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Explore the growth and perception of e-commerce from Gartner's predictions to real outcomes in 2004. Dive into examples of artificial intelligence, databases, neural networks, and the challenges of combinatorial growth. Uncover the failures, perceived growth, and the importance of accurate information in decision-making across various industries like engineering, manufacturing, finance, and marketing. Learn about the evolution of data to information through mediation layers and the role of mediators in transforming resources for applications. Discover the impact of human-computer interaction, user interfaces, and application-specific codes in shaping internet trends and global connectivity.
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Discussion Material for Globo TV Interview "'Raquel Novaes'" <raquel.novaes@tvglobo.com.br> . Internet Growth History and Prospects Gio Wiederhold, Kincho Law, Stefan Decker 12 July 2002
Growth and Perception E-commerce • Gartner: 2000 prediction for 2004: 7.3 T$ • Revision:2001 prediction for 2004: 5.9 T$ drastic loss? 50 companies, each after 20% of the market Examples Artificial Intelligence Databases Neural networks E-commerce Extrapolated growth Disap- pointment Combi- natorial growth Realistic growth Failures Perceived growth Perception level Perceived initial growth Invisible growth 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
Research & Tool Inno - building Consumer vation Product Pull building & General marketing Technology Push Information Business Technology needs Government responsibilities Interactions
90 80 70 60 50 40 30 20 10 0 Centroid, in 1999 ~1% of total market % Ü 98 99 00 01 02 03 04 0.3 1 3 9 27 81 ** Year / % Ü T r e n d s 1998 : 1999 • Users of the Internet 40% Ü 52% of U.S. population • Growth of Net Sites (now 2.2M public sites with 288M pages) • Expected growth in E-commerce by Internet users[BW, 6 Sep.1999] segment 1998 1999 • books 7.2% Ü 16.0% • music & video 6.3% Ü 16.4% • toys 3.1% Ü 10.3% • travel 2.6% Ü 4.0% • tickets 1.4% Ü 4.2% • Overall 8.0% Ü 33.0% = $9.5Billion An unstainable trend cannot be sustained [Herbert Stein] Ü new services E-penetration Toys
Missed Valid Information (False Negatives ) causes lost opportunities cheapest shovel, . . . suboptimal decision-making by x valid suppliers Cost- benefit 1 Excess Information (False Positives ) has to be investigated attractive-looking supplier - makes toys 2 Space of results, ordered Cost of Error types differs Having many cases of excess information costs more than some missing information
A Major Cause of Errors Searches extend over many domains • Domains have their own terminologies • Need autonomy to deal with knowledge growth • The usage of terms in a domain is efficient • Appropriate granularity • Mechanic working on a truck vs. logistics manager • Shorthand notations • PSU vs. PSU • Functions differ in scope • Payroll versus Personnel • getting paid vs. available (includes contract staff)
Industry Needs Information • Engineering and Manufacturing • own capability ésuppliers’ capabilities • demand églobal demand • Distribution and Transportation • costs for alternate means of shipping • Finance • project demand 3 project cost of funds • Marketing and Service • taste and style édemographics more from remote sources
Information overload Data starvation • More databases • public & corporate • Faster communication • digital • packeting: TCP-IP, ATM • World-wide connectivity • internet • world-wide web • Disintermediation • ubiquitous publishing
Transforming Data to Information Application Layer Mediation Layer Foundation Layer users at workstations value-added services data and simulation resources
Definition* A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications. It should be small and simple, so that it can be maintained by one expert or, at most, a small and coherent group of experts. * Wiederhold: IEEE Computer March 1992
Human-computer Interaction User interface Application- specific code Service interface Domain- specific code MEDIATION Resource access interface Source- specific code Real-world interface Functional Layer
applications A3 A4 A2 A5 A1 A6 integrators a. I2 I1 mediators M1 b. M2 network c. d. e. wrappers D1 W3 D6 W2 D5 D4 W1 D2 D3 datasources Evolution of mediation
Central Solutions do not Scale What works with 7 modules and one person in charge fails when there are 100 modules and a committee is needed Any changes in resources affect the central module
resource reuse Growing Systems: n modules Federated: to deal with many servers and clients changes are difficult affect many clients
Systems with Mediators Applications . . . . Gio Wiederhold. 1995 Mediators . . . . . . Data Resources . . .
Growth through Reuse New Application Gio Wiederhold. 1995 Prior & Revised Mediators Extended Data Resources
Application Interface Changes of user needs Domain changes Owner/ Creator Maintainer Lessor - Seller Advertisor Resource Interfaces A mediator Is not just static software Software & People Models, programs, rules, caches, . . . Resource changes
13 12 ? 11 100% 10 years 90 9 80 8 70 7 60 6 50 5 40 4 30 3 lifetime 20 2 10 1 0 automobile software hardware Maintenance is good for you relative annual maintenance cost depreciation = 1 / lifetime
Domain-specific Mediation • User application • Workstations • Mediator • Expert-owned nodes • Data sources • Remote primary and byproduct services
Integration at two levels Application • Informal, pragmatic • User-control Mediation • Formal service • Domain-Expert control
Application C Application B Application I M2 M N 1 N 2 DB P DB Q Allocation Flexibility User Interfaces copy Provider of medi- ator N Provider of Mediator M Copy- if high intensity of interaction with 1. Application (M2) 2. Resources (N1,2) 3. Processing (M1) HPC N M1 Mediators are only code DBS R Databases
Application Programs object model Object Layer object model generator cus- tomer object interface controller object instantiator GUI object decomposer designer structural schema Relational Layer Data Base(s) DBMS data schema Penguin Object-relational-based Architecture
hospital disease insurance personnel patient pharmacy country doctor visit nurse drug Structural Model in Healthcare.
object schema structural schema data schema Generation Expert System Object Layer object generator object interface controller user GUI object instantiator object decomposer designer Relational Layer Data Base(s) DBMS
Object Creation hospital 1. Start at disease insurance Pivot personnel patient pharmacy country doctor visit nurse 2. Omit weak links 3. Ask object designer drug
Pivot visit nurse insurance patient country drug disease country pharmacy personnel doctor country Object Structuring
object schema structural schema data schema Instantiating Expert System Object Layer object generator object interface controller user GUI object instantiator object decomposer designer Relational Layer Data Base(s) DBMS
object schema structural schema data schema Decomposing Expert System Object Layer object generator object interface controller user GUI object instantiator object decomposer designer Relational Layer Data Base(s) DBMS
Network Policy [Francois Barr] ideal `USA’ Flexibility virtual integration composition virtual differentiation specialization ^ | Network Diversity `France’ Stability Network Coherence -->
Value of Mediated Architectures • System maintenance • linear cost of introducing a new or improved application through the entire layersing • better information • More information per byte
NEEDS Access to relevant Information Rapid response to changing situations Remain current with global conditions External services can be shared effectively FEATURES Linkages to networks and resources Incremental update of information systems avoids legacy problem Equal access to local and remote sources Value-added services live in the network Industrial Needs Served