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Enhanced Clients for Data Stores and Cloud Services

delete2019-10-01
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Arun Iyengar *
DOI：10.1109/TKDE.2018.2873321delete
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Abstract

Abstract

En 中文
Data stores and cloud services are typically accessed using a client-server paradigm wherein the client runs as part of an application process which is trying to access the data store or cloud service. This paper presents the design and implementation of enhanced clients for improving both the functionality and performance of applications accessing data stores or cloud services. Our enhanced clients can improve performance via multiple types of caches, encrypt data for providing confidentiality before sending information to a server, and compress data for reducing the size of data transfers. Our clients can perform data analysis to allow applications to more effectively use cloud services. They also provide both synchronous and asynchronous interfaces. An asynchronous interface allows an application program to access a data store or cloud service and continue execution before receiving a response which can significantly improve performance. We present a Universal Data Store Manager (UDSM) which allows an application to access multiple different data stores and provides a common interface to each data store. The UDSM also can monitor the performance of different data stores. A workload generator allows users to easily determine and compare the performance of different data stores. We also present NLU-SA, an application for performing natural language understanding and sentiment analysis on text documents. NLU-SA is implemented on top of our enhanced clients and integrates text analysis with Web searching. We present results from NLU-SA on sentiment on the Web towards major companies and countries. We also present a performance analysis of our enhanced clients.
Keywords:
Data store client
database client
cloud service client
cognitive service client
web service client
caching
client caching
sentiment analysis
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Journal

IEEE Transactions on Knowledge and Data Engineering cover
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
Papers:
6.8K
Citations:
3.2W

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