Multi-Step Processing of Spatial Joins

Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in twodimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last year’s conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidates which is handled by the following two steps. First of all, sophisticated approximations are used to identify answers as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. In the last step, the exact geometry of the remaining candidates has to be tested against the join predicate. The time required for computing spatial join predicates can essentially be reduced when objects are adequately organized in main memory. In our approach, objects are first decomposed into simple components which are exclusively organized by a main-memory resident spatial data structure. Overall, we present a complete approach of spatial join processing on complex spatial objects. The performance of the individual steps of our approach is evaluated with data sets from real cartographic applications. The results show that our approach reduces the total execution time of the spatial join by factors

Co-Clustering Network-Constrained Trajectory Data

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network

Thiomicrospira kuenenii sp. nov., and Thiomicrospira frisia sp. nov., two mesophilic obligately chemolithoautotrophic sulfur-oxidizing bacteria isolated from an intertidal mud flat

Two new members of the genus Thiomicrospira were isolated from an intertidal mud flat sample with thiosulfate as the electron donor and CO2 as carbon source. On the basis of differences in genotypic and phenotypic characteristics, it is proposed that strain JB-A1(T) (= DSM 12350(T)) and strain JB-A2(T) (= DSM 12351(T)) are members of two new species, Thiomicrospira kuenenii and Thiomicrospira frisia, respectively. The cells were Gram-negative vibrios or slightly bent rods. Strain JB-A1(T) was highly motile, whereas strain JB-A2(T) showed a much lower degree of motility combined with a strong tendency to form aggregates. Both organisms were obligately autotrophic and strictly aerobic. Nitrate was not used as electron acceptor. Chemolithoautotrophic growth was observed with thiosulfate, tetrathionate, sulfur and sulfide. Neither isolate was able to grow heterotrophically. For strain JB-A1(T), growth was observed between pH values of 4.0 and 7.5 with an optimum at pH 6.0, whereas for strain JB-A2(T), growth was observed between pH 4.2 and 8.5 with an optimum at pH 6.5. The temperature limits for growth were between 3.5 and 42 degrees C and 3.5 and 39 degrees C, respectively. The optimum growth temperature for strain JB-A1(T) was between 29 and 33.5 degrees C, whereas strain JB-A2(T) showed optimal growth between 32 and 35 degrees C. The mean maximum growth rate on thiosulfate was 0.35 h(-1) for strain JB-A1(T) and 0.45 h(-1) for strain JB-A2(T)

Thiomicrospira arctica sp nov and Thiomicrospira psychrophila sp nov., psychrophilic, obligately chemolithoautotrophic, sulfur-oxidizing bacteria isolated from marine Arctic sediments

Two psychrophilic, chemolithoautotrophic, sulfur-oxidizing bacteria were isolated from marine Arctic sediments sampled off the coast of Svalbard with thiosulfate as the electron donor and CO(2) as carbon source. Comparative analysis of 16S rRNA gene sequences suggested that the novel strains, designated SVAL-D(T) and SVAL-E(T), represent members of the genus Thiomicrospira. Further genotypic (DNA-DNA relatedness, DNA G+C content) and phenotypic characterization revealed that the strains represent members of two novel species. Both organisms are obligately autotrophic and strictly aerobic. Nitrate was not used as an electron acceptor. Chemolithoautotrophic growth was observed with thiosulfate, tetrathionate and sulfur. The temperature limits for growth of both strains were between -2 degrees C and 20.8 degrees C, with optima of 11.5-13.2 degrees C (SVAL-E(T)) and 14.6-15.4 degrees C (SVAL-D(T)), which is about 13-15 degrees C lower than the optima of all other recognized Thiomicrospira species. The maximum growth rate on thiosulfate at 14 degrees C was 0.14 h(-1) for strain SVAL-E(T) and 0.2 h(-1) for strain SVAL-D(T). Major fatty acids of SVAL-D(T) are C(16 : 1), C(18 : 0) and C(16 : 0), and those of SVAL-E(T) are C(16 : 1), C(18 : 1), C(16 : 0) and C(14 : 1). Cells of SVAL-D(T) and SVAL-E(T) are rods, like those of their closest relatives. To our knowledge the novel strains are the first psychrophilic, chemolithoautotrophic, sulfur-oxidizing bacteria so far described. The names Thiomicrospira arctica sp. nov. and Thiomicrospira psychrophila sp. nov. are proposed for SVAL-E(T) (=ATCC 700955(T)=DSM 13458(T)) and SVAL-D(T) (=ATCC 700954(T)=DSM 13453(T)), respectively

Efficient Processing of Spatial Joins Using R-Trees

Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems

Filling some black holes: modeling the connection between urbanization, infrastructure, and global service intensity

This empirical article combines insights from previous research on the level of knowledge-intensive service in metropolitan areas with the aim to develop an understanding of the spatial structure of the global service economy. We use a stepwise regression model with the Globalization and World Cities research network's measure of globalized service provisioning as the dependent variable and a range of variables focusing on population, infrastructure, urban primacy, and national regulation as independent variables. The discussion of the results focuses on model parameters as well as the meaning of outliers and is used to explore some avenues for future research

Vertical distribution and risk assessment of pharmaceuticals and other micropollutants in southern North Sea coastal waters

Pharmaceutical compounds are micropollutants of emerging concern, as well as other classes of chemicals such as UV filters and artificial sweeteners. They enter marine environments via wastewater treatment plants, aquaculture runoff, hospital effluents, and shipping activities. While many studies have investigated the presence and distribution of these pollutants in numerous coastal areas, our study is the first to focus on their occurrence, spatial distribution, and vertical distribution in the sea surface microlayer (SML) and the near-surface layer of marine environments. We analyzed 62 pharmaceutical compounds, one UV filter, and six artificial sweeteners from the SML to the corresponding underlying water (0 cm, 20 cm, 50 cm, 100 cm, and 150 cm) at four stations in the southern North Sea. One station is the enclosed Jade Bay, one is the Weser estuary at Bremerhaven, and the other two stations (NS_7 and NS_8) are in the open German Bight. Jade Bay receives pollutants from surrounding wastewater treatment plants, while the Weser estuary receives pollutants from cities like Bremerhaven, which has dense populations and industrial activities. Concentrations of pharmaceutical compounds were higher in the upper water layers (from the SML to 20 cm). Eleven pharmaceutical compounds (caffeine, carbamazepine, gemfibrozil, ibuprofen, metoprolol, salicylic acid, clarithromycin, novobiocin, clindamycin, trimethoprim, and tylosin) were detected in >95 % of our samples. One UV filter (benzophenone-4) was found in 83 % and three artificial sweeteners (acesulfame, saccharin, and sucralose) in 100 % of all our samples. All artificial sweeteners posed high risks to the freshwater invertebrate Daphnia magna. Understanding the spatial and vertical distribution of pharmaceuticals and other micropollutants in marine environments may be essential in assessing their dispersal and detection in other aquatic environments

Inspection of Computed Tomography (CT) Data and Finite Element (FE) Simulation of Additive Manufactured (AM) Components

This is the author accepted manuscript. The final version is available from the publisherOne of the challenges of working with Additive Manufactured (AM) metal parts involves checking accuracy and reliability before production. Techniques used Computed Tomography (CT) scans, 3D image processing, and Finite Element (FE) simulation help detect problems prior to costly faults. A workflow has been developed by Synopsys, ANSYS, North Star Imaging, and the University of Pittsburgh to streamline this often-complex process, with applications to analyzing metal AM-produced lightweight brackets and a component from Moog, Inc. Software like Synopsys Simpleware™ is used to generate robust models from 3D scans of AM parts to compare original CAD models with ‘as-built’ geometries, and to export a FE mesh for simulation in ANSYS. This method enables identification of design deviations early in the design process, and how their impact might be tackled prior to production. For the Moog application, unexpected defects were identified for aerospace parts to inform future design iteration

Speed Partitioning for Indexing Moving Objects

Indexing moving objects has been extensively studied in the past decades. Moving objects, such as vehicles and mobile device users, usually exhibit some patterns on their velocities, which can be utilized for velocity-based partitioning to improve performance of the indexes. Existing velocity-based partitioning techniques rely on some kinds of heuristics rather than analytically calculate the optimal solution. In this paper, we propose a novel speed partitioning technique based on a formal analysis over speed values of the moving objects. We first show that speed partitioning will significantly reduce the search space expansion which has direct impacts on query performance of the indexes. Next we formulate the optimal speed partitioning problem based on search space expansion analysis and then compute the optimal solution using dynamic programming. We then build the partitioned indexing system where queries are duplicated and processed in each index partition. Extensive experiments demonstrate that our method dramatically improves the performance of indexes for moving objects and outperforms other state-of-the-art velocity-based partitioning approaches