General Graph Data De-Anonymization

This study explores the use of a new protocol in hypertension care, in which continuous patient-generated data reported through digital technology are presented in graphical form and discussed in follow-up consultations with nurses. This protocol is part of an infrastructure design project in which patients and medical professionals are co-designers. The approach used for the study was interaction analysis, which rendered possible detailed in situ examination of local variations in how nurses relate to the protocol. The findings show three distinct engagements: (1) teasing out an average blood pressure, (2) working around the protocol and graph data and (3) delivering an analysis. It was discovered that the graphical representations structured the consultations to a great extent, and that nurses mostly referred to graphs that showed blood pressure values, which is a measurement central to the medical discourse of hypertension. However, it was also found that analysis of the data alone was not sufficient to engage patients: nurses' invisible and inclusion work through eliciting patients' narratives played an important role here. A conclusion of the study is that nurses and patients both need to be more thoroughly introduced to using protocols based on graphs for more productive consultations to be established.

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Searching Keyword on Uncertain Graph Data by Using Enhanced Approach

International Journal of Innovative Research in Computer and Communication Engineering ◽

10.15680/ijircce.2015.0307015 ◽

2015 ◽

Vol 03 (07) ◽

pp. 6464-6470

Author(s):

Ashwini V. Urade, Pravin Kulurkar

Keyword(s):

Uncertain Graph ◽

Graph Data

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Using RAPIDS AI to Accelerate Graph Data Science Workflows

2020 IEEE High Performance Extreme Computing Conference (HPEC) ◽

10.1109/hpec43674.2020.9286224 ◽

2020 ◽

Author(s):

Todd Hricik ◽

David Bader ◽

Oded Green

Keyword(s):

Data Science ◽

Graph Data

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Integrating molecular graph data of drugs and multiple -omic data of cell lines for drug response prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2021.3096960 ◽

2021 ◽

pp. 1-1

Author(s):

Giang Thi Thu Nguyen ◽

Duc-Hoa Vu ◽

Duc-Hau Le

Keyword(s):

Cell Lines ◽

Drug Response ◽

Molecular Graph ◽

Response Prediction ◽

Graph Data ◽

Omic Data

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Graph Data Management Systems

Encyclopedia of Big Data Technologies ◽

10.1007/978-3-319-63962-8_82-1 ◽

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

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Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data

Journal of Database Management ◽

10.4018/jdm.2019100102 ◽

2019 ◽

Vol 30 (4) ◽

pp. 24-40

Author(s):

Lei Li ◽

Fang Zhang ◽

Guanfeng Liu

Keyword(s):

Pattern Matching ◽

Particle Swarm Optimization Algorithm ◽

Nsga Ii ◽

Specific Domain ◽

Graph Pattern Matching ◽

Graph Data ◽

Graph Pattern ◽

Big Graph ◽

Complex Relationships ◽

Pattern Graph

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multi-fuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.

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