterprise System
Challenges
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23. Enterprise Business Challenges
Enterprise business people care about two
primary objectives:
●
Reducing costs:
●
automating/eliminating internal processes
●
reducing operating costs for enterprise systems
●
reducing ownership costs for enterprise systems
●
Increasing revenue:
●
Winning new customers
●
Retaining existing customers
●
Getting more business from existing customers
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24. Enterprise System Challenges
1) Multi-channel applications
- acting consistently to the customer
2) Multi-business service
- providing multiple offers consistently
3) Effective customer knowledge
- acting more intelligently to the customer
4) Effective market knowledge
- foreseeing what customers will want next
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25. Multi-Channel Applications

Many enterprise systems are designed to
support particular sales channels, eg.:

Store checkout systems

Kiosk/ticketing systems

Call centre systems

Web based systems

Mobile systems

Business offer may depend on channel, but

Applications should treat the customer
consistently, whichever channel he/she uses
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26. 2
Typical M Architecture
sync msgs
async msgs
sync msgs
Line of business
Presentation / Integration server Application and
Channel server(s) - Tight coupling Data server(s)
- Static/ Dynamic web - Loose coupling
pages - Stand-in processing
- Portals - Flow ctrl/compensation
- Channel specific
M2 = Multi-Channel, Multi-Business
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27. Customer Knowledge
●
Many web systems allow customer to explore
options before & after a transaction:
●
high “browse to buy” ratio in web shopping
●
evaluations of product, service, etc.
●
If we identify the customer, we can study:
●
search patterns
●
history of actual transactions
●
customer likes & dislikes
●
May enable better offers to the customer
●
need more data & may require real time parallel
computation
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28. Market Knowledge
●
Many enterprise systems collect data about a
mass of customer transactions:
●
Collected/refined in data warehouse
●
Linked with tools for analytics / Bus Intelligence
●
Used to produce periodic reports & analyses
●
This process may be ineffective:
●
Too slow/costly for business needs
●
Only structured data – much unstructured data
●
New methods use very large data sets
●
Best practice uses highly parallel processing
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29. “Highly Parallel” Processing
●
Google is the best known exponent
●
Many processes crawling the Web in parallel
●
Combine results using MapReduce technique
●
Store results in Google File System
●
Effectively substitutes concurrency for parallelism
●
Also widely used in scientific applications:
●
e.g. SETI @Home used subscriber PCs
●
IBM “Blue Gene” protein modelling project
●
4K processors generated 10 μsec simulation
●
Uses hardware cluster plus GPRS
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30. Summary
●
When building an Enterprise System, we are
building a model of (part of) the enterprise:
●
Model must be real time and scalable
●
Customer can use anywhere, anytime, any device
●
Access any business offering consistently
●
Know and respond intelligently to each customer
●
Meta-Enterprise System should analyse system &
aggregate behaviour of customers
●
Detect trends and respond to them
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