Modelling temporal data in knowledge graphs: a systematic review protocol

Background: The benefits of having high-quality healthcare data are well established. However, high-dimensionality and irregularity of healthcare data pose challenges in their management. Knowledge graphs have gained increasing popularity in many domains, as a method for representing data to overcome such challenges. However, little is known about their suitability for use with healthcare data. One important factor in representing data is “time”.Data with time related attributes are considered, temporal data. Temporal data are frequently observed in healthcare and the management of rapidly changing patient data is an ongoing challenge. Traditionally, data models have focused on presenting static data and do not account for temporal data. Temporal data models ensure time consistency in data models and assist analysing the history of data and predicting the future trends in data. Knowledge graphs can include temporal data models and are therefore of interest to the field of healthcare data management. As such, the herein aim is to outline a protocol for an inter-disciplinary systematic review of approaches, applications and challenges in modelling temporal data in knowledge graphs so that we can inform the application of knowledge graphs to healthcare data. Method: The research questions is, what are the existing approaches in modelling temporal data in knowledge graphs. Two sub-questions on applications, and challenges will also be evaluated. ACM digital library, IEEEXplore and ScienceDirect will be searched for this review. The search will be limited to peer-reviewed literature referring to knowledge graphs based on Resource Description Framework (RDF). A narrative synthesis of the papers will be conducted. Conclusion: The findings of this systematic review will be useful for data engineers to better represent data and perform analytics through temporal data modelling. They can be applied in the context of healthcare data and the current challenges faced in managing rapidly changing patient data.

Modeling Environmental Impacts on Viticultural Ecosystems

This paper focuses on simulating environmental impacts on grapevine behavioral dynamics and vineyard management strategies. The methodology presented uses technology from geomatics object oriented databases and spatio-temporal data models. Our approach has two principle objectives, first, to simulate grapevine phenology and grape ripening under spatial and temporal environmental conditions and constraints and secondly, to simulate viticultural practices and adaptation strategies under various constraints (environmental, economical, socio-technical). The approach is based on a responsive agent-based structure where environmental conditions and constraints are considered as a set of forcing data (biophysical, socio-economic and regulatory data) that influences the modelled activities. The experiment was conducted in the regulated wine producing appellation Grand Cru “Quarts de Chaume”, situated in the middle Loire Valley, France. All of the methodology, from the implementation of the knowledge database to the analysis of the first simulation, is presented in this paper.

Modeling Temporal Information With JSON

As a ubiquitous form of data in human natural life, time has been widely used in military, finance, medical treatment, environment and other fields. Therefore, temporal data models used to express the dynamic development process of data have been proposed constantly. Currently, the main research achievements focus on temporal database and temporal XML. With the rapid development and popularization of network technology, the requirement of efficiency and security is getting higher and higher. JSON, a new generation of data exchange language, has been widely used because of its lightweight, fast parsing and high transmission efficiency. However, modeling temporal information with JSON has not been studied enough. The chapter proposes a temporal data model based on JSON. What is more, the temporal query language and the JSON Schema is also mentioned.

Modeling Environmental Impacts on Viticultural Ecosystems

This paper focuses on simulating environmental impacts on grapevine behavioral dynamics and vineyard management strategies. The methodology presented uses technology from geomatics object oriented databases and spatio-temporal data models. Our approach has two principle objectives, first, to simulate grapevine phenology and grape ripening under spatial and temporal environmental conditions and constraints and secondly, to simulate viticultural practices and adaptation strategies under various constraints (environmental, economical, socio-technical). The approach is based on a responsive agent-based structure where environmental conditions and constraints are considered as a set of forcing data (biophysical, socio-economic and regulatory data) that influences the modelled activities. The experiment was conducted in the regulated wine producing appellation Grand Cru “Quarts de Chaume”, situated in the middle Loire Valley, France. All of the methodology, from the implementation of the knowledge database to the analysis of the first simulation, is presented in this paper.