A DNA-Based Semantic Fusion Model for Remote Sensing Data
Identifieur interne : 000379 ( Main/Merge ); précédent : 000378; suivant : 000380A DNA-Based Semantic Fusion Model for Remote Sensing Data
Auteurs : Heng Sun [République populaire de Chine] ; Jian Weng [République populaire de Chine] ; Guangchuang Yu [République populaire de Chine] ; Richard H. Massawe [République populaire de Chine]Source :
- PLoS ONE [ 1932-6203 ] ; 2013.
Abstract
Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.
Url:
DOI: 10.1371/journal.pone.0077090
PubMed: 24116207
PubMed Central: 3792926
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Massawe</name><affiliation wicri:level="3"><nlm:aff id="aff3"><addr-line>International School, Jinan University, Guangzhou, People's Republic of China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>International School, Jinan University, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Adleman, Lm" uniqKey="Adleman L">LM Adleman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lipton, R" uniqKey="Lipton R">R Lipton</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bancroft, C" uniqKey="Bancroft C">C Bancroft</name></author><author><name sortKey="Bowler, T" uniqKey="Bowler T">T Bowler</name></author><author><name sortKey="Bloom, B" uniqKey="Bloom B">B Bloom</name></author><author><name sortKey="Clelland, Ct" uniqKey="Clelland C">CT Clelland</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Renear, A" uniqKey="Renear A">A Renear</name></author><author><name sortKey="Palmer, C" uniqKey="Palmer C">C 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Sattler</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Lewis, Jj" uniqKey="Lewis J">JJ Lewis</name></author><author><name sortKey="Callaghan, Rjo" uniqKey="Callaghan R">RJO Callaghan</name></author><author><name sortKey="Nikolov, Sg" uniqKey="Nikolov S">SG Nikolov</name></author><author><name sortKey="Bull, Dr" uniqKey="Bull D">DR Bull</name></author><author><name sortKey="Canagarajah, N" uniqKey="Canagarajah N">N Canagarajah</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Merge/RBID.i -Sk "pubmed:24116207" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Merge/biblio.hfd \
| NlmPubMed2Wicri -a CyberinfraV1
| This area was generated with Dilib version V0.6.25. |  |