Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/287080
Title: Modelling and Evaluation Of Real Time Scheduling Updates In Streaming Data Warehouses
Researcher: Misbha D.S
Guide(s): Jeba J.R
Keywords: Engineering and Technology,Computer Science,Computer Science Artificial Intelligence
University: Noorul Islam Centre for Higher Education
Completed Date: 15/09/2018
Abstract: ABSTRACT newlineA data warehouse is used for reporting and data analysis. It is a central repository of data newlinewhich is created by integrating data from one or more disparate sources. Data warehouses newlinestore current as well as historical data and are used for creating trending reports for senior newlinemanagement reporting such as annual and quarterly comparisons. The data stored in the newlinewarehouse are uploaded from the operational systems such as marketing, sales etc. The newlinedata may pass through an operational data store for additional operations before they are newlineused in the DW for reporting. The typical Extract Transform and Load (ETL)-based data newlinewarehouse uses staging, data integration, and access layers to house its key functions. The newlinestaging layer or staging database stores raw data extracted from each of the disparate source newlinedata systems. The integration layer integrates the disparate data sets by transforming the newlinedata from the staging layer often storing this transformed data in an Operational Data Store newline(ODS) database. The integrated data are then moved to yet another database, often called newlinethe data warehouse database, where the data is arranged into hierarchical groups often called newlinedimensions and into facts and aggregate facts. newlineThe need to provide up-to-date information, streaming warehouses are used to screen newlinemultipart systems such as web site complexes, data centers, and world-wide networks, newlinecongregating and comparing encumbered collections of happenings and measurements. For newlineprofound analysis and for rapid responses, both chronological data and concurrent data that newlineraising the problems in streaming warehouses were used. The data warehouse gathers a large newlinenumber of streaming data provisions that are generated by external sources and reach target newlineasynchronously. Scheduling updates is the most important processes that are concerned newlineseverely in streaming warehouses. newlineIn Optimized Cackoo Scale Scheduling(OCSS) relevant data sets and the user newlinesubmitted jobs are transmitted to the real-time controller. The real-time controller spli
Pagination: 132
URI: http://hdl.handle.net/10603/287080
Appears in Departments:Department of Computer Applications

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