Data Warehouse Tools Research Articles

Location-Based Tracking and Monitoring Infrastructural Construction Works by Using Business Intelligence Tool

Tracking and monitoring construction projects are essential tasks to make decisions on the projects. There are various techniques to collect and store data from the construction site. In the current construction sector, informatics play an important role in collecting and interpreting data from construction processes. In recent studies, various technologies such as RFID, UAV are used for surveying the construction site. However, there is a study about collecting and interpreting the collected data is still needed. In this study, the collected data is categorized by labeling with several dimensions (e.g. location, contractor) using business intelligence and data warehouse tools. Moreover, this technique allows us to construct relationships between activities and generate practical information. The major implication of this study for engineering managers is monitoring the public demands and construction processes within one framework.

WITHDRAWN: Bhattacharyya coefficient target feature matching based weighted emphasis adaptive boosting classification for predictive analytics with big data

Abstract Data warehousing is the most significant systems that hold large amounts of information. The data warehouse integrates the data from a large and inconsistent database that are located in different locations. Nowadays, data warehouse tools and technologies difficult to handle the analytic process of data since the dimensions of data volumes get increased rapidly. A novel technique Bhattacharyya Coefficient Target Matching Pursuit based Weighted Emphasis Adaptive Boosting Classification (BCTMP-WEABC) technique is introduced for predicting future outcomes with higher accuracy and lesser time consumption. The BCTMP-WEABC consists of two modules namely attribute (i.e., features) selection and classification for handling a large amount of data in the warehouse. In the BCTMP-WEABC technique, Bhattacharyya Coefficient Target Matching Pursuit is applied to select the relevant attributes for performing the classification process based on the similarity measure. After that, Weighted Emphasis Adaptive Boosting Classification (WEABC) process is carried out to perform predictive analytics with the selected attributes for minimizing the time consumption. The WEABC provides an accurate classification by implementing the set of weak learners as a quadratic discriminant classifier. The weak learner performs a likelihood ratio test for distinguishing the given input data into different classes based on mean and deviation. The WEABC combines the weak learners and provides accurate classification and also minimizes the quadratic error. Result evaluation is carried out with factors such as feature selection rate, classification accuracy, classification time, and false-positive rate with respect to the number of features. The evaluation and conferred statistical results notify the development of the proposed BCTMP-WEABC technique in terms of higher classification accuracy, feature selection rate, and minimum time consumption as well as false positives than the conventional methods.

Effect of anaemia on the diagnosis of rheumatic heart disease using World Heart Federation criteria

There is overlap between pathological mitral regurgitation seen in borderline rheumatic heart disease using World Heart Federation echocardiography criteria and physiologic regurgitation found in normal children. One possible contributing factor is higher rates of anaemia in endemic countries. To investigate the contribution of anaemia as a potential confounder in the diagnosis of rheumatic heart disease detected in echocardiographic screening. A novel Server 2012 data warehouse tool was used to incorporate haematology and echocardiography databases. The study included a convenience sample of patients from 5 to 18 years old without structural or functional heart disease that had a haemoglobin value within 1 month prior to an echocardiogram. Echocardiogram images were reviewed to determine presence or absence of World Heart Federation criteria for rheumatic heart disease. The rate of rheumatic heart disease among anaemic and non-anaemic children according to gender- and age-based norms groups was compared. Of the 935 patients who met the study inclusion criteria, 406 were classified as anaemic. There was no difference in the rate of echocardiograms meeting criteria for borderline rheumatic heart disease in anaemic (2.0%, 95% CI 0.6-3.3%) and non-anaemic children (1.3%, 95% CI 0.3-2.3%). However, there was a statistically significant increase in rates of mitral regurgitation of unclear significance among anaemic versus non-anaemic patients (8.6 versus 3.6%; p = 0.0012). Anaemia does not increase the likelihood of meeting echocardiographic criteria for borderline rheumatic heart disease. Future studies should evaluate for the correlation between anaemia and mitral regurgitation in endemic settings.

The data warehouse for down payment administration in the Constitutional Court of Republic of Indonesia

The Constitutional Court of The Republic of Indonesia is an institution which has authority to judge and examine constitutions. In this institution, each component has responsibility to manage their own finance and budgeting. Based on this case, they need a solution to summarize the amount and absorption of the budget in every component. This paper proposes a solution to design a data warehouse model to process and analyse the down payment administration data. The nine steps design methodology for developing data warehouse is implemented in this work. This paper aims to bring some benefit for The Constitutional Court, especially in form of information about down payment absorption for every component each year. The result is presented in form of pdf report and informative dashboard using data warehouse tools.

Data Warehouse with Big Data Technology for Higher Education

Nowadays, data warehouse tools and technologies cannot handle the load and analytic process of data into meaningful information for top management. Big data technology should be implemented to extend the existing data warehouse solutions. Universities already collect vast amounts of data so the academic data of university has been growing significantly and become a big academic data. These datasets are rich and growing. University’s top-level management needs tools to produce information from the records. The generated information is expected to support the decision-making process of top-level management. This paper explores how big data technology could be implemented with data warehouse to support decision making process. In this framework, we propose Hadoop as big data analytic tools to be implemented for data ingestion/staging. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on Big Data.

Realistic Analysis of Data Warehousing and Data mining Application in Education Domain

increase in the number of branches and intake. Now a day, any reputed Institute’s enrollments count in to thousands. In view of these factors the challenges for the management are meeting the diverse needs of students and facing increased complexity in academic processes. The complexity of these challenges requires continual improvements in operational strategies based on accurate, timely and consistent information. The cost of building a data warehouse is expensive for any educational institution as it requires data warehouse tools for building data warehouse and extracting data using data mining tools from data warehouse. The present study provides an option to build data warehouse and extract useful information using data warehousing and data mining open source tools. In this paper we have explored the need of data warehouse / business intelligence for an educational institute, the operational data of an educational institution has been used for experimentation. The study may help decision makers of educational institutes across the globe for better decisions..

A Virtual Data Warehouse for Manufacturing Industry

Data warehousing is the best solution to relieve the database management systems to hold the long standing data which will go on mounting from day to day business transactions. But creating and managing a data warehouse are complex tasks and in addition, data warehouse tools available in the market are very expensive. To make the data warehouse solutions cost-effective, creation of virtual data warehouse is one option in the case of applications having limited scope or limited duration. This paper deals with the development of such virtual data warehouse for effective supply chain management for a multi-national software development and consultancy company dealing with big semiconductor manufacturing industries. The design and development of the virtual data warehouse is described in detail using the existing database management systems and the existing huge database of the companies. The data warehouse has been created based on the important business requirements, identifying the relevant existing data sources that can be integrated together to generate respective dimensional tables and thereby the fact tables and then finally completing the multi-dimensional models to derive answers to the respective queries. The paper also presents some screen shots of the application interface.

Qualidade da informação em duas empresas que utilizam Data Warehouse na perspectiva do consumidor de informação: um estudo de caso

Atualmente, convivemos com um grande volume de dados, redundantes e inconsistentes, que necessitam cada vez mais serem administrados com eficiência e transformados em informação pertinente para fins de suporte à decisão. A competitividade de uma empresa é diretamente proporcional à sua capacidade de obter informação, processá-la e disponibilizá-la de forma rápida, segura e com qualidade. A implantação da ferramenta Data Warehouse (DW) altera essa possibilidade para uso da informação analítica, tornando-se necessário um mecanismo de gerência da qualidade desses dados. Tendo-se em vista a necessidade de se conhecer a qualidade dessas informações, o presente estudo tem por objetivo analisar, em empresas que utilizam a ferramenta DW, a qualidade da informação na perspectiva do Consumidor de Informação. A análise da qualidade da informação será feita por meio de dimensões e categorias. Para maior familiaridade com o problema, com vistas a torná-lo explícito, foram feitas uma revisão da literatura e entrevistas em duas empresas, com o emprego de dois questionários, um em nível Gerencial e outro para os Consumidores de Informação. Como resultado, obteve-se a resposta de como é a qualidade da informação nestas empresas que se utilizam do DW.

A Framework for Developing Enterprise Data Warehouses

Learn to follow a five-stage framework designed to accommodate the common challenges encountered by project teams when building enterprise data warehousing solutions. This framework is an architecture-driven approach that subsequently involves selection of add-on data warehouse tools and meeting ongoing administration requirements.