scholarly journals Selecting and using faculty data management software systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rita Jeanne Shea-Van Fossen ◽  
Rosa Di Virgilio Taormina ◽  
JoDee LaCasse
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Business School ◽  
School Administrators ◽  
Management Systems ◽  
Software Systems ◽  
Data Management Systems ◽  
Content Type ◽  
System Selection ◽  
Data Management Software

Purpose The purpose of this paper is to determine which software systems business school administrators use to support accreditation efforts and how administrators select and use these systems. This study also provides best practice suggestions from institutions using faculty data management systems to support accreditation efforts. Design/methodology/approach This study used a sequential explanatory design using an internet-based survey for business school administrators involved with accreditation reporting with follow-up interviews with survey respondents. Findings There are four major software vendors that most respondents use for managing reporting of faculty research activity and sufficiency. The location of the school appears to influence the system selected. For assurance of learning reporting, most schools used an in-house or manual system. Respondents highlighted the importance of doing a thorough needs analysis before selecting a system. Research limitations/implications Although respondents were geographically diverse, having a larger sample with schools in developing regions would provide greater generalizability of results. Practical implications This study gives business school leaders a comprehensive overview of the business schools’ data management systems, criteria used in system selection and best practices for system selection and implementation, faculty engagement and ongoing maintenance. Originality/value This study addresses the limited attention given to resources and best practices for selecting and implementing faculty data management software for accreditation in the academic and industry literature despite the significant investment of resources for schools and the importance such systems play in a successful accreditation effort.

Effective Materials Property Information Management for the 21st Century

2009 ◽  
Author(s):  
Weiju Ren ◽  
David Cebon ◽  
Steven M. Arnold
Keyword(s):  
Data Management ◽  
Information Management ◽  
Business Models ◽  
Constitutive Models ◽  
Management Systems ◽  
Software Systems ◽  
Sensitive Information ◽  
Data Management Systems ◽  
Wide Range ◽  
Management Functions

This paper discusses key principles for the development of materials property information management software systems. There are growing needs for automated materials information management in various organizations. In part these are fuelled by the demands for higher efficiency in material testing, product design and engineering analysis. But equally important, organizations are being driven by the need for consistency, quality and traceability of data, as well as control of access to sensitive information such as proprietary data. Further, the use of increasingly sophisticated nonlinear, anisotropic and multi-scale engineering analyses requires both processing of large volumes of test data for development of constitutive models and complex materials data input for Computer-Aided Engineering (CAE) software. And finally, the globalization of economy often generates great needs for sharing a single “gold source” of materials information between members of global engineering teams in extended supply-chains. Fortunately, material property management systems have kept pace with the growing user demands and evolved to versatile data management systems that can be customized to specific user needs. The more sophisticated of these provide facilities for: (i) data management functions such as access, version, and quality controls; (ii) a wide range of data import, export and analysis capabilities; (iii) data “pedigree” traceability mechanisms; (iv) data searching, reporting and viewing tools; and (v) access to the information via a wide range of interfaces. In this paper the important requirements for advanced material data management systems, future challenges and opportunities such as automated error checking, data quality characterization, identification of gaps in datasets, as well as functionalities and business models to fuel database growth and maintenance are discussed.

Towards Observability Data Management at Scale

2021 ◽  
Vol 49 (4) ◽  
pp. 18-23
Author(s):  
Suman Karumuri ◽  
Franco Solleza ◽  
Stan Zdonik ◽  
Nesime Tatbul
Keyword(s):  
Data Management ◽  
Large Scale ◽  
Management Systems ◽  
Software Systems ◽  
Data Management Systems ◽  
Database Research ◽  
Challenges And Opportunities ◽  
Current Experience

Observability has been gaining importance as a key capability in today's large-scale software systems and services. Motivated by current experience in industry exemplified by Slack and as a call to arms for database research, this paper outlines the challenges and opportunities involved in designing and building Observability Data Management Systems (ODMSs) to handle this emerging workload at scale.

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.

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

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.

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