ι. Overview and Concepts Data Warehousing and Business Intelligence Why reρortinq and analysinq data. Raw data to valuaωe information-Lifecycle of data WI1at is business intelliqence ΒΙ and DW in today's perspectίve What is data warehousίnq The builclin\I blocRs : l)efinin\I features 1),Ιtθ warehouses c1ncl clata mc1rts Overview of the coωponents - Metadata in the data warehouse - Need for data ware!1ousίnq - Basic elements of data warehoιιsinq - Trends ίη data warehousinq. (Chapter - 1) 2. The Architecture of ΒΙ and DW ΒΙ θηcΙ DW θrchitectures nncl its tyρes Re!Btion between ΒΙ θηcΙ DW OLAI> (Online Analytical Processinq) definitions Difference between OLAP and OLTP Dimensional analysis What are cubes ? Drill-down and roll-up Slice and dice or rotation OLAP rnodels - ROLAP versus MOLAP - Definin scheωas : Stars, Snowflakes and fact constellations. (Chapter - 2) 3. lntroduction to Data Mining (DM) Motivation for d,ltθ minin\I 1),Jtθ mininΙJ-clefinition ancl functio11cllities Classific,ltion of DM systerns DM task prirnitil7es Inteqration of a data rnininq systeπ1 witl1 a database or a data warehoιιse - Issιιes ίη DM - KDD process. (Chapter - 3) 4. Data Pre-processing Why to ρre-ρrocess dcJtB? Data clennin!J: Missin!J values. Noisy dnta Data inteωtion nnd transformation Data reduction : Data cιιbe aqqreqation, Dimensionality redιιction Data compression Numerosity reduction Data mininq primitives ιan\Iuaqes and system architectures : Task rele17ant data - Kind of knowledqe to be π1ined - Discretization and concept hίerarchy. (Chapter - 4) 5. Concept Description and Association Rule Mining What is concept descrίρtion ? - Data !Jeneralization and summarization-based characterization -Attribute relevance - Class comparisons association rule minin!J: Market basket analysis - Basic concepts - Findin!J frequent item sets : Apriori al!Jorithm - Generatin!J rules - lmproved Aprίori al!Jorithm - Incremental ARM - Associative classification - Rule minin!J. (Chapter - 5)
