On-line analytical processing in distributed data warehouses

The concepts of 'data warehousing' and 'on-line analytical processing' have seen a growing interest in the research and commercial product community. Today, the trend moves away from complex centralized data warehouses to distributed data marts integrated in a common conceptual schema. However, as the first part of this paper demonstrates, there are many problems and little solutions for large distributed decision support systems in worldwide operating corporations. After showing the benefits and problems of the distributed approach, this paper outlines possibilities for achieving performance in distributed online analytical processing. Finally, the architectural framework of the prototypical distributed OLAP system CUBESTAR is outlined

A Collaborative Platform to Support the Enterprise 2.0 in Active Interactions with Customers

In recent years a new model of Enterprise 2.0, which interacts actively with customers using web 2.0 tools (chat, forum, blog, wiki), is developing. The enterprises, listening opinions and suggestions of customers, can improve the product/service. For a company, customer's opinions are very important both for the improvement of products and also for the reinforcement of the customer loyalty. The customer will be motivated to be loyal if the enterprise shows a strong attention to his/her needs. This paper presents a model of a collaborative and interactive platform that supports the Enterprise 2.0 in the management of communications and relationships with all stakeholder of the supply chain and in particular with customers. A good e-reputation of the company improves business performances

Relatório de Estágio - Solução de BI Roaming Data Science (RoaDS) em ambiente Vodafone

A telecom company (Vodafone), had the need to implement a Business Intelligence solution for Roaming data across a wide set of different data sources. Based on the data visualization of this solution, its key users with decision power, can make a business analysis and needs of infrastructure and software expansion. This document aims to expose the scientific papers produced with the various stages of production of the solution (state of the art, architecture design and implementation results), this Business Intelligence solution was designed and implemented with OLAP methodologies and technologies in a Data Warehouse composed of Data Marts arranged in constellation, the visualization layer was custom made in JavaScript (VueJS). As a base for the results a questionnaire was created to be filled in by the key users of the solution. Based on this questionnaire it was possible to ascertain that user acceptance was satisfactory. The proposed objectives for the implementation of the BI solution with all the requirements was achieved with the infrastructure itself created from scratch in Kubernetes. This BI platform can be expanded using column storage databases created specifically with OLAP workloads in mind, removing the need for an OLAP cube layer. Based on Machine Learning algorithms, the platform will be able to perform the predictions needed to make decisions about Vodafone's Roaming infrastructure

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

Using Semantic Web technologies in the development of data warehouses: A systematic mapping

The exploration and use of Semantic Web technologies have attracted considerable attention from researchers examining data warehouse (DW) development. However, the impact of this research and the maturity level of its results are still unclear. The objective of this study is to examine recently published research articles that take into account the use of Semantic Web technologies in the DW arena with the intention of summarizing their results, classifying their contributions to the field according to publication type, evaluating the maturity level of the results, and identifying future research challenges. Three main conclusions were derived from this study: (a) there is a major technological gap that inhibits the wide adoption of Semantic Web technologies in the business domain;(b) there is limited evidence that the results of the analyzed studies are applicable and transferable to industrial use; and (c) interest in researching the relationship between DWs and Semantic Web has decreased because new paradigms, such as linked open data, have attracted the interest of researchers.This study was supported by the Universidad de La Frontera, Chile, PROY. DI15-0020. Universidad de la Frontera, Chile, Grant Numbers: DI15-0020 and DI17-0043

Hybrid Classification of OLAP Queries in Cloud Computing Environment

Generally, the execution time of the decision requests on large tables is very high which degrades the performance of data warehouses (DW). On the other hand, having high traffic can influence the response time of queries. Cloud Computing (CC) offers a solution to this kind of problem by providing a flexible environment in which data is highly available since it is stored and duplicated in different nodes. Optimizing the performance of an DW deployed on CC is indispensable task that aims to make cloud services conform to customer expectations by increasing performance at a minimum cost. This optimization is based on the improvement of various factors such as the response time to the client queries, availability, scalability, etc. Thus, having a voluminous and dynamic queries load can make the task of optimization difficult. For this purpose, we propose in this paper a hybrid classification technique of queries, in order to minimize his number and reduce the total cost of hosting the DW on the CC

Modeling Data Analytics Architecture for Smart Cities Data-Driven Applications using DAT

Extracting valuable insights from vast amounts of information is a critical process that involves acquiring, storing, managing, analyzing, and visualizing data. Providing an abstract overview of data analytics applications is crucial to ensure that collected data is transformed into meaningful information. One effective way of achieving this objective is through Data Architecture. This article shares our experiences in developing a Data Analytics Architecture (DAA) using model-driven engineering for Data-Driven Smart Cities applications utilizing DAT