On-line analytical processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft

Business Intelligence Approach In A Business Performance Context

Subordinated to performance management, Business Intelligence approaches help firms to optimize business performance. Key performance indicators will be added to the multidimensional model grounding the performance perspectives. With respect to the Business Intelligence value chain, a theoretical approach was introduced and a practice example, based on Microsoft SQL Server specific services, for the customer perspective was implemented.business intelligence, performance management, key performance indicators

The interrogation of the OLAP CUBE by using multidimensional analyses

The present paper proposes to speak about the interrogation of the OLAP cubes, by using the multidimensional analyses. The first part of the paper deals with the dynamics of the business environment, a fact which triggers the necessity of the informatics systems for decision assistance. The second point of the paper is the presentation of the online analytical processing (OLAP). Connected to this point is the declarative interrogation language MDX, which assures the access to the OLAP interrogation, functions, offering us the possibility of defining the calculated members of the dimensions. A few examples show us how they work. In the end, we draw the conclusions, stressing the importance and the benefits of the intelligent solutions

CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1, 2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agent-based simulation platform. In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models. The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach

Solutions for decision support in university management

The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors’ experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.university management, decision support, multidimensional analysis, data warehouse, OLAP

Decision making based on data analyses using data warehouses

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making

Decision making based on data analyses using data warehouses

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making