A Partial Solution To The Fourth Dimension Problem In Digital Data Reduction--Turnaround Time

1979 ◽  
Author(s):  
David M. Stern
Keyword(s):  
Data Reduction ◽  
Partial Solution ◽  
Turnaround Time ◽  
Digital Data ◽  
Fourth Dimension

Instance Selection

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.

A Global Survey on Data Deduplication

2018 ◽  
Vol 10 (4) ◽  
pp. 43-66 ◽  
Author(s):  
Shubhanshi Singhal ◽  
Pooja Sharma ◽  
Rajesh Kumar Aggarwal ◽  
Vishal Passricha
Keyword(s):  
Data Reduction ◽  
Cloud Storage ◽  
Storage Systems ◽  
Distributed Storage ◽  
Digital Data ◽  
Data Deduplication ◽  
Computationally Efficient ◽  
Redundant Data ◽  
Single Instance ◽  
Local Storage

This article describes how data deduplication efficiently eliminates the redundant data by selecting and storing only single instance of it and becoming popular in storage systems. Digital data is growing much faster than storage volumes, which shows the importance of data deduplication among scientists and researchers. Data deduplication is considered as most successful and efficient technique of data reduction because it is computationally efficient and offers a lossless data reduction. It is applicable to various storage systems, i.e. local storage, distributed storage, and cloud storage. This article discusses the background, components, and key features of data deduplication which helps the reader to understand the design issues and challenges in this field.

Specplot - An Interactive Data Reduction and Display Program for Spectral Data

1979 ◽  
Vol 23 ◽  
pp. 305-311 ◽  
Author(s):  
Raymond P. Goehner
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Usual Method ◽  
Spectral Lines ◽  
Digital Data ◽  
Strip Chart Recorder ◽  
Automatic Data ◽  
Chart Recorder ◽  
Rapid Pace ◽  
Interactive Data

The automation of analytical equipment is proceeding at a rapid pace, particularly since the introduction of inexpensive microcomputer systems. Most of this equipment has one characteristic in common, that is, they produce digital spectral data. The usual method of recording spectral data has been the strip chart recorder. Strip charts require the hand encoding of position and intensities of the spectral lines. This requires that all of the lines be on scale or that the sample be run several times in order to amplify weaker lines. This problem is eliminated by recording the data digitally. Digital data can then be rapidly plotted on a cathode ray terminal to any desired scale. The user of digital data has access to a great variety of automatic data reduction programs.

Digital Data Reduction Of Rotationally Invariant Interferograms.

1987 ◽  
Author(s):  
J. J. Sanchez-Mondragon ◽  
D. Malacara ◽  
A. Davila
Keyword(s):  
Data Reduction ◽  
Digital Data ◽  
Rotationally Invariant

COLLECTION AND PROCESSING OF DIGITIZED ACOUSTIC LOG DATA

Geophysics ◽  
1960 ◽  
Vol 25 (4) ◽  
pp. 939-947 ◽  
Author(s):  
R. A. Broding ◽  
J. L. Poole
Keyword(s):  
Data Reduction ◽  
Synthetic Seismograms ◽  
Digital Data ◽  
Precision Data ◽  
Log Data ◽  
Reduction Processes ◽  
Geophysical Logs ◽  
Analog Form ◽  
Electronic Computers ◽  
Computational Procedures

The mathematical processes involved in making acoustic logs useful for the geophysicist or production analyst have prompted the handling of log data by digital means. Digital punched tape logs from retraced analog recordings as well as digital recordings made by punching tapes directly from the recording galvanometer signal obtained during a logging survey are being used as input to electronic computers. These computers can be programmed to perform the normal computations now used in data reduction. This process yields higher precision data and allows computational procedures to be performed that previously were considered burdensome or tedious. The technique is attractive in preparation of geophysical logs, synthetic seismograms, calculating effective porosities and saturations. However, the requirement of converting recorded digital data to be compatible with the input of different digital computers and to all the various formats for individual programs is a problem that will require standardization in formats. It is evident that the advantages to be realized in data reduction processes will require the log of the future to be recorded in digital as well as analog form.

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