Contributions in Merlin

Real-world data management applications generally manage temporal data, i.e., they manage multiple states of time-varying data. Many contributions have been made by the research community for how to better model, store, and query temporal data. In particular, several dozen temporal data models and query languages have been proposed. Motivated in part by the emergence of non-traditional data management applications and the increasing proliferation of temporal data, this paper puts focus on the aggregation of temporal data. In particular, it provides a general framework of temporal aggregation concepts, and it discusses the abilities of five approaches to the design of temporal query languages with respect to temporal aggregation. Rather than providing focused, polished results, the paper's aim is to explore the inherent support for temporal aggregation in an informal manner that may serve as a foundation for further exploration.

null

This paper examines the causes and effects of mobile number portability (MNP) and provides a survey of its implementation in Europe. It first examines the competitive effects and costs of introducing MNP. Next, it discusses how to charge for MNP. It argues that a price cap regime starting from the average cost of porting is likely to provide appropriate incentives. Finally, it reviews recent experience with implementing MNP in Europe. Differences in the speed of porting and porting charges appear to explain part of the differences in the use of MNP across countries.

We describe ongoing work on the mapping between At- tempto Controlled English (ACE) and OWL DL. ACE is a well-studied controlled language, with a parser that converts ACE texts into Discourse Representation Structure (DRS). We describe a relatively direct mapping of a subset of the DRS language to OWL DL. This mapping renders ACE an interesting companion to existing OWL front-ends. and anaphoric references to noun phrases through proper names, definite noun phrases, pronouns, and variables. The intention behind ACE is to minimize the number of syn- tax and interpretation rules needed to predict the resulting DRS, or for the end-user, the reasoning results. The small number of ACE function words have a clear and predictable

Mobile technologies offer the opportunity to collaborate spontaneously any time and any place. While researchers have begun to understand the skills and motivational consequences of distributed office meetings, we are only beginning to understand them for ad-hoc collaboration. This paper reports on an analysis of two exploratory experiments dating from 2004 and 2005. Ad-hoc collaboration requires specific skills for process facilitation, communication, planning, media usage, multi-tasking, as well as specific social skills. Those skills need to be different in their characteristic than those skills necessary for traditional face-to-face and distributed meetings. A fast action and reaction cycle leads to raised excitement and motivation despite the difficulties the group has in organizing their work.

In two simulation models the benefit of schooling underpredatory pressure is investigated. It appears that if a predator cannot become confused by prey, grouping is seldom beneficial. If prey, however, can confuse a predator, schooling appears to protect prey under a whole range of parameters. Using an evolutionary approach we found that, in the case of a confusable predator, cohesive groups with a consistent forward movement evolve most frequently, but that milling stationary groups also prove to be effective. We suggest that the predator protection in moving and stationary groups rely on different mechanisms, among other things, on a kind of altruistic behaviour.

How do consumers choose from a menu of contracts? We analyze a novel dataset from three U.S. health clubs with information on both the contractual choice and the day-to-day attendance decisions of 7,752 members over three years. The observed consumer behavior is difficult to reconcile with standard preferences and beliefs. First, members who choose a contract with a flat monthly fee of over \$70 attend on average 4.3 times per month. They pay a price per expected visit of more than \$17, even though they could pay \$10 per visit using a 10-visit pass. On average, these users forgo savings of \$600 during their membership. Second, consumers who choose a monthly contract are 17 percent more likely to stay enrolled beyond one year than users committing for a year. This is surprising because monthly members pay higher fees for the option to cancel each month. We also document cancellation delays and attendance expectations, among other findings. Leading explanations for our findings are overconfidence about future self-control or about future efficiency. Overconfident agents overestimate attendance as well as the cancellation probability of automatically renewed contracts. Our results suggest that making inferences from observed contract choice under the rational expectation hypothesis can lead to biases in the estimation of consumer preferences.

Probleme der Finanzierung sind für die Marktdiffusion innovativer Energietechnologien ebenso wichtig wie ihre technischen Eigenschaften oder der erwartete Nutzen aus der Technologieanwendung. Welche Rolle spielt dabei die Eigentümerstruktur des Innovators? Welchen Einfluss hat die Finanzierungsform des Investitionsvorhabens—und damit das finanzielle Risiko—auf die Attraktivität einer Energieinvestition?

Background: Current established models predicting mortality in acute coronary syndrome (ACS) patients are derived from randomised controlled trials performed in the 1990�s, and are thus based on and predictive for selected populations. These scores perform inadequately in patients treated according to current guidelines. The aim of this study was to develop a model with improved predictive performance applicable to all kinds of ACS, based on outcomes in real world patients from the new millennium. Methods: The AMIS-Plus registry prospectively collects data from ACS patients admitted to 56 Swiss hospitals. Patients included in this registry between October 2001 and May 2005 (n = 7520) were the basis for model development. Modern data mining computational methods using new classification learning algorithms were tested to optimise mortality risk prediction using well-defined and non-ambiguous variables available at first patient contact. Predictive performance was quantified as �area under the curve� (AUC, range 0 � 1) in a receiver operator characteristic, and was compared to the benchmark risk score from the TIMI study group. Results were verified using 10-fold cross-validation. Results: Overall, hospital mortality was 7.5%. The final prediction model was based on the �Averaged One-Dependence Estimators� algorithm and included the following 7 input variables: 1) Age, 2) Killip class, 3) systolic blood pressure, 4) heart rate, 5) pre-hospital mechanical resuscitation, 6) history of heart failure, 7) history of cerebrovascular disease. The output of the model was an estimate of in-hospital mortality risk for each patient. The AUC for the entire cohort was 0.875, compared to 0.803 for the TIMI risk score. The AMIS model performed equally well for patients with or without ST-Elevation (AUC 0.879 and 0.868, respectively). Subgroup analysis according to the initial revascularisation modality indicated that the AMIS model performed best in patients undergoing PCI (AUC 0.884 vs. 0.783 for TIMI) and worst for patients receiving no revascularisation therapy (AUC 0.788 vs. 0.673 for TIMI). The model delivered an accurate and reproducible prediction over the complete range of risks and for all kinds of ACS. Conclusions: The AMIS model performs about 10% better than established risk prediction models for hospital mortality in patients with all kinds of ACS in the modern era. Modern data mining algorithms proved useful to optimise the model development.

null