Study on Data Warehouse Based Equipment Support Data Management

2013 ◽  
Vol 462-463 ◽  
pp. 1072-1075
Author(s):  
Feng Xie ◽  
Jiang Sheng Sun ◽  
Wei Jie Liang ◽  
Dong Sheng Dai
Keyword(s):  
Decision Making ◽  
Data Management ◽  
Data Warehouse ◽  
Heterogeneous Data ◽  
Data Partitioning ◽  
Point Of View ◽  
Data Management System ◽  
Management Systems ◽  
Equipment Management ◽  
Data Granularity

There are more and more data produced in equipment management systems. For decision making, we must collect and integrate all kinds of data and information from different management systems. By analyzing the distributional and heterogeneous data resources, the architecture of equipment management data warehouse is proposed in this paper. We discussed important components of data warehouse for equipment support. The components include metadata, data market, data granularity and data partitioning. By discussing the organization of the data warehouse from the macroscopic point of view, the equipment support data management system with the function of decision making will be used in the future.

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

A Review of Clinical Data Management Systems Used in Clinical Trials

2019 ◽  
Vol 14 (1) ◽  
pp. 10-23 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Data Management System ◽  
Management Systems ◽  
Management Process ◽  
Data Management Systems ◽  
Ovid Medline ◽  
Research Findings ◽  
Accuracy Of Results

Background:A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.Methods:This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.Results:The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.Conclusion:Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

PO-0933: A centralized data management system of heterogeneous data sources for radiation therapy

2014 ◽  
Vol 111 ◽  
pp. S120
Author(s):  
O. Diesenbacher ◽  
M. Memelink ◽  
F. Sedlmayer ◽  
H. Deutschmann ◽  
P. Steininger
Keyword(s):  
Radiation Therapy ◽  
Data Management ◽  
Management System ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Data Management System ◽  
Heterogeneous Data Sources

Based on the Condition of the Equipment Management

2012 ◽  
Vol 490-495 ◽  
pp. 2091-2094 ◽  
Author(s):  
Bing Jian Wu ◽  
Hui Tian Zhu ◽  
Yuan Wang
Keyword(s):  
Decision Making ◽  
Data Management ◽  
Scientific Basis ◽  
Equipment Management ◽  
Production Stage ◽  
Comprehensive Data ◽  
Key Technologies ◽  
Equipment Development

With the edge of the application and key technologies, the management ideas that are the integration of equipment development and production stage of technology status management put forward and combed this project, providing comprehensive data management environment, providing rapid decision-making, timely information and scientific basis

scholarly journals A Coding-Free Software Framework for Developing Lightweight Web Data Management Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 865
Author(s):  
Can Yang ◽  
Shiying Pan ◽  
Runmin Li ◽  
Yu Liu ◽  
Lizhang Peng
Keyword(s):  
Data Management ◽  
Management System ◽  
System Development ◽  
Data Management System ◽  
Management Systems ◽  
Web Data ◽  
Data Management Systems ◽  
Construction Methods ◽  
Free Construction ◽  
Web Data Management

Increasingly more enterprises are intending to deploy data management systems in the cloud. However, the complexity of software development significantly increases both time and learning costs of data management system development. In this paper, we investigate the coding-free construction of a data management system based on Software-as-a-Service (SaaS) architecture, in which a practical application platform and a set of construction methods are proposed. Specifically, by extracting the common features of data management systems, we design a universal web platform to quickly generate and publish customized system instances. Then, we propose a method to develop a lightweight data management system using a specific requirements table in a spreadsheet. The corresponding platform maps the requirements table into a system instance by parsing the table model and implementing the objective system in the running stage. Finally, we implement the proposed framework and deploy it on the web. The empirical results demonstrate the feasibility and availability of the coding-free method for developing lightweight web data management systems.

scholarly journals Overview of the ATLAS distributed computing system

2019 ◽  
Vol 214 ◽  
pp. 03010 ◽  
Author(s):  
Johannes Elmsheuser ◽  
Alessandro Di Girolamo
Keyword(s):  
Distributed Computing ◽  
Data Management ◽  
New Technologies ◽  
Computing System ◽  
Data Management System ◽  
Management Systems ◽  
New Developments ◽  
Atlas Experiment ◽  
Physics Program ◽  
Workflow System

The CERN ATLAS experiment successfully uses a worldwide computing infrastructure to support the physics program during LHC Run 2. The Grid workflow system PanDA routinely manages 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 370 PB of data is distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing LHC luminosity in future runs new developments are underway to even more efficiently use opportunistic resources such as HPCs and utilize new technologies. This paper will review and explain the outline and the performance of the ATLAS distributed computing system and give an outlook to new workflow and data management ideas for the beginning of the LHC Run 3. It will be discussed that the ATLAS workflow and data management systems are robust, performant and can easily cope with the higher Run 2 LHC performance. There are presently no scaling issues and each subsystem is able to sustain the large loads.

scholarly journals THESE MODELS NEED ENTERPRISE DATA MANAGEMENT!

10.28945/3651 ◽  
2017 ◽  
Vol 6 ◽  
pp. 01
Author(s):  
Jay Hoecker ◽  
Debbie Bernal ◽  
Alex Brito ◽  
Arda Ergonen ◽  
Richard Stiftinger
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Management System ◽  
Current Data ◽  
Scientific Models ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technology Platform

The current data management systems for the life cycle of scientific models needed an upgrade. What technology platform offered the best option for an Enterprise Data Management system?

scholarly journals Rancangan Desain Data Warehouse Kunjungan Wisatawan Ke Bali Baw Tour & Travel

2021 ◽  
Vol 3 (1) ◽  
pp. 14-18
Author(s):  
Ratna Sari ◽  
I Putu Agus Eka Pratama
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Data Warehouse ◽  
Business Development ◽  
Daily Process ◽  
Fierce Competition ◽  
Travel Needs

This business development in the globalized era provoked fierce competition especially with the application of it to support his daily process especially in data management, therefore, the BAW Tour & Travel needs a system for data processing so that data can be stored and integrated with each other or what's called data warehouse and followed by other regulatory ETL, OLAP methods in analysis and quick decision-making. Using the data warehouse can figure out a spike in visitors' visits so it can predict people to be able to predict the number of guides during a month in case there's a spike that reduces anyone's denial of acceptance on the grounds of lack of a guide and to booked a motel because it's already full.

PolyFrame

2021 ◽  
Vol 14 (11) ◽  
pp. 2296-2304
Author(s):  
Phanwadee Sinthong ◽  
Michael J. Carey
Keyword(s):  
Data Management ◽  
Data Analytics ◽  
Data Science ◽  
Database Systems ◽  
Query Languages ◽  
Machine Learning Techniques ◽  
Data Management System ◽  
Management Systems ◽  
Learning Techniques ◽  
Scaling Out

In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision-making and applications. Scaling data analyses to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by providing the data scientists' familiar interface, Pandas Dataframe, and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a large volume of data, the initial version only generated SQL++ queries and only operated against AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems, making it more flexible for deployment against other data management systems with composable query languages.

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