SWORD: workload-aware data placement and replica selection for cloud data management systems

2014 ◽  
Vol 23 (6) ◽  
pp. 845-870 ◽  
Author(s):  
K. Ashwin Kumar ◽  
Abdul Quamar ◽  
Amol Deshpande ◽  
Samir Khuller
Keyword(s):  
Data Management ◽  
Data Placement ◽  
Management Systems ◽  
Data Management Systems ◽  
Cloud Data ◽  
Replica Selection ◽  
Selection For ◽  
Cloud Data Management

Towards a Generic Security Framework for Cloud Data Management Environments

2012 ◽  
Vol 3 (1) ◽  
pp. 17-34 ◽  
Author(s):  
Alexandra Carpen-Amarie ◽  
Alexandru Costan ◽  
Catalin Leordeanu ◽  
Cristina Basescu ◽  
Gabriel Antoniu
Keyword(s):  
Data Management ◽  
Denial Of Service ◽  
Management Systems ◽  
Security Level ◽  
Security Framework ◽  
Data Repositories ◽  
Data Management Systems ◽  
Cloud Data ◽  
Management Platform ◽  
Cloud Data Management

Providing an adequate security level in Cloud Environments is currently an extremely active research area. More specifically, malicious behaviors targeting large-scale Cloud data repositories (e.g., Denial of Service attacks) may drastically degrade the overall performance of such systems and cannot be detected by typical authentication mechanisms. This article proposes a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. This security framework is designed to detect and stop a large array of attacks defined through an expressive policy description language and to be easily interfaced with various data management systems. The authors show that they can efficiently protect a data storage system by evaluating the security framework on top of the BlobSeer data management platform. The authors evaluate the benefits of preventing a DoS attack targeted towards BlobSeer through experiments performed on the Grid’5000 testbed.

Benchmarking and Analyzing the Energy Consumption of Cloud Data Management System

2014 ◽  
Vol 36 (7) ◽  
pp. 1485-1499 ◽  
Author(s):  
Jie SONG ◽  
Tian-Tian LI ◽  
Zhi-Liang ZHU ◽  
Yu-Bin BAO ◽  
Ge YU
Keyword(s):  
Energy Consumption ◽  
Data Management ◽  
Management System ◽  
Data Management System ◽  
Cloud Data ◽  
Cloud Data Management

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.

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