We outline a blueprint for the development of new computer science approaches for the management and analysis of big data problems for biodiversity science. Such problems are characterized by a combination of different data sources each of which owns at least one of the typical characteristics of big data (volume, variety, velocity, or veracity). For these problems, we envision a solution that covers different aspects of integrating data sources and algorithms for their analysis on one of the following three layers: At the data layer, there are various data archives of heterogeneous, unstructured, and uncertain data. At the functional layer, the data are analyzed for each archive individually. At the meta-layer, multiple functional archives are combined for complex analysis.

The use of model-based code-generators for construction of controller software increases the reliability of the software in two ways: first, the models often can be checked for safety conditions. Second the use of code-generators prevents manual implementation faults. However, the reliability depends on the correctness of these code-generators, i.e., whether they really generate code that correctly implements the model. In this paper, we show how this correctness can be checked automatically for code-generators for PLCs.

Natural semantics specifications have become mainstream in the formal specification of programming language semantics during the last 10 years. In this article, we set up sorted natural semantics as a specification framework which is able to express static semantic information of programming languages declaratively in a uniform way and allows one at the same time to generate corresponding analyses. Such static semantic information comprises context-sensitive properties which are checked in the semantic analysis phase of compilers as well as further static program analyses such as, for example, classical data and control flow analyses or type and effect systems. The latter require fixed-point analyses to determine their solutions. We show that, given a sorted natural semantics specification, we can generate the corresponding analysis. Therefore, we classify the solution of such an analysis by the notion of a proof tree. We show that a proof tree can be computed by solving an equivalent residuation problem. In case of the semantic analysis, this solution can be found by a basic algorithm. We show that its efficiency can be enhanced using solution strategies. We also demonstrate our prototype implementation of the basic algorithm which proves its applicability in practical situations. With the results of this article, we have established natural semantics as a framework which closes the gap between declarative and operational specification methods for static semantic properties as well as between specification frameworks for the semantic analysis. In particular, we show that natural semantics is expressive enough to define fixed-point program analyses.

This article discusses the problem of constructing robust class libraries. Further design criteria include the flexibility of class libraries, the efficiency of the implementations, and their safe extensibility. We show that it is possible to design robust libraries to satisfy any two of the requirements at the same time. Although the solution may require an exponential growth in the number of classes compared to the original design, this apparent class explosion can be controlled by generating only the necessary additional classes automatically. As an application demonstrating both the theoretical problems and the power of our generator approach, the design of a library modelling data structures and algorithms for graphs is considered. Both the discussion and the results in this article generalize to other domains. Copyright © 2000 John Wiley & Sons, Ltd.

In sequential computing the step from programming in machine code to programming in machine independent high level languages has been done for decades. Although high level programming languages are available for parallel machines today's parallel programs highly depend on the architectures they are intended to run on. Designing efficient parallel programs is a difficult task that can be performed by specialists only. Porting those programs to other parallel architectures is nearly impossible without a considerable loss of performance. Abstract machine models for parallel computing like the PRAM-model are accepted by theoreticians but have no practical relevance since these models don't take into account properties of existing architectures. However, the PRAM is easy to program. Recently, Culler et al. defined the LogP machine model which better reflects the behaviour of massively parallel computers. In this work, we show transformations of a subclass of PRAM-programs leading to efficie...