Asymmetric Index Management Scheme for High-capacity Compressed Databases

Byun, Siwoo; Jang, Seok-Woo

doi:10.5762/kais.2016.17.7.293

Cited by 3 publications

(5 citation statements)

References 4 publications

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“…In addition, medical data can be compressed or encrypted to export to higher systems, such as medical cloud. Thus, edge gateways require efficient data management to perform critical functions such as data analysis, compression, filtering, encryption, and so on [6].…”

Section: Related Workmentioning

confidence: 99%

Design of Context-aware Resource Management for Healthcare IoT

Byun¹

2020

IJSDA

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Edge computing is emerging as a technology that complements cloud computing in an IoT environment where huge amounts of data are generated in real time. This paper introduces recent IoT network and edge computing technology and describes healthcare IoT network and related edge technologies. This paper also proposes efficient resource management called CaRM to ensure stable data services for sensor nodes and edge gateways in the edgebased IoT environment. In CaRM, the context-aware classification and dynamic resource management are devised to improve the stability and performance for healthcare IoT service. Based on the context-aware classification, the sensor-level resource tree and applicationlevel resource tree are generated. After creating a service-context tree that incorporates these two trees, CaRM controls the permitted range of individual resource consumption at each service level to guarantee ensure service.

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Section: Related Workmentioning

confidence: 99%

Design of Context-aware Resource Management for Healthcare IoT

Byun¹

2020

IJSDA

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“…Traditional row(record) based storage systems have to read a lot of unrelated data from data repository, so they have performance limitations when handling a number of concurrent, diverse queries. In contrast, in column-based storage systems, only the columns related to the query need to be taken from the data repository which is densely compressed to improve read efficiency [10,11]. In particular, column-wise storing skill enables significant improvements in data compression, reduces the number of storage drives while maintaining high performance.…”

Section: Compression For Iot Datamentioning

confidence: 99%

“…As a simple example, each city name, such as Los Angeles, can be mapped to an integer value such as 1397, which requires storage of two bytes instead of 11 bytes. Run-length encoding is another simple compression technique that uses counts of repetitions, which can be used to reduce space needs [10]. Compared to simple row-oriented databases, column storage methods need to be reconstructed for most database access standards (e.g., ODBC, JDBC).…”

Section: Compression For Iot Datamentioning

confidence: 99%

Gateway-based Resource Control for Reliable IoT Environments

Byun¹

2019

IJATCSE

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Edge computing refers to decentralized computing technology to reduce cloud computing's overload or security problems that redirect local data to a central data center. Edge computing is emerging as a technology that complements cloud computing in an IoT environment where huge amounts of data are generated in real time. This study introduces architectures of IoT sensor network, data control schemes, fog computing and edge computing technology. This study also reviews the requirements of IoT service from the aspects of transmission, storage and computing, and analyzes data balancing and data recovery. This paper also proposes a data management scheme called DaRM, utilizing dual IoT gateways to provide efficient and stable data service in IoT environment. DaRM exploits both edge gateway and fog gateway to improve data reliability and communication efficiency. DaRM can transmit compressed sensor data by reducing overhead in advance through delayed replication and column-based data compression.

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“…Due to the characteristics of its architecture, though, flash memory's erasing/reading/writing capabilities are much slower than PRAM. That is, PRAM is much faster than flash memory and ten times more durable [1].…”

Section: Motivationmentioning

confidence: 99%

“…Its density and I/O performance have been improved to a level at which it can be used not only as a main storage for portable smart systems but also as a main memory for hybrid database storages. [1] Although flash memory is not as fast as RAM, it is a hundred times faster than a hard disk in data access operations. Currently, flash memory SSD is largely recognized as the preferred storage media for the high-speed analysis systems.…”

Section: Introductionmentioning

confidence: 99%