Synergetic use of polarimetric Doppler radars at Ka- and C- band for retrieval of water drop and ice particle size distributions

The transition from bulk to spectral bin microphysics schemes within numerical weather prediction (NWP) models promises more realistic simulations of cloud resolving processes and finally an improving of weather forecasting quality. However the needed size distributions of water drops and ice particles were parameterized from rare observations, typically from ground based rain distrometers or aircraft in-situ measurements. The current work introduces a retrieval method to derive size distributions from synergetic use of vertical pointed Ka-band and polarimetric C-band radar. The method is based on using the full height-resolved Doppler spectra instead of mean values of reflectivity and radial velocity inside the radar bin volume. Within a Mie and T-matrix based radar forward operator, Doppler spectra were simulated from assumed size distributions, taking into account the attenuation at Ka-band. In an iterative way, the parameters of distributions were varied until differences between simulated and observed radar profiles could minimized. Additional data e.g. from radiosounding and SODAR wind profilers were used to estimate and minimize the most relevant error sources expected from vertical air motion and turbulence. First results were achieved from a case study of 8th July 2007 during the Convection and Orographically Induced Precipitation Study (COPS)

Bulkloading and Maintaining XML Documents

The popularity of XML as a exchange and storage format brings about massive amounts of documents to be stored, maintained and analyzed -- a challenge that traditionally has been tackled with Database Management Systems (DBMS). To open up the content of XML documents to analysis with declarative query languages, efficient bulk loading techniques are necessary. Database technology has traditionally been offering support for these tasks but yet falls short of providing efficient automation techniques for the challenges that large collections of XML data raise. As storage back-end, many applications rely on relational databases, which are designed towards large data volumes. This paper studies the bulk load and update algorithms for XML data stored in relational format and outlines opportunities and problems. We investigate both (1) bulk insertion and deletion as well as (2) updates in the form of edit scripts which heavily use pointer-chasing techniques which often are considered orthogonal to the algebraic operations relational databases are optimized for. To get the most out of relational database systems, we show that one should make careful use of edit scripts and replace them with bulk operations if more than a very small portion of the database is updated. We implemented our ideas on top of the Monet Database System and benchmarked their performance

Memory aware query scheduling in a database cluster

Query throughput is one of the primary optimization goals in interactive web-based information systems in order to achieve the performance necessary to serve large user communities. Queries in this application domain differ significantly from those in traditional database applications: they are of lower complexity and almost exclusively read-only. The architecture we propose here is specifically tailored to take advantage of the query characteristics. It is based on a large parallel shared-nothing database cluster where each node runs a separate server with a fully replicated copy of the database. A query is assigned and entirely executed on one single node avoiding network contention or synchronization effects. However, the actual key to enhanced throughput is a resource efficient scheduling of the arriving queries. We develop a simple and robust scheduling scheme that takes the currently memory resident data at each server into account and trades off memory re-use and execution time, reordering queries as necessary. Our experimental evaluation demonstrates the effectiveness when scaling the system beyond hundreds of nodes showing super-linear speedup

Omega Omega -storage : a self organizing multi-attribute storage technique for very large main memories

Main memory is continuously improving both in price and capacity. With this comes new storage problems as well as new directions of usage. Just before the millennium, several main memory database systems are becoming commercially available. The hot areas include boosting the performance of web-enabled systems, such as search-engines, and auctioning systems. We present a novel data storage structure -- the {em $Omega$-storage structure, a high performance data structure, allowing automatically indexed storage of {em very large amounts of multi-attribute data. The experiments show excellent performance for point retrieval, and highly efficient pruning for {em pattern searches. It provides the balanced storage previously achieved by random kd-trees, but avoids their increased pattern match search times, by an effective assignment bits of attributes. Moreover, it avoids the sensitivity of the kd-tree to insert orders

Scalable storage for a DBMS using transparent distribution

Scalable Distributed Data Structures (SDDSs) provide a self-managing and self-organizing data storage of potentially unbounded size. This stands in contrast to common distribution schemas deployed in conventional distributed DBMS. SDDSs, however, have mostly been used in synthetic scenarios to investigate their properties. In this paper we concentrate on the integration of the LH* SDDS into our efficient and extensible DBMS, called Monet. We show that this merge permits processing very large sets of distributed data. In our implementation we extended the relational algebra interpreter in such a way that access to data, whether it is distributed or locally stored, is transparent to the user. The on-the-fly optimization of operations --- heavily used in Monet --- to deploy different strategies and scenarios inside the primary operators associated with an SDDS adds self-adaptiveness to the query system; it dynamically adopts itself to unforeseen situations. We illustrate the performance efficiency by experiments on a network of workstations. The transparent integration of SDDSs opens new perspectives for very large self-managing database systems

First experiences with the ATLAS Pixel Detector Control System at the Combined Test Beam 2004

Detector control systems (DCS) include the read out, control and supervision of hardware devices as well as the monitoring of external systems like cooling system and the processing of control data. The implementation of such a system in the final experiment has also to provide the communication with the trigger and data acquisition system (TDAQ). In addition, conditions data which describe the status of the pixel detector modules and their environment must be logged and stored in a common LHC wide database system. At the combined test beam all ATLAS subdetectors were operated together for the first time over a longer period. To ensure the functionality of the pixel detector a control system was set up. We describe the architecture chosen for the pixel detector control system, the interfaces to hardware devices, the interfaces to the users and the performance of our system. The embedding of the DCS in the common infrastructure of the combined test beam and also its communication with surrounding systems will be discussed in some detail.Comment: 6 pages, 9 figures, Pixel 2005 proceedings preprin