scholarly journals Data mining for marine data analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Valery Maximov ◽  
Kseniya Reznikova ◽  
Dmitry Popov
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Information Technologies ◽  
Software Tool ◽  
Production Costs ◽  
Highly Qualified ◽  
Use Of Data ◽  
Marine Data ◽  
Definition Of ◽  
Effective Use

There is practically no industry left where modern information technologies would not be used. Data mining approaches are very popular today. Using this technology allows to transform huge amounts of data into useful information. In the article, the authors present the definition of data mining technology and frequently used methods. Some of the popular data mining techniques include classification, clustering, machine learning, and prediction. The authors paid special attention to such a clustering method as the k-means. The algorithm’s essence is to distribute the dataset into clusters. The finished results can be visualized and detect the scatter by naked eye, which implies heterogeneity in the data. By further investigating these variations, the analyst can find errors and weaknesses in the study area according to the task at hand. Accurate and complete data is essential in maritime activities. In the field of shipbuilding data analysis and well-made operational decisions can affect the speed and quality of ship construction or even reduce production costs. In shipping and logistics, they can be used to optimize routes and improve the safety of seafarers. Effective use of data mining usually requires highly qualified database specialists and programmers. In this work, the authors have demonstrated a variant of using the Orange Data Mining software tool. This program does not require programming skills from the user, which makes it a useful tool for people far from writing program code. The article explores the application of the Orange Data Mining program for automated mining of marine data. The results obtained show that the program can be effectively used in maritime activities.

scholarly journals Research of the possibilities of application of the Data Warehouse in the construction area

2018 ◽  
Vol 251 ◽  
pp. 03062 ◽  
Author(s):  
Alexandr Konikov ◽  
Ekaterina Kulikova ◽  
Olga Stifeeva
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Warehouse ◽  
Information Technologies ◽  
Use Of Data ◽  
Building Area ◽  
Olap Analysis ◽  
Rapid Processing

Today, in information technologies, the direction associated with the use of Data Warehouse (DW) is evolving very dynamically. Using DW, it is possible to implement two types of data analysis: OLAP-analysis: a set of technologies for the rapid processing of data presented as a multidimensional cube; Data Mining is an intelligent, deep analysis of data to detect previously unknown, practically useful patterns (in our case, the construction area). It is noted, that of all the methods used in technology Data Mining, cluster analysis is especially useful for the construction area. At present, the role of DW has increased, significantly due to the fact, that many methods and approaches of Data Mining have formed the basis of a new, promising method of Big Data. We will specify that, that Data processing from the Data Warehouse with the help of technology Big Data, allows to deduce researches in a building area to the higher level. The purpose of this work is to research of the possibilities of application of the Data Warehouse in the construction area. The article suggests the new approach to data analysis in the construction area, based on the use of Big Data technology and elements of OLAP - analysis. In the section “Discussion” is considering the possibility of the new promising business in the construction field, based on the application of Data Warehouse and technology Big Data.

Applications of Data Mining in Software Development Life Cycle

2014 ◽  
pp. 67-79
Author(s):  
Naveen Dahiya ◽  
Vishal Bhatnagar ◽  
Manjeet Singh ◽  
Neeti Sangwan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Software Development ◽  
Future Research ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Development Life Cycle ◽  
Efficient Information ◽  
Software Development Life Cycle ◽  
Effective Use

Data mining has proven to be an important technique in terms of efficient information extraction, classification, clustering, and prediction of future trends from a database. The valuable properties of data mining have been put to use in many applications. One such application is Software Development Life Cycle (SDLC), where effective use of data mining techniques has been made by researchers. An exhaustive survey on application of data mining in SDLC has not been done in the past. In this chapter, the authors carry out an in-depth survey of existing literature focused towards application of data mining in SDLC and propose a framework that will classify the work done by various researchers in identification of prominent data mining techniques used in various phases of SDLC and pave the way for future research in the emerging area of data mining in SDLC.

scholarly journals An enhanced rule mining algorithm to detect suspects of crime against women in the state of Tamil Nadu

2018 ◽  
Vol 7 (4.5) ◽  
pp. 713
Author(s):  
D. Usha ◽  
D. Chitradevi
Keyword(s):  
Data Mining ◽  
Tamil Nadu ◽  
Law Enforcement Officers ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Crime Trend ◽  
Effective Use ◽  
Women In India ◽  
Crime Reports

Crime against women in India has become a prominent topic of argument in the recent years and the issue has been brought to the foreground for concern due to the increasing trend in crimes performed against women. It is the major challenge to the investigators to detect and prevent crimes, particularly crime against women. Most of the crimes get reported and a massive dataset is being generated every year. Analyzing the crime reports can help the law enforcement officers to take preventive measures for reducing the crime, but processing this voluminous data is backbreaking and error prone. So, the application of various data mining techniques can help in visualizing the crime trend. Crime is one of the interesting applications where data mining plays an important role in terms of prediction and analysis in the interest of society. This paper covers in detail analysis of modus operandi of committing crimes and effective use of data mining techniques and algorithms in narrow down to identify the criminals at a short span of time.  

Big Data Analysis and Implementation in Different Areas Using IoT

2017 ◽  
Vol 1 (2) ◽  
pp. 12-25
Author(s):  
Aqeel ur Rehman ◽  
Muhammad Fahad ◽  
Rafi Ullah ◽  
Faisal Abdullah
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Healthcare Applications ◽  
New Techniques ◽  
Standard Data ◽  
Huge Data ◽  
Data Rates ◽  
Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

scholarly journals Architecture of a Compact Data GRID Cluster for Teaching Modern Methods of Data Mining in the Virtual Computer Lab

2020 ◽  
Vol 226 ◽  
pp. 03004
Author(s):  
Mikhail Belov ◽  
Vladimir Korenkov ◽  
Nadezhda Tokareva ◽  
Eugenia Cheremisina
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Information Technologies ◽  
System Analysis ◽  
Nuclear Research ◽  
Data Grid ◽  
Highly Qualified ◽  
Distributed Data Storage ◽  
Computer Lab ◽  
Virtual Computer

This paper discusses the architecture of a compact Data GRID cluster for teaching new methods of Big Data analytics in the Virtual Computer Lab. Its main destination is training highly qualified IT-professionals able to solve efficiently problems of distributed data storage and processing, drawing insights, data mining, and mathematical modeling based on these data. The Virtual Computer Lab was created and successfully operated by the experts of the System Analysis and Control Department at the Dubna State University in collaboration with the Laboratory of Information Technologies (Joint Institute for Nuclear Research).

scholarly journals ANALYSIS OF A LARGE VOLUME OF DATA ON THE STATE OF HIGH-TECH EQUIPMENT

2017 ◽  
pp. 90-95
Author(s):  
Д.С. ШИБАЕВ ◽  
В.В. ВЫЧУЖАНИН ◽  
Н.О. ШИБАЕВА
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Storage ◽  
Large Volume ◽  
Modern Architecture ◽  
Map Reduce ◽  
High Tech ◽  
Short Term ◽  
Use Of Data ◽  
Time Of Operation

The ideological basis of the study is to analyze the data obtained in the result of a large number of high-tech equipment. The data is distributed in databases, depending on various characteristics. The complexity of the sub-sequent processing depends on the amount of information you need to perform, as well as architectural type of data storage. The use of data mining technology allows to significantly improve the analysis of information and subsequent short-term search value. The use of this technology will improve the efficiency of the archives of marine indicators for all time of operation of the vessel. The technology of data analysis is not  tho-rough and requires permanent modification to increase their own efficiency. The addition of modern architecture through data in the databases, will allow to increase efficiency of data analysis, consisting of a large number of indicators of the condition of the vessel and its equipment. One of these    architectures is Map-Reduce.

scholarly journals A Rational Framework for Production Decision Making in Blood Establishments

2012 ◽  
Vol 9 (3) ◽  
pp. 69-79 ◽  
Author(s):  
Augusto Ramoa ◽  
Salomé Maia ◽  
Anália Lourenço
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Quality Management System ◽  
Analysis Software ◽  
Production Chain ◽  
Use Of Data ◽  
Production Strategies ◽  
Definition Of ◽  
General Operation ◽  
Production Decision

Summary SAD_BaSe is a blood bank data analysis software, created to assist in the management of blood donations and the blood production chain in blood establishments. In particular, the system keeps track of several collection and production indicators, enables the definition of collection and production strategies, and the measurement of quality indicators required by the Quality Management System regulating the general operation of blood establishments.This paper describes the general scenario of blood establishments and its main requirements in terms of data management and analysis. It presents the architecture of SAD_BaSe and identifies its main contributions. Specifically, it brings forward the generation of customized reports driven by decision making needs and the use of data mining techniques in the analysis of donor suspensions and donation discards.

Applications of Data Mining in Software Development Life Cycle

2016 ◽  
pp. 558-570
Author(s):  
Naveen Dahiya ◽  
Vishal Bhatnagar ◽  
Manjeet Singh ◽  
Neeti Sangwan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Software Development ◽  
Future Research ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Development Life Cycle ◽  
Efficient Information ◽  
Software Development Life Cycle ◽  
Effective Use

Data mining has proven to be an important technique in terms of efficient information extraction, classification, clustering, and prediction of future trends from a database. The valuable properties of data mining have been put to use in many applications. One such application is Software Development Life Cycle (SDLC), where effective use of data mining techniques has been made by researchers. An exhaustive survey on application of data mining in SDLC has not been done in the past. In this chapter, the authors carry out an in-depth survey of existing literature focused towards application of data mining in SDLC and propose a framework that will classify the work done by various researchers in identification of prominent data mining techniques used in various phases of SDLC and pave the way for future research in the emerging area of data mining in SDLC.

scholarly journals Pattern Identification and Predictions in Data Analysis

2018 ◽  
Vol 7 (03) ◽  
pp. 23686-23691
Author(s):  
Mrs.M. Sasikala ◽  
Ms.D. Deepika ◽  
Mr.S.Shiva Shankar
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Pattern Identification ◽  
Use Of Data ◽  
Large Sets ◽  
Analytic Process ◽  
Predictive Data Mining ◽  
Analytical System ◽  
Mining Work ◽  
And Behavior

Data Mining is an analytic process to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. The main target of data mining application is prediction. Predictive data mining is important and it has the most direct business applications in world. The paper briefly explains the process of data mining which consists of three stages: (1) the Initial exploration, (2) Pattern identification with validation, and (3) Deployment (application of the model to new data in order to generate predictions). Data Mining is being done for Patterns and Relationships recognitions in Data analysis, with an emphasis on large Observational data bases. From a statistical perspective Data Mining is viewed as computer automated exploratory data analytical system for large sets of data and it has huge Research challenges in India and abroad as well. Machine learning methods form the core of Data Mining and Decision tree learning. Data mining work is integrated within an existing user environment, including the works that already make use of data warehousing and Online Analytical Processing (OLAP). The paper describes how data mining tools predict future trends and behavior which allows in making proactive knowledge-driven decisions.

scholarly journals STRUCTURE OF THE INFORMATION EDUCATION ENVIRONMENT AND ITS USE IN PREPARATION OF FUTURE EDUCATION PROFESSIONALS

2019 ◽  
pp. 63-76
Author(s):  
Kateryna Danylyshyna
Keyword(s):  
Information Technologies ◽  
Professional Training ◽  
Educational Environment ◽  
Highly Qualified ◽  
Education Professionals ◽  
Main Page ◽  
Education Environment ◽  
Definition Of ◽  
Definition Of Information ◽  
Structural Blocks

The article analyzes the definition of information educational environment by national and foreign scientists, on the basis of which the author defines the specified concept; structural blocks, functions, principles, components of the information educational environment are analyzed; the structure of information educational environment of Vinnytsia State Pedagogical University named after Mikhail Kotsyubynsky and the possibility of its use in the process of formation of information competence of future teacher of professional training are considered; the structure of the main page of the University site is described, the its sections and their purpose are characterized, the view of the site of the electronic library of the educational establishment is characterized, its structure and purpose are characterized, the structure of the information and educational portal of the Department of Innovative and Information Technologies in Education, Educational and Scientific Institute of Psychology, training of highly qualified specialists were reviewed. Based on the analysis of scientific works by foreign and national scientists, the main characteristics of the informational educational environment were determined: openness, possibility of expansion, scalability, integration, adaptability, and their definitions were provided; the functions of the informational educational environment are defined: information, interactive, communication, coordinating, developmental, occupational. The scientific and pedagogical directions of formation of information educational environment are characterized: organizational, methodical, technical, resource, explanation of these principles is given. The structure and possibilities of using the educational environment are described on the example of the portal of the Chair of Innovative and Information Technologies in Education. The article also presents the results of a pedagogical study regarding the use of an informational educational environment to improve the quality of training of future vocational education teachers.

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