The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user's perspective, languages, and notations, which easily decrease recommendation's efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.

In Vietnam, Artemisia annua (Asteraceae) is a popular medicinal plant, indicated to relieve fever and to stop malarial attacks (1). The plant's active principle, artemisinin, is currently being developed to a registrated antimalarial drug, requiring plants with a high artemisinin content (2). We followed the development of the artemisinin content (HPLC) in A. annua plants and of the biosynthetically related sesquiterpenes arteannuic acid, arteannuin B, and artemisitene (GC), during a vegetation period in Vietnam. In addition, the essential oil content (hydrodistillation) and composition (GC, GC-MS) were studied.Seeds of A. annua were sown near Hanoi, mid-January 1992. Samples of leaves, buds, flowers, or ceased flowers and seeds were taken at different stages: vegetative (5, 6, and 8 months old), at mass formation of buds (9 months), at full blooming (10 months), and at cease of blooming (10'/, months). They were air-dried and analysed (3, 4).The highest artemisinin content in the leaves (0.86% dry wt) was found in 5-months-old plants. At this stage they had almost reached their full size and possessed the highest leaf yield (21.1 g dry wt per plant). Then, the artemisinin content gradualiy dropped, through 0.42% when bnds had formed, to 0.16% postflowering. At the age of 5 months also the highest arteannuinic acid and arteannuin B contents, 0.16 and 0.08 % dry wt, respectively, were found. Artemisitene was present at all stages of development, ranging from 0.002 to 0.09%drywt.With 1.9% v/w, the essential oil content was maximal just before flowering and was composed of 55% monoterpenes and 45% sesquiterpenes. At all other stages (0.4 -1.0% v/w oil) this ratio was 30 %170 %. The main components of the oil were camphor and germacrene-D, as found previously (5). Just before flowering, especially the monoterpenes myrcene and 1,8-cineole were abundant, with 8.5 and 7.3%, respectively. At the end of the vegetation period, the trans-flfarnesene content was significantly increased to ca. 13 % of the oil.We conclude that the highest artemisinin content as well as the highest possible yield in Vietnamese plants are foi.md at an early vegetative stage (5 months old), and not just prior to flowering, as found in all other studies performed on this subject so far. Acknowledgements

No abstract

No abstract

International audienceDeveloping process variants has been proven as a principle task to flexibly adapt a business process model to different markets. Con-temporary research on variant development has focused on conceptual process models. However, process models do not always exist, even when process logs are available in information systems. Moreover, process logs are often more detailed than process models and reflect more closely to the behavior of the process. In this paper, we propose an activity rec-ommendation approach that takes into account process logs for assisting the development of executable process variants. To this end, we define a notion of neighborhood context for each activity based on logs, which captures order constraints between activities with their occurrence fre-quency. The similarity of the neighborhood context between activities provides us then with a basis to recommend activities during the process of creating a new process model. The approach has been implemented as a plug-in for ProM. Furthermore, we conducted experiments on a large collection of process logs. The results indicate that our approach is feasible and applicable in real use cases