scholarly journals Introduction—State of the Art of Technology and Market Potential for Big Data in Forestry

2021 ◽  
pp. 299-307
Author(s):  
Jukka Miettinen ◽  
Renne Tergujeff

AbstractForest monitoring is undergoing rapid changes due to the growing data volumes, developing data processing technologies and increasing monitoring requirements. The DataBio forestry pilots set out to demonstrate how big data approaches can support the forestry sector to get full benefit of the evolving technologies and to meet the increasing monitoring requirements. In this introductory chapter, we describe underlying technical and market forces driving the forestry sector toward big data approaches, and give short overviews on the forestry pilots to be presented in the following chapters.

2019 ◽  
Vol 160 ◽  
pp. 561-566 ◽  
Author(s):  
Nataliya Shakhovska ◽  
Nataliya Boyko ◽  
Yevgen Zasoba ◽  
Eleonora Benova

Author(s):  
Sherif Sakr ◽  
Fuad Bajaber ◽  
Ahmed Barnawi ◽  
Abdulrahman Altalhi ◽  
Radwa Elshawi ◽  
...  

2018 ◽  
Vol 3 (1) ◽  
pp. 49-60
Author(s):  
M. Elshayeb ◽  
◽  
Leelavathi Rajamanickam ◽  

Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. In order to analyse complex data and to identify patterns it is very important to securely store, manage, and share large amounts of complex data. In recent years an increasing of database size according to the various forms (text, images and videos), in huge volumes and with high velocity, the services issues that use internet and desires big data come to leading edge (data-intensive services), (HDFS) Apache’s Hadoop distributed file system is in progress as outstanding software component for cloud computing joint with integrated pieces such as MapReduce. GoogleMapReduce implemented an open source which is Hadoop, having a distributed file system, present to software programmers the perception of the map and reduce. The research shows the security approaches for Big Data Hadoop distributed file system and the best security solution, also this research will help business by big data visualization which will help in better data analysis. In today’s data-centric world, big-data processing and analytics have become critical to most enterprise and government applications.


2021 ◽  
Vol 50 (2) ◽  
pp. 18-29
Author(s):  
Christos Doulkeridis ◽  
Akrivi Vlachou ◽  
Nikos Pelekis ◽  
Yannis Theodoridis

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.


Author(s):  
A. E. Dolganov ◽  
K. E. Gavrov ◽  
S. V. Sumarokov ◽  
F. O. Novoselsky

Author(s):  
Savo Stupar ◽  
Emir Kurtović ◽  
Mirha Bičo Ćar

The aim of this chapter is to enable marketing managers to gain basic knowledge of the capabilities of the latest data management technology, big data, which has the potential of digitally storing huge amounts of data, processing and utilizing the results of processing different types of data, as well as data of different formats in real-time. Due to the enormous potential of implementing the big data, there are also tremendous expectations in terms of the direct financial benefits of its implementation. Realizing all these expectations is a very complex task, which is set to marketing and other managers. The knowledge and skills of managers acquired by education will greatly help to understand the benefits of faster adoption and implementation of new data management paradigms. This chapter emphasizes the differences between the big data concept and conventional data processing technologies, as well as the benefits and potentials that this concept offers, especially when it comes to the process of making quick marketing decisions or making decisions in a reasonably short time.


Author(s):  
T Q Urazmatov ◽  
B B Nurmetova ◽  
X Sh Kuzibayev

2019 ◽  
Vol 10 (3) ◽  
pp. 380-394 ◽  
Author(s):  
A. E. Karlik ◽  
V. V. Platonov ◽  
M. V. Tihonova ◽  
E. A. Jakovleva

Purpose: it is to determine the most important factors that condition the ability of an enterprise to successfully implement the big data as a new economic resource.Methods: the methodological foundation of this research is the analytical framework of the resource-based view, which is applied to highlight the most important factors of the organizational capacity for the implementation of big data into economic activity. These factors are classified by blocks of internal factors of the organizational capacity in two hierarchical levels (organizational and individual). The study is based on the primary information obtained through a survey in the form of semi-structured interviews of managers and experts of the companies pioneering in implementation of big data.Results: a based on the analysis of scientific publications in accordance with positive and normative approaches to the understanding of big data, the concept of "big data" as an economic resource is developed. Its attributes are identified with emphasis on the heterogeneity of big data which allows filtering information about the subsystems of a complex economic system representing the modern enterprise. This information cannot be obtained from traditional sources of economic data. By systematizing the primary information on the projects of implementation of big data into economic activity by foreign companies by applying the analytical framework of the resource-based view, the key conceptual factors of the organizational capacity for the use of big data and relationship among significant factors have been identified. These key internal factors emerge as a result of the revolution in information technology and represent the necessary condition to ensure the transformation of the analytical procedures for decision making at the corporate level based on big data. The study reveals that sufficient condition represents a system of intangible resources and organizational capabilities, the most important of which is the capability to coordinate data processing and analysis. This capability, in a system with the other key organizational level capabilities, enables the integration of analytical and data processing technologies, on the one hand, and individual competencies of employees, on the other.Conclusions and Relevance: the implications of this study are aimed at researchers studying the problems of the information and networked economy, and practitioners of the Russian companies that are implementing or consider the implementation of the big data into economic activity. In business perspective, the most important implication of this research is that effective implementation of big data is not a technical challenge but an organizational and economic one. The basis of the organizational capacity for the implementation of big data is information resources, human resources and corporate culture and systems (technologies) for data processing and analysis.


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