scholarly journals Implementasi Sistem Business Intelligence Untuk Data Penelitian di Perguruan Tinggi

2019 ◽  
Vol 4 ◽  
pp. I1-I10
Author(s):  
Firman Noor Hasan
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Olap Analysis

Data didalam sebuah perguruan tinggi merupakan aset, aset yang senantiasa berkembang dan membutuhkan pengelolaan secara khusus, baik dari sisi pemanfaatannya maupun dari sisi penyimpanannya. Pembangunan business intelligence merupakan salah satu cara untuk dapat mengekstrak informasi penting di sebuah perguruan tinggi. Kondisi sistem yang berjalan saat ini di Perguruan Tinggi XYZ yaitu laporan tentang data-data penelitian belum mempunyai database dan hanya dihasilkan langsung dengan file yang berbentuk excel, dan setiap program studi mempunyai format yang berbeda-beda. Oleh karena itu dibutuhkan sistem business intelligence yang perancangannya meliputi perancangan data warehouse, perancangan olap analysis, perancangan report, dan perancangan dashboard. Penelitian ini menghasilkan sebuah sistem business intelligence di Perguruan Tinggi XYZ yang sangat membantu didalam mengumpulkan data-data penelitian sehingga dapat dianalisis. Report yang dirancang sangat membantu didalam membuat laporan penelitian dan dapat disesuaikan dengan kebutuhan, sehingga masalah yang sering terjadi dalam hal kebergantungan dalam perolehan laporan diharapkan tidak terjadi kembali. Dashboard penelitian yang dirancang membantu pimpinan didalam menganalisis data untuk mempelajari tren penelitian yang dilakukan di Perguruan Tinggi XYZ, serta dapat dijadikan untuk mendukung pengambilan keputusan dan nantinya dapat juga sebagai pengukur kinerja dosen.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

scholarly journals Business intelligence for social media interaction in the travel industry in Indonesia

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Michael Yulianto ◽  
Abba Suganda Girsang ◽  
Reinert Yosua Rumagit
Keyword(s):  
Social Media ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Marketing Activity ◽  
Social Media Data ◽  
Current Technology ◽  
Media Interaction ◽  
Travel Industry ◽  
Media Data ◽  
Analyze Data

Electronic ticket (eticket) provider services are growing fast in Indonesia, makingthe competition between companies increasingly intense. Moreover, most of them have the sameservice or feature for serving their customers. To get back the feedback of their customers, manycompanies use social media (Facebook and Twitter) for marketing activity or communicatingdirectly with their customers. The development of current technology allows the company totake data from social media. Thus, many companies take social media data for analyses. Thisstudy proposed developing a data warehouse to analyze data in social media such as likes,comments, and sentiment. Since the sentiment is not provided directly from social media data,this study uses lexicon based classification to categorize the sentiment of users’ comments. Thisdata warehouse provides business intelligence to see the performance of the company based ontheir social media data. The data warehouse is built using three travel companies in Indonesia.As a result, this data warehouse provides the comparison of the performance based on the socialmedia data.

scholarly journals Business Intelligence and Data Warehouse Technologies for Traffic Accident Data Analysis in Botswana

2021 ◽  
Author(s):  
Monkgogi Mudongo ◽  
Edwin Thuma ◽  
Nkwebi Peace Motlogelwa ◽  
Tebo Leburu-Dingalo ◽  
Pulafela Majoo
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Managerial Decision ◽  
Vehicle Registration ◽  
Accident Severity ◽  
Accident Data ◽  
Causes Of Deaths

Road traffic accidents are a serious problem for the nation of Botswana. A large amount of money is used to compensate those who are affected by road accidents. Traffic accidents are one of the major causes of Deaths in Botswana. It is important for relevant organizations to have a reliable source of data for accurate evaluation of traffic accidents. Similarly, data on vehicle registration must be transformed and be readily available to assist managerial decision makers. In this article, we deploy a Business Intelligence (BI) and Data Warehouse (DW) solution in an attempt to assist the relevant departments in their road traffic accidents and vehicle registration evaluation. In Our evaluation of the traffic accidents our findings suggest that across accident severity, Damage Only accidents had the most interesting recent trend with a 11.93% decrease in the last 3 years on record. Count of Accident Severity for Damage Only accidents dropped from 13,491 to 11,881 between 2018 and 2020 whilst Minor accidents experienced the longest period of growth. Most accidents take place in rural locations and more accidents take place during the weekend. At 28,439, Sunday had the highest number of accidents and was 47.59% higher than Wednesday, which had the lowest count of accidents at 19,269. The results for vehicle registration reveal that the number of vehicle registration decreased for the last 3 years on record. The number of vehicles registered dropped from 65535 to 24457 during its steepest decline between 2019 and 2021.

IT Architecture and Information Quality in Data Warehouse and Business Intelligence Environments

2014 ◽  
pp. 112-127
Author(s):  
Samuel Otero Schmidt ◽  
Edmir Parada Vasques Prado
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Information Quality ◽  
Relevant Literature ◽  
Research Model ◽  
Structured Interviews ◽  
It Architecture ◽  
Financial Company ◽  
The Relationship

Organizations are currently investing more in information technology to store and process a vast amount of information. Generally, this information does not comply with any standard, which hinders the decision-making process. The cause of the difficulties can be attributed to Information Quality (IQ), which has technical characteristics related to the architecture used in Data Warehouse (DW) and Business Intelligence (BI) environments. On the basis of the relevant literature in IQ, DW, and BI, a research model was created to identify the relations between components of DW/BI architecture and IQ dimensions. This research model was applied in a real case study (Big Financial Company in Brazil). This case study involved semi-structured interviews with managers and analysts. This chapter attempts to provide a better understanding of the relations between IT architecture and IQ in DW and BI environments. The authors hope to motivate the discussion around the development of IQ-oriented architectures for BI and the relationship between these concepts.

Data Warehouse Performance

2011 ◽  
pp. 580-585 ◽  
Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Performance Tuning ◽  
Data Warehouses ◽  
User Query ◽  
Data Volume ◽  
Predictive Data Mining ◽  
Support Decision Making ◽  
Over Time

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.

Seismological Data Warehousing and Mining

2011 ◽  
pp. 22-37
Author(s):  
Marketos Gerasimos ◽  
Theodoridis Yannis ◽  
S. Kalogeras Ioannis
Keyword(s):  
Data Warehouse ◽  
Earth Science ◽  
Post Processing ◽  
Mining System ◽  
Seismological Data ◽  
Comparative Survey ◽  
Data Collections ◽  
Olap Analysis ◽  
Science Information ◽  
Earthquake Data

Earthquake data composes an ever increasing collection of earth science information for post-processing analysis. Earth scientists, local or national administration officers and so forth, are working with these data collections for scientific or planning purposes. In this article, we discuss the architecture of a so-called seismic data management and mining system (SDMMS) for quick and easy data collection, processing, and visualization. The SDMMS architecture includes, among others, a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining. We provide template schemes for these two components as well as examples of their functionality towards the support of decision making. We also provide a comparative survey of existing operational or prototype SDMMS.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

Do Users Go Both Ways?

2010 ◽  
Vol 1 (3) ◽  
pp. 15-33
Author(s):  
Hamid Nemati ◽  
Brad Earle ◽  
Satya Arekapudi ◽  
Sanjay Mamani
Keyword(s):  
Literature Review ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Information Needs ◽  
Project Team ◽  
Market Place ◽  
Tool Selection ◽  
Road Map ◽  
Perceived Values ◽  
User Groups

A challenging task for a data warehouse team is identifying users by their information needs and skills, and then providing the BI (Business Intelligence) tools that support each group to do their job effectively and efficiently. Recent studies have shown that the BI market place is saturated with a bewildering array of capabilities, functions and software suites. The current lack of consistent interpretation of Business Intelligence has created some confusion in the market place. This paper defines a framework to identify different user groups in an organization and map their needs and requirements to the different functionalities offered by different BI tool vendors. Through literature review, clear definitions of users were created and a set of BI tools that identifies functional needs was established. From that information, a questionnaire was developed that probed for the relationships between user types, tools, functions and other perceived values. Responses from 154 professionals were then used to develop a road map for the data warehouse project team in BI tool selection.

Sign in / Sign up

Export Citation Format

Share Document