Results 81 to 90 of about 2,924 (186)
GASSER: An Auto-Tunable System for General Sliding-Window Streaming Operators on GPUs
IEEE Access, 2019 Today's stream processing systems handle high-volume data streams in an efficient manner. To achieve this goal, they are designed to scale out on large clusters of commodity machines.
Tiziano De Matteis +4 more
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Extracting Build Changes with BUILDDIFF
, 2017 Build systems are an essential part of modern software engineering projects. As software projects change continuously, it is crucial to understand how the build system changes because neglecting its maintenance can lead to expensive build breakage ...
Macho, Christian +2 more
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Adaptive Data Quality Monitoring: A Comprehensive Framework for Streaming Data Integrity
IEEE AccessReal-time streaming pipelines are increasingly central to data-driven product decisions, yet the data quality literature has focused predominantly on batch systems.
Tulika Bhatt, Saurabh Jaluka
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A tool for improving the delivery of integrated intensive health care performance
International Journal of Integrated Care, 2019 Introduction: In intensive and intermediate care units, patient data consists of lower frequency asynchronous clinical data and higher frequency synchronous physiological data streams generated by medical equipment.
Javier Balladini +6 more
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New techniques to lower the tail latency in stream processing systems [PDF]
, 2016 Over the past decade, the demand for real time processing of huge amount of streaming data has emerged and grown rapidly. Apache Storm, Apache Flink, Samza and many other stream processing frameworks have been proposed and implemented to meet this need ...
Du, Guangxiang
core
Evaluating Apache Spark and Apache Flink for Modern Data Streaming Solutions
International Journal For Multidisciplinary Research, 2021 Real-time data processing enables swift decisions based on continuous data streams and immediate insights from various data sources, modern data architectures now rely mostly on it. The paper reviews two of the most widely known distributed systems for real-time analytics: Apache Spark and Apache Flink, along with their unique challenges and solutions.
openaire +1 more source
Lightweight Asynchronous Snapshots for Distributed Dataflows
, 2015 Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput.
Carbone, Paris +4 more
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CLEF NewsREEL 2016: Comparing Multi-Dimensional Offline and Online Evaluation of News Recommender Systems [PDF]
, 2016 Running in its third year at CLEF, NewsREEL challenged participants to develop news recommendation algorithms and have them benchmarked in an online (Task 1) and offline setting (Task 2), respectively.
Brodt, Torben +7 more
core
Prink: $$k_s$$-Anonymization for Streaming Data in Apache Flink
In this paper, we present Prink, a novel and practically applicable concept and fully implemented prototype for ks-anonymizing data streams in real-world application architectures. Building upon the pre-existing, yet rudimentary CASTLE scheme, Prink for the first time introduces semantics-aware ks-anonymization of non-numerical (such as categorical or ...
Philip Groneberg +6 more
openaire +2 more sources
Ìнформаційні технології та компʼютерна інженеріяThe study aimed to analyse and substantiate effective methods for analysing inefficiently structured data using neural networks to provide operational decision support in complex environments.
M. Demchyna
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