scholarly journals Nuevas tendencias para la optimización de los procesos de Inteligencia de Negocios

2021 ◽  
Vol 11 (1) ◽  
pp. 524-539
Author(s):  
Rosa Mirelly García Jara ◽  
◽  
Katty Susana Gutiérrez Villanueva ◽  
Katherin Vanessa Rodríguez Zevallos ◽  
Frank Edmundo Escobedo Bailón
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Response Times ◽  
Open Data ◽  
Decision Makers ◽  
Process Automation ◽  
Effectiveness And Efficiency ◽  
Automated Support ◽  
Data Acquisition Process

Business Intelligence is becoming more and more relevant in companies, this is due to the fact that decision makers rely on it to perform the work itself. Based on this assertion, it is necessary to facilitate the BI process, reducing response times and increasing effectiveness and efficiency. This article shows different ways to perform Business Intelligence, from the origin, i.e. the extraction of data, to the last link of the process related to decision making. To this extent, new alternatives are presented that with the necessary study have shown that they go beyond what we now know as BI, allowing not only to make decisions but also to propose that these have an automated support, also allowing the data to be processed practically alone and to handle more real reports based on data from various sources. The objective of the study is to analyze the new trends for the development of processes related to business intelligence, for which a meticulous bibliographic review has been carried out by consulting scientific articles, books and scientific conferences. First, the description of terms and the staging of the information collected through the research, dedicated to the various innovative trends for the deployment of Business Intelligence, showing new definitions, architectures and trends that are currently being carried out, have been developed. Finally, SOA architecture proposals, open data acquisition, process automation and data warehouse reengineering would allow the optimization of business intelligence through their alternatives.

scholarly journals Business Intelligence System via Group Decision Making

2016 ◽  
Vol 16 (3) ◽  
pp. 219-229 ◽  
Author(s):  
Daniela Borissova ◽  
Ivan Mustakerov ◽  
Dilian Korsemov
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Business Intelligence ◽  
Optimization Model ◽  
Web Application ◽  
Group Decision ◽  
Evaluation Criteria ◽  
Decision Makers ◽  
Intelligence System ◽  
Three Layer Architecture

Abstract In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.

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.

Analysis of Business Intelligence Solutions in the SME Sectors

2015 ◽  
Vol 795 ◽  
pp. 123-128
Author(s):  
Leszek Kiełtyka ◽  
Klaudia Smoląg
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Complex Analysis ◽  
Small And Medium Enterprises ◽  
Decision Makers ◽  
Current Events ◽  
Decision Making Processes ◽  
Barriers To Implementation ◽  
Medium Enterprises ◽  
Support Decision Making

Business intelligence (BI) solutions are aimed to help managers make decisions in enterprises. Through complex analysis, decision-makers are supported in building strategies of operation. Managers in small and medium-sized enterprises (SME) are also becoming more aware of the fact that conventional methodology of analysis of current events is insufficient. Therefore, the need arises for using the solutions that support the processes of data analysis, finding relationships between each other or pointing to important tendencies and anomalies. These systems were primarily oriented at larger enterprises. However, BI solutions are more and more often adjusted to SME enterprises, offering a complex tool to support decision-making processes. This paper presents key stages in evolution of BI systems and characterizes selected BI systems dedicated to small and medium enterprises (SMEs). Substantial barriers to implementation of BI systems in SMEs were also indicated.

Materialized View Selection Using Set Based Particle Swarm Optimization

2018 ◽  
Vol 12 (3) ◽  
pp. 18-39 ◽  
Author(s):  
Amit Kumar ◽  
T.V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Response Times ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
Continuous Growth ◽  
View Selection ◽  
Materialized View Selection

A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

Business intelligence in academic libraries in Jordan: Opportunities and challenges

IFLA Journal ◽  
2020 ◽  
pp. 034003522093188 ◽  
Author(s):  
Faten Hamad ◽  
Razan Al-Aamr ◽  
Sinaria Abdel Jabbar ◽  
Hussam Fakhuri
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Academic Libraries ◽  
Academic Library ◽  
Decision Makers ◽  
Successful Implementation ◽  
Advanced Level ◽  
Department Staff ◽  
Library Users ◽  
Analytical Tools

Data plays a major role in helping to understand clearly the changing needs of academic library users, and in helping libraries to innovate their services and procedures accordingly. Data needs to be transformed into information for decision-making and strategic planning. Business intelligence offers powerful analytical tools, such as visualization and data-mining tools, which lead to informed decisions and hence transform the user’s experience, bringing it to a more advanced level. This research investigates the concept of business intelligence from the perceptions of information department staff at academic libraries in Jordan. The opportunities and challenges associated with it are also discussed and explored. As indicated by the results, information department staff agree that business intelligence improves decision-making, helping decision-makers to make the most accurate and timely decisions for the library. The results also indicate that an appropriate infrastructure is important for the successful implementation of business intelligence in academic libraries in Jordan.

A New Architecture for Large Data Warehouse

2011 ◽  
Vol 55-57 ◽  
pp. 87-90
Author(s):  
Gang Li ◽  
Xing San Qian ◽  
Chun Ming Ye
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
High Performance ◽  
Lower Cost ◽  
Large Data ◽  
Current Data

Data warehouse is playing a more and more important role in company’s decision making; it is the basis for a typical business intelligence solution. The paper points out the reasons why data warehouse projects failed and by analyzing the current data warehouse architectures, as well as technologies used in industry, a new data warehouse architecture is proposed which has many advantages over current ones, for example, it is extensible, reusable, flexible and with high performance and lower cost.

scholarly journals Construction and functioning for the data warehouse in the enterprise in the context of the effective decision making

Equilibrium ◽  
2009 ◽  
Vol 2 (1) ◽  
pp. 171-180
Author(s):  
Michał Kukliński
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Warehouses ◽  
Economic Decisions ◽  
Business Intelligence Systems ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Answering Questions

In the twenty-four hours of computerised enterprises, recruiting huge amounts of data, processing them in the traditional way would be highly ineffective and it will not deliver to us so much interesting information, forecasts and the relation, as Business Intelligence systems, of which Data Warehouses are a basis. The publication is answering questions: what the data warehouse is what is serving for and what are examples of applying. Stages of the build of the Data Warehouse and factors assuring achieving success in taking economic decisions will be introduced.

Commercial and Open Source Business Intelligence Platforms for Big Data Warehousing

2019 ◽  
pp. 158-181 ◽  
Author(s):  
Jorge Bernardino ◽  
Joaquim Lapa ◽  
Ana Almeida
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Comparative Analysis ◽  
Open Source ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Selection Process ◽  
Decision Making Processes ◽  
Big Data Warehouse

A big data warehouse enables the analysis of large amounts of information that typically comes from the organization's transactional systems (OLTP). However, today's data warehouse systems do not have the capacity to handle the massive amount of data that is currently produced. Business intelligence (BI) is a collection of decision support technologies that enable executives, managers, and analysts to make better and faster decisions. Organizations must make good use of business intelligence platforms to quickly acquire desirable information from the huge volume of data to reduce the time and increase the efficiency of decision-making processes. In this chapter, the authors present a comparative analysis of commercial and open source BI tools capabilities, in order to aid organizations in the selection process of the most suitable BI platform. They also evaluated and compared six major open source BI platforms: Actuate, Jaspersoft, Jedox/Palo, Pentaho, SpagoBI, and Vanilla; and six major commercial BI platforms: IBM Cognos, Microsoft BI, MicroStrategy, Oracle BI, SAP BI, and SAS BI & Analytics.

A Framework for Improving Knowledge Management Using Cloud-Based Business Intelligence

2019 ◽  
pp. 322-345
Author(s):  
Wasan Shaker Awad ◽  
Dalal N. Al-Noaimi
Keyword(s):  
Decision Making ◽  
Information System ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Management Information System ◽  
Research Method ◽  
Quantitative Research ◽  
Business Environment ◽  
Decision Makers ◽  
Global Changes

Business environment is becoming more complex which creates a big pressure on organizations to increase the performance and decrease the budget and time. Typical management information system failed to reach decision makers' expectations. In order to adapt to the global changes and support decision makers, organizations may implement different solutions and strategies. One of the solutions is to implement business intelligence (BI) in large organizations. The aim of this chapter is to assess the effectiveness of BI solutions and propose a solution for improving knowledge management using BI and cloud computing. A quantitative research method is used which includes survey and interviews. The results will be analyzed to evaluate the current BI solutions in order to identify the problems of knowledge management and decision-making process. Accordingly, a solution will be proposed to overcome the identified problem using cloud BI.

scholarly journals Data Template For District Economic Planning

2012 ◽  
Vol 8 (2) ◽  
Author(s):  
Sharadini Rath
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Local Governance ◽  
Open Data ◽  
Economic Planning ◽  
Local Economy ◽  
Decision Makers ◽  
Structured Decision Making ◽  
Digital Maps ◽  
The Status

Defining ‘open data’ to mean insights from accessible data becoming available to elected decision makers within governance as structured inputs, this paper sets out the status in India in a study done in 2008-10 in the case of Madhubani district in the state of Bihar for district economic planning. The paper demonstrates that a large amount of accessible data can generate highly structured inputs, such as village level digital maps and comparative analysis to set out sectoral, temporal and geographical dimensions of the changes in the local economy. It also identifies disconnects within institutional and political functioning of governance that impedes such opening of data to structured decision making in local governance.

Sign in / Sign up

Export Citation Format

Share Document