The impact of cluster characteristics on HiveQL query optimization

Joldzic, Ognjen; Vuković, Dijana

doi:10.1109/telfor.2013.6716360

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…Besides the voices of the users that may be directly stored in HDFS, logs and other data may be stored in a traditional database such as MySQL or a NoSQL database such as Redis and MongoDB. Some information that reflects the health of an RS system is extracted immediately from the logs by Flume NG [15], Sqoop [7] and HiveQL [9]. The health-related information of the system components can be received by monitors, and then the supervision tools can adjust the configuration and tune the running status of the system if some unusual events have happened.…”

Section: System Architecturementioning

confidence: 99%

Knowledge Transfer Using User-Generated Data within Real-Time Cloud Services

Zhang¹,

Pan²,

Cai³

et al. 2020

KSII TIIS

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When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

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Section: System Architecturementioning

confidence: 99%