Multiple Query Optimization For Data Analysis Applications on Clusters of SMPs

This paper is concerned with the efficient execution of multiple query workloads on a cluster of SMPs. We target applications that access and manipulate large scientific datasets. Queries in these applications involve user-defined processing operations on data and distributed data structures to hold intermediate and final results. Our goal is to implement system components to leverage previously computed query results and to effectively utilize processing power and aggregated I/O bandwidth on SMP nodes so that both single queries and multi-query batches can be efficiently executed. (Also referenced as UMIACS-TR-2001-78

Secreção de aldosterona em pacientes com choque séptico : estudo prospectivo

Objective: To assess serum levels of the main factors that regulate the activation of the zona glomerulosa and aldosterone production in patients with septic shock, as well as their response to a high-dose (250 μg) adrenocorticotropic hormone (ACTH) stimulation test. Subjects and methods: In 27 patients with septic shock, baseline levels of aldosterone, cortisol, ACTH, renin, sodium, potassium, and lactate were measured, followed by a cortrosyn test. Results: Renin correlated with baseline aldosterone and its variation after cortrosyn stimulation. Baseline cortisol and its variation did not correlate with ACTH. Only three patients had concomitant dysfunction of aldosterone and cortisol secretion. Conclusions: Activation of the zona glomerulosa and zona fasciculata are independent. Aldosterone secretion is dependent on the integrity of the renin-angiotensin-aldosterone system, whereas cortisol secretion does not appear to depend predominantly on the hypothalamic-pituitaryadrenal axis. These results suggest that activation of the adrenal gland in critically ill patients occurs by multiple mechanisms.Objetivo: Avaliar os níveis séricos dos principais fatores que regulam a ativação da zona glomerulosa e a produção de aldosterona em pacientes com choque séptico, assim como sua resposta ao teste de cortrosina em alta dose (250 μg). Sujeitos e métodos: Em 27 portadores de choque séptico, foram aferidos níveis basais de aldosterona, cortisol, ACTH, renina, sódio, potássio e lactato, bem como realizado teste de cortrosina. Resultados: Renina se correlacionou com níveis basais de aldosterona e sua variação após teste de cortrosina. Cortisol basal e sua variação não se correlacionaram com ACTH. Apenas três pacientes apresentaram disfunção concomitante da secreção de aldosterona e cortisol. Conclusões: Ativação das zonas fasciculada e glomerulosa são independentes. Secreção de aldosterona é dependente da integridade do sistema renina-angiotensina-aldosterona, enquanto secreção de cortisol não parece predominantemente dependente do eixo hipotálamo-hipófise-adrenal. Esses resultados sugerem que a ativação da adrenal em pacientes críticos ocorre por múltiplos mecanismos

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Active Proxy-G: Optimizing the Query Execution Process in the Grid

The Grid environment facilitates collaborative work and allows many users to query and process data over geographically dispersed data repositories. Over the past several years, there has been a growing interest in developing applications that interactively analyze datasets, potentially in a collaborative setting. We describe an Active Proxy-G service that is able to cache query results, use those results for answering new incoming queries, generate subqueries for the parts of a query that cannot be produced from the cache, and submit the subqueries for final processing at application servers that store the raw datasets. We present an experimental evaluation to illustrate the effects of various design tradeoJj5 . We also show the benefits that two real applications gain from using the middleware

Exploiting Functional Decomposition for Efficient Parallel Processing of Multiple Data Analysis Queries

Reuse is a powerful method for improving system performance. In this paper, we examine functional decomposition for improving data reuse, and therefore overall query execution performance, in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated during the execution of a previous query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using the techniques presented in the paper

Decision Tree Construction for Data Mining on Cluster of Shared-Memory Multiprocessors

Classification of very large datasets is a challenging problem in data mining. It is desirable to have decision-tree classifiers that can handle large datasets, because a large dataset often increases the accuracy of the resulting classification model. Classification tree algorithms can benefit from parallelization because of large memory and computation requirements for handling large datasets. Clusters of shared-memory multiprocessors (SMPs), in which each shared-memory node has a small number of processors (e.g., 2--8 processors) and is connected to the other nodes via a high-speed inter-connect, have become a popular alternative to pure distributed-memory and shared-memory machines. A cluster of SMPs provides a two-tier architecture, in which a combination of shared-memory and distributed-memory paradigms can be employed. In this paper we investigate decision tree construction on a cluster of SMPs. We present an algorithm that employs a hybrid approach. The classification training dataset is partitioned across the SMP nodes so that each SMP node performs tree construction using a subset of the records in the dataset. Within each SMP node, on the other hand, tasks associated with an attribute are dynamically scheduled to the light-weight threads running on the SMP node. We present experimental results on a Linux PC cluster with dual-processor SMP nodes. (Also cross-referenced as UMIACS-TR-2000-78

Scheduling multiple data visualization query workloads on a shared memory machine

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