8.2 Agricultural Big Data
8.2.1 Special Features of Agro-Big Data
Realizing the socioeconomic values of information and knowledge contained in
huge volumes of the data, in recent years, significant research efforts have been spent
on maximizing the utilization of the data by mining the information and knowledge
from the data (Manyika 2011). The term “big data” is coined to refer to the huge
amount of data those efforts deal with. According to Wikipedia, “Big data is a broad
term for datasets so large or complex that traditional data processing applications are
inadequate.” (https://en.wikipedia.org/w/index.php?title¼Big_data&
oldid¼925811014. Accessed 14 Nov 2019).
Big data are commonly characterized with five Vs (Hitzler and Janowicz 2013):
• Volume refers to the huge amounts of data generated every day. For example,
millions of cameras have been installed worldwide to monitor the Earth’s environment, traffic conditions, public safety, etc. year around. The volume of data
generated by those cameras is unimaginable.
• Velocity refers to the speed at which data is generated and moved around. Every
second the world generates petabytes of data, which need to be managed and
analyzed, and near-real-time decision might be made based on the analysis
results.
• Variety refers to the different types of data the world generates and uses. For
example, in the geospatial field, we now need to deal with data from in situ,
airborne, satellite platforms, and citizen scientists’ mobile devices. The data type
can range from hyperspectral images, videos, model outputs, and sensor measurement to social media conversations.
• Veracity refers to the trustworthiness of the data. In the scientific world, the
quality and accuracy of the data are one of the biggest concerns that every
scientific experiment has to consider. In the big data era, because the sources of
the data are numerous and the qualifications of the organizations or individuals
who collect the data are not equal, the quality and accuracy of the data are less
controllable.
• Value refers to the usefulness of the information and knowledge we can derive
from the data. Therefore, value is the most important V of big data. In any
applications of big data, we have first to question what the value we can get
from the big data.
Contrary to the traditional data management and analysis technologies, big data
management and analysis must consider and properly deal with big data’s five V
characteristics. Significant progresses have been made in both big data management,
which deals with data capture, curation, archive, storage, cataloging, discovery,
search, access, sharing, quality control, privacy, etc., and big data analytics, which
includes big data analysis, transformation, mining, visualization, knowledge discovery, etc. However, challenges still exist in all above areas.
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8.2.1 Special Features of Agro-Big Data
Realizing the socioeconomic values of information and knowledge contained in
huge volumes of the data, in recent years, significant research efforts have been spent
on maximizing the utilization of the data by mining the information and knowledge
from the data (Manyika 2011). The term “big data” is coined to refer to the huge
amount of data those efforts deal with. According to Wikipedia, “Big data is a broad
term for datasets so large or complex that traditional data processing applications are
inadequate.” (https://en.wikipedia.org/w/index.php?title¼Big_data&
oldid¼925811014. Accessed 14 Nov 2019).
Big data are commonly characterized with five Vs (Hitzler and Janowicz 2013):
• Volume refers to the huge amounts of data generated every day. For example,
millions of cameras have been installed worldwide to monitor the Earth’s environment, traffic conditions, public safety, etc. year around. The volume of data
generated by those cameras is unimaginable.
• Velocity refers to the speed at which data is generated and moved around. Every
second the world generates petabytes of data, which need to be managed and
analyzed, and near-real-time decision might be made based on the analysis
results.
• Variety refers to the different types of data the world generates and uses. For
example, in the geospatial field, we now need to deal with data from in situ,
airborne, satellite platforms, and citizen scientists’ mobile devices. The data type
can range from hyperspectral images, videos, model outputs, and sensor measurement to social media conversations.
• Veracity refers to the trustworthiness of the data. In the scientific world, the
quality and accuracy of the data are one of the biggest concerns that every
scientific experiment has to consider. In the big data era, because the sources of
the data are numerous and the qualifications of the organizations or individuals
who collect the data are not equal, the quality and accuracy of the data are less
controllable.
• Value refers to the usefulness of the information and knowledge we can derive
from the data. Therefore, value is the most important V of big data. In any
applications of big data, we have first to question what the value we can get
from the big data.
Contrary to the traditional data management and analysis technologies, big data
management and analysis must consider and properly deal with big data’s five V
characteristics. Significant progresses have been made in both big data management,
which deals with data capture, curation, archive, storage, cataloging, discovery,
search, access, sharing, quality control, privacy, etc., and big data analytics, which
includes big data analysis, transformation, mining, visualization, knowledge discovery, etc. However, challenges still exist in all above areas.
8 Big Data and Its Applications in Agro-Geoinformatics
147