Spatial patterns of knowledge-intensive business services in cities of various sizes, morphologies and economies

We compare intra-urban localization patterns of advertising and IT companies in three large Czech cities. The main aim of our analysis is an empirically-based contribution to the question to what extent do knowledge bases affect the spatial distribution of various knowledge-intensive business industries. The central research question is: To what extent is the localization of these two industries influenced by different modes of innovation/knowledge bases (symbolic vs. synthetic) and to what extent by contextual factors, such as urban size, morphology, position in the urban hierarchy and economic profile of the given city. We found that the urban contexts shape the localization patterns of advertising and IT companies more than differences in knowledge bases-both industries cluster primarily in the inner cities and urban cores. Formation of more suburban IT "scientific neighborhoods" is limited.Web of Science125art. no. 184

Experience with the Open Source based implementation for ATLAS Conditions Data Management System

Conditions Data in high energy physics experiments is frequently seen as every data needed for reconstruction besides the event data itself. This includes all sorts of slowly evolving data like detector alignment, calibration and robustness, and data from detector control system. Also, every Conditions Data Object is associated with a time interval of validity and a version. Besides that, quite often is useful to tag collections of Conditions Data Objects altogether. These issues have already been investigated and a data model has been proposed and used for different implementations based in commercial DBMSs, both at CERN and for the BaBar experiment. The special case of the ATLAS complex trigger that requires online access to calibration and alignment data poses new challenges that have to be met using a flexible and customizable solution more in the line of Open Source components. Motivated by the ATLAS challenges we have developed an alternative implementation, based in an Open Source RDBMS. Several issues were investigated land will be described in this paper: -The best way to map the conditions data model into the relational database concept considering what are foreseen as the most frequent queries. -The clustering model best suited to address the scalability problem. -Extensive tests were performed and will be described. The very promising results from these tests are attracting the attention from the HEP community and driving further developments.Comment: 8 pages, 4 figures, 3 tables, conferenc

Bayesian modeling of networks in complex business intelligence problems

Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specific products, along with co-subscription networks encoding multiple purchasing behavior. Data are available for several agencies within the same insurance company, and our goal is to efficiently exploit co-subscription networks to inform targeted advertising of cross-sell strategies to currently mono-product customers. We address this goal by developing a Bayesian hierarchical model, which clusters agencies according to common mono-product customer choices and co-subscription networks. Within each cluster, we efficiently model customer behavior via a cluster-dependent mixture of latent eigenmodels. This formulation provides key information on mono-product customer choices and multiple purchasing behavior within each cluster, informing targeted cross-sell strategies. We develop simple algorithms for tractable inference, and assess performance in simulations and an application to business intelligence