scholarly journals BIG DATA AND DECISION-MAKING SUPPORT

2021 ◽  
Vol 2 ◽  
pp. 87-92
Author(s):  
Peter Procházka
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Methodological Approach ◽  
Timely Manner ◽  
Domestic Literature ◽  
Risk Definition ◽  
Similar Solutions ◽  
Support Decision Making ◽  
Big Data Technology

INTRODUCTION: Nowadays, Big Data is created in previously unimaginable quantities. Newly generated data from various Internet of Things (IoT) sensors and their use have never reached their current dimensions. Along with this trend, the availability of devices capable of collecting this data increases, the time for their evaluation is reduced and the volume of data collected at the same time increases. The most important task of research and development in this area is to bring solutions suitable for processing large amounts of data because our current storage and processing capabilities are limited and unable to compete with the storage, processing and publication of the resulting data. OBJECTIVES: Point out the importance of implementing Big Data technology. METHODS: To achieve the goal, the following methodological approach was chosen: study and processing of foreign and domestic literature, acquaintance with similar solutions for data processing, definition of Big Data and IoT, proposal for using Big Data solution to support decision-making, risk definition and evaluation. RESULTS: With the growing amount of disparate and incoherent data and the further growth of the Internet of Things, it is now almost impossible to evaluate all available information correctly and in a timely manner. Without this knowledge, the company loses its competitive advantage and is unable to respond in a timely manner to client requests. CONCLUSION: Implementing a solution for processing Big Data to support decision-making in the company is a complex process. As part of the implementation and use of the Big Data solution to support decision-making, the company must be prepared for the emergence of various problems. We can assume that Big Data technology will constantly be evolving in terms of streamlining analytical tools for obtaining information from large volumes of generated data. Therefore, it is appropriate to create space for the implementation of Big Data technology.

scholarly journals IoT Based Decision Making System to Improve Veracity of Big Data

2018 ◽  
Vol 7 (3.1) ◽  
pp. 63 ◽  
Author(s):  
R Revathy ◽  
R Aroul Canessane
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Streams ◽  
Data Cleaning ◽  
Data Handling ◽  
Secure Data ◽  
Decision Making System ◽  
Data Investigation ◽  
Impact Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.  

scholarly journals Characterizing Big Data Management

10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2019 ◽  
Vol 10 (4) ◽  
pp. 1-23
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Conceptual Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

scholarly journals A functional framework based on big data analytics for smart farming

2021 ◽  
Vol 24 (3) ◽  
pp. 1772
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
New Products ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Time Production ◽  
Smart Farming ◽  
The Future

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

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