EECE 433 Chapter 25, Spring 2012

1 Overview of Data Warehouse and OLAP Technology Ch. 25 Hazem Hajj Source: Data Mining Course Presentations WHY DATA WAREHOUSE? October 19, 2026 Data Mining: Concepts and Techniques 2 October 19, 2026 Data Mining: Concepts and Techniques 3 Stock Prediction October 19, 2026 Mining and Searching Graphs in Graph Databases 4 Graph, Graph, Everywhere: Hidden Pattern? Aspirin Yeast protein interaction network from H. Jeong et al Nature 411, 41 (2001) Internet Co-author network October 19, 2026 Data Mining: Concepts and Techniqu es 5 Social Network: Community behavior Nodes: individuals Links: social relationship (family/work/friendship/etc. ) S. Milgram (1967) Social networks: Many individuals with diverse social interactions between them. John Guare Six Degrees of Separation (also referred to as the "Human Web") refers to the idea that, if a person is one step away from each person they know and two steps away from each person who is known by one of the people they know, then everyone is at most six steps away from any other person on Earth. 10/19/26 Data Mining: Principles and Algorithms 6 WEB Search Engine – Two Rank Functions Web Pages Meta Data Forward Index Inverted Index Forward Link Backward Link (Anchor Text) Web Topology Graph Web Page Parser Indexer Anchor Text Generator Web Graph Constructor Importance Ranking (Link Analysis) Rank Functions URL Dictioanry Term Dictionary (Lexicon) Search Relevance Ranking Ranking based on link structure analysis Similarity based on content or text 10/19/26 Data Mining: Principles and Algorithms 7 Web Mining Importance = Med Importance = Low Importance = High Web Page Blocks October 19, 2026 Data Mining: Concepts and Techniques 8 Knowledge Discovery (KDD) Process  Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation October 19, 2026 Data Mining: Concepts and Techniques 9 Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Know ledge -Base Database Data Warehouse World-Wide Web Other Info Repositories OLAP October 19, 2026 Data Mining: Concepts and Techniques 10 Data Warehousing and OLAP Technology: An Overview  What is a data warehouse?: Definition, OLAP VS OLTP,  A multi-dimensional data model  Data warehouse architecture  Data warehouse implementation  From data warehousing to data mining October 19, 2026 Data Mining: Concepts and Techniques 11 What is Data Warehouse?  “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon October 19, 2026 Data Mining: Concepts and Techniques 12 Data Warehouse—Subject-Oriented  Organized around major subjects, such as sales  (versus operational DB that may be storing every single transaction that happened in a store: time, teller, product items, barcode, price per item, customer info,…)  Focusing on the modeling and analysis of data for decision makers,  not on daily operations or transaction processing (e.g.: sales/year, sales/branch,….) October 19, 2026 Data Mining: Concepts and Techniques 13 Data Warehouse—Integrated  Constructed by integrating multiple, heterogeneous data sources  relational databases, flat files, on-line transaction records  Data cleaning and data integration techniques are applied.  Ensure consistency among different data sources  E.g., Hotel price: currency, tax, breakfast covered, etc.  When data is moved to the warehouse, it is converted. October 19, 2026 Data Mining: Concepts and Techniques 14 Data Warehouse—Time Variant  The time horizon for the data warehouse is significantly longer than that of operational systems  Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)  Every key structure in the data warehouse Contains an element of time, explicitly or implicitly October 19, 2026 Data Mining: Concepts and Techniques 15 Data Warehouse—Nonvolatile  A physically separate store of data transformed from the operational environment  Requires two operations in data accessing:  loading of data and access of data October 19, 2026 Data Mining: Concepts and Techniques 16 Data Warehouse vs. Heterogeneous DBMS  Traditional heterogeneous DB integration: A query driven approach  When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set  Data warehouse: update-driven, high performance  Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis October 19, 2026 Data Mining: Concepts and Techniques 17 Data Warehouse vs. Operational DBMS  OLTP (on-line transaction processing)  Major task of traditional relational DBMS  Day-to-day operations.  Example at Intel: OLTP stores equipment and wafer data. (Not overall process health)  OLAP (on-line analytical processing)  Major task of data warehouse system  Data analysis and decision making  Example at Intel: WIPT (Wip Turns)  Distinct features (OLTP vs. OLAP):  Data contents: current, detailed vs. historical, consolidated  Database design: ER vs. star + subject October 19, 2026 Data Mining: Concepts and Techniques 18 OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated historical, summarized, multidimensional integrated, consolidated usage repetitive ad-hoc access read/write index/hash on prim. key lots of scans unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response October 19, 2026 Data Mining: Concepts and Techniques 19 Data Warehousing and OLAP Technology: An Overview  What is a data warehouse?  A multi-dimensional data model: Schema design, Cubes, and typical OLAP  Data warehouse architecture  Data warehouse implementation  From data warehousing to data mining October 19, 2026 Data Mining: Concepts and Techniques 20 From Tables and Spreadsheets to Data Cubes  A data warehouse is based on a multidimensional data model which views data in the form of a data cube  A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions  Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)  Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables  In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube. October 19, 2026 Data Mining: Concepts and Techniques 21 Cube: A Lattice of Cuboids Aggregation with GROUP BY time,item time,item,location time, item, location, supplier all time item location supplier time,location time,supplier item,location item,supplier location,supplier time,item,supplier time,location,supplier item,location,supplier 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D cuboids 4-D(base) cuboid October 19, 2026 Data Mining: Concepts and Techniques 22 Conceptual Modeling of Data Warehouses  Modeling data warehouses: dimensions & measures  Star schema: A fact table in the middle connected to a set of dimension tables  Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake  Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation October 19, 2026 Data Mining: Concepts and Techniques 23 Example of Star Schema time_key day day_of_the_week month quarter year time location_key street city state_or_province country location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch October 19, 2026 Data Mining: Concepts and Techniques 24 Example of Snowflake Schema time_key day day_of_the_week month quarter year time location_key street city_key location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city state_or_province country city October 19, 2026 Data Mining: Concepts and Techniques 25 Example of Fact Constellation time_key day day_of_the_week month quarter year time location_key street city province_or_state country location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped shipper_key shipper_name location_key shipper_type shipper October 19, 2026 Data Mining: Concepts and Techniques 26 Multidimensional Data  Sales volume as a function of product, month, and region Product Region Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day October 19, 2026 Data Mining: Concepts and Techniques 27 A Sample Data Cube Total annual sales of TV in U.S.A. Date Product Country sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum October 19, 2026 Data Mining: Concepts and Techniques 28 Cuboids Corresponding to the Cube all product date country product,date product,country date, country product, date, country 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D(base) cuboid October 19, 2026 Data Mining: Concepts and Techniques 29 Browsing a Data Cube  Visualization  OLAP capabilities  Interactive manipulation October 19, 2026 Data Mining: Concepts and Techniques 30 Typical OLAP Operations  Roll up (drill-up): summarize data  by climbing up hierarchy or by dimension reduction  Drill down (roll down): reverse of roll-up  from higher level summary to lower level summary or detailed data, or introducing new dimensions  Slice and dice: slice: select on one dimension of the cube; dice: select on two or more dimensions.  Pivot (rotate):  reorient the cube, visualization, 3D to series of 2D planes  Other operations  drill across: involving (across) more than one fact table  drill through: through the bottom level of the cube to its back-end relational tables (using SQL) October 19, 2026 Data Mining: Concepts and Techniques 31 Fig. 3.10 Typical OLAP Operations October 19, 2026 Data Mining: Concepts and Techniques 32 Data Warehousing and OLAP Technology: An Overview  What is a data warehouse?  A multi-dimensional data model  Data warehouse System architecture, Development approach and Back-end tools  Data warehouse implementation  From data warehousing to data mining October 19, 2026 Data Mining: Concepts and Techniques 33 Data Warehouse: A Multi-Tiered Architecture Data Warehouse: A Multi-Tiered Architecture Data Warehouse Extract Transform Load Refresh OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Operational DBs Other sources Data Storage OLAP Server October 19, 2026 Data Mining: Concepts and Techniques 34 Three Data Warehouse Models  Enterprise warehouse  collects all of the information about subjects spanning the entire organization  Data Mart  a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart  Virtual warehouse  A set of views over operational databases  Only some of the possible summary views may be materialized October 19, 2026 Data Mining: Concepts and Techniques 35 Data Warehouse Development: A Recommended Approach Define a high-level corporate data model Data Mart Data Mart Distributed Data Marts Multi-Tier Data Warehouse Enterprise Data Warehouse Model refinement Model refinement October 19, 2026 Data Mining: Concepts and Techniques 36 Data Warehouse Back-End Tools and Utilities  Data extraction  get data from multiple, heterogeneous, and external sources  Data cleaning  detect errors in the data and rectify them when possible  Data transformation  convert data from legacy or host format to warehouse format  Load  sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions  Refresh  propagate the updates from the data sources to the warehouse October 19, 2026 Data Mining: Concepts and Techniques 37 Data Warehousing and OLAP Technology: An Overview  What is a data warehouse?  A multi-dimensional data model  Data warehouse architecture  Data warehouse implementation: Efficient Cube computations  From data warehousing to data mining October 19, 2026 Data Mining: Concepts and Techniques 38 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  How many cuboids in an n-dimensional cube with L levels?  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. ) 1 1 (     n i Li T October 19, 2026 Data Mining: Concepts and Techniques 39 Cube Operation  Cube definition and computation in DMQL define cube sales[item, city, year]: sum(sales_in_dollars) compute cube sales  Transform it into a SQL-like language (with a new operator cube by, introduced by Gray et al.’96) SELECT item, city, year, SUM (amount) FROM SALES CUBE BY item, city, year  Need compute the following Group-Bys (date, product, customer), (date,product),(date, customer), (product, customer), (date), (product), (customer) () (item) (city) () (year) (city, item) (city, year) (item, year) (city, item, year) October 19, 2026 Data Mining: Concepts and Techniques 40 Iceberg Cube  Computing only the cuboid cells whose count or other aggregates satisfying the condition like HAVING COUNT(*) >= minsup  Motivation  Only a small portion of cube cells may be “above the water’’ in a sparse cube  Only calculate “interesting” cells—data above certain threshold  Avoid explosive growth of the cube  Suppose 100 dimensions, only 1 base cell. How many aggregate cells if count >= 1? What about count >= 2? October 19, 2026 Data Mining: Concepts and Techniques 41 Data Warehousing and OLAP Technology: An Overview  What is a data warehouse?  A multi-dimensional data model  Data warehouse architecture  Data warehouse implementation  From data warehousing to data mining October 19, 2026 Data Mining: Concepts and Techniques 42 Data Warehouse Usage  Three kinds of data warehouse applications  Information processing  supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs  Analytical processing  multidimensional analysis of data warehouse data  supports basic OLAP operations, slice-dice, drilling, pivoting  Some Data mining  knowledge discovery from hidden patterns  supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools  More DM may be needed for complex data or more automated analysis. October 19, 2026 Data Mining: Concepts and Techniques 43 From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM)  Why online analytical mining?  High quality of data in data warehouses  DW contains integrated, consistent, cleaned data  Available information processing structure surrounding data warehouses  ODBC, Web accessing, service facilities, reporting and OLAP tools  OLAP-based exploratory data analysis  Mining with drilling, dicing, pivoting, etc.  On-line selection of data mining functions  Integration and swapping of multiple mining functions, algorithms, and tasks October 19, 2026 Data Mining: Concepts and Techniques 44 An OLAM System Architecture Data Warehouse Meta Data MDDB OLAM Engine OLAP Engine User GUI API Data Cube API Database API Data cleaning Data integration Layer3 OLAP/OLAM Layer2 MDDB Layer1 Data Repository Layer4 User Interface Filtering&Integration Filtering Databases Mining query Mining result October 19, 2026 Data Mining: Concepts and Techniques 45 Data Warehousing and OLAP Technology: An Overview  What is a data warehouse?  A multi-dimensional data model  Data warehouse architecture  Data warehouse implementation  From data warehousing to data mining  Summary October 19, 2026 Data Mining: Concepts and Techniques 46 Key notes about Data Warehouse and OLAP Technology  Why data warehousing?  A multi-dimensional model of a data warehouse  Star schema, snowflake schema, fact constellations  A data cube consists of dimensions & measures  OLAP operations: drilling, rolling, slicing, dicing and pivoting  Data warehouse architecture  OLAP servers: ROLAP, MOLAP, HOLAP  Efficient computation of data cubes  Partial vs. full vs. no materialization  Indexing OALP data: Bitmap index and join index  OLAP query processing  From OLAP to OLAM (on-line analytical mining) October 19, 2026 Data Mining: Concepts and Techniques 47 References (I)  S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDB’96  D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. SIGMOD’97  R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDE’97  S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997  E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July 1993.  J. Gray, et al. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997.  A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and Applications. MIT Press, 1999.  J. Han. Towards on-line analytical mining in large databases. ACM SIGMOD Record, 27:97-107, 1998.  V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. SIGMOD’96 October 19, 2026 Data Mining: Concepts and Techniques 48 References (II)  C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design: Relational and Dimensional Techniques. John Wiley, 2003  W. H. Inmon. Building the Data Warehouse. John Wiley, 1996  R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2ed. John Wiley, 2002  P. O'Neil and D. Quass. Improved query performance with variant indexes. SIGMOD'97  Microsoft. OLEDB for OLAP programmer's reference version 1.0. In http://www.microsoft.com/data/oledb/olap, 1998  A. Shoshani. OLAP and statistical databases: Similarities and differences. PODS’00.  S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. ICDE'94  OLAP council. MDAPI specification version 2.0. In http://www.olapcouncil.org/research/apily.htm, 1998  E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley, 1997  P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987.  J. Widom. Research problems in data warehousing. CIKM’95.