Data mart

About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.

A Review of Star Schema and Snowflakes Schema

Abstract: In the new age, digital data is the most important source of acquiring knowledge. For this purpose, collect data from various sources like websites, blogs, webpages, and most important databases. Database and relational databases both provide help to decision making in the future work. Nowadays these approaches become time and resource consuming there for new concept use name data warehouse. Which can analyze many databases at a time on a common plate from with very efficient way. In this paper, we will discuss the database and migration from the database to the data warehouse. Data Warehouse (DW) is the special type of a database that stores a large amount of data. DW schemas organize data in two ways in which star schema and snowflakes schema. Fact and dimension tables organize in them. Distinguished by normalization of tables. Nature of data leads the designer to follow the DW schemas on the base of data, time and resources factor. Both design-modeling techniques compare with the experiment on the same data and results of applying the same query on them. After the performance evaluation, using bitmap indexing to improve the schemas performance. We also present the design modeling techniques with respect to data mining and improve query optimization technique to save time and resource in the analysis of data.

Challenges and Benefits of Deploying Big Data Storage Solution

Abstract: Since data is at the heart of information systems, new technologies and approaches dealing with storing, processing and analyzing data have proliferated. Data Warehouses are among the most known approaches that tackle data storing and processing. However, they reached their limits in dealing with large quantities of data as those of Big Data. Consequently, a new concept which is an evolution of Data Warehouse known as "Data Lake" is emerging. This paper presents a detailed analysis that compares Data Lake and Data Warehouse key concepts. It sheds lights on the aspects and characteristics for the sake of revealing similarities and differences. It also emphasizes the complementary of the two technologies by showing the most appropriate use case of each of them.

Association Rule Mining and Its Application

Abstract: Several algorithms in data mining technique have been studied recently, among which association is one of the most important techniques. In this paper, we introduce theory of association rule in data mining, and analyze the characteristics of postal EMS service. We create a data warehouse model for EMS services and give the procedure of applying association rule mining based on it. In the end, we give an example of the whole mining procedure. This EMS Data warehouse model and association rule mining technique have been applied in a practical Postal CRM System.

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

Abstract: Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their application is due to the risk of teachers’ replacement. However, artificial intelligence systems should be seen as companions to empower teachers during the teaching and learning process. During the past years, the Universitat Oberta de Catalunya has advanced developing a data mart where all data about learners and campus utilization are stored for research purposes. The extensive collection of these educational data has been used to build a trustworthy early warning system whose infrastructure is introduced in this paper. The infrastructure supports such a trustworthy system built with artificial intelligence procedures to detect at-risk learners early on in order to help them to pass the course. To assess the system’s trustworthiness, we carried out an evaluation on the basis of the seven requirements of the European Assessment List for trustworthy artificial intelligence (ALTAI) guidelines that recognize an artificial intelligence system as a trustworthy one. Results show that it is feasible to build a trustworthy system wherein all seven ALTAI requirements are considered at once from the very beginning during the design phase.

Sherwin-Williams' Data Mart Strategy: Creating Intelligence Across the Supply Chain

Abstract: Companies can build a data warehouse using a top-down or a bottom-up approach, and each has its advantages and disadvantages. With the top-down approach, a project team creates an enterprise data warehouse that combines data from across the organization, and end-user applications are developed after the warehouse is in place. This strategy is likely to result in a scaleable data warehouse, but like most large IT projects, it is time consuming, expensive, and may fail to deliver benefits within a reasonable timeframe. With the bottom-up approach, a project team begins by creating a data mart that has a limited set of data sources and that meets very specific user requirements. After the data mart is complete, subsequent marts are developed, and they are conformed to data structures and processes that are already in place. The data marts are incrementally architected into an enterprise data warehouse that meets the needs of users across the organization. The appeal of the data mart strategy is that a mart can be built quickly, at relatively little cost and risk, while providing a