Interdisciplinary system architectures in agile modular development in the product generation development model using the example of a machine tool manufacturer

This paper considers the orientation of product development structures towards interdisciplinary system architectures using the example of a tool machine manufacturer. Due to the change from simple mechanical products to extensively designed systems, whose successful development requires the integration of all disciplines involved, it is analyzed which requirements there are for these interdisciplinary system architectures in today\u27s development environment. In addition, it is validated on the basis of the investigation environment that interdisciplinary system structures are necessary for the development on the different levels of the system view. In doing so, the investigation environment addresses the concept of extracting customer-relevant features (systems) from a physical-tailored modular system (supersystem) in order to develop and test them autonomously, as well as to transfer them to the entire product range in a standardized manner. The elaboration identifies basic requirements for the development of a knowledge base in interdisciplinary system structures and places them into the context of an agile modular kit development

Model-based probabilistic frequent itemset mining

Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain database induces an exponential number of possible worlds. To tackle this problem, we propose a novel methods to capture the itemset mining process as a probability distribution function taking two models into account: the Poisson distribution and the normal distribution. These model-based approaches extract frequent itemsets with a high degree of accuracy and support large databases. We apply our techniques to improve the performance of the algorithms for (1) finding itemsets whose frequentness probabilities are larger than some threshold and (2) mining itemsets with the {Mathematical expression} highest frequentness probabilities. Our approaches support both tuple and attribute uncertainty models, which are commonly used to represent uncertain databases. Extensive evaluation on real and synthetic datasets shows that our methods are highly accurate and four orders of magnitudes faster than previous approaches. In further theoretical and experimental studies, we give an intuition which model-based approach fits best to different types of data sets. © 2012 The Author(s).published_or_final_versio

Pest population dynamics are related to a continental overwintering gradient

Overwintering success is an important determinant of arthropod populations that must be considered as climate change continues to influence the spatiotemporal population dynamics of agricultural pests. Using a long-term monitoring database and biologically relevant overwintering zones, we modeled the annual and seasonal population dynamics of a common pest, Helicoverpa zea (Boddie), based on three overwintering suitability zones throughout North America using four decades of soil temperatures: the southern range (able to persist through winter), transitional zone (uncertain overwintering survivorship), and northern limits (unable to survive winter). Our model indicates H. zea population dynamics are hierarchically structured with continental-level effects that are partitioned into three geographic zones. Seasonal populations were initially detected in the southern range, where they experienced multiple large population peaks. All three zones experienced a final peak between late July (southern range) and mid-August to mid-September (transitional zone and northern limits). The southern range expanded by 3% since 1981 and is projected to increase by twofold by 2099 but the areas of other zones are expected to decrease in the future. These changes suggest larger populations may persist at higher latitudes in the future due to reduced low-temperature lethal events during winter. Because H. zea is a highly migratory pest, predicting when populations accumulate in one region can inform synchronous or lagged population development in other regions. We show the value of combining long-term datasets, remotely sensed data, and laboratory findings to inform forecasting of insect pests

Comments Regarding "Subclinical Hyperthyroidism in Cats: A Spontaneous Model of Subclinical Toxic Nodular Goiter in Humans?'' Response

Many spatial query problems defined on uncertain data are computationally expensive, in particular, if in addition to spatial attributes, a time component is added. Although there exists a wide range of applications dealing with uncertain spatio-temporal data, there is no solution for efficient management of such data available yet. This paper is the first work to propose general models for spatiotemporal uncertain data that have the potential to allow efficient processing on a wide range of queries. The main challenge here is to unfold this potential by developing new algorithms based on these models. In addition, we give examples of interesting spatiotemporal queries on uncertain data. Copyright 2011 ACM.link_to_subscribed_fulltex