Topic
Data mart
About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.
Papers published on a yearly basis
Papers
More filters
01 Jan 2009
TL;DR: The Emergo Project as mentioned in this paper assesses psychology students using a data mart with multiple-choice questions from national exams and students' answers, identifying patterns for the evolution of correct answers across semester enrolled.
Abstract: National-level, objective assessment in higher education has been a practice in Brazil since 1996, surviving political shifts that frequently dismantle public policies. This paper presents the Emergo Project – the assessment of Psychology students using a data mart with multiple-choice questions from national exams and students’ answers. We run two annual examinations, giving individual feedback and discussing aggregate results with faculty and students. We identified patterns for the evolution of correct answers across semester enrolled – Growing, Decreasing, Peak, Constant, and Other. Actual results in the national exam suggest that the feedback and discussions might have helped achieving superior performance standards.
30 Oct 2008
TL;DR: This article describes the viability of implementing scripts for handling extensive datasets of SNP genotypes with low computational costs, and shows that the updating of these data marts is straightforward, permitting easy implementation of new external data and the computation of new statistical indices.
Abstract: Databases containing very large amounts of SNP (Single Nucleotide Polymorphism) data are now freely available for researchers interested in medical or population genetics applications. While many of these SNP repositories have implemented data retrieval tools for general purpose mining, these alone cannot cover the broad spectrum of needs of most medical and population genetics studies. To address this limitation, we propose building in-house customized data marts from the raw data provided by the largest public databases. In particular, for population genetics analysis based on genotypes we propose building a set of data processing scripts that would deal with raw data coming from the major SNP variation databases (e.g. HapMap, Perlegen) that can be stripped into single genotypes and then grouped into populations. This allows not only in-house standardization and normalization of the genotyping data retrieved from different repositories, but also the calculation of statistical indices from simple allele frequency estimates up to elaborate genetic differentiation tests within populations, together with the ability to combine population samples from different databases. This article describes the viability of implementing scripts for handling extensive datasets of SNP genotypes with low computational costs, and shows that the updating of these data marts is straightforward, permitting easy implementation of new external data and the computation of new statistical indices.
Patent•
09 Feb 2018
TL;DR: In this article, a method and an apparatus for sharing indexes among data marts is described, which relates to the field of computer science and relates to our field of information theory.
Abstract: The invention discloses a method and an apparatus for sharing indexes among data marts, and relates to the field of computers. A specific embodiment of the method comprises the steps of determining atleast one shared index data table in index data tables of multiple data marts, and according to any one of the at least one shared index data table, creating a test case corresponding to the shared index data table, wherein the shared index data table comprises index data shared among the data marts; copying the shared index data table and the test case to a shared mart, and testing the corresponding shared index data table in the shared mart by utilizing the test case; and copying any tested shared index data table to each data mart which does not store the shared index data table. The indexdata can be shared among the data marts, so that definitions of indexes and mathematic models adopted by the indexes are enabled to be consistent among the data marts.
01 Jan 2013
TL;DR: In this paper, the authors have used Laplacian method for ranking, which enables to make efficient decision, which makes in order to increase the sales promotion in sales data mart using Hyper ETL (Extract, Transform and Load).
Abstract: The multiplication in the number of corporations lo oking for data mart solutions, with the aim of adding major business gains, has created the nee d for a decision right data mart system. Due to the indistinct concept often represented in decision facilitate decision matrix analysis, with considera tion given to both technical and managerial criteri a. This paper illustrates the item- wise and place Hyper ETL (Extract, Transform and Load) tool. We have used Laplacian method for ranking, which enables to make efficient decision - this paper is t o determine the alternative courses of action (the movement of sales quantity and also the movement particular item in number of places) from which the ultimate choice to be made. This approach supports the business goals and requirements of an organiz appropriate attributes or criteria for evaluation. This improvement communicates information quickly and enlarges the aggregation process and helps to t ake an efficient decision. The multiplication in the number of corporations lo oking for data mart solutions, with the aim of adding major business gains, has created the nee d for a decision -aid has come near in preferring the Due to the indistinct concept often represented in decision - facilitate decision matrix analysis, with considera tion given to both technical and managerial criteri a. wise and place - wise analysis of sa les promotion in sales data mart using Hyper ETL (Extract, Transform and Load) tool. We have used Laplacian method for ranking, which - making in order to increase the sales promotion. The main objective of o determine the alternative courses of action (the movement of sales quantity and also the movement particular item in number of places) from which the ultimate choice to be made. This approach supports the business goals and requirements of an organiz ation and to identify the appropriate attributes or criteria for evaluation. This improvement communicates information quickly and enlarges the aggregation process and helps to t ake an efficient decision. The multiplication in the number of corporations lo oking for data mart solutions, with the aim aid has come near in preferring the -making procedure, to facilitate decision matrix analysis, with considera tion given to both technical and managerial criteri a. les promotion in sales data mart using Hyper ETL (Extract, Transform and Load) tool. We have used Laplacian method for ranking, which making in order to increase the sales promotion. The main objective of o determine the alternative courses of action (the movement of sales quantity and also the movement particular item in number of places) from which the ultimate choice to be made. This ation and to identify the appropriate attributes or criteria for evaluation. This improvement communicates information quickly
01 Jan 2012
TL;DR: To improve the decision support system to extract the required user demanded data from data mart, inductive rule mining is used and induced rules are proposed to be the supportive knowledge for identifying the user needed information.
Abstract: A data warehouse is a database used for reporting and analyzing the data stored in the repositories. A data mart acts as the accessing form of the data warehouse situation used to obtain the data out to the users. Accessing of data in the data warehouse is a challenging approach since the user needs better understanding of the data structure stored in the repositories. To handle this issue, data mart is introduced. Data marts built separate functional data repository layers based on the requirements and applications of the corporate data applications. However function requirements of users are not easily understood by the data warehouse model. It needs efficient decision support system to extract the required user demanded data from data warehouse. Existing work only identified the functional activities of the data mart based on attribute relativity in the data mart and it does not extract the user demanded information from the repositories. To improve the decision support system to extract the required user demanded data from data mart, inductive rule mining is used. The decision support is done with inductive rule mining on functional data marts segregates of the layered data repository and extracted the required information for the user. The induced rules are proposed to be the supportive knowledge for identifying the user needed information. An experimental evaluation is conducted with benchmark datasets from UCI repository data sets and compared with existing functional behavior pattern for data mart based on attribute relativity in terms of number of decision rules, extracted data relativity, analysis of functional behavior.