Contributions in Merlin

A formal ontology does not contain lexical knowledge; it is by nature language-independent. Mappings can be added between the ontology and, arbitrarily, many lexica in any number of languages. The result of this operation is what is here referred to as a cross-language ontology. A cross-language ontology can be a useful resource for machine translation or cross-language information retrieval. This chapter focuses on ways of automatically building an ontology by exploiting cross-language information from parallel corpora. The goal is to improve the automatic learning results compared to learning an ontology from resources in a single language. The authors present a framework for cross-language ontology learning, providing a setting in which cross-language evidence (data) can be integrated and quantified. The aim is to investigate the following question: Can cross-language data teach us more than data from a single language for the ontology learning task?

Purpose – The purpose of this paper is to examine the influence of an open outside-in innovation management strategy on companies' innovativeness and innovation performance. Specifically, it focuses on the adoption of the open innovation paradigm in practice and the extent of collaboration with different stakeholders. Design/methodology/approach – The proposed hypotheses are tested empirically using survey data collected from stock-listed companies in Germany, Switzerland and Austria. The data include the complete responses from 141 R&D managers for the period from 2004 to 2008. Findings – The openness of the outside-in process in R&D management is of crucial importance for achieving high direct and indirect innovation output effects. In particular, openness towards customers, suppliers and universities has a significant positive impact on the different innovation performance measures. Regarding openness towards cross-sector companies, the analysis reveals a significant negative effect on innovation performance. Research limitations/implications – The utilization of cross-sectional data and its dependency on the perceptions and experiences of the respondents has its limitations. Thus, future research should be based on a more longitudinal design that emphasizes quantitative measurement techniques. Originality/value – To date, the adoption of open innovation in practice has not been examined in depth. This study provides empirical insights into the open innovation approaches in German-speaking countries and, by drawing important conclusions for managers involved in the R&D processes, fills a gap in the innovation management literature.

Modern server systems schedule large amounts of concurrent requests constrained by, e.g., correctness criteria and service-level agreements. Since standard database management systems provide only limited consistency levels, the state of the art is to develop schedulers imperatively which is time-consuming and error-prone. In this poster, we present Smile (declarative Scheduling MIddLEware), a tool for developing domain-specific scheduling protocols declaratively. Smile decreases the effort to implement and adapt such protocols because it abstracts from low level scheduling details allowing developers to focus on the protocol implementation. We demonstrate the advantages of our approach by implementing a domain-specific use case protocol.

Sequenced spatiotemporal aggregation (SSTA) is an important query for many applications of spatiotemporal databases, such as traffic analysis. Conceptually, an SSTA query returns one aggregate value for each individual spatiotemporal granule. While the data is typically recorded at a fine granularity, at query time a coarser granularity is common. This calls for efficient evaluation strategies that are granularity aware. In this paper, we formally define an SSTA operator that includes a data-to-query granularity conversion. Based on a discrete time model and a discrete 1.5 dimensional space model, we generalize the concept of time constant intervals to constant rectangles, which represent maximal rectangles in the spatiotemporal domain over which an aggregation result is constant. We propose an efficient evaluation algorithm for SSTA queries that takes advantage of a coarse query granularity. The algorithm is based on the plane sweep paradigm, and we propose a granularity aware event point schedule, termed gaEPS, and a granularity aware sweep line status, termed gaSLS. These data structures store space and time points from the input relation in a compressed form using a minimal set of counters. In extensive experiments, we show that for coarse query granularities gaEPS significantly outperforms a basic EPS that is based on an extension of previous work, both in terms of memory usage and runtime.

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Self-assembly is a phenomenon broadly observed in nature where a vast number of various molecules spontaneously synthesize complex structures. In this article, prompted by the need for the realization of highly autonomous self-assembly systems that employ magnetism as a driving force, we discuss fundamental issues associated with magnetically driven self-assembly systems. We first introduce some examples from our case studies, in which the models all subscribe to a distributed approach, and thus lack central control. Then we categorize them by their type of magnetic attachment. The issues discussed include several fundamental properties, such as the effect of morphology, stochasticity, the difference between two-dimensional models and three-dimensional models, emergence, allostericity, and parallelism. The conclusions obtained support our stance that the appropriate morphology lightens the control cost for the assembly, providing primal but engaging instances of magnetic self-assembly systems that warrant further study.

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