Requirements and Data Content Evaluation of Industry In-House Data Management System

2010 ◽  
Author(s):  
Beshoy Morkos ◽  
Shraddha Joshi ◽  
Joshua D. Summers ◽  
Gregory G. Mocko
Keyword(s):  
Data Management ◽  
Management System ◽  
Large Scale ◽  
Data Management System ◽  
Industrial Case Study ◽  
Large Scale Data ◽  
Data Content ◽  
Scale Data ◽  
Future Growth

This paper presents an industrial case study performed on an in-house developed data management system for an automation firm. This data management system has been in use and evolving over a span of fifteen years. To ensure the system is robust to withstand the future growth of the corporation, a study is done to identify deficiencies that may prohibit efficient large scale data management. Specifically, this case study focused on the means in which project requirements are managed and explored the issues of perceived utility in the system. Two major findings are presented: completion metrics are not consistent or expressive of the actual needs and there is no linking between the activities and the original client requirements. Thus, the results of the study were used to depict the potential vulnerability of such deficiencies.

Design and Implementation of Large-Scale Data Management System Based on C/S Structure

2014 ◽  
Vol 602-605 ◽  
pp. 3265-3267
Author(s):  
Zhan Kun Zhao
Keyword(s):  
Data Management ◽  
Management System ◽  
Large Scale ◽  
Data Management System ◽  
Scientific Management ◽  
Employee Productivity ◽  
Report Generation ◽  
Large Scale Data ◽  
Depth Analysis ◽  
Scale Data

With the development of computer and network technology, the storage and analysis for complex and large-scale of data can be implemented. According to the requirement, this paper designs a large-scale data management system based on C/S structure which realizes concentration, integration, security management, in-depth analysis and report generation of the field data. Application of the system can improve the scientific management level of enterprises, enhance employee productivity, ensure security, integrity and accuracy of the data, and provide important data for the performance review and decisions of enterprises development, thus improving economic efficiency of enterprises.

Data Modeling for Big Data

2015 ◽  
Vol 6 (1) ◽  
pp. 1-11 ◽  
Author(s):  
M Misbachul Huda ◽  
Dian Rahma Latifa Hayun ◽  
Zhin Martun
Keyword(s):  
Big Data ◽  
Data Management ◽  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Large Scale ◽  
Database Management System ◽  
Large Scale Data ◽  
Relational Database Management ◽  
Scale Data

Today the rapid growth of the internet and the massive usage of the data have led to the increasing CPU requirement, velocity for recalling data, a schema for more complex data structure management, the reliability and the integrity of the available data. This kind of data is called as Large-scale Data or Big Data. Big Data demands high volume, high velocity, high veracity and high variety. Big Data has to deal with two key issues, the growing size of the datasets and the increasing of data complexity. To overcome these issues, today researches are devoted to kind of database management system that can be optimally used for big data management. There are two kinds of database management system, relational database management system and nonrelational system that can be optimally used for big data management. There are two kinds of database management, Relational Database Management and Non-relational Database Management. This paper will give reviews about these two database management system, including description, vantage, structure and the application of each DBMS. Index Terms - Big Data, DBMS, Large-scale Data, Non-relational Database, Relational Database.

Exploration of Tourist Activities in Urban Destination Using Venue Check-In Data

2019 ◽  
Vol 44 (3) ◽  
pp. 472-498
Author(s):  
Huy Quan Vu ◽  
Jian Ming Luo ◽  
Gang Li ◽  
Rob Law
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Traditional Approach ◽  
Urban Tourism ◽  
Data Set ◽  
Tourism Marketing ◽  
Large Scale Data ◽  
New Type ◽  
Scale Data

Understanding the differences and similarities in the activities of tourists from various cultures is important for tourism managers to develop appropriate plans and strategies that could support urban tourism marketing and managements. However, tourism managers still face challenges in obtaining such understanding because the traditional approach of data collection, which relies on survey and questionnaires, is incapable of capturing tourist activities at a large scale. In this article, we present a method for the study of tourist activities based on a new type of data, venue check-ins. The effectiveness of the presented approach is demonstrated through a case study of a major tourism country, France. Analysis based on a large-scale data set from 19 tourism cities in France reveals interesting differences and similarities in the activities of tourists from 14 markets (countries). Valuable insights are provided for various urban tourism applications.

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