Exploring series of multivariate censored temporal data through fuzzy coding and correspondence analysis

2006 ◽  
Vol 25 (10) ◽  
pp. 1741-1750 ◽  
Author(s):  
Bernard Goldfarb ◽  
Catherine M. Pardoux
Keyword(s):  
Correspondence Analysis ◽  
Temporal Data ◽  
Fuzzy Coding

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Clustering Mutual Outline for Multi Assessment Temporal Data and cancer Data

2018 ◽  
Vol 6 (1) ◽  
pp. 71-79
Author(s):  
Sathishkumar. K ◽  
◽  
V. Thiagarasu ◽  
E. Balamurugan ◽  
David Otto Arthur ◽  
...  
Keyword(s):  
Temporal Data ◽  
Cancer Data

Giscience in the Mountain Geography

2010 ◽  
Vol 27 (1-2) ◽  
pp. 81-90
Author(s):  
Krishna Poudel
Keyword(s):  
Spatial Data ◽  
Environmental Data ◽  
Temporal Data ◽  
Observation Systems ◽  
Research Findings ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Mountain Geography ◽  
Different Sources

Mountains have distinct geography and are dynamic in nature compared to the plains. 'Verticality' and 'variation' are two fundamental specificities of the mountain geography. They possess distinct temporal and spatial characteristics in a unique socio-cultural setting. There is an ever increasing need for spatial and temporal data for planning and management activities; and Geo Information (GI) Science (including Geographic Information and Earth Observation Systems). This is being recognized more and more as a common platform for integrating spatial data with social, economic and environmental data and information from different sources. This paper investigates the applicability and challenges of GISscience in the context of mountain geography with ample evidences and observations from the mountain specific publications, empirical research findings and reports. The contextual explanation of mountain geography, mountain specific problems, scientific concerns about the mountain geography, advances in GIScience, the role of GIScience for sustainable development, challenges on application of GIScience in the contexts of mountains are the points of discussion. Finally, conclusion has been made with some specific action oriented recommendations.

Contribution a la rationalisation du reseau qualite de l’eau de la Baie James

1977 ◽  
Vol 12 (1) ◽  
pp. 51-76
Author(s):  
B. Bobée ◽  
D. Cluis ◽  
A. Tessier
Keyword(s):  
Correspondence Analysis ◽  
Major Ions ◽  
Base Station ◽  
Base Stations ◽  
James Bay ◽  
Mean Values ◽  
Multivariate Technique ◽  
Qualité De L’Eau ◽  
Concentration Changes ◽  
The Relationship

Abstract A water quality sampling programme for James Bay territory established in a previous study has been carried out for the Department of Natural Resources of the Province of Quebec. The network is composed of 5 base-stations, sampled every fortnight to determine the variability with time of the parameters and 16 satellite-stations, sampled five times yearly with a view to determine the spatial variability. The data (major ions and certain nutrients) gathered during the 1974–1975 field survey are subjected to an analysis by a multivariate technique (correspondence analysis) in addition to certain classical statistical methods. The latter have shown that the mean values obtained at satellite stations were representative of the annual mean. In addition, the results permit the determination for a given parameter, of the relationship between stations and, for a given station, the relationship between parameters. In both cases, the formulation of predictive equations was attempted. An overall evaluation of the data by correspondence analysis has permitted: - a more precise definition of the qualitative behaviour of the different sub-basins of the James Bay territory and characterization of their waters;- a proof of the existence of gradual concentration changes in both East-West and North-South directions. Within the original objectives of the network, the results of the study have led to the following recommendations: - to continue synchronised samplings;- to transform a base station with a low information content into a satellite station;- to create a new base station in the eastern part of the territory.

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

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