Decomposition and Merging Algorithms for Noncrossing Forests

2021 ◽  
Vol 38 (1) ◽  
Author(s):  
Sabrina X. M. Pang ◽  
Lun Lv ◽  
Xiaoming Deng
Keyword(s):  
Merging Algorithms

Procedure Based on Semantic Similarity for Merging Ontologies by Non-Redundant Knowledge Enrichment

2018 ◽  
Vol 14 (2) ◽  
pp. 16-36 ◽  
Author(s):  
Carlos Ramón Rangel ◽  
Junior Altamiranda ◽  
Mariela Cerrada ◽  
Jose Aguilar
Keyword(s):  
Semantic Similarity ◽  
Similarity Measures ◽  
The Other ◽  
Other Hand ◽  
Knowledge Enrichment ◽  
Merging Algorithms

The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good results are obtained in terms of completeness, relationships and properties, without creating redundancy.

scholarly journals On the skill of high frequency precipitation analyses

2014 ◽  
Vol 11 (10) ◽  
pp. 11605-11636
Author(s):  
A. Kann ◽  
I. Meirold-Mautner ◽  
F. Schmid ◽  
G. Kirchengast ◽  
J. Fuchsberger
Keyword(s):  
High Frequency ◽  
Cross Validation ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Convective Condition ◽  
Station Network ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Merging Algorithms ◽  
Very High

Abstract. The ability of radar-rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling. However, due to drawbacks of methods like cross-validation and due to the limited availability of reference datasets on high temporal and spatial scale, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense station network (WegenerNet), operated in a limited domain of the south-eastern parts of Austria (Styria). Based on case studies and a longer term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown, although the temporal and spatial variability of the errors is – by convective nature – high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

Comparison of stream merging algorithms for media-on-demand

2004 ◽  
Vol 9 (5) ◽  
pp. 411-423 ◽  
Author(s):  
Amotz Bar-Noy ◽  
Justin Goshi ◽  
Richard E. Ladner ◽  
Kenneth Tam
Keyword(s):  
On Demand ◽  
Stream Merging ◽  
Merging Algorithms

scholarly journals Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

2019 ◽  
Vol 11 (2) ◽  
pp. 717-739 ◽  
Author(s):  
Alexander Gruber ◽  
Tracy Scanlon ◽  
Robin van der Schalie ◽  
Wolfgang Wagner ◽  
Wouter Dorigo
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Scientific Literature ◽  
Passive Microwave ◽  
Climate Data ◽  
Regular Grid ◽  
Change Initiative ◽  
Merging Algorithm ◽  
Merging Algorithms

Abstract. The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined active–passive product, which are sampled to daily global images on a 0.25∘ regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithms, versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018) and 4 (https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e; Dorigo et al., 2019), and provides an outlook on the expected improvements planned for the next algorithm, version 5.

Set Merging Algorithms

1973 ◽  
Vol 2 (4) ◽  
pp. 294-303 ◽  
Author(s):  
J. E. Hopcroft ◽  
J. D. Ullman
Keyword(s):  
Merging Algorithms
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