Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the s...Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the state of open data in countries and organisations,several open data assessment frameworks were developed.Despite high scores in these assessment frameworks,the actual(re)use of open government data(OGD)fails to live up to its expectations.Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem.We have developed a framework,which assesses open data supply,open data governance,and open data user characteristics holistically.This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention.Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data,such as healthcare data.Therefore,open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.展开更多
Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards the...Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.展开更多
文摘Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the state of open data in countries and organisations,several open data assessment frameworks were developed.Despite high scores in these assessment frameworks,the actual(re)use of open government data(OGD)fails to live up to its expectations.Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem.We have developed a framework,which assesses open data supply,open data governance,and open data user characteristics holistically.This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention.Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data,such as healthcare data.Therefore,open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.
文摘Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.
