Survey on Medical Data Storage Systems

Nowadays digital data storage and digital communication are widely used in the healthcare sector. Since data in the digital form significantly easier to store, retrieve, manipulate, analyses, and manage. Also, digital data eliminate the threat of data loss considerably. These advantages pushing many hospitals to store their data digitally. But, as the patients reveal their private and important information to the doctor, it is very crucial to maintain the privacy, security, and reliability of the healthcare data. In this process of handling the data securely, several technologies are being used like cloud storage, data warehousing, blockchain, etc. The main aim of this survey is to study the different models and technologies in the healthcare sector and analyses them on different parameters like security, privacy, performance, etc. This study will help the new developing healthcare systems to choose appropriate technology and approach to build a more efficient, robust, secure, and reliable system.

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New Trends in Digital Data Storage for the Internet of Things

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.359363 ◽

2018 ◽

Vol 6 (3) ◽

pp. 359-363

Author(s):

A. Saxena ◽

◽

S. Sharma ◽

S. Dangi ◽

A. Sharma ◽

...

Keyword(s):

Internet Of Things ◽

Data Storage ◽

Digital Data ◽

The Internet ◽

Digital Data Storage ◽

The Internet Of Things

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Analog and digital data storage in a phase-coded holographic memory

10.1117/12.304980 ◽

1998 ◽

Cited By ~ 3

Author(s):

Kai-Oliver Mueller ◽

Cornelia Denz ◽

Torsten Rauch ◽

Thorsten Heimann ◽

J. Trumpfheller ◽

...

Keyword(s):

Data Storage ◽

Digital Data ◽

Holographic Memory ◽

Digital Data Storage

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Instance Selection

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch161 ◽

2011 ◽

pp. 1041-1045

Author(s):

Huan Liu

Keyword(s):

Data Mining ◽

Data Storage ◽

Data Reduction ◽

Effective Means ◽

Digital Data ◽

Data Selection ◽

Instance Selection ◽

Redundant Data ◽

Performance Deterioration ◽

Digital Data Storage

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.

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