1. Ksenija Jovanović

2. Cilj vežbe
Cilj studije slučaja je određivanje osnovnih elemenata poslovnog modela na primeru svetske mreže Google

3. Poslovni model
Poslovni model opisuje kako jedna organizacija kreira, isporučuje ekonomsku, socijalnu i druge vrednosti svojim korisnicima

4. Poslovni model Poslovni model se može analizirati na više načina
Jedan od načina je analiza lanca vrednosti po metodlogiji M. Portera
Za razumevanje suštine poslovnog modela analiziraju se elementi glavnog procesa

5. Lanac vrednosti

6. Inovacije Invencija je izum, ideja, poboljšanje
Inovacija (uvođenje nečeg novog) predstavlja proces primene invencije u praksu
Osnovne vrste inovacija (Izvor OEBS):
Inovacije proizvoda
Inovacije procesa
Marketing inovacije
Organizaciona inovacija

7. Zadaci Pažljivo pročitajte tekst nekoliko puta
Hronološki složite najznačajnije činjenice
Koje su lične karakteristike osnivača Google?
Koji su ključni elementi poslovnog modela?
Navedite primenjene inovacije?
Kojoj grupi inovacija pripadaju?

8. David Leedal1, Keith Beven1,2, Jeff Neal3, Paul Bates3, Neil Hunter4
BHS2010: A case study of tools for manipulating and visualizing large flood risk management data sources
David Leedal1, Keith Beven1,2, Jeff Neal3, Paul Bates3, Neil Hunter4
1Lancaster Environment Centre, Lancaster University.
2Geocentrum, Uppsala University, Uppsala, Sweden.
3Bristol University.
4 JBA consulting.

9. Introduction
Three objectives for a useful flood risk map visualisation tool:
Aggregate information in a form that can be assimilated and queried by the end user
Provide sensory stimulus and user interaction to aid understanding of unfamiliar concepts
Produce a modular unit that can function as the target for disparate model results within broader web/GIS environments –encourage code/API standardisation

10. Information aggregation
Spatial modelling studies produce large amounts of data. A 2D inundation study becomes 3D when considering velocity which becomes 4D when considering uncertainty. We need to aggregate the information.
Client and server approach: database (PostgreSQL, mySQL) + scripting (PHP, pearl)
Central data store, secure, probably the right way to go
Slower, more complicated, needs administering
Local storage approach: save data on local machine (matlab file, CSV) + associated data processing program (matlab, R)
Fast and independent
Only suitable for small scale applications

11. User interaction
Once the data is stored, there must be some method for the user to interact with it
Static interaction (GIS)
An experienced user can retrieve, act on and plot probabilistic inundation data and map backdrops producing high quality analysis and visual results
Steep learning curve
Dynamic interaction (matlab, Google maps+DHTML)
Familiar ‘widgets’ such as a slider can be used to dynamically query the underlying data base –intuitive user experience
Limited to the functionality provided by the application designer

12. Modularity and standardisation
In general, each risk map study produces data in an ad-hoc format depending on the preferences of the programmer. Instead, it would be nice to have a standard format for output to work towards. If widely adopted, a visualisation tool could supply:
A set of standard requirements for source data eg., attribute table headings
Documentation for the georeferencing format and extent of the study site eg., a bounding box in a standard coordinate reference frame In return, the visualisation tool should comply with external standards such as those of the Google maps API if used

13. Examples (matlab)
Matlab graphic user interface interactive visualisation tool using results produced by Neil Hunter (JBA) for Mexborough (S. Yorkshire UK)
Local data storage
General interface: third party risk map study provides inundation probability data matrix and background image – program does the rest
Interface includes:
Slider for probability selection
Point and click marker for single location query
Popup menu for return period/depth scenario
Depth probability figure dynamically linked to point marker

14. Examples (matlab)

15. Examples (matlab) Zoomed view shows: •popup menu •point marker
•interactive graph

16. Examples (matlab) Show all probabilities option: •view selector
•point marker
•colour-coded probabilities
•interactive g

17. Examples (Google maps)

18. Examples (Google maps)

19. Examples (Google maps)

20. Examples (Google maps)

21. Examples (Google maps)
Google maps data viewer includes:
•Slider for interactive probability selection
•Text box for word-based descriptions
•Access to standard Google maps tools (pan zoom etc)
•Tutorial tips and definitions

22. Examples (QGis) QGisdata viewer includes: •Colour coding
•Topology (linking between layers)
•Advanced georeferencing
•Map switching

23. Examples (QGis)

24. Acknowledgement www.floodrisk.org.uk EPSRC Grant: EP/FP202511/1
The research reported in this presentation was conducted as part of the Flood Risk Management Research Consortium with support from the:
•Engineering and Physical Sciences Research Council
•Department of Environment, Food and Rural Affairs/Environment Agency Joint Research Programme
•United Kingdom Water Industry Research
•Office of Public Works Dublin
•Northern Ireland Rivers Agency
Data were provided by the EA and the Ordnance Survey.
EPSRC Grant: EP/FP202511/1