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 35 for “"Apache Spark"”.
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Work-sharing framework for Apache Spark
Apache Spark is a popular framework for distributed data processing that generalizes the MapReduce model and significantly improves the performance of many use cases. People can use Spark to query enormous data sets faster than before to gain insights for a competitive edge in industry. Often these …
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Parallel SPARQL Query Execution using Apache Spark
… of cluster computing. Big data ecosystems like Apache Spark provide new opportunities for designing scalable RDF indexing and query processing techniques. In this thesis, we present new ideas on storing, indexing, and query processing of RDF datasets with billions of RDF statements. In our …
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Distributed graph decomposition algorithms on Apache Spark
… this paper, we propose distributed algorithms on Apache Spark for k-truss and k-core decomposition of a graph. We also compare the performance of our algorithm with state-of-the-art Map-Reduce and parallel algorithms using openly available real world network data. Our proposed algorithms have …
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Exploration of fault tolerance in Apache Spark
… solving fault tolerance for batch processing in Apache Spark. We evaluate the benefits and challenges of these approaches. Apache Spark is a cluster computing system comprised of three main components: the driver program, the cluster manager, and the worker nodes. Spark already tolerates the loss …
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Shade : a differentially private wrapper around Apache Spark
… present an efficient and scalable framework for Apache Spark, a cluster computing framework, that provides strong privacy guarantees for users even in the presence of an informed adversary, while still providing high utility for analysts in an interactive wrapper. The framework, titled Shade, …
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A Distributed Approach to EpiFast using Apache Spark
… and provides two different implementations: Spark-EpiFast based on the Apache Spark big data processing engine and Charm-EpiFast based on the Charm++ parallel programming framework. The study focuses on exploiting features of both systems that we believe could potentially benefit in terms of …
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Data analytics with mapreduce in apache spark and hadoop systems
MapReduce comes from a traditional problem solving method: separating a big problem and solving each small parts. With the target of computing larger dataset in more efficient and cheaper way, this is implement into a programming mode to deal with massive quantity of data. The users get a map …
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A faster reinforcement learning approach to efficient job scheduling in Apache Spark
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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Exploring and Evaluating the Scalability and Efficiency of Apache Spark using Educational Datasets
… four popular data mining methods are applied to Apache Spark, using large volumes of datasets from Online Cognitive Learning Systems to explore the scalability and efficiency of Spark. Various volumes of datasets are tested on Spark MLlib with different running configurations and parameter …
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Improving the Performance of Parallel SPARQL Query Processing on Apache Spark Using Bloom Filters
… the different platforms for cluster computing, Apache Spark has emerged as the industry leader with a huge development community. In this thesis, we extend our previous work on parallel SPARQL query processing using Apache Spark, which proposed a simple partitioning strategy for a large RDF …
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Modelem řízený vývoj Spark úloh pomocí Eclipse Acceleo
… riadeným vývojom Big Data úloh v prostredí Apache Spark. Na začiatok je čitateľovi predstavený framework Apache Spark a potrebné detaily. Ďalej sa priblíži problematika modelom riadeného vývoja a popíšu sa jeho výhody a nevýhody. V druhej časti je popísaný navrhnutý meta-model pre …
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Mitigating Spark straggler tasks for iterative applications by data re-partitioning
… on a parallel processing framework like Apache Spark, one problem that affects the running time is the straggler, where a disproportionate long-running task slows down the entire cluster. In this work we present a straggler mitigation technique tailored for applications that run small …
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What about big data?
… on the two major current proposed solutions: Apache Hadoop and Apache Spark. As we will see, both are open source big data processing frameworks, but each one uses the batch processing solution in a different manner.
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Dynamic Large-Scale Graph Processing over Data Streams with Community Detection as a Case Study
… of maintaining a dynamic graph on top of Apache Spark, a data processing engine. The chosen solutions included RDDs, IndexedRDDs, and Redis. Results from our experimental indicated that Redis performed the best, and thus we concluded that storing the graph in an external big data store …
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A system for storage and analysis of machine learning operations
… thesis describes ModelDB Server and the ModelDB Spark Client, which record operations as the data scientist performs them, stores the operations and models in a central database, and exposes an API for gleaning information from the operations and models. ModelDB Server is library agnostic and it …
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Automated Discovery of Big Data Workload Types
… are applied in this research to detect Apache Spark and Hadoop workloads independent of historical data. Clustering techniques are compared in terms of different evaluation metrics, and the ones with the highest performance are introduced. The DBSCAN algorithm has shown the best …
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Large-scale storage, analysis and integration of metabolomics data
… to quantify and identify metabolite features. Apache Spark is responsible for the distribution of these processing jobs to the nodes of the compute cluster where they are parallelized. Every node in the cluster downloads an experiment from the object storage, where it was previously uploaded, …
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An Approach for Fast Score Computation in Bayesian Network Structure Learning Over Large-Scale Distributed Data
… by the distributed computation framework Apache Spark on a variety of synthetic and real datasets with a low accuracy trade-off.
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An adaptive partitioning scheme for ad-hoc and time-varying database analytics
… and an efficient implementation on top of Apache Spark and HDFS. Our experimental results show that the Amoeba storage system provides improved query performance for ad-hoc workloads, adapts to changes in the query workloads, and converges to a steady state in case of recurring workloads. …
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KNN Optimization for Multi-Dimensional Data
… of Big Data by parallelizing the work using Apache Spark. The experimental results of the Spark implementation demonstrate that it outperforms the serial, or local, implementation of this optimization algorithm after the dataset size reaches a specific threshold. Thus, further improving the …
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