An efficient index for massive IOT data in cloud environment

Ma, Youzhong; Rao, Jia; Hu, Weisong; Meng, Xiaofeng; Han, Xu; Zhang, Yu; Chai, Yunpeng; Liu, Chunqiu

doi:10.1145/2396761.2398587

Cited by 56 publications

(30 citation statements)

References 10 publications

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“…The IoT systems produce huge amounts of data after a long time of usage [32]. This massive data can be kept in cloud-based data centres for processing [33]. A proper analysis of stored data in a cloud may reveal certain patterns based on which medical information analyst can predict the health risks for a person with disability and generate appropriate health alerts or basic suggestions for treatment, which are very helpful, particularly when applied in homes or communities.…”

Section: Discussionmentioning

confidence: 99%

The Application of the Internet of Things in Healthcare

Alqahtani¹

2018

IJCA

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Communication and sensing devices, as well as their respective software, have become versatile for Information Technology (IT) solutions in different contexts. The emergent technology known as the Internet of Things (IoT) has helped both practitioners and researchers to design innovative solutions in healthcare. The IoT-enabled healthcare research is significant due to its valuable implications, including higher quality and lower cost of services and reliable preventive care. This paper reviews the current literature on the IoT in healthcare and discusses its applications and enabling technologies, as well as critical challenges.

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Section: Discussionmentioning

confidence: 99%

The Application of the Internet of Things in Healthcare

Alqahtani¹

2018

IJCA

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“…Recent work in Internet of Things (IOT) [24] proposes an optimization based on high update throughput and query efficient index framework including pre-splitting the HBase region for reducing the cost of data movement. Likewise, [22] addresses the problem of the HBase multidimensional data queries (upto four-dimension) in IOT with better response time.…”

Section: Related Workmentioning

confidence: 99%

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

Bao

Plassard

Landman

et al. 2017

2017 IEEE International Conference on Cloud Engineering (IC2E)

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Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based “medical image processing-as-a-service” offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop’s distributed file system. Despite this promise, HBase’s load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage.

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“…They provide a small-scale performance evaluation of their framework. Ma et al consider an indexing mechanism for "massive" IoT data in a cloud environment [19]. Their performance evaluation involves four orders of magnitude fewer records that our study.…”

Section: Related Workmentioning

confidence: 99%

IoTAbench

Arlitt

Marwah

Bellala

et al. 2015

Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

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The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This "Internetof-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer "Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform "apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.

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An efficient index for massive IOT data in cloud environment

Cited by 56 publications

References 10 publications

The Application of the Internet of Things in Healthcare

The Application of the Internet of Things in Healthcare

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

IoTAbench

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