Data mart

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

Real-Time Data Warehousing: A Rewrite/Merge Approach

Abstract: This paper focuses on Real-Time Data Warehousing systems, a relevant class of Data Warehouses where the main requirement consists in executing classical data warehousing operations (e.g., loading, aggregation, indexing, OLAP query answering, and so forth) under real-time constraints. This makes classical DW architectures not suitable to this goal, and puts the basis for a novel research area which has tight relationship with emerging Cloud architectures. Inspired by this motivation, in this paper we proposed a novel framework for supporting Real-Time Data Warehousing which makes use of a rewrite/merge approach. We also provide an extensive experimental campaign that confirms the benefits deriving from our framework.

Model Base On Human Resource System Using Classification Technique

Abstract: In higher education such as university, academic is becoming major asset. The performance of academic has become a yardstick of university performance. Therefore it's important to know the talent of academicians in their university, so that the management can plan for enhancing the academic talent using human resource data. Therefore, this research aims to develop an academic talent model using data mining based on several related human resource systems. The case study used 7 human resources systems in one of government university in Malaysia. This study shows how automated human talent data mart is developed to get the most important attribute's of academic talent from 15 different tables like demographic data, publications, supervision, conferences, research, and others. Apart from the talent attribute collected, the forecasting talent academician model developed using the classification technique involving 14 classification algorithm in the experiment for example J48, Random Forest, BayesNet, Multilayer perceptron, JRip and others. Several experiments are conducted to get the most highest accuracy by applying discretization process, dividing the data set in the different interval year (1,2,3,4, no interval) and also changing the number of classes from 24 to 6 and 4. The best model is obtained 87.47% accuracy using data set interval 4 years and 4 classes with J48 algorithm

TSMC turnkey data mart

Abstract: TSMC turnkey data mart is the collection of databases, designed to help managers make strategic decisions about their business. It focuses on operation management department and aid their managers and planners to get useful information from the system. The data mart creation service moves data from a variety of sources such as ERP system, B2B transaction system, Supply Chain Management system, EDA system and MES databases to build the Turnkey data mart. The data mart creation is used by IT administrators to deliver star or snowflake schemas, which present multi-dimensional models of data warehouses. The dimensional model contains the same information as found in a complex Entity Relationship model, but it makes the information easier to understand, facilitates querying and is resilient to change.

Data mining and modeling method for incremental sales

Abstract: The invention relates to a data mining and modeling method for incremental sales, which comprises the following steps of: selecting corresponding data from a data warehouse according to the service demand; preprocessing the data, and building a data mart facing a service theme; sampling the data from the data mart for a marketing test; recording the data of the marketing test, and forming a data set; building an incremental sale model based on the data set; applying the incremental sale model; and correcting the incremental sale model according to a result of applying the incremental sale model, and the data set formed by the marketing test Compared with the prior art, the method can accurately mine marketing target customer base