Decrease of Highly Educated Technology Workforce
Description:
Following discussion as part of the European Lisboa conference (2000), the government of The Netherlands has set itself a target to become one of the most competitive and dynamic knowledge economies of the world. In order to realize this goal, the government wants to increase R&D spending to 3% of GDP vs. current spending of 1.8% only (2004). One of the main drivers of the succesful realisation of this goal comprises the availability of sufficient numbers of highly educated technological professionals for both public and private research institutions.
As it currently stands, the number of technological students in The Netherlands has been declining for many years in a row. In addition it appears, that after graduation, a large part of the technological professionals prefer to work in general business roles rather than scientific or engineering roles. Consequently, the number of technological affluent employees is expected to continue to decline going forward.
Enablers:
Recent advances in database, such as data mining, data warehouse.
Technogical advances in parallel programming
Web services
.NET technologies
Software for interactive control of programs and instruments
Scientific applications in areas such energy physics, bioinformatics, computational astronomy, computational biology, material sciences, archeology, and oceanography.
Inhibitors:
Limitted large computational systems, data storage and specialized experimental facilities. Scheduling difficulty in distributed environment: i.e resource utilization, response time, global and local allocation policies.
Paradigms:
Data-intensive computations arise in many domains of scientific and engineering research. Itself is not a driving force that would change people's view on the world, however, it does driven the development of Grid technology, because of its demanding requirement for large exchange and storage of datasets, and response time, which forces a concept of building common platform between geographically distributed processors. At the same time, with the advances in the development and maturity of data-intensive computation itself, many formidable problems in areas such as physics, bioinformatics, computational astronomy, computational biology, material sciences, archeology, and oceanography may in the future be sloved, which in turn would bring new research discoveries and reasonably new perspectives of the world into existance.
Experts:
PNNL [1] (http://www.pnl.gov/news/2004/04-64.htm) ORNL [2] (http://www.ornl.gov/)
Timing:
The development of data-intensive computation is more or less involved with the development of each of its application areas. It's hard to find it as a separate discipline and get clear milestones.