Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 162 for “"Data-Intensive"”.
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Workload Management for Data-Intensive Services
<p>Data-intensive web services are typically composed of three tiers: i) a display tier that interacts with users and serves rich content to them, ii) a storage tier that stores the user-generated or machine-generated data used to create this content, and iii) an analytics tier that runs data …
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Data-Intensive Biocomputing in the Cloud
… However, these NGS technologies generate data at a rate that far outstrips Moore\'s Law. As a consequence, analyzing this exponentially increasing data deluge requires enormous computational and storage resources, resources that many life science institutions do not have access to. As …
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Software and Hardware Support for Data Intensive Computing
In the architectural aspect, we propose a Near-Memory Processor (NMP), a heterogeneous architecture that couples on one chip a commodity microprocessor together with a coprocessor that is designed to run well applications that have poor locality or that require bit manipulations. The coprocessor …
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EXCALIBRATE Bayesian calibration for data-intensive astrophysical experimentation
… same environment as observational measurements. Datasets taken at various points of the receiver development are evaluated with EXCALIBRATE which achieves calibration accuracies of about 1 kelvin or less. Upon numerous adjustments to both the physical receiver unit and our code, we demonstrate …
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Optimizing Data-Intensive Computing with Efficient Configuration Tuning
… systems’ environment (e.g., increase in input data size, changes in the allocation of resources). Paradoxically, existing solutions for workload tuning either assume static tuning environment or workloads that are inexpensive to run (i.e. requiring hundreds of execution samples). Recently, …
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Insight Driven Sampling for Interactive Data Intensive Computing
Data Visualization is used to help humans perceive high dimensional data, but it is unable to be applied in real time to data intensive computing applications. Attempts to process and apply traditional information visualization techniques to such applications result in slow or non-responsive …
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SMART DATA-INTENSIVE SERVICE COMPOSITION IN CLOUD-EDGE CONTINUUM
… dell’analisi in tempo reale e di applicazioni data-intensive ha messo in discussione l’adeguatezza del cloud computing tradizionale, che, pur offrendo elevata scalabilità, spesso non riesce a soddisfare i requisiti stringenti di latenza, banda e affidabilità richiesti dai servizi moderni. …
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Accelerating data-intensive scientific visualization and computing through parallelization
… applications generate colossal amounts of data that require an increasing number of processors for parallel processing. The research in this dissertation is focused on optimizing the performance of data-intensive parallel scientific visualization and computing. In parallel scientific …
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Optimizing Data Accesses for Scaling Data-intensive Scientific Applications
Data-intensive scientific applications often process an enormous amount of data. The scalability of such applications depends critically on how to manage the locality of data. Our study explores two common types of applications that are vastly different in terms of memory access pattern and …
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DATA GOVERNANCE FRAMEWORK FOR ML-BASED, DATA-INTENSIVE DISTRIBUTED SYSTEMS
The data processing landscape has been fundamentally transformed in recent decades, evolving from monolithic systems to complex distributed architectures where multiple services operated by different parties collaborate to deliver sophisticated analytics capabilities. Machine Learning (ML) and …
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H-SIMD machine : configurable parallel computing for data-intensive applications
… a hierarchical single-instruction multiple-data (H-SLMD) configurable computing architecture to facilitate the efficient execution of data-intensive applications on field-programmable gate arrays (FPGAs). H-SIMD targets data-intensive applications for FPGA-based system designs. The H-SIMD …
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Satisfying strong application requirements in data-intensive cloud computing environments
In today's data-intensive cloud systems, there is a tension between resource limitations and strict requirements. In an effort to scale up in the cloud, many systems today have unfortunately forced users to relax their requirements. However, users still have to deal with constraints, such as strict …
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Data Intensive Method For Processing Defect Detection and Mitigation For Composites
… is limited by the lack of in situ imaging data and effective anomaly detection models trained specifically for defect detection during composite curing. In this study, different methods were proposed to detect and reduce processing defects in conventional and additive manufacturing of …
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Data-intensive spatial pattern discovery based on generalized spatial point representations
Geospatial big data consisting of records at the individual level or with fine spatial resolutions, such as geo-referenced social media posts and movement records collected using GPS, provide tremendous opportunities to understand complex geographic phenomena and their space-time dynamics. Such …
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User Experiences with Data-Intensive Bioinformatics Resources: A Distributed Cognition Perspective
… and dissemination of a wide range of big data platforms such as bioinformatics into the biomedical and life sciences environments. Bioinformatics brings the promise of enabling life scientists to easily and effectively access large and complex data sets in new ways, thus promoting …
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Cost-Effective Resource Configurations for Executing Data-Intensive Workloads in Public Clouds
The rate of data growth in many domains is straining our ability to manage and analyze it. Consequently, we see the emergence of computing systems that attempt to efficiently process data-intensive applications or I/O bound applications with large data. Cloud computing offers “infinite” resources …
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Optimizing Timeliness, Accuracy, and Cost in Geo-Distributed Data-Intensive Computing Systems
Big Data touches every aspect of our lives, from the way we spend our free time to the way we make scientific discoveries. Netflix streamed more than 42 billion hours of video in 2015, and in the process recorded massive volumes of data to inform video recommendations and plan investments in new …
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Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges
… video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs …
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An integrated cyberGIS and machine learning framework for data-intensive urban analytics
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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