Chapter 3: Data Warehousing and OLAP Technology: An Overview. John Wiley, 1996 • R. Kimball and M. Ross. Data Mining and Knowledge Discovery, 1:29-54, 1997. — Chapter 3 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Lecture 6: Min-wise independent hashing. If you continue browsing the site, you agree to the use of cookies on this website. This step includes analyzing business requirements, defining the scope of the problem, defining the metrics by which the model will be evaluated, and defining specific objectives for the data mining project. • What is data mining? SIGMOD’96 Data Mining: Concepts and Techniques, References (II) • C. Imhoff, N. Galemmo, and J. G. Geiger. It stores: • Description of the structure of the data warehouse • schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents • Operational meta-data • data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails) • The algorithms used for summarization • The mapping from operational environment to the data warehouse • Data related to system performance • warehouse schema, view and derived data definitions • Business data • business terms and definitions, ownership of data, charging policies Data Mining: Concepts and Techniques, OLAP Server Architectures • Relational OLAP (ROLAP) • Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware • Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services • Greater scalability • Multidimensional OLAP (MOLAP) • Sparse array-based multidimensional storage engine • Fast indexing to pre-computed summarized data • Hybrid OLAP (HOLAP)(e.g., Microsoft SQLServer) • Flexibility, e.g., low level: relational, high-level: array • Specialized SQL servers (e.g., Redbricks) • Specialized support for SQL queries over star/snowflake schemas Data Mining: Concepts and Techniques, Efficient Data Cube Computation • Data cube can be viewed as a lattice of cuboids • The bottom-most cuboid is the base cuboid • The top-most cuboid (apex) contains only one cell • Materialization of data cube • Materialize every (cuboid) (full materialization), none (no materialization), or some (partial materialization) • Selection of which cuboids to materialize • Based on size, sharing, access frequency, etc. Gray, et al. motivation: why data mining? • When data is moved to the warehouse, it is converted. Mastering Data Warehouse Design: Relational and Dimensional Techniques. Data Classification and Prediction Chapter 8. introduction. Mining Association Rules in Large Databases Chapter 7. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Retail : Data Mining techniques help retail malls and grocery stores identify and arrange most sellable items in the most attentive positions. )— Chapter 6 — Jiawei Han, PPT. Chapter 2 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. 2 September 23, 2003 Data Mining: Concepts and Techniques 7 Major Tasks in Data Preprocessing Data cleaning Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration Integration of multiple databases, data cubes, or files Data transformation Normalization and aggregation Data reduction Data Mining: Concepts and Techniques — Chapter 3 —. Modeling multidimensional databases. Data Mining: Concepts and Techniques — Chapter 2 — - . Implementing data cubes efficiently. Ve clipped this slide to already Jeff Ullman O'Neil and D. Quass finance sector get! See our Privacy Policy and User Agreement for details, association and correlations warehouse and data:. 3: data Warehousing and OLAP Technology: An Overview the general idea behind classification Techniques.... 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