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.

Analysising on the Library Services in the Age of Big Data

2014 ◽  
Vol 1044-1045 ◽  
pp. 1066-1070
Author(s):  
Chen Wei ◽  
Xiao Di Wang ◽  
Ran Ma ◽  
Bing Qi Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Social Networking ◽  
Data Storage ◽  
Rapid Development ◽  
Structured Data ◽  
The Internet ◽  
Library Services ◽  
Library Service

The advent of the age of big data brings not only the rapid development of the Internet, scientific research, social networking and other fields, but also help and challenges to the application of library. For example, the library service applications in data storage, data mining, data analysis, etc. can identify hidden values behind the data only through systematic organization and analysis of massive structured, unstructured, and semi-structured data, ​​in order to predict the future development of library and promote its better development.

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.

Association Rule Hiding Methods

2011 ◽  
pp. 71-75
Author(s):  
Vassilios S. Verykios
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Research Area ◽  
Privacy And Security ◽  
The Public ◽  
Use Of Data ◽  
Processing Power ◽  
Interesting Approach ◽  
Data Auditing ◽  
New Research

The enormous expansion of data collection and storage facilities has created an unprecedented increase in the need for data analysis and processing power. Data mining has long been the catalyst for automated and sophisticated data analysis and interrogation. Recent advances in data mining and knowledge discovery have generated controversial impact in both scientific and technological arenas. On the one hand, data mining is capable of analyzing vast amounts of information within a minimum amount of time, an analysis that has exceeded the expectations of even the most imaginative scientists of the last decade. On the other hand, the excessive processing power of intelligent algorithms which is brought with this new research area puts at risk sensitive and confidential information that resides in large and distributed data stores. Privacy and security risks arising from the use of data mining techniques have been first investigated in an early paper by O’ Leary (1991). Clifton & Marks (1996) were the first to propose possible remedies to the protection of sensitive data and sensitive knowledge from the use of data mining. In particular, they suggested a variety of ways like the use of controlled access to the data, fuzzification of the data, elimination of unnecessary groupings in the data, data augmentation, as well as data auditing. A subsequent paper by Clifton (2000) made concrete early results in the area by demonstrating an interesting approach for privacy protection that relies on sampling. A main result of Clifton’s paper was to show how to determine the right sample size of the public data (data to be disclosed to the public where sensitive information has been trimmed off), by estimating at the same time the error that is introduced from the sampling to the significance of the rules. Agrawal and Srikant (2000) were the first to establish a new research area, the privacy preserving data mining, which had as its goal to consider privacy and confidentiality issues originating in the mining of the data. The authors proposed an approach known as data perturbation that relies on disclosing a modified database with noisy data instead of the original database. The modified database could produce very similar patterns with those of the original database.

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 An Intelligent Big Data Analytics System using Enhanced Map Reduce Techniques

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1006-1010
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Storage ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Map Reduce ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Effective Manner

An Intelligent Big Data Analytics System using Enhanced Map Reduce Techniques include a set of Methods, applications and strategy which helps the organization and industry to bring together the data and information from outside sources and internal systems, as well as it is used to collect , classify, analysis and run the queries against the data and prepare the report for effective decision making. The Enhanced Map Reduced Techniques based on K-Nearest Neighbor (KNN) clustering Strategy works efficient as well as in an effective manner. We found that the existing MR – mafia sub space clustering Strategy have not performed effectively .Many clustering techniques are adopted in real world data analysis for example customer behavior analysis, medical data analysis, digital forensics, etc. The existing MR- mafia sub space clustering Strategy is inefficient because of continuously increase in the data size, and overlaying of the data blocks .The proposed KNN clustering Strategy mainly focused on the enhanced the Map Reduce techniques, and then to avoid the unnecessary input and output data, optimize the data storage in order to achieve the best out sourcing of data privacy. The proposed KNN clustering Strategy works effectively and that can be outsourced to cloud server.

scholarly journals Preparing Low Cost Solution Based On Customized Process Of Parallel Clustering Solution

2016 ◽  
Vol 12 (21) ◽  
pp. 159
Author(s):  
E. Manigandan ◽  
V. Shanthi ◽  
Magesh Kasthuri
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Large Volume ◽  
Low Cost ◽  
Cost Effective ◽  
Data Sets ◽  
Data Mining Technique ◽  
Increasing Demand ◽  
Traditional Approaches ◽  
Parallel Clustering

Big Data analysis is the field of data processing where it involves collections of large volume of data sets which are generally so large and really complex in nature and also there is no unified scientific solution globally for any data analysis due to its nature of difficulties to process them by adopting traditional approaches and technologies. Handling large volume of data and preparing them for deep analysis to evaluate them and prepare required information as required by the mining process is the most complex and sometimes costlier task in real-time. There are many solutions for the data mining process like clustering, special mining, k-means mining to name a few. But the real challenge in data mining process is choosing the correct solution or algorithm to apply for mining the input data and tuning the processing step in such a way that we establish a cost effective solution for the entire mining process. There may be many solutions where mining is efficient but cost of operation is not effective and sometimes it is vice-versa. Hence there is always an ever increasing demand for an efficient solution which is cost effective as well as efficient in data mining technique. The intent of this paper is researching on how we implement a concept called Parallel clustering which gives higher benefit in terms of cost and time in data mining processing without compromising the efficiency and accuracy in expected result. This paper discusses one such custom algorithm and its performance as compared to other solutions.

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.

Analisis Pengaruh Kecerdasan Intelektual Terhadap Kemampuan Akuntansi dengan Kecerdasan Emosional dan Spiritual sebagai Pemoderasi

2018 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Ria Manurung
Keyword(s):  
Data Analysis ◽  
Graduate Program ◽  
Descriptive Analysis ◽  
Research Result ◽  
Secondary Data ◽  
Spiritual Intelligence ◽  
Primary Data ◽  
Moderating Variables ◽  
Use Of Data ◽  
Independent Variable

Research conducted to obtain empirical evidence how the influence of independent variables of intellectual intelligence to accounting with moderating variables of emotional and spiritual intelligence. The research method used is descriptive quantitative with explanatory descriptive or explanatory research. This method is an explanatory research that proves the existence of causal relationship of independent variable (independent variable) that is intellectual intelligence; moderating variable (emotional and spiritual intelligence); and dependent variable (accounted dependent variable). Research begins by conducting library search, followed by primary data collection conducted by using questionnaires and secondary data through data analysis. And for the use of data analysis consists of descriptive analysis, classical assumption test and verification analysis with the method of Moderated Regression Analysis (MRA). This study is a census study with homogeneous and limited population of 92 students, all students of Accounting Graduate Program at UNSOED. Conclusion of research result that is: (1) Intellectual intelligence have influence either positively or signifikan to accountancy. Thus intellectual intelligence can lead students to more easily understand accounting, (2) Intellectual intelligence can be strengthened by emotional intelligence on accounting both positively and significantly. (3) Spiritual intelligence can strengthen the influence of intellectual intelligence on accounting both positively and significantly.

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