Data mart

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

Design of Dimensional Model for Clinical Data Storage and Analysis

Abstract: Current research in the field of Life and Medical Sciences is generating chunk of data on daily basis. It has thus become a necessity to find solutions for efficient storage of this data, trying to correlate and extract knowledge from it. Clinical data generated in Hospitals, Clinics & Diagnostics centers is falling under a similar paradigm. Patient's records in various hospitals are increasing at an exponential rate, thus adding to the problem of data management and storage. Major problem being faced corresponding to storage, is the varied dimensionality of the data, ranging from images to numerical form. Therefore there is a need for development of efficient data model which can handle this multi-dimensionality data issue and store the data with historical aspect. For the stated problem lying in facade of clinical informatics we propose a clinical dimensional model design which can be used for development of a clinical data mart. The model has been designed keeping in consideration temporal storage of patient's data with respect to all possible clinical parameters which can include both textual and image based data. Availability of said data for each patient can be then used for application of data mining techniques for finding the correlation of all the parameters at the level of individual and population.

Photovoltaic power generation prediction method based on data mining

Abstract: The invention provides a photovoltaic power generation prediction method based on data mining, and relates to the technical field of new energy The operation steps are as follows: collecting a large quantity of data to establish a data source; extracting data from the data source to establish a data warehouse for storing and managing the extracted data; carrying out ETL processing on the data in the data warehouse, establishing a data mart, and classifying and storing the processed data; and processing and analyzing all kinds of data in the data mart and predicted weather environment data by adopting cluster analysis and an association rule, and comprehensively analyzing the results of the two methods to obtain a photovoltaic power generation prediction result The prediction method is simple, the accuracy of the prediction result is high, and the photovoltaic power generation prediction precision is higher

Design and Study of 7 – Tier Architecture Model for Opening Account in Present Financial System

Abstract: Today core banking has become the major part of financial system. Presently, to fulfill the basic financial needs, the customer open different account numbers in different banks, financial institutions, Provident funds, Mutual funds, Insurances, etc. for various transactions. The major drawback of this system is that, there is no unique identification code of a customer to maintain various transactions and different accounts detail in different branches of different banks and financial institutions. In this paper, we have tried to focus to remove the above drawback by introducing the 7-tier architecture to maintain the Bank unique identification code (BUID code). Here the 7tier architecture model is designed with the seven layers with the use of biometric tools and the data of customers are stored in Data Warehouse through data mart. This model will help in decision making process by using OLAP and OLTP tools. The main advantage of this model is that the 7-tier architecture model with BUID can easily blend with current finance system. Thus the 7-tier architectural model can become a robust to perform a role to enhance the present account opening process in financial system.

Organization of data mart using clustered key

Abstract: A data mart may be organized using a clustered key, thereby allowing certain efficiencies in data search and retrieval to be realized. In one example, the clustered key is made of a plurality of attributes. The attributes may be chosen based on their likelihood of being using as search criteria. The likelihood of a given attribute being used as a search criterion may be determined through historical analysis of search requests. Records in the data mart may be sorted based on the attributes in the clustered key, thereby producing records that are organized by attribute in sequential runs. When a search uses an attribute in the clustered key as a search criterion, the records that are being sought may appear in one or more sequential runs, thereby leveraging the efficiency of sequential reads as opposed to random reads.