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This paper describes an evaluation of Automated Theorem Proving (ATP) systems on problems taken from the QMLTP library of first-order modal logic problems. Principally, the problems are translated to higher-order logic in the TPTP language using an embedding approach, and solved using higher-order logic ATP systems. Additionally, the results from native modal logic ATP systems are considered, and compared with those from the embedding approach. The findings are that the embedding process is reliable and successful, the choice of backend ATP system can significantly impact the performance of the embedding approach, native modal logic ATP systems outperform the embedding approach, and the embedding approach can cope with a wider range modal logics than the native modal systems considered., Comment: 17 pages, 1 figure

We present a new statistical method that detects industrial clusters at a firm level. The proposed method does not divide space into subunits whereby it is not affected by the Modifiable Areal Unit Problem (MAUP). Our metric differs both in its calculation and interpretation from existing distance-based metrics and shows four central properties that enable its meaningful usage for cluster analysis. The method fulfills all five criteria for a test of localization proposed by Duranton and Overman (2005).

Distance-based methods for measuring spatial concentration such as the Duranton-Overman index undergo an increasing popularity in the spatial econometrics community. However, a limiting factor for their usage is their computational complexity since both their memory requirements and running-time are in O(n2). In this paper, we present an algorithm with constant memory requirements and an improved running time, enabling the Duranton-Overman index and related distance-based methods to run big data analysis. Furthermore, we discuss the index by Scholl and Brenner (2012) whose mathematical concept allows an even faster computation for large datasets than the improved algorithm does.

This paper studies the industry-specific relationship between industrial clustering and firm growth. Micro-geographically defined agglomeration measures, free of the modifiable areal unit problem (MAUP), are used to study 23 industries. The spatial impacts of agglomeration of related economic and knowledge generating activities are examined by using travel time distances, a flexible log-logistic decay function framework and quantile regression techniques. We find that firms'€™ growth prospects tend to be hampered by the agglomeration of own-industry employment, but improved by proximate scientific activity. Results depend on the kind and age of industry. Furthermore, the optimal decay functions that measure agglomeration effects considerably vary both between the industries and variables. Three illustrative cases of industries are discussed in more details.

We investigate company foundations in the German micro technology industry (MST) by means of a spatial-temporal micro-geographic analysis. Our dataset includes the exact location (latitude, longitude) of all German MST firms, relevant research institutions and MST-patent owners including their date of inception for a timeframe of 12 years. Furthermore, we obtain employment data at a municipality level for three related industries. In order to deal with our unusual detailed data, we develop a new distance-based framework for a logistic regression that enables the usage of micro geographic data that is free of any spatial aggregation. A temporal component is integrated by means of a Markov chain so that our model allows both, a micro spatial and a micro temporal analysis. The model detects spatial dependencies on a firm level instead on an aggregated regional level. This allows an econometric modeling without the Modifiable Areal Unit Problem (MAUP), a well-known problem in spatial econometrics. Recently, the MAUP has been tackled by new metrics such as the Duranton-Overman-Index that however only allow the observance of (co-) localization but cannot include multiple variables. Our framework for a logistic regression is able to fill this methodological gap as it is distance-based, allows the observation of multiple variables and gives results for global models. Locations of company foundations are investigated with respect to their spatial proximity to similar firms, patent owner, related industries and research institutions and are benchmarked with the overall distribution of company foundations in Germany. We perform one investigation for the whole timeframe and three separate investigations with a three years period in order to test for temporal caused dynamics. The empirical results reveal some interesting findings that stand in line with well-established theories: Spatial proximity to other MST firms and to relevant research institutions has a clear positive influence on where a new firm is founded. This might be due to positive local spillover or to spinoffs from firms or research institutions. While this confirms that MST-firms depend on a high local innovation capacity, the proximity to owners of patents and to qualified workers in related industries has little or even a negative influence. Furthermore, the influence of the different actors is not constant over time but evolves with the industry?s life cycle. The increasing importance of spatial proximity to firms stands in line with the self-augmenting process theory, while the parallel decreasing importance of proximity to research institutions suggests a shift from tacit to explicit knowledge. The analysis shows that the MST industry becomes more self-contained and that proximity to related industries is of decreasing importance.

We present a new statistical method that describes the localization patterns of industries in a continuous space. The proposed method does not divide space into subunits whereby it is not affected by the Modifiable Areal Unit Problem (MAUP). Our method fulfils all five criteria for a spatial statistical test of localization proposed by Duranton and Overman (2005) and improves them with respect to the significance of its results. Additionally, our test allows inference to the localization of highly clustered firms. Furthermore, the algorithm is efficient in its computation, which eases the usage in research.

E-science communities have put tremendous efforts into providing global access to their distributed scientific data sets to foster data and knowledge sharing. Beyond already existing huge data volumes, the researchers face major challenges in managing the anticipated data deluge of forthcoming projects with expected data rates of several terabytes a day. This thesis presents community-driven data grids which target at domain-specific federations and provide a scalable, distributed, and collaborative data management. Our infrastructure optimizes the overall query throughput by employing dominant data characteristics and query patterns. By combining well-established techniques for data partitioning and replication with P2P technologies, we address several challenging issues: data load balancing, efficient data dissemination and query processing, and the adaption to short-term query hot spots as well as to long-term load redistributions. Viele e-Science Forschungsverbünde veröffentlichen ihre Ergebnisse weltweit, um einen regen Daten- und Wissensaustausch innerhalb ihrer Community anzuregen. Über die bereits vorhandenen Datenvolumina hinaus, stellt insbesondere die antizipierte Datenflut geplanter e-Science-Projekte die Forscher vor neue Herausforderungen. Diese Arbeit stellt mit Community-Driven Data Grids einen Ansatz zur Datenverwaltung innerhalb verteilter e-Science Forschungsverbünde vor, der ein durchsatzoptimiertes Datenmanagement ermöglicht, indem die jeweils vorherrschenden Dateneigenschaften und Anfragemuster in die Optimierung miteinbezogen werden. Durch die Kombination von Ansätzen zur Datenpartitionierung und Replikation sowie von Peer-To-Peer Technologien behandelt diese Arbeit interessante Themenkomplexe wie Datenlastbalancierung, Berücksichtigung von Anfrage-Hotspots und die Anpassung sowohl an kurzfristige als auch an langfristige Lastverschiebungen.