GaaS-X: Graph Analytics Accelerator Supporting Sparse Data Representation using Crossbar Architectures

2020 ◽  
Author(s):  
Nagadastagiri Challapalle ◽  
Sahithi Rampalli ◽  
Linghao Song ◽  
Nandhini Chandramoorthy ◽  
Karthik Swaminathan ◽  
...  
Keyword(s):  
Sparse Data ◽  
Data Representation ◽  
Graph Analytics

scholarly journals Sparse Data Representation of Random Fields

PAMM ◽  
2009 ◽  
Vol 9 (1) ◽  
pp. 587-588 ◽  
Author(s):  
Alexander Litvinenko ◽  
Hermann G. Matthies
Keyword(s):  
Random Fields ◽  
Sparse Data ◽  
Data Representation

scholarly journals Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach

2021 ◽  
Vol 11 (23) ◽  
pp. 11425
Author(s):  
Nikolaos Giarelis ◽  
Nikos Karacapilidis
Keyword(s):  
Named Entity Recognition ◽  
Data Representation ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Graph Database ◽  
Keyphrase Extraction ◽  
Graph Analytics ◽  
Aggregated Data ◽  
Horizon 2020 ◽  
Named Entity

This paper aims to meaningfully analyse the Horizon 2020 data existing in the CORDIS repository of EU, and accordingly offer evidence and insights to aid organizations in the formulation of consortia that will prepare and submit winning research proposals to forthcoming calls. The analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced. The modelling of data is performed through a knowledge graph-based approach, aiming to semantically capture existing relationships and reveal hidden information. The main contribution of this work lies in the proper utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of an efficient graph database. The proposed approach enables users to ask complex questions about the interconnection of various entities related to previously funded research projects. A set of representative queries demonstrating our data representation and analysis approach are given at the end of the paper.

scholarly journals MATAR: Data-Oriented Sparse Data Representation

2021 ◽  
Author(s):  
Tanya Tafolla ◽  
Eappen Nelluvelil ◽  
Jacob Moore ◽  
Daniel Dunning ◽  
Nathaniel Morgan ◽  
...  
Keyword(s):  
Sparse Data ◽  
Data Representation

Sparse graph-regularized dictionary learning for suppressing random seismic noise

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. V215-V231 ◽  
Author(s):  
Lina Liu ◽  
Jianwei Ma ◽  
Gerlind Plonka
Keyword(s):  
Similarity Measure ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Laplacian Matrix ◽  
Sparse Data ◽  
Data Representation ◽  
Image Patches ◽  
Learned Dictionary ◽  
Value Decomposition

We have developed a new regularization method for the sparse representation and denoising of seismic data. Our approach is based on two components: a sparse data representation in a learned dictionary and a similarity measure for image patches that is evaluated using the Laplacian matrix of a graph. Dictionary-learning (DL) methods aim to find a data-dependent basis or a frame that admits a sparse data representation while capturing the characteristics of the given data. We have developed two algorithms for DL based on clustering and singular-value decomposition, called the first and second dictionary constructions. Besides using an adapted dictionary, we also consider a similarity measure for the local geometric structures of the seismic data using the Laplacian matrix of a graph. Our method achieves better denoising performance than existing denoising methods, in terms of peak signal-to-noise ratio values and visual estimation of weak-event preservation. Comparisons of experimental results on field data using traditional [Formula: see text]-[Formula: see text] deconvolution (FX-Decon) and curvelet thresholding methods are also provided.

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