scholarly journals ABOUT THE GENERAL CONCEPT OF THE UNIVERSAL STORAGE SYSTEM AND PRACTICE-ORIENTED DATA PROCESSING

2017 ◽  
pp. 12-19 ◽  
Author(s):  
L. V. Rudikova
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Processing ◽  
Data Warehouse ◽  
General Concept ◽  
Process Data ◽  
Important Place ◽  
Universal System ◽  
Mining Methods And Algorithms ◽  
Subject Domains

Approaches evolution and concept of data accumulation in warehouse and subsequent Data Mining use is perspective due to the fact that, Belarusian segment of the same IT-developments is organizing. The article describes the general concept for creation a system of storage and practice-oriented data analysis, based on the data warehousing technology. The main aspect in universal system design on storage layer and working with data is approach uses extended data warehouse, based on universal platform of stored data, which grants access to storage and subsequent data analysis different structure and subject domains have compound’s points (nodes) and extended functional with data structure choice option for data storage and subsequent intrasystem integration. Describe the universal system general architecture of storage and analysis practice-oriented data, structural elements. Main components of universal system for storage and processing practice-oriented data are: online data sources, ETL-process, data warehouse, subsystem of analysis, users. An important place in the system is analytical processing of data, information search, document’s storage and providing a software interface for accessing the functionality of the system from the outside. An universal system based on describing concept will allow collection information of different subject domains, get analytical summaries, do data processing and apply appropriate Data Mining methods and algorithms.

A systematic view on data descriptors for the visual analysis of tabular data

2016 ◽  
Vol 16 (3) ◽  
pp. 232-256 ◽  
Author(s):  
Hans-Jörg Schulz ◽  
Thomas Nocke ◽  
Magnus Heitzler ◽  
Heidrun Schumann
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Data Gathering ◽  
Climate Impact ◽  
General Concept ◽  
Basic Knowledge ◽  
Data Types ◽  
Process Data ◽  
Tabular Data ◽  
Analysis Process

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.

scholarly journals Penggunaan Algoritma Clustering K-means Untuk Melihat Daerah-Daerah Penyuplai Mahasiswa Di Universitas Kristen Satya Wacana, Salatiga.

2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Rivort Pormes ◽  
Daniel H. F. Manongga
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Processing ◽  
Penetration Process ◽  
Christian University ◽  
Student Data

The use of k-means algorithm in student supply process is done to see students supply tendency in each region who study at satya wacana christian university (SWCU). This data processing with k-meansis useful for extracting information and knowledge from students data at SWCU. With data mining processing, the stakeholders at SWCU can take strategic steps for the penetration process in regions with potential student supply indication. The use of k-meansalgorithms facilitates the process of data analysis as well as grouping on student data during a 5-year period. The aim of this study is to provide on target strategic picture for regions which potentially have a significant impact on students supply each year.

scholarly journals ABOUT DATA MODELING SUBJECT DOMAINS PRACTICE-ORIENTED DIRECTION FOR UNIVERSAL SYSTEM OF STORAGE AND PROCESSING DATA

2017 ◽  
pp. 4-12 ◽  
Author(s):  
L. V. Rudikova ◽  
E. V. Zhavnerko
Keyword(s):  
Data Warehouse ◽  
Data Storage ◽  
Data Modeling ◽  
Analysis Data ◽  
Data Models ◽  
Universal System ◽  
Current Time ◽  
High Data ◽  
General Data Model ◽  
Subject Domains

This article describes data modeling for practice-oriented subject domains they are basis of general data model for data warehouse creation. Describes short subject domains characteristic relationship to different types of any human activities at the current time. Offered appropriate data models, considered relationship between them as data processing and data warehouse creation, which can be built on information data storage technology and which has some characteristics as extensible complex subject domain, data integration, which get from any data sources, data time invariance with required temporal marks, relatively high data stability, search necessary compromises in data redundancy, system blocks modularity, flexibility and extensibility of architecture, high requirements to data storage security. It’s proposed general approach of data collection and data storage, appropriate data models, in the future, will integrate in one database scheme and create generalized scheme of data warehouse as type «constellation of facts». For getting of data models applies structural methodology and consider general principles of conceptual design. Using complex system, which can work with some information sources and represent data in convenient view for users will in-demand for analysis data selected subject domains and determination of possible relationships.

scholarly journals Evaluation of Rapidminer-Aplication in Data Mining Learning using PeRSIVA Model

2020 ◽  
Vol 4 (2) ◽  
pp. 164-171
Author(s):  
Muhammad Uska ◽  
◽  
Rasyid Wirasasmita ◽  
Usuluddin Usuluddin ◽  
Baiq Arianti ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Collection ◽  
Completion Rate ◽  
Descriptive Statistics ◽  
Think Aloud ◽  
Process Data ◽  
Data Collection Technique ◽  
Evaluation Of Data ◽  
Analyze Data

RapidMiner is an application that is used to analyze data quantities and qualitatively to obtain information and knowledge as expected. This software is implemented to process data using several methods or algorithms in Data Minig learning. However, when using this software, users sometimes cannot distinguish between various methods or algorithms in Data Mining. Therefore, it is necessary to evaluate to optimize the use of this software in data mining learning. This study focuses on RapidMiner evaluation of data mining learning using the Persiva model. This model consists of aspects of satisfaction, behavior, impact, and effectiveness. The data collection technique was in the form of a questionnaire with 48 subjects. Data analysis used is descriptive statistics to determine satisfaction, behavior and effects. Meanwhile, Think-Aloud Retrospective technique is used to determine the effectiveness of RapidMiner. Our findings show that users are satisfied with the results of respondents on average agreeing (80%), in the aspect of behavior and impact, the percentage results are above 80%, and the use of this application has been effective with a completion rate above 90%. So it can be concluded that by using this application in data mining learning users can easily complete tasks, and be motivated, and add insights and knowledge in relevant disciplines.

scholarly journals A Feasible Data-Driven Mining System to Optimize Wastewater Treatment Process Design and Operation

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1342 ◽  
Author(s):  
Yong Qiu ◽  
Ji Li ◽  
Xia Huang ◽  
Hanchang Shi
Keyword(s):  
Data Mining ◽  
Wastewater Treatment ◽  
Data Warehouse ◽  
Process Design ◽  
High Efficiency ◽  
Data Driven ◽  
Process Data ◽  
Mining System ◽  
Wastewater Treatment Process ◽  
Data Mining System

Achieving low costs and high efficiency in wastewater treatment plants (WWTPs) is a common challenge in developing countries, although many optimizing tools on process design and operation have been well established. A data-driven optimal strategy without the prerequisite of expensive instruments and skilled engineers is thus attractive in practice. In this study, a data mining system was implemented to optimize the process design and operation in WWTPs in China, following an integral procedure including data collection and cleaning, data warehouse, data mining, and web user interface. A data warehouse was demonstrated and analyzed using one-year process data in 30 WWTPs in China. Six sludge removal loading rates on water quality indices, such as chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP), were calculated as derived parameters and organized into fact sheets. A searching algorithm was programmed to find out the five records most similar to the target scenario. A web interface was developed for users to input scenarios, view outputs, and update the database. Two case WWTPs were investigated to verify the data mining system. The results indicated that effluent quality of Case-1 WWTP was improved to meet the discharging criteria through optimal operations, and the process design of Case-2 WWTP could be refined in a feedback loop. A discussion on the gaps, potential, and challenges of data mining in practice was provided. The data mining system in this study is a good candidate for engineers to understand and control their processes in WWTPs.

scholarly journals Innovative Approaches for Efficiently Warehousing Complex Data from the Web

Data Mining ◽  
2013 ◽  
pp. 1422-1448
Author(s):  
Fadila Bentayeb ◽  
Nora Maïz ◽  
Hadj Mahboubi ◽  
Cécile Favre ◽  
Sabine Loudcher ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Warehouse ◽  
Design Management ◽  
Complex Data ◽  
Data Warehouses ◽  
Process Data ◽  
Access Methods ◽  
Olap Analysis ◽  
The Web

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.

scholarly journals PERANCANGAN SISTEM DATA WAREHOUSE PEMBELAJARAN PADA PONDOK PESANTREN AL-FURQON

2017 ◽  
Vol 3 (1) ◽  
pp. 110-122
Author(s):  
Padeli Padeli ◽  
Muhammad Dzulfikar Allam ◽  
Nurviani Riska Suharto
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Data Warehouse ◽  
Teaching And Learning ◽  
Storage Capacity ◽  
Historical Data ◽  
Boarding School ◽  
Process Data ◽  
Faith Based ◽  
Collection Methods

Al-furqon is foundation of faith-based education that is commonly called the boarding school. Along with the increasing number of students each year the more the teacher or teachers who will take part in the activities of teaching and learning. Thus the more the data that goes into the institution or Pondok Pesantren Al-furqon, these data would need to be stored, processed, and analyzed to produce a useful information for the institution, and reported to the Ministry of Religious Affairs in charge of boarding school. Meanwhile, to make the report, it takes more time to collect information and process data stored in operational databases in the institution. Since it is considered necessary to manage large amounts of data in order to produce an information quickly, then made a design of the data warehouse to facilitate the conduct of data processing, analyzing and reporting the data analysis. The method used the data collection methods, such as observation, interviews, and literature review. After getting the data and then analyzed with the SWOT method, this method describes the shortcomings, strengths and opportunities for improvement in the system. This research resulted in a data warehouse for Al-furqon institution, as well as the amount of storage capacity needed for historical data.

scholarly journals ARRANGEMENT AND MODULATION OF ETL PROCESS IN THE STORAGE

2020 ◽  
pp. 3-8
Author(s):  
Jala Aghazada
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Data Warehouse ◽  
Analytical Data ◽  
Online Analytical Processing ◽  
Processing Data ◽  
Analytical Processing ◽  
Different Sources ◽  
Operational Data ◽  
Important Significance

Data warehouse (DW) is the basis of systems for operational data analysis (OLAP-Online Analytical Processing). Data extracted from different sources transforms and load in DW. Proper organization of this process, which is called ETL (Extract, Transform, Load) has important significance in creation of DW and analytical data processing. Forms of organization, methods of realization and modeling of ETL processes are considered in this paper.

scholarly journals Implementasi Data Warehouse dan Data Mining: Studi Kasus Analisis Peminatan Studi Siswa

2011 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Eka Miranda
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Decision Making ◽  
Data Analysis ◽  
Data Warehouse ◽  
Ad Hoc ◽  
Data Distribution ◽  
Program Selection ◽  
Mining Design ◽  
The University

This paper discusses the implementation of data mining and their role in helping decision-making related to students’ specialization program selection. Currently, the university uses a database to store records of transactions which can not directly be used to assist analysis and decision making. Based on these issues then made the data warehouse design used to store large amounts of data and also has the potential to gain new data distribution perspectives and allows to answer the ad hoc question as well as to perform data analysis. The method used consists of: record analysis related to students’ academic achievement, designing data warehouse and data mining. The paper’s results are in a form of data warehouse and data mining design and its implementation with the classification techniques and association rules. From these results can be seen the students’ tendency and pattern background in choosing the specialization, to help them make decisions. 

Sign in / Sign up

Export Citation Format

Share Document