Page 4 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

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Data Warehousing and Data Mining
In this, Students study the issues involved in planning, designing, building, populating, and maintaining a successful data
warehouse. It’s objective is to:
l Analyze query plans of execution.
l Manage data loads and transactions
l Describe methods and tools for accessing and analyzing data warehouse.

l Describe various technologies to implement data warehouse.

s. no. Description
1 Data Warehouse Practice: Data warehouse components, Designing the Data Warehouse, Getting Heterogeneous Data into the
Warehouse, Getting Multidimensional Data out of the Warehouse.
2 Data Warehouse Research-Issues and Research: Data Extraction and Reconciliation, Data Aggregation and Customization, Query
Optimization, Update Propagation, Modelling and Measuring Data Warehouse Quality, Some Major Research Projects in Data
Warehousing, Three Perspectives of Data Warehouse Metadata.
3 Source Integration: The Practice of Source Integration, Research in Source Integration, Towards Systematic Methodologies for
Source Integration.
4 Data Warehouse Refreshment: Data Warehouse Refreshment, Incremental Data Extraction, Data Cleaning,
5 Data Warehouse Refreshment: Update Propagation into Materialized Views, Towards a Quality-Oriented Refreshment Process,
Implementation of the Approach
6 Multidimensional Data Models and Aggregation: Multidimensional View of Information, ROLAP Data Model, MOLAP Data
Model, Logical Models for Multidimensional Information, Conceptual Models for Multidimensional Information
7 Query Processing and Optimization: Description and Requirements for Data Warehouse Queries, Query Processing Techniques.
8 Metadata and Warehouse Quality: Matadata Management in Data Warehouse Practice, A repository Model for the DWQ
Framework, Defining Data Warehouse Quality.
9 Metadata and Data Warehouse Quality: Representing and Analyzing Data Warehouse Quality, Quality Analysis in Data
Staging.
10 Quality-Driven Data Warehouse Design: Interactions between Quality Factors and DW Tasks, The DWQ Data Warehouse
Design Methodology, Optimizing the Materialization of DW Views