Dualizing marked Petri nets results in tokens for transitions (t-tokens). A marked transition can strictly not be enabled, even if there are sufficient "enabling" tokens (p-tokens) on its input places. On the other hand, t-tokens can be moved by the firing of places. This permits flows of t-tokens which describe sequences of non-events. Their benefiit to simulation is the possibility to model (and observe) causes and effects of non-events, e.g. if something is broken down.

The paper deals with a specific introduction into probability propagation nets. Starting from dependency nets (which in a way can be considered the maximum information which follows from the directed graph structure of Bayesian networks), the probability propagation nets are constructed by joining a dependency net and (a slightly adapted version of) its dual net. Probability propagation nets are the Petri net version of Bayesian networks. In contrast to Bayesian networks, Petri nets are transparent and easy to operate. The high degree of transparency is due to the fact that every state in a process is visible as a marking of the Petri net. The convenient operability consists in the fact that there is no algorithm apart from the firing rule of Petri net transitions. Besides the structural importance of the Petri net duality there is a semantic matter; common sense in the form of probabilities and evidencebased likelihoods are dual to each other.

Wild boars belong to the most wide spread ungulates in the world. They are characterized by a well performed adaption to their environment mainly due to their omnivorous dietary. The wild boar population in Germany increased during the past three decades. Nowadays their high density leads to problems in agricultural areas due to damage of crops and plays a significant role as disease vector as the classical swine fever. For an effective population management population size information is of crucial importance. Different traditional methods exist to estimate population sizes as direct sightnings, faecal drop counts or hunting harvest which provide only relative estimates and population trends. Absolute population sizes could be yielded by a Capture-Mark-Recapture (CMR) approach. However, capturing of wild boars is difficult to realize and costly in terms of personnel and field effort. Furthermore the capture probabilities are heterogeneous due to the variable behaviour of individuals influenced by age, sex, and experience of the animals. Non-invasive genetic methods are a promising complement to the traditional methods for population size estimation particularly for wild boar. These methods reduce stress and capture bias and increase the number of re-captures. Faeces proved to be a suitable DNA source for wild boar genotyping, due to almost equal capture probability. However working with faeces implicates difficulties such as low DNA rnquality and quantity, genotyping errors as dropout and false alleles. The main aim of the present study was to develop a reliable, cost-efficient, reproducible and practicable method for wild boar genotyping. This method should provide a reliable dataset of genotypes obtained from the collected faeces samples. Individual identification forms the basis for an improved mark-recapture approach. As there is no sound method for absolute population counts in free living wild boar, reference values for the validation of this new approach are missing. Therefore, different routines to reduce and to assess genotyping errors were compared within this thesis. For maximum amplification rate, the storage, the extraction methods and the PCR-procedure were optimised. A step by step procedure was evaluated in order to determine the minimum required microsatellite (MS) number for reliable individual identification including a test with family groups (female and embryo tissue) to distinguish even between close relatives. A multiple-tubes approach, post-amplification checking and different correction procedures were applied to reduce genotyping errors. In order to quantify real genotyping error rates (GER) of datasets derived from sampling in the Palatinate Forest in western Germany, different methods for GER determination were compared with each other, obtaining GERs between 0% and 57.5%. As a consequence, more strict criteria for the multi-tube approach and increased repetition number of homozygous samples were used. An additional method validation was the implementation of a blind test to achieve the reliability of the genotyping and error checking procedure. Finally a strict and practicable proposal for the lab procedure was developed, by beginning with faecal sample collection and ending with a reliable dataset with genotypes of each sample. The results of the presented method were derived from two sampling periods in a 4000 ha area in the Palatinate Forest in Rhineland-Palatinate in December 2006 and 2007. Both provided high confidence intervals (CI) applying inaccurate estimates (eg. for 2006 population size amounted to 215 with CI 95% of 156-314 and for 2007 population size amounted to 415 with CI 95% of 318-561) due to low sampling sizes (for 2006 n = 141 and for 2007 n = 326), successfully analysed samples (for 2006 n = 89 and for 2007 n = 156) and recapture numbers (for 2006 n = 12 and for 2007 n = 24). Furthermore, the population estimates even for the lowest values were considerably higher than previously assumed by hunting statistics, which implicates an ineffective hunting regime in the study area. For the future prospect, to obtain more precise population size estimations the increase of sampling sizes is inevitable, because absolute and reliable estimates are highly desirable for wildlife management and the control of diseases transmission. Nevertheless, the method for individual genotyping of wild boars evaluated in this thesis could be successfully established resulting in reliable datasets for population estimation modelling with sufficiently low GER.

Regarding the rapidly growing amount of data produced every year and the increasing acceptance of Enterprise 2.0 enterprises have to care about the management of their data more and more. Content created and stored in an uncoordinated manner can lead to data-silos (Williams & Hardy 2011, p.57), which result in long search times, inaccessible data and in consequence monetary losses. The "expanding digital universe" forces enterprises to develop new archiving solutions and records management policies (Gantz et al. 2007, p.13). Enterprise Content Management (ECM) is the research field that deals with these challenges. It is placed in the scientific context of Enterprise Information Management. This thesis aims to find out to what extent current Enterprise Content Management Systems (ECMS) support these new requirements, especially concerning the archiving of Enterprise 2.0 data. For this purpose, three scenarios were created to evaluate two different kinds of ECMS (one Open Source - and one proprietary system) chosen on the basis of a short marketrnresearch. The application of the scenarios reveals that the system vendors actually face the industry- concerns: both tools provide functionality for the archiving of data arising from online collaboration and also business records management capabilities but the integration of those topics is not, or is only inconsistently solved. At this point new questions - such as, "Which datarngenerated in an Enterprise 2.0 is worth being a record?" - arise and should be examined in future research.

Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources´ properties. In this paper, we analyze the correlation between the two sources of schema information. To this end, we have extracted schema information regarding the types and properties defined in two datasets of different size. One dataset is a LOD crawl from TimBL- FOAF profile (11 Mio. triple) and the second is an extract from the Billion Triples Challenge 2011 dataset (500 Mio. triple). We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in this two manifestations of schema information. Our analysis provides insights into the information encoded in the different schema characteristics. It shows that a schema based on either types or properties alone will capture only about 75% of the information contained in the data. From these observations, we derive conclusions about the design of future schemas for LOD.