scholarly journals OLAP SYSTEMS AS THE MODERN DATA PREPARATION TOOLS FOR OUTDOOR ADVERTISING DATA MINING

2020 ◽  
Vol 1 (2) ◽  
pp. 60-66
Author(s):  
Oleksandr Shelest ◽  
Bella Holub
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Data Warehouse ◽  
Data Access ◽  
Analysis Data ◽  
Subject Area ◽  
Point Of View ◽  
Outdoor Advertising ◽  
Text Documents ◽  
Operational Data

Today, most organizations use databases and at worst text documents and spreadsheet files as sources for data analysis, which prevent correct and error-free analysis. At best, the data can be constantly adjusted due to ambiguities and inaccuracies. The subject of the study is the intellectual analysis of outdoor advertising data. The methodology of successful data analysis is the correct storage of data, which is the basis for clear data analysis. Modern computer systems and computer networks allow the accumulation of large arrays of data to solve problems of processing and analyzing. Unfortunately, the machine form of data presentation itself contains the information that a person needs in a hidden form, and you need to use special methods of data analysis to obtain it. In order to get what you want, you need to create not just a database, but a data warehouse with a special storage structure. Thus, the data warehouse allows you to collect data from various sources, databases, table files and other things, store them throughout history and, unlike conventional databases, allows you to create systems for fast and accurate data analysis. Data warehouse is the basis for building decision support systems. Operational data is checked, cleared and aggregated before entering the data warehouse. Such integrated data is much easier to analyze. Different sources of operational data may contain data describing the same subject area from different points of view (for example, from the point of view of accounting, inventory control, planning department, etc.). A decision made on the basis of only one point of view can be ineffective or even erroneous. The goal is to use a data warehouse to integrate information that reflects different perspectives on the same subject area. Focus on the object, which will also allow the data warehouse to store only the data you need to analyze it. It will also significantly increase the speed of data access both due to the possible redundancy of the stored information and due to the exclusion of modification operations. Conclusion: the decision support system will ensure reliable storage of large amounts of data. Tasks will also be assigned to prevent unauthorized access, data backup, archiving, etc.

scholarly journals PENGEMBANGAN DATA WAREHOUSE CALL CENTER DENGAN METODOLOGI KIMBALL NINE-STEP PADA BINA SARANA INFORMATIKA

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
ADJAT SUDRADJAT
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Data Warehouse ◽  
Call Center ◽  
Educational Institution ◽  
Public Information ◽  
Strategic Information ◽  
Web Based ◽  
Academic Services ◽  
Operational Data

ABSTRACT - As an educational institution, Bina Sarana Informatika of course requires summary of information which is comprehensive and sustainable as a support to management in doing evaluation, planning and decision-making in the field of academic services. But the information system from operational data processing today can’t meet the needs, because it’s only able to produce detailed reports periodically. The research studies the development of a data warehouse for Call Center on The Division of Public Information of Bina Sarana Informatika in order to explore the strategic information contained in the operational database and present them in the form of summary information which is useful as input in improving the quality of academic services. By using a nine steps kimball approach, the research produce a data warehouse which is equipped with a web-based presentation application that can be easily accessed by all stakeholders of Bina Sarana Informatika. The development of data warehouse has been able to extract operational data into strategic information summaries that are useful to Bina Sarana Informatika management as supporting in doing evaluation, planning and decision-making in the field of academic services. Keywords : Nine Steps Kimball, Call Center, Data Warehouse, Decision Support. ABSTRAKSI - Sebagai sebuah institusi pendidikan, Bina Sarana Informatika tentu membutuhkan ringkasan informasi yang komprehensif dan berkesinambungan sebagai penunjang bagi manajemen dalam melakukan evaluasi, perencanaan dan pengambilan keputusan di bidang pelayanan akademik. Namun sistem informasi yang berasal dari pengolahan data operasional saat ini tidak dapat memenuhi kebutuhan tersebut, karena hanya mampu menghasilkan laporan–laporan yang bersifat detail dan periodik. Penelitian ini mengkaji pengembangan data warehouse Call Center pada Divisi Informasi Publik Bina Sarana Informatika untuk menggali informasi strategis yang terdapat pada database operasional dan menyajikannya dalam bentuk ringkasan informasi yang berguna sebagai masukan dalam usaha peningkatan kualitas pelayanan akademik. Dengan menggunakan metodologi kimball nine-step, penelitian menghasilkan sebuah data warehouse dilengkapi dengan aplikasi presentasi berbasis web yang dapat diakses dengan mudah oleh seluruh stakeholder Bina Sarana Informatika. Pengembangan data warehouse telah mampu mengekstrak data operasional menjadi ringkasan informasi strategis yang berguna bagi manajemen Bina Sarana Informatika sebagai penunjang dalam melakukan evaluasi, perencanaan dan pengambilan keputusan di bidang pelayanan akademik. Kata Kunci : Nine Steps Kimball, Call Center, Data Warehouse, Decision Support.

Data Streams

2011 ◽  
pp. 561-565
Author(s):  
João Gama ◽  
Pedro Pereira Rodrigues
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Data Base ◽  
Data Streams ◽  
Active Role ◽  
Analysis Data ◽  
Current Data ◽  
Data Mining Algorithms ◽  
Exact Answer ◽  
Approximate Answers

Nowadays, data bases are required to store massive amounts of data that are continuously inserted, and queried. Organizations use decision support systems to identify potential useful patterns in data. Data analysis is complex, interactive, and exploratory over very large volumes of historic data, eventually stored in distributed environments. What distinguishes current data sources from earlier ones are the continuous flow of data and the automatic data feeds. We do not just have people who are entering information into a computer. Instead, we have computers entering data into each other (Muthukrishnan, 2005). Some illustrative examples of the magnitude of today data include: 3 billion telephone calls per day, 4 Giga Bytes of data from radio telescopes every night, 30 billion emails per day, 1 billion SMS, 5 Giga Bytes of Satellite Data per day, 70 billion IP Network Traffic per day. In these applications, data is modelled best not as persistent tables but rather as transient data streams. In some applications it is not feasible to load the arriving data into a traditional Data Base Management Systems (DBMS), and traditional DBMS are not designed to directly support the continuous queries required in these applications (Alon et al., 1996; Babcock et al. 2002; Cormode & Muthukrishnan, 2003). These sources of data are called Data Streams. Computers play a much more active role in the current trends in decision support and data analysis. Data mining algorithms search for hypothesis, evaluate and suggest patterns. The pattern discovery process requires online ad-hoc queries, not previously defined, that are successively refined. Due to the exploratory nature of these queries, an exact answer may be not required: a user may prefer a fast but approximate answer to a exact but slow answer. Processing queries in streams require radically different type of algorithms. Range queries and selectivity estimation (the proportion of tuples that satisfy a query) are two illustrative examples where fast but approximate answers are more useful than slow and exact ones. Approximate answers are obtained from small data structures (synopsis) attached to data base that summarize information and can be updated incrementally

scholarly journals Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

2021 ◽  
Author(s):  
Vladimir Badenko ◽  
Nikolai Bolshakov ◽  
Alexander Fedotov ◽  
Florian Becker ◽  
Aleksandra Müller
Keyword(s):  
Decision Support ◽  
Digital Technologies ◽  
Data Access ◽  
Point Of View ◽  
Optimal Decision ◽  
Economic Point ◽  
Factory Planning ◽  
Building Information ◽  
Digital Asset ◽  
Factory Of The Future

Abstract Industrial objects nowadays rapidly transform due to the development of digital technologies. The concept of the Factory of the Future (FoF) involves digitization of all parts of the factory. In this paper two technologies are motivated and considered as the basic technologies that should be used in FoF: Digital Shadow (DS) and Building Information Modelling (BIM). Basic theory on these issues is given and potentials of BIM and DS integration is formulated. Based on the ability of digital technologies, their integration and convergence to generate value, definition Digital Asset is introduced from the economic point of view as a digital resource which brings economic benefit. A concept of integrating BIM and DS technologies for decision support in factory planning is formulated, including Life Cycle Assessment (LCA) and semantic modeling. The concept includes a description of the aggregate of technologies and their interconnections as a Digital Asset of the FoF. Further research objectives are focused on integration of BIM and DS which requires their interoperability ensured by an Ontology-Based Data Access (OBDA) approach, based on the Semantic web.

Data Warehousing, Multi-Dimensional Data Models and OLAP

2005 ◽  
pp. 134-139
Author(s):  
Prasad M. Deshpande ◽  
Karthikeyan Ramasamy
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Analysis Data ◽  
Multidimensional Analysis ◽  
Query Evaluation ◽  
Business Information ◽  
Analytical Processing ◽  
The 1960S ◽  
A Company

Since the advent of information technology, businesses have been collecting vast amounts of data about their daily transactions. For example, a company keeps track of data regarding the sales of its various products at different stores over a period of time. Businesses can gain valuable insights by analyzing this data to spot trends and correlations in the data. Data warehousing, multidimensional analysis, and online analytical processing (OLAP) refer to a set of technologies that address the problem of business data analysis. Data warehousing has become the established paradigm for knowledge workers to sift through mountains of historical data in order to extract nuggets of business information. Data analysis tools have been used in various forms historically since the 1960s (Pendse, 2003). Recently, there has been a rapid growth in the industry, with the total worldwide OLAP market estimated at about $3.7 billion in 2003 (Pendse). This has also been an active area of research with many contributions in data warehouse design, storage, view selection, cube computation, indexing, and query evaluation.

scholarly journals Big Data Warehouse for Healthcare-Sensitive Data Applications

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2353
Author(s):  
Arsalan Shahid ◽  
Thien-An Ngoc Nguyen ◽  
M-Tahar Kechadi
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Childhood Obesity ◽  
Access Control ◽  
Data Warehouse ◽  
System Architecture ◽  
Data Access ◽  
Data Representation ◽  
Data Driven ◽  
Privacy And Security

Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

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.

Data Warehousing, Multi-Dimensional Data Models and OLAP

2008 ◽  
pp. 179-186
Author(s):  
Prasad M. Deshpande ◽  
Karthikeyan Ramasamy
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Analysis Data ◽  
Multidimensional Analysis ◽  
Query Evaluation ◽  
Business Information ◽  
Analytical Processing ◽  
The 1960S ◽  
A Company

Since the advent of information technology, businesses have been collecting vast amounts of data about their daily transactions. For example, a company keeps track of data regarding the sales of its various products at different stores over a period of time. Businesses can gain valuable insights by analyzing this data to spot trends and correlations in the data. Data warehousing, multidimensional analysis, and online analytical processing (OLAP) refer to a set of technologies that address the problem of business data analysis. Data warehousing has become the established paradigm for knowledge workers to sift through mountains of historical data in order to extract nuggets of business information. Data analysis tools have been used in various forms historically since the 1960s (Pendse, 2003). Recently, there has been a rapid growth in the industry, with the total worldwide OLAP market estimated at about $3.7 billion in 2003 (Pendse). This has also been an active area of research with many contributions in data warehouse design, storage, view selection, cube computation, indexing, and query evaluation.

scholarly journals KEBUTUHAN BERAFILIASI, INTROVERSI KEPRIBADIAN SERTA KETERGANTUNGAN PADA FACEBOOK PADA MAHASISWA

Proyeksi ◽  
1970 ◽  
Vol 6 (2) ◽  
pp. 68
Author(s):  
Tri Nurmala Dewi ◽  
Joko Kuncoro
Keyword(s):  
Regression Analysis ◽  
Social Network ◽  
Data Analysis ◽  
Positive Relationship ◽  
Analysis Data ◽  
Point Of View ◽  
The Third ◽  
The Social ◽  
Facebook Addiction ◽  
The Relationship

Manusia adalah mahkluk sosial dengan tipe kepribadian yang berbeda-beda. Setiap individu selalu berhubungan dengan orang lain meski berbeda cara dan intensitasnya. Kecenderungan ini dikenal sebagai kebutuhan afiliasi. Kebutuhan ini melekat pada tiap individu termasuk yang berkepribadian introvert. Ada banyak cara dan media yang dapat digunakan untuk memuaskan kebutuhan ini. Jaringan sosial Facebook adalah salah satunya. Kebiasan mengakses jaringan sosial ini diduga dapat menimbulkan ketagihan dan ketergantungan. Tujuan peneilitian ini adalah mengetahuai keterkaitan antara introversi kepribadian dengan ketergantungan terhadap facebook. Populasi penelitian adalah mahasiswa UNISSULA dengan sampel sebanyak 167 yang diambil secara proporsional.Data ketergantungan terhadap facebook diukur dengan menggunakan skala yang disusun berdasar karakteristik dari Young dan skala afilisasi dari Murray untuk mengukur kebutuhan afiliasi. Data introversi kepribadian diukur dengan skala introversi kepribadian dari Jung.  Ada tiga hipotesis yang akan diuji. Pertama adalah ada keterkaitan antara kebutuhan afiliasi dan introversi kepribadian dengan ketergantungan terhadap facebook. Kedua adalah ada hubungan positif antara kebutuhan afiliasi dengan ketergantungan terhadap facebook dan ketiga adalah ada hubungan positif antara introversi kepribadian dengan ketergantungan terhadap fecbook. Analisis data dilakukan dengan teknik statistic regresi ganda.Hasil analisis menunjukan besarnya Koefisien regresi ganda R = 0.278, F = 6.863 dan p = 0.001 (p < 0.01) yang berarti ada hubungan yang signifikan antara kebutuhan afiliasi dan introversi kepribadian dengan ketergantungan terhadap facebook.  Uji hipotesis kedua menunjukan ry1 = - 0.163 (p = 0.036) yang beratri hipotesis kedua ditolak sedangkan uji hipotesis ketiga menunjukan Ry2 = 0.189 dan p = 0.015 (p < 0.05) yang berarti hipotesis ketiga diterima. Kata Kunci :   introversi kepribadian, kebutuhan afiliasi, ketergantungan facebook.  THE NEED OF AFFILIATION, INTROVERSION OF PERSONALITY, AND FACEBOOK ADDICTION AMONG UNIVERSITY STUDENTS Abstract Human are social beings that have personality type. Each individual is always in relationship with others, although in different manner and intensity. This tendency is called the need of affiliation. This need is necessary for everyone include persons with an introversion personality, who focus on the inside world and observe the outside world selectively according to their own point of view. There are many ways and media can be used to satisfy this need. One of the most widely used today is the social network facebook. Facebook’s function related to the fulfillment of need which related to an individual, can lead to an addiction. The purpose of this research was to know the relationship between the need of affiliation and introversion personality with facebook addiction. Populations in this research were students in UNISSULA with 167 samples which were determined based on the proportional sampling technique.This research used facebook addiction scale based on Young’s characteristic, Murray’s need of affiliation’s scale, and introversion personality’s scale based on Jung’s characteristic. The first hypothesis was that there is relationship between the need of affiliation and introversion personality with facebook addiction. The second hypothesis was that there is positive relationship between the need of affiliation and facebook addiction; and the third hypothesis was that there is positive relationship between introversion personality and facebook addiction.The test of item difference power and reliability used product moment and alpha cronbach’s coefficient. The test hypothesis used regression analysis. Data analysis resulted in R = 0,278, F = 6,863 with p = 0,001 (p<0, 01). This result shows that the first hypothesis was accepted. The second hypothesis analysis result showed ry1 = - 0,163 with p = 0,036 (p<0, 05), this means that the second hypothesis was rejected, while the third hypothesis analysis result showed ry2 = 0,189 with p = 0,015 (p<0, 05) which means the third hypothesis was accepted. Keyword: Need of affiliation, introversion personality, facebook addiction

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

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