Forestry big data platform by Knowledge Graph

Using the advantages of web crawlers in data collection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with the mature big data technology at its present stage, Hadoop’s distributed system was selected to solve the storage problem of massive forestry...

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Published inJournal of forestry research Vol. 32; no. 3; pp. 1305 - 1314
Main Authors Zhao, Mengxi, Li, Dan, Long, Yongshen
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021
Springer
Springer Nature B.V
Northeast Forestry University , Harbin 150040 , People's Republic of China
Subjects
Online AccessGet full text
ISSN1007-662X
1993-0607
DOI10.1007/s11676-020-01130-w

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Abstract Using the advantages of web crawlers in data collection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with the mature big data technology at its present stage, Hadoop’s distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data. The forestry data contains a wealth of information, and mining this information is of great significance for guiding the development of forestry. We conducts co-word and cluster analyses on the keywords of forestry data, extracts the rules hidden in the data, analyzes the research hotspots more accurately, grasps the evolution trend of subject topics, and plays an important role in promoting the research and development of subject areas. The co-word analysis and clustering algorithm have important practical significance for the topic structure, research hotspot or development trend in the field of forestry research. Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms. Therefore, the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.
AbstractList Using the advantages of web crawlers in data collection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with the mature big data technology at its present stage, Hadoop’s distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data. The forestry data contains a wealth of information, and mining this information is of great significance for guiding the development of forestry. We conducts co-word and cluster analyses on the keywords of forestry data, extracts the rules hidden in the data, analyzes the research hotspots more accurately, grasps the evolution trend of subject topics, and plays an important role in promoting the research and development of subject areas. The co-word analysis and clustering algorithm have important practical significance for the topic structure, research hotspot or development trend in the field of forestry research. Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms. Therefore, the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.
Using the advantages of web crawlers in data col-lection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with the mature big data technology at its present stage, Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data. The forestry data contains a wealth of information, and min-ing this information is of great significance for guiding the development of forestry. We conducts co-word and cluster analyses on the keywords of forestry data, extracts the rules hidden in the data, analyzes the research hotspots more accu-rately, grasps the evolution trend of subject topics, and plays an important role in promoting the research and develop-ment of subject areas. The co-word analysis and clustering algorithm have important practical significance for the topic structure, research hotspot or development trend in the field of forestry research. Distributed storage framework and par-allel computing have greatly improved the performance of data mining algorithms. Therefore, the forestry big data min-ing system by big data technology has important practical significance for promoting the development of intelligent forestry.
Audience Academic
Author Zhao, Mengxi
Long, Yongshen
Li, Dan
AuthorAffiliation Northeast Forestry University , Harbin 150040 , People's Republic of China
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Copyright Northeast Forestry University 2020
COPYRIGHT 2021 Springer
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Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
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– notice: Northeast Forestry University 2020.
– notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
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Snippet Using the advantages of web crawlers in data collection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with...
Using the advantages of web crawlers in data col-lection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with...
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SubjectTerms Algorithms
Big Data
Biomedical and Life Sciences
Clustering
Computer networks
Data collection
Data mining
evolution
Forest management
Forestry
Forestry research
Forests and forestry
Geospatial data
Information storage and retrieval
Knowledge representation
Life Sciences
Original Paper
R&D
Research & development
research and development
Title Forestry big data platform by Knowledge Graph
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Volume 32
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