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In this paper, an one-click method for coronary artery ostia extraction from computed tomography angiography (CTA) scans is presented and a strict definition of coronary artery ostia in geometric graphics is given. Due to the presence of diverse anatomical structures surrounding the heart of cardiacCTA data, computerized segmentation of the coronary arteries is a challenging procedure. Many coronary...
Though an abundance of novel "data transformation" technologies have been developed (such as compression, level-of-detail, layout optimization, and indexing), there remains a notable gap in the adoption of such services by scientific applications. In response, we develop an in situ data transformation framework in the ADIOS I/O middleware with a "plug in" interface, thus greatly...
The size and scope of cutting-edge scientific simulations are growing much faster than the I/O and storage capabilities of their run-time environments. The growing gap is exacerbated by exploratory, data-intensive analytics, such as querying simulation data with multivariate, spatio-temporal constraints, which induces heterogeneous access patterns that stress the performance of the underlying storage...
The size and scope of cutting-edge scientific simulations are growing much faster than the I/O and storage capabilities of their runtime environments. The growing gap gets exacerbated by exploratory dataâ"intensive analytics, such as querying simulation data for regions of interest with multivariate, spatio-temporal constraints. Query-driven data exploration induces heterogeneous access...
The size and scope of cutting-edge scientific simulations are growing much faster than the I/O subsystems of their runtime environments, not only making I/O the primary bottleneck, but also consuming space that pushes the storage capacities of many computing facilities. These problems are exacerbated by the need to perform data-intensive analytics applications, such as querying the dataset by variable...
The growing gap between the massive amounts of data generated by petascale scientific simulation codes and the capability of system hardware and software to effectively analyze this data necessitates data reduction. Yet, the increasing data complexity challenges most, if not all, of the existing data compression methods. In fact, loss less compression techniques offer no more than 10% reduction on...
Efficient analytics of scientific data from extreme-scale simulations is quickly becoming a top-notch priority. The increasing simulation output data sizes demand for a paradigm shift in how analytics is conducted. In this paper, we argue that query-driven analytics over compressed — rather than original, full-size — data is a promising strategy in order to meet storage-and-I/O-bound application challenges...
Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource allocations automatically. Our approach leverages...
To reduce cloud system resource cost, application consolidation is a must. In this paper, we present a novel pattern driven application consolidation (PAC) system to achieve efficient resource sharing in virtualized cloud computing infrastructures. PAC employs signal processing techniques to dynamically discover significant patterns called signatures of different applications and hosts. PAC then performs...
Cloud computing has emerged as a promising platform that grants users with direct yet shared access to computing resources and services without worrying about the internal complex infrastructure. Unlike traditional batch service model, cloud service model adopts a pay-as-you-go form, which demands explicit and precise resource control. In this paper, we present SigLM, a novel Signature-driven Load...
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