Graph Data Management Systems

2018 ◽  
pp. 1-9
Author(s):  
Marcus Paradies ◽  
Stefan Plantikow ◽  
Oskar van Rest
Keyword(s):  
Data Management ◽  
Management Systems ◽  
Data Management Systems ◽  
Graph Data ◽  
Graph Data Management

scholarly journals Graph data management systems for new application domains

2011 ◽  
Vol 4 (12) ◽  
pp. 1510-1511 ◽  
Author(s):  
Philippe Cudré-Mauroux ◽  
Sameh Elnikety
Keyword(s):  
Data Management ◽  
Management Systems ◽  
Data Management Systems ◽  
Graph Data ◽  
Graph Data Management

Graph Data Management Systems

2019 ◽  
pp. 822-830
Author(s):  
Marcus Paradies ◽  
Stefan Plantikow ◽  
Oskar van Rest
Keyword(s):  
Data Management ◽  
Management Systems ◽  
Data Management Systems ◽  
Graph Data ◽  
Graph 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.

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.

ByShard

2021 ◽  
Vol 14 (11) ◽  
pp. 2230-2243
Author(s):  
Jelle Hellings ◽  
Mohammad Sadoghi
Keyword(s):  
Data Management ◽  
Phase Locking ◽  
Large Data ◽  
General Purpose ◽  
Management Systems ◽  
Two Phase ◽  
Data Management Systems ◽  
Malicious Behavior ◽  
Byzantine Failures ◽  
Do So

The emergence of blockchains has fueled the development of resilient systems that can deal with Byzantine failures due to crashes, bugs, or even malicious behavior. Recently, we have also seen the exploration of sharding in these resilient systems, this to provide the scalability required by very large data-based applications. Unfortunately, current sharded resilient systems all use system-specific specialized approaches toward sharding that do not provide the flexibility of traditional sharded data management systems. To improve on this situation, we fundamentally look at the design of sharded resilient systems. We do so by introducing BYSHARD, a unifying framework for the study of sharded resilient systems. Within this framework, we show how two-phase commit and two-phase locking ---two techniques central to providing atomicity and isolation in traditional sharded databases---can be implemented efficiently in a Byzantine environment, this with a minimal usage of costly Byzantine resilient primitives. Based on these techniques, we propose eighteen multi-shard transaction processing protocols. Finally, we practically evaluate these protocols and show that each protocol supports high transaction throughput and provides scalability while each striking its own trade-off between throughput, isolation level, latency , and abort rate. As such, our work provides a strong foundation for the development of ACID-compliant general-purpose and flexible sharded resilient data management systems.

Sign in / Sign up

Export Citation Format

Share Document