CF-RDF: A Lightweight and Efficient Large Scale RDF Data Management System

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.

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Meta-data Management System for High-Performance Large-Scale Scientific Data Access

High Performance Computing — HiPC 2000 - Lecture Notes in Computer Science ◽

10.1007/3-540-44467-x_26 ◽

2000 ◽

pp. 293-300 ◽

Cited By ~ 1

Author(s):

Wei-keng Liao ◽

Xaiohui Shen ◽

Alok Choudhary

Keyword(s):

Data Management ◽

Management System ◽

High Performance ◽

Large Scale ◽

Data Access ◽

Scientific Data ◽

Data Management System ◽

Meta Data

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161 The NeuroStar Outcomes Registry

CNS Spectrums ◽

10.1017/s1092852918000524 ◽

2018 ◽

Vol 23 (1) ◽

pp. 98-98

Author(s):

Miriam Mina ◽

David G. Brock ◽

W. Scott West ◽

Todd Hutton ◽

Kenneth P. Pages ◽

...

Keyword(s):

Data Collection ◽

Data Management ◽

Management System ◽

Large Scale ◽

Remission Rate ◽

Severity Of Illness ◽

Study Data ◽

Data Management System ◽

Patient Treatment ◽

Open Label

AbstractThe NeuroStar Outcomes RegistryObjectiveNeuroStar transcranial magnetic stimulation (TMS) is an effective acute treatment for patients with major depressive disorder (MDD). In order to further understand use of the NeuroStar in a clinical setting, Neuronetics has established a patient treatment and outcomes registry to collect and analyze utilization information on patients receiving treatment with the NeuroStar.MethodsIndividual NeuroStar providers are invited to participate in the registry and agree to provide their de-identified patient treatment data. The NeuroStar has an integrated electronic data management system (TrakStar) which allows for the data collection to be automated. The data collected for the registry include Demographic Elements (age, gender), Treatment Parameters, and Clinical Ratings. Clinical assessments are: Clinician Global Impression - Severity of Illness (CGI-S) and thePatient Health Questionnaire 9-item (PHQ-9). De-identified patient data is uploaded to Registry server; an independent statistical service then creates final data reports.ResultsOver 500 patients have entered the NeuroStar Outcomes Registry since Sept 2016. Mean patient age: 48.0 (SD±16.0); 64% Female. Baseline PHQ-9, mean 18.8 (SD±5.0.) Response/Remission Rate, PHQ-9: 61%/33% CGI-S: 78%/59%.ConclusionsFor the initial 500 patients in the Outcomes Registry, approximately 2/3 patients achieve respond and 1/3 patients achieve remission with an acute course of NeuroStar. These treatment outcomes consistent with NeuroStar open-label study data (Carpenter, 2012). The TrakStar data management system makes large scale data collection feasible. The NeuroStarOutcomes Registry is ongoing, and expected to reach 6000 outpatients from more than 47 clinical sites in 36 months.Funding AcknowledgementsNeuronetics, Inc.

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