scholarly journals LPG Representation of the Reification of RDF

2018 ◽  
Vol 7 (3.34) ◽  
pp. 562 ◽  
Author(s):  
Zhanfang Zaho ◽  
Sung Kook Han ◽  
Ju Ri Kim
Keyword(s):  
Data Model ◽  
Graph Data ◽  
Semantic Relationships ◽  
Resource Modeling ◽  
Novel Approach ◽  
Flexible Resource ◽  
Complex Structural ◽  
Complex Knowledge ◽  
Rdf Graphs ◽  
Future Work

Background/Objectives: It is still a challenging issue to represent the reification effectively since the reification representation of RDF standard has been revealed some drawbacks.Methods/Statistical analysis: Currently, there are two main graph data models: RDF and LPG. LPG is a popular graph data model that is usually applied to NoSQL graph databases.This paper derives three types of the reification structures in terms of the structural and semantic relationships of the reification statements. The detailed representation of each type of the reification is presented with the extended LPG model.Findings: This paper proposes a novel approach to represent the reification structure of RDF from the perspective of LPG. The paper explores the formal, conceptual properties of the conventional LPG models and proposes their extension to capture more complex knowledge structures efficiently. These augmentations of LPG can achieve more efficient and flexible resource modeling. This paper derives three types of the reification structures in terms of the structural and semantic relationships of the reification statements: assertion, quantification, and entailment.The proposed approach not only preserves the structure and semantics of the reification but also enables LPG modeling of the complex structural statements to be easy and intuitive.This can contribute to transfer RDF graphs into LPGs.Improvements/Applications: The implementation of the extended LPG and the query processing of the reification remain future work. 

scholarly journals The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

2020 ◽  
Vol 17 (2-3) ◽  
Author(s):  
Dagmar Waltemath ◽  
Martin Golebiewski ◽  
Michael L Blinov ◽  
Padraig Gleeson ◽  
Henning Hermjakob ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Data Model ◽  
Computational Models ◽  
Life Sciences ◽  
The Past ◽  
Challenges And Opportunities ◽  
Stakeholder Workshop ◽  
Software Engineers ◽  
Future Work ◽  
Modeling In Biology

AbstractThis paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

Growing Neural Forest-Based Color Quantization Applied to RGB Images

2017 ◽  
Vol 7 (3) ◽  
pp. 13-25
Author(s):  
Jesús Benito-Picazo ◽  
Ezequiel López-Rubio ◽  
Enrique Domínguez
Keyword(s):  
Data Streams ◽  
Image Data ◽  
Color Quantization ◽  
Perceptual Distortion ◽  
Self Organized ◽  
Novel Approach ◽  
Image Quantization ◽  
Rgb Images ◽  
Storage Technologies ◽  
Future Work

Although last improvements in both physical storage technologies and image handling techniques have eased image managing processes, the large amount of information handled nowadays constantly demands more efficient ways to store and transmit image data streams. Among other alternatives for such purpose, the authors find color quantization, which consists of color indexing for minimal perceptual distortion image compression. In this context, artificial intelligence-based algorithms and more specifically, Artificial Neural Networks, have been consolidated as a powerful tool for unsupervised tasks, and therefore, for color quantization purposes. In this work, a novel approach to color quantization is presented based on the Growing Neural Forest (GNF), which is a Growing Neural Gas (GNG) variation where a set of trees is learnt instead of a general graph. Experimental results support the use of GNF for image quantization tasks where it overcomes other self-organized models including SOM, GHSOM and GNG. Future work will include more datasets and different competitive models to compare to.

scholarly journals Measuring Vascular Recovery Rate After Exercise

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 12 ◽  
Author(s):  
Halil Dijab ◽  
Jordi Alastruey ◽  
Peter Charlton
Keyword(s):  
Heart Rate ◽  
Recovery Rate ◽  
Wearable Sensors ◽  
Cardiovascular Fitness ◽  
Wave Shape ◽  
Future Studies ◽  
Additional Information ◽  
Novel Approach ◽  
Pulse Waves ◽  
Future Work

The rate at which an individual recovers from exercise is known to be indicative of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study, we present a novel approach to assess vascular recovery rate (VRR) using the photoplethysmogram (PPG) signal, which is monitored by smart wearables. The VORTAL dataset (http://peterhcharlton.github.io/RRest/) was used for this study, containing PPG signals from 39 healthy subjects before (baseline) and after exercise. 31 VRR indices were extracted from the PPG pulse wave shape, as well as heart rate for comparison. The rate at which indices returned to baseline after exercise was quantified, and the consistency of changes between subjects was assessed statistically. Many VRR indices exhibited changes after exercise which were consistent between subjects. Indices derived from the timings and second derivative of pulse waves were identified as candidates for future work. The rate at which the indices returned to baseline differed between indices and subjects, indicating that they may provide additional information beyond that of heart rate, and that they may be useful for stratifying subjects. This study demonstrated the feasibility of assessing VRR after exercise from the PPG. Future studies should investigate whether VRR indices are associated with cardiovascular fitness, and the potential utility of incorporating the indices into wearable sensors.

scholarly journals Building an integrated enhanced virtual research environment metadata catalogue

2019 ◽  
Vol 37 (6) ◽  
pp. 929-951 ◽  
Author(s):  
Laurent Remy ◽  
Dragan Ivanović ◽  
Maria Theodoridou ◽  
Athina Kritsotaki ◽  
Paul Martin ◽  
...  
Keyword(s):  
Data Model ◽  
Data Science ◽  
Science Research ◽  
Dublin Core ◽  
Content Type ◽  
Knowledge Based ◽  
Novel Approach ◽  
Virtual Research Environment ◽  
Definition Of ◽  
Practical Implications

Purpose The purpose of this paper is to boost multidisciplinary research by the building of an integrated catalogue or research assets metadata. Such an integrated catalogue should enable researchers to solve problems or analyse phenomena that require a view across several scientific domains. Design/methodology/approach There are two main approaches for integrating metadata catalogues provided by different e-science research infrastructures (e-RIs): centralised and distributed. The authors decided to implement a central metadata catalogue that describes, provides access to and records actions on the assets of a number of e-RIs participating in the system. The authors chose the CERIF data model for description of assets available via the integrated catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings. Findings An integrated catalogue of research assets metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E and CKAN formats can be integrated into the catalogue. Metadata are stored in CERIF RDF in the integrated catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats. Practical implications The integrated catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications The integrated catalogue should boost the performance of multi-disciplinary research; thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value A novel approach for creation of the integrated catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.

Graph Data Model

1990 ◽  
pp. 7-20
Author(s):  
Hideko S. Kunii
Keyword(s):  
Data Model ◽  
Graph Data

scholarly journals Money: An Integrated Review and Synthesis From a Psychological Perspective

2020 ◽  
Vol 24 (2) ◽  
pp. 172-190 ◽  
Author(s):  
Xijing Wang ◽  
Zhansheng Chen ◽  
Eva G. Krumhuber
Keyword(s):  
Empirical Studies ◽  
Goal Pursuit ◽  
Self Esteem ◽  
Psychological Perspective ◽  
Review Of The Literature ◽  
Social Distinction ◽  
Novel Approach ◽  
Broad Concept ◽  
Underlying Mechanisms ◽  
Future Work

Many empirical studies have demonstrated the psychological effects of various aspects of money, including the aspiration for money, mere thoughts about money, possession of money, and placement of people in economic contexts. Although multiple aspects of money and varied methodologies have been focused on and implemented, the underlying mechanisms of the empirical findings from these seemingly isolated areas significantly overlap. In this article, we operationalize money as a broad concept and take a novel approach by providing an integrated review of the literature and identifying five major streams of mechanisms: (a) self-focused behavior; (b) inhibited other-oriented behavior; (c) favoring of a self–other distinction; (d) money’s relationship with self-esteem and self-efficacy; and (e) goal pursuit, objectification, outcome maximization, and unethicality. Moreover, we propose a unified psychological perspective for the future—money as an embodiment of social distinction—which could potentially account for past findings and generate future work.

scholarly journals Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

2020 ◽  
Vol 10 (9) ◽  
pp. 3116 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Jenny Carter ◽  
Fabio Caraffini
Keyword(s):  
School Leadership ◽  
Educational Administration ◽  
School Attendance ◽  
Novel Approach ◽  
Negative Impacts ◽  
Using Data ◽  
The Uk ◽  
Leadership Research ◽  
Future Work

Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools.

Sign in / Sign up

Export Citation Format

Share Document