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Business process management experiences a large uptake by the industry, and process models play an important role in the analysis and improvement of processes. While an increasing number of staff becomes involved in actual modeling practice, it is crucial to assure model quality and homogeneity along with providing suitable aids for creating models. In this paper we consider the problem of offering recommendations to the user during the act of modeling. Our key contribution is a concept for defining and identifying so-called action patterns - chunks of actions often appearing together in business processes. In particular, we specify action patterns and demonstrate how they can be identified from existing process model repositories using association rule mining techniques. Action patterns can then be used to suggest additional actions for a process model. Our approach is challenged by applying it to the collection of process models from the SAP Reference Model.
We present the results of Monte Carlo mass-loss predictions for massive stars covering a wide range of stellar parameters. We critically test our predictions against a range of observed massloss rates – in light of the recent discussions on wind clumping. We also present a model to compute the clumping-induced polarimetric variability of hot stars and we compare this with observations of Luminous Blue Variables, for which polarimetric variability is larger than for O and Wolf-Rayet stars. Luminous Blue Variables comprise an ideal testbed for studies of wind clumping and wind geometry, as well as for wind strength calculations, and we propose they may be direct supernova progenitors.
The optical density of human macular pigment was measured for 50 observers ranging in age from 10 to 90 years. The psychophysical method required adjusting the radiance of a 1°, monochromatic light (400–550 nm) to minimize flicker (15 Hz) when presented in counterphase with a 460 nm standard. This test stimulus was presented superimposed on a broad-band, short-wave background. Macular pigment density was determined by comparing sensitivity under these conditions for the fovea, where macular pigment is maximal, and 5° temporally. This difference spectrum, measured for 12 observers, matched Wyszecki and Stiles's standard density spectrum for macular pigment. To study variation in macular pigment density for a larger group of observers, measurements were made at only selected spectral points (460, 500 and 550 nm). The mean optical density at 460 nm for the complete sample of 50 subjects was 0.39. Substantial individual differences in density were found (ca. 0.10–0.80), but this variation was not systematically related to age.
As mid-19th-century American Jews introduced radical changes to their religious observance and began to define Judaism in new ways, to what extent did they engage with European Jewish ideas? Historians often approach religious change among Jews from German lands during this period as if Jewish immigrants had come to America with one set of ideas that then evolved solely in conversation with their American contexts. Historians have similarly cast the kinds of Judaism Americans created as both unique to America and uniquely American. These characterizations are accurate to an extent. But to what extent did Jewish innovations in the United States take place in conversation with European Jewish developments? Looking to the 19th-century American Jewish press, this paper seeks to understand how American Jews engaged European Judaism in formulating their own ideas, understanding themselves, and understanding their place in world Judaism.
We launched an original large-scale experiment concerning informatics learning in French high schools. We are using the France-IOI platform to federate resources and share observation for research. The first step is the implementation of an adaptive hypermedia based on very fine grain epistemic modules for Python programming learning. We define the necessary traces to be built in order to study the trajectories of navigation the pupils will draw across this hypermedia. It may be browsed by pupils either as a course support, or an extra help to solve the list of exercises (mainly for algorithmics discovery). By leaving the locus of control to the learner, we want to observe the different trajectories they finally draw through our system. These trajectories may be abstracted and interpreted as strategies and then compared for their relative efficiency. Our hypothesis is that learners have different profiles and may use the appropriate strategy accordingly. This paper presents the research questions, the method and the expected results.
We describe a framework to support the implementation of web-based systems to manipulate data stored in relational databases. Since the conceptual model of a relational database is often specified as an entity-relationship (ER) model, we propose to use the ER model to generate a complete implementation in the declarative programming language Curry. This implementation contains operations to create and manipulate entities of the data model, supports authentication, authorization, session handling, and the composition of individual operations to user processes. Furthermore and most important, the implementation ensures the consistency of the database w.r.t. the data dependencies specified in the ER model, i.e., updates initiated by the user cannot lead to an inconsistent state of the database. In order to generate a high-level declarative implementation that can be easily adapted to individual customer requirements, the framework exploits previous works on declarative database programming and web user interface construction in Curry.
We prove in this paper an existence result for infinite-dimensional stationary interactive Brownian diffusions. The interaction is supposed to be small in the norm ||.||∞ but otherwise is very general, being possibly non-regular and non-Markovian. Our method consists in using the characterization of such diffusions as space-time Gibbs fields so that we construct them by space-time cluster expansions in the small coupling parameter.
The present thesis introduces an iterative expert-based Bayesian approach for assessing greenhouse gas (GHG) emissions from the 2030 German new vehicle fleet and quantifying the impacts of their main drivers. A first set of expert interviews has been carried out in order to identify technologies which may help to lower car GHG emissions and to quantify their emission reduction potentials. Moreover, experts were asked for their probability assessments that the different technologies will be widely adopted, as well as for important prerequisites that could foster or hamper their adoption. Drawing on the results of these expert interviews, a Bayesian Belief Network has been built which explicitly models three vehicle types: Internal Combustion Engine Vehicles (which include mild and full Hybrid Electric Vehicles), Plug-In Hybrid Electric Vehicles, and Battery Electric Vehicles. The conditional dependencies of twelve central variables within the BBN - battery energy, fuel and electricity consumption, relative costs, and sales shares of the vehicle types - have been quantified by experts from German car manufacturers in a second series of interviews. For each of the seven second-round interviews, an expert's individually specified BBN results. The BBN have been run for different hypothetical 2030 scenarios which differ, e.g., in regard to battery development, regulation, and fuel and electricity GHG intensities. The present thesis delivers results both in regard to the subject of the investigation and in regard to its method. On the subject level, it has been found that the different experts expect 2030 German new car fleet emission to be at 50 to 65% of 2008 new fleet emissions under the baseline scenario. They can be further reduced to 40 to 50% of the emissions of the 2008 fleet though a combination of a higher share of renewables in the electricity mix, a larger share of biofuels in the fuel mix, and a stricter regulation of car CO$_2$ emissions in the European Union. Technically, 2030 German new car fleet GHG emissions can be reduced to a minimum of 18 to 44% of 2008 emissions, a development which can not be triggered by any combination of measures modeled in the BBN alone but needs further commitment. Out of a wealth of existing BBN, few have been specified by individual experts through elicitation, and to my knowledge, none of them has been employed for analyzing perspectives for the future. On the level of methods, this work shows that expert-based BBN are a valuable tool for making experts' expectations for the future explicit and amenable to the analysis of different hypothetical scenarios. BBN can also be employed for quantifying the impacts of main drivers. They have been demonstrated to be a valuable tool for iterative stakeholder-based science approaches.
ANNIS
(2004)
In this paper, we discuss the design and implementation of our first version of the database "ANNIS" ("ANNotation of Information Structure"). For research based on empirical data, ANNIS provides a uniform environment for storing this data together with its linguistic annotations. A central database promotes standardized annotation, which facilitates interpretation and comparison of the data. ANNIS is used through a standard web browser and offers tier-based visualization of data and annotations, as well as search facilities that allow for cross-level and cross-sentential queries. The paper motivates the design of the system, characterizes its user interface, and provides an initial technical evaluation of ANNIS with respect to data size and query processing.