Future Trends in Computer Applications

1980 ◽  
Vol 45 (3) ◽  
pp. 462-471 ◽  
Author(s):  
Sylvia W. Gaines ◽  
Warren M. Gaines
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Significant Role ◽  
Computer Technology ◽  
Computer Applications ◽  
Archaeological Research ◽  
Future Trends ◽  
New Techniques ◽  
Processing And Storage ◽  
And Storage

Rapidly evolving computer technology can play a significant role in all phases of future archaeological research. Technical trends are summarized, including hardware (data processing and storage), software (languages and data management), and data processing and entry. The utility and benefits of these new techniques are explored in relation to several current archaeological projects.

Keysystems in large systems implementing distributed data processing and storage technologies

2021 ◽  
Author(s):  
V.G. Belenkov ◽  
V.I. Korolev ◽  
V.I. Budzko ◽  
D.A. Melnikov
Keyword(s):  
Data Processing ◽  
Distributed Processing ◽  
Specific Work ◽  
Distributed Data ◽  
Distributed Data Processing ◽  
Technical Specifications ◽  
The Creation ◽  
Processing And Storage ◽  
Storage Technologies ◽  
And Storage

The article discusses the features of the use of the cryptographic information protection means (CIPM)in the environment of distributed processing and storage of data of large information and telecommunication systems (LITS).A brief characteristic is given of the properties of the cryptographic protection control subsystem - the key system (CS). A description is given of symmetric and asymmetric cryptographic systems, required to describe the problem of using KS in LITS.Functional and structural models of the use of KS and CIPM in LITS, are described. Generalized information about the features of using KS in LITS is given. The obtained results form the basis for further work on the development of the architecture and principles of KS construction in LITS that implement distributed data processing and storage technologies. They can be used both as a methodological guide, and when carrying out specific work on the creation and development of systems that implement these technologies, as well as when forming technical specifications for the implementation of work on the creation of such systems.

Use of RFID in Supply Chain Data Processing

2008 ◽  
pp. 1184-1191
Author(s):  
Jan Owens ◽  
Suresh Chalasani ◽  
Jayavel Sounderpandian
Keyword(s):  
Supply Chain ◽  
Data Processing ◽  
Radio Frequency ◽  
Supply Chains ◽  
Radio Frequency Identification ◽  
Department Of Defense ◽  
Frequency Identification ◽  
Processing And Storage ◽  
And Storage ◽  
Existing Data

The use of Radio Frequency Identification (RFID) is becoming prevalent in supply chains, with large corporations such as Wal-Mart, Tesco, and the Department of Defense phasing in RFID requirements on their suppliers. The implementation of RFID can necessitate changes in the existing data models and will add to the demand for processing and storage capacities. This article discusses the implications of the RFID technology on data processing in supply chains.

Use of RFID in Supply Chain Data Processing

2011 ◽  
pp. 1160-1165 ◽  
Author(s):  
Jan Owens ◽  
Suresh Chalasani ◽  
Jayavel Sounderpandian
Keyword(s):  
Supply Chain ◽  
Data Processing ◽  
Radio Frequency ◽  
Supply Chains ◽  
Radio Frequency Identification ◽  
Department Of Defense ◽  
Frequency Identification ◽  
Processing And Storage ◽  
And Storage ◽  
Existing Data

The use of Radio Frequency Identification (RFID) is becoming prevalent in supply chains, with large corporations such as Wal-Mart, Tesco, and the Department of Defense phasing in RFID requirements on their suppliers. The implementation of RFID can necessitate changes in the existing data models and will add to the demand for processing and storage capacities. This article discusses the implications of the RFID technology on data processing in supply chains.

scholarly journals ATLAS: Big Data in a Small Package?

2015 ◽  
Vol 10 (S318) ◽  
pp. 299-305
Author(s):  
Larry Denneau
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Moving Objects ◽  
Low Earth Orbit ◽  
Near Misses ◽  
Transient Sources ◽  
Geo Satellites ◽  
Processing And Storage ◽  
Near Earth Asteroids ◽  
And Storage

AbstractFor even small astronomy projects, the petabyte scale is now upon us. The Asteroid Terrestrial-impact Last Alert System (Tonry 2011) will survey the entire visible sky from Hawaii multiple times per night to search for near-Earth asteroids on impact trajectories. While the ATLAS optical system is modest by modern astronomical standards — two 0.5 m F/2.0 telescopes — each night the ATLAS system will measure nearly 109 astronomical sources to a photometric accuracy of <5%, totaling 1012 individual observations over its initial 3-year mission. This ever-growing dataset must be searched in real-time for moving objects and transients then archived for further analysis, and alerts for newly discovered near-Earth asteroids (NEAs) disseminated within tens of minutes from detection. ATLAS's all-sky coverage ensures it will discover many ‘rifle shot’ near-misses moving rapidly on the sky as they shoot past the Earth, so the system will need software to automatically detect highly-trailed sources and discriminate them from the thousands of low-Earth orbit (LEO) and geosynchronous orbit (GEO) satellites ATLAS will see each night. Additional interrogation will identify interesting phenomena from millions of transient sources per night beyond the solar system. The data processing and storage requirements for ATLAS demand a ‘big data’ approach typical of commercial internet enterprises. We describe our experience in deploying a nimble, scalable and reliable data processing infrastructure, and suggest ATLAS as steppingstone to data processing capability needed as we enter the era of LSST.

The upgrade of data processing and storage system for EAST NBI

2021 ◽  
Vol 173 ◽  
pp. 112849
Author(s):  
Yu Gu ◽  
Mengqi Fan ◽  
Yuanzhe Zhao ◽  
Xiaodan Zhang
Keyword(s):  
Data Processing ◽  
Storage System ◽  
Processing And Storage ◽  
And Storage

scholarly journals Data Science: Measuring Uncertainties

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1438
Author(s):  
Carlos Alberto de Braganca Pereira ◽  
Adriano Polpo ◽  
Agatha Sacramento Rodrigues
Keyword(s):  
Data Processing ◽  
Data Science ◽  
Storage Capacity ◽  
Processing And Storage ◽  
And Storage

With the increase in data processing and storage capacity, a large amount of data is available [...]

Sign in / Sign up

Export Citation Format

Share Document