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Computer Science
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Database Management
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Data Warehousing |
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Need of Data Warehousing
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Why a DWH, Warehousing
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The Basic Concept of Data Warehousing
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Classical SDLC and DWH SDLC, CLDS, Online Transaction Processing
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Types of Data Warehouses: Financial, Telecommunication, Insurance, Human Resource
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Normalization: Anomalies, 1NF, 2NF, INSERT, UPDATE, DELETE
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De-Normalization: Balance between Normalization and De-Normalization
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DeNormalization Techniques: Splitting Tables, Horizontal splitting, Vertical Splitting, Pre-Joining Tables, Adding Redundant Columns, Derived Attributes
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Issues of De-Normalization: Storage, Performance, Maintenance, Ease-of-use
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Online Analytical Processing OLAP: DWH and OLAP, OLTP
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OLAP Implementations: MOLAP, ROLAP, HOLAP, DOLAP
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ROLAP: Relational Database, ROLAP cube, Issues
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Dimensional Modeling DM: ER modeling, The Paradox, ER vs. DM,
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Process of Dimensional Modeling: Four Step: Choose Business Process, Grain, Facts, Dimensions
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Issues of Dimensional Modeling: Additive vs Non-Additive facts, Classification of Aggregation Functions
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Extract Transform Load ETL: ETL Cycle, Processing, Data Extraction, Data Transformation
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Issues of ETL: Diversity in source systems and platforms
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Issues of ETL: legacy data, Web scrapping, data quality, ETL vs ELT
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ETL Detail: Data Cleansing: data scrubbing, Dirty Data, Lexical Errors, Irregularities, Integrity Constraint Violation, Duplication
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Data Duplication Elimination and BSN Method: Record linkage, Merge, purge, Entity reconciliation, List washing and data cleansing
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Introduction to Data Quality Management: Intrinsic, Realistic, Orr’s Laws of Data Quality, TQM
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DQM: Quantifying Data Quality: Free-of-error, Completeness, Consistency, Ratios
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Total DQM: TDQM in a DWH, Data Quality Management Process
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Need for Speed: Parallelism: Scalability, Terminology, Parallelization OLTP Vs DSS
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Need for Speed: Hardware Techniques: Data Parallelism Concept
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Conventional Indexing Techniques: Concept, Goals, Dense Index, Sparse Index
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Special Indexing Techniques: Inverted, Bit map, Cluster, Join indexes
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Join Techniques: Nested loop, Sort Merge, Hash based join
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Data mining (DM): Knowledge Discovery in Databases KDD
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Data Mining: CLASSIFICATION, ESTIMATION, PREDICTION, CLUSTERING,
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Data Structures, types of Data Mining, Min-Max Distance, One-way, K-Means Clustering
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DWH Lifecycle: Data-Driven, Goal-Driven, User-Driven Methodologies
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DWH Implementation: Goal Driven Approach
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DWH Implementation: Goal Driven Approach
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DWH Life Cycle: Pitfalls, Mistakes, Tips
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Course Project
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Contents of Project Reports
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Case Study: Agri-Data Warehouse
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Web Warehousing: Drawbacks of traditional web sear ches, web search, Web traffic record: Log files
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Web Warehousing: Issues, Time-contiguous Log Entries, Transient Cookies, SSL, session ID Ping-pong, Persistent Cookies
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Data Transfer Service (DTS)
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Lab Data Set: Multi -Campus University
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Extracting Data Using Wizard
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Data Profiling
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Related Documents:
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