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11 Data Mining: Concepts and Techniques (3rd ed.) ā€” Chapter 4 ā€” Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University Ā©2011 Han, Kamber & Pei. All rights reserved.
2 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
3 What is a Data Warehouse? ā—¼ Defined in many different ways, but not rigorously. ā—¼ A decision support database that is maintained separately from the organizationā€™s operational database ā—¼ Support information processing by providing a solid platform of consolidated, historical data for analysis. ā—¼ ā€œA data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of managementā€™s decision-making process.ā€ā€”W. H. Inmon ā—¼ Data warehousing: ā—¼ The process of constructing and using data warehouses
4 Data Warehouseā€”Subject-Oriented ā—¼ Organized around major subjects, such as customer, product, sales ā—¼ Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing ā—¼ Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process
5 Data Warehouseā€”Integrated ā—¼ Constructed by integrating multiple, heterogeneous data sources ā—¼ relational databases, flat files, on-line transaction records ā—¼ Data cleaning and data integration techniques are applied. ā—¼ Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources ā—¼ E.g., Hotel price: currency, tax, breakfast covered, etc. ā—¼ When data is moved to the warehouse, it is converted.
6 Data Warehouseā€”Time Variant ā—¼ The time horizon for the data warehouse is significantly longer than that of operational systems ā—¼ Operational database: current value data ā—¼ Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) ā—¼ Every key structure in the data warehouse ā—¼ Contains an element of time, explicitly or implicitly ā—¼ But the key of operational data may or may not contain ā€œtime elementā€
7 Data Warehouseā€”Nonvolatile ā—¼ A physically separate store of data transformed from the operational environment ā—¼ Operational update of data does not occur in the data warehouse environment ā—¼ Does not require transaction processing, recovery, and concurrency control mechanisms ā—¼ Requires only two operations in data accessing: ā—¼ initial loading of data and access of data
8 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
9 Why a Separate Data Warehouse? ā—¼ High performance for both systems ā—¼ DBMSā€” tuned for OLTP: access methods, indexing, concurrency control, recovery ā—¼ Warehouseā€”tuned for OLAP: complex OLAP queries, multidimensional view, consolidation ā—¼ Different functions and different data: ā—¼ missing data: Decision support requires historical data which operational DBs do not typically maintain ā—¼ data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources ā—¼ data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled ā—¼ Note: There are more and more systems which perform OLAP analysis directly on relational databases
10 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
11 Three Data Warehouse Models ā—¼ Enterprise warehouse ā—¼ collects all of the information about subjects spanning the entire organization ā—¼ Data Mart ā—¼ 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 ā—¼ Independent vs. dependent (directly from warehouse) data mart ā—¼ Virtual warehouse ā—¼ A set of views over operational databases ā—¼ Only some of the possible summary views may be materialized
12 Extraction, Transformation, and Loading (ETL) ā—¼ Data extraction ā—¼ get data from multiple, heterogeneous, and external sources ā—¼ Data cleaning ā—¼ detect errors in the data and rectify them when possible ā—¼ Data transformation ā—¼ convert data from legacy or host format to warehouse format ā—¼ Load ā—¼ sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions ā—¼ Refresh ā—¼ propagate the updates from the data sources to the warehouse
13 Metadata Repository ā—¼ Meta data is the data defining warehouse objects. It stores: ā—¼ Description of the structure of the data warehouse ā—¼ schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents ā—¼ Operational meta-data ā—¼ 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) ā—¼ The algorithms used for summarization ā—¼ The mapping from operational environment to the data warehouse ā—¼ Data related to system performance ā—¼ warehouse schema, view and derived data definitions ā—¼ Business data ā—¼ business terms and definitions, ownership of data, charging policies
14 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
15 From Tables and Spreadsheets to Data Cubes ā—¼ A data warehouse is based on a multidimensional data model which views data in the form of a data cube ā—¼ A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions ā—¼ Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year) ā—¼ Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables ā—¼ 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.
16 Cube: A Lattice of Cuboids 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
17 Conceptual Modeling of Data Warehouses ā—¼ Modeling data warehouses: dimensions & measures ā—¼ Star schema: A fact table in the middle connected to a set of dimension tables ā—¼ 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 ā—¼ Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation
18 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
19 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
20 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
21 A Concept Hierarchy: Dimension (location) all Europe North_America MexicoCanadaSpainGermany Vancouver M. WindL. Chan ... ...... ... ... ... all region office country TorontoFrankfurtcity
22 Data Cube Measures: Three Categories ā—¼ Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning ā—¼ E.g., count(), sum(), min(), max() ā—¼ Algebraic: if it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function ā—¼ E.g., avg(), min_N(), standard_deviation() ā—¼ Holistic: if there is no constant bound on the storage size needed to describe a subaggregate. ā—¼ E.g., median(), mode(), rank()
23 View of Warehouses and Hierarchies Specification of hierarchies ā—¼ Schema hierarchy day < {month < quarter; week} < year ā—¼ Set_grouping hierarchy {1..10} < inexpensive
24 Multidimensional Data ā—¼ Sales volume as a function of product, month, and region Product Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day
25 A Sample Data Cube Total annual sales of TVs in U.S.A.Date Country sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum
26 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
27 Typical OLAP Operations ā—¼ Roll up (drill-up): summarize data ā—¼ by climbing up hierarchy or by dimension reduction ā—¼ Drill down (roll down): reverse of roll-up ā—¼ from higher level summary to lower level summary or detailed data, or introducing new dimensions ā—¼ Slice and dice: project and select ā—¼ Pivot (rotate): ā—¼ reorient the cube, visualization, 3D to series of 2D planes ā—¼ Other operations ā—¼ drill across: involving (across) more than one fact table ā—¼ drill through: through the bottom level of the cube to its back-end relational tables (using SQL)
28 Fig. 3.10 Typical OLAP Operations
29 A Star-Net Query Model Shipping Method AIR-EXPRESS TRUCK ORDER Customer Orders CONTRACTS Customer Product PRODUCT GROUP PRODUCT LINE PRODUCT ITEM SALES PERSON DISTRICT DIVISION OrganizationPromotion CITY COUNTRY REGION Location DAILYQTRLYANNUALY Time Each circle is called a footprint
30 Browsing a Data Cube ā—¼ Visualization ā—¼ OLAP capabilities ā—¼ Interactive manipulation
31 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
32 Design of Data Warehouse: A Business Analysis Framework ā—¼ Four views regarding the design of a data warehouse ā—¼ Top-down view ā—¼ allows selection of the relevant information necessary for the data warehouse ā—¼ Data source view ā—¼ exposes the information being captured, stored, and managed by operational systems ā—¼ Data warehouse view ā—¼ consists of fact tables and dimension tables ā—¼ Business query view ā—¼ sees the perspectives of data in the warehouse from the view of end-user
33 Data Warehouse Design Process ā—¼ Top-down, bottom-up approaches or a combination of both ā—¼ Top-down: Starts with overall design and planning (mature) ā—¼ Bottom-up: Starts with experiments and prototypes (rapid) ā—¼ From software engineering point of view ā—¼ Waterfall: structured and systematic analysis at each step before proceeding to the next ā—¼ Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around ā—¼ Typical data warehouse design process ā—¼ Choose a business process to model, e.g., orders, invoices, etc. ā—¼ Choose the grain (atomic level of data) of the business process ā—¼ Choose the dimensions that will apply to each fact table record ā—¼ Choose the measure that will populate each fact table record
34 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 refinementModel refinement
35 Data Warehouse Usage ā—¼ Three kinds of data warehouse applications ā—¼ Information processing ā—¼ supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs ā—¼ Analytical processing ā—¼ multidimensional analysis of data warehouse data ā—¼ supports basic OLAP operations, slice-dice, drilling, pivoting ā—¼ Data mining ā—¼ knowledge discovery from hidden patterns ā—¼ supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools
36 From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM) ā—¼ Why online analytical mining? ā—¼ High quality of data in data warehouses ā—¼ DW contains integrated, consistent, cleaned data ā—¼ Available information processing structure surrounding data warehouses ā—¼ ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools ā—¼ OLAP-based exploratory data analysis ā—¼ Mining with drilling, dicing, pivoting, etc. ā—¼ On-line selection of data mining functions ā—¼ Integration and swapping of multiple mining functions, algorithms, and tasks
37 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
38 Efficient Data Cube Computation ā—¼ Data cube can be viewed as a lattice of cuboids ā—¼ The bottom-most cuboid is the base cuboid ā—¼ The top-most cuboid (apex) contains only one cell ā—¼ How many cuboids in an n-dimensional cube with L levels? ā—¼ Materialization of data cube ā—¼ Materialize every (cuboid) (full materialization), none (no materialization), or some (partial materialization) ā—¼ Selection of which cuboids to materialize ā—¼ Based on size, sharing, access frequency, etc. )1 1 ( +ļƒ• = = n i iLT
39 The ā€œCompute Cubeā€ Operator ā—¼ Cube definition and computation in DMQL define cube sales [item, city, year]: sum (sales_in_dollars) compute cube sales ā—¼ Transform it into a SQL-like language (with a new operator cube by, introduced by Gray et al.ā€™96) SELECT item, city, year, SUM (amount) FROM SALES CUBE BY item, city, year ā—¼ 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)
40 Indexing OLAP Data: Bitmap Index ā—¼ Index on a particular column ā—¼ Each value in the column has a bit vector: bit-op is fast ā—¼ The length of the bit vector: # of records in the base table ā—¼ The i-th bit is set if the i-th row of the base table has the value for the indexed column ā—¼ not suitable for high cardinality domains ā—¼ A recent bit compression technique, Word-Aligned Hybrid (WAH), makes it work for high cardinality domain as well [Wu, et al. TODSā€™06] Cust Region Type C1 Asia Retail C2 Europe Dealer C3 Asia Dealer C4 America Retail C5 Europe Dealer RecID Retail Dealer 1 1 0 2 0 1 3 0 1 4 1 0 5 0 1 RecIDAsia Europe America 1 1 0 0 2 0 1 0 3 1 0 0 4 0 0 1 5 0 1 0 Base table Index on Region Index on Type
41 Indexing OLAP Data: Join Indices ā—¼ Join index: JI(R-id, S-id) where R (R-id, ā€¦) ļ€¾ļ€¼ S (S-id, ā€¦) ā—¼ Traditional indices map the values to a list of record ids ā—¼ It materializes relational join in JI file and speeds up relational join ā—¼ In data warehouses, join index relates the values of the dimensions of a start schema to rows in the fact table. ā—¼ E.g. fact table: Sales and two dimensions city and product ā—¼ A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city ā—¼ Join indices can span multiple dimensions
42 Efficient Processing OLAP Queries ā—¼ Determine which operations should be performed on the available cuboids ā—¼ Transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g., dice = selection + projection ā—¼ Determine which materialized cuboid(s) should be selected for OLAP op. ā—¼ Let the query to be processed be on {brand, province_or_state} with the condition ā€œyear = 2004ā€, and there are 4 materialized cuboids available: 1) {year, item_name, city} 2) {year, brand, country} 3) {year, brand, province_or_state} 4) {item_name, province_or_state} where year = 2004 Which should be selected to process the query? ā—¼ Explore indexing structures and compressed vs. dense array structs in MOLAP
43 OLAP Server Architectures ā—¼ Relational OLAP (ROLAP) ā—¼ Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware ā—¼ Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services ā—¼ Greater scalability ā—¼ Multidimensional OLAP (MOLAP) ā—¼ Sparse array-based multidimensional storage engine ā—¼ Fast indexing to pre-computed summarized data ā—¼ Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer) ā—¼ Flexibility, e.g., low level: relational, high-level: array ā—¼ Specialized SQL servers (e.g., Redbricks) ā—¼ Specialized support for SQL queries over star/snowflake schemas
44 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
45 Attribute-Oriented Induction ā—¼ Proposed in 1989 (KDD ā€˜89 workshop) ā—¼ Not confined to categorical data nor particular measures ā—¼ How it is done? ā—¼ Collect the task-relevant data (initial relation) using a relational database query ā—¼ Perform generalization by attribute removal or attribute generalization ā—¼ Apply aggregation by merging identical, generalized tuples and accumulating their respective counts ā—¼ Interaction with users for knowledge presentation
46 Attribute-Oriented Induction: An Example Example: Describe general characteristics of graduate students in the University database ā—¼ Step 1. Fetch relevant set of data using an SQL statement, e.g., Select * (i.e., name, gender, major, birth_place, birth_date, residence, phone#, gpa) from student where student_status in {ā€œMscā€, ā€œMBAā€, ā€œPhDā€ } ā—¼ Step 2. Perform attribute-oriented induction ā—¼ Step 3. Present results in generalized relation, cross-tab, or rule forms
47 Class Characterization: An Example Name Gender Major Birth-Place Birth_date Residence Phone # GPA Jim Woodman M CS Vancouver,BC, Canada 8-12-76 3511 Main St., Richmond 687-4598 3.67 Scott Lachance M CS Montreal, Que, Canada 28-7-75 345 1st Ave., Richmond 253-9106 3.70 Laura Lee ā€¦ F ā€¦ Physics ā€¦ Seattle, WA, USA ā€¦ 25-8-70 ā€¦ 125 Austin Ave., Burnaby ā€¦ 420-5232 ā€¦ 3.83 ā€¦ Removed Retained Sci,Eng, Bus Country Age range City Removed Excl, VG,.. Gender Major Birth_region Age_range Residence GPA Count M Science Canada 20-25 Richmond Very-good 16 F Science Foreign 25-30 Burnaby Excellent 22 ā€¦ ā€¦ ā€¦ ā€¦ ā€¦ ā€¦ ā€¦ Birth_Region Gender Canada Foreign Total M 16 14 30 F 10 22 32 Total 26 36 62 Prime Generalized Relation Initial Relation
48 Basic Principles of Attribute-Oriented Induction ā—¼ Data focusing: task-relevant data, including dimensions, and the result is the initial relation ā—¼ Attribute-removal: remove attribute A if there is a large set of distinct values for A but (1) there is no generalization operator on A, or (2) Aā€™s higher level concepts are expressed in terms of other attributes ā—¼ Attribute-generalization: If there is a large set of distinct values for A, and there exists a set of generalization operators on A, then select an operator and generalize A ā—¼ Attribute-threshold control: typical 2-8, specified/default ā—¼ Generalized relation threshold control: control the final relation/rule size
49 Attribute-Oriented Induction: Basic Algorithm ā—¼ InitialRel: Query processing of task-relevant data, deriving the initial relation. ā—¼ PreGen: Based on the analysis of the number of distinct values in each attribute, determine generalization plan for each attribute: removal? or how high to generalize? ā—¼ PrimeGen: Based on the PreGen plan, perform generalization to the right level to derive a ā€œprime generalized relationā€, accumulating the counts. ā—¼ Presentation: User interaction: (1) adjust levels by drilling, (2) pivoting, (3) mapping into rules, cross tabs, visualization presentations.
50 Presentation of Generalized Results ā—¼ Generalized relation: ā—¼ Relations where some or all attributes are generalized, with counts or other aggregation values accumulated. ā—¼ Cross tabulation: ā—¼ Mapping results into cross tabulation form (similar to contingency tables). ā—¼ Visualization techniques: ā—¼ Pie charts, bar charts, curves, cubes, and other visual forms. ā—¼ Quantitative characteristic rules: ā—¼ Mapping generalized result into characteristic rules with quantitative information associated with it, e.g., .%]47:["")(_%]53:["")(_ )()( tforeignxregionbirthtCanadaxregionbirth xmalexgrad =ļƒš= ļƒžļƒ™
51 Mining Class Comparisons ā—¼ Comparison: Comparing two or more classes ā—¼ Method: ā—¼ Partition the set of relevant data into the target class and the contrasting class(es) ā—¼ Generalize both classes to the same high level concepts ā—¼ Compare tuples with the same high level descriptions ā—¼ Present for every tuple its description and two measures ā—¼ support - distribution within single class ā—¼ comparison - distribution between classes ā—¼ Highlight the tuples with strong discriminant features ā—¼ Relevance Analysis: ā—¼ Find attributes (features) which best distinguish different classes
52 Concept Description vs. Cube-Based OLAP ā—¼ Similarity: ā—¼ Data generalization ā—¼ Presentation of data summarization at multiple levels of abstraction ā—¼ Interactive drilling, pivoting, slicing and dicing ā—¼ Differences: ā—¼ OLAP has systematic preprocessing, query independent, and can drill down to rather low level ā—¼ AOI has automated desired level allocation, and may perform dimension relevance analysis/ranking when there are many relevant dimensions ā—¼ AOI works on the data which are not in relational forms
53 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
54 Summary ā—¼ Data warehousing: A multi-dimensional model of a data warehouse ā—¼ A data cube consists of dimensions & measures ā—¼ Star schema, snowflake schema, fact constellations ā—¼ OLAP operations: drilling, rolling, slicing, dicing and pivoting ā—¼ Data Warehouse Architecture, Design, and Usage ā—¼ Multi-tiered architecture ā—¼ Business analysis design framework ā—¼ Information processing, analytical processing, data mining, OLAM (Online Analytical Mining) ā—¼ Implementation: Efficient computation of data cubes ā—¼ Partial vs. full vs. no materialization ā—¼ Indexing OALP data: Bitmap index and join index ā—¼ OLAP query processing ā—¼ OLAP servers: ROLAP, MOLAP, HOLAP ā—¼ Data generalization: Attribute-oriented induction
55 References (I) ā—¼ S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDBā€™96 ā—¼ D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. SIGMODā€™97 ā—¼ R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDEā€™97 ā—¼ S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997 ā—¼ E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July 1993. ā—¼ 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. ā—¼ A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and Applications. MIT Press, 1999. ā—¼ J. Han. Towards on-line analytical mining in large databases. ACM SIGMOD Record, 27:97-107, 1998. ā—¼ V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. SIGMODā€™96 ā—¼ J. Hellerstein, P. Haas, and H. Wang. Online aggregation. SIGMOD'97
56 References (II) ā—¼ C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design: Relational and Dimensional Techniques. John Wiley, 2003 ā—¼ W. H. Inmon. Building the Data Warehouse. John Wiley, 1996 ā—¼ R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2ed. John Wiley, 2002 ā—¼ P. Oā€™Neil and G. Graefe. Multi-table joins through bitmapped join indices. SIGMOD Record, 24:8ā€“ 11, Sept. 1995. ā—¼ P. O'Neil and D. Quass. Improved query performance with variant indexes. SIGMOD'97 ā—¼ Microsoft. OLEDB for OLAP programmer's reference version 1.0. In http://www.microsoft.com/data/oledb/olap, 1998 ā—¼ S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. ICDE'94 ā—¼ A. Shoshani. OLAP and statistical databases: Similarities and differences. PODSā€™00. ā—¼ D. Srivastava, S. Dar, H. V. Jagadish, and A. V. Levy. Answering queries with aggregation using views. VLDB'96 ā—¼ P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987. ā—¼ J. Widom. Research problems in data warehousing. CIKMā€™95 ā—¼ K. Wu, E. Otoo, and A. Shoshani, Optimal Bitmap Indices with Efficient Compression, ACM Trans. on Database Systems (TODS), 31(1): 1-38, 2006
57 Surplus Slides
58 Compression of Bitmap Indices ā—¼ Bitmap indexes must be compressed to reduce I/O costs and minimize CPU usageā€”majority of the bits are 0ā€™s ā—¼ Two compression schemes: ā—¼ Byte-aligned Bitmap Code (BBC) ā—¼ Word-Aligned Hybrid (WAH) code ā—¼ Time and space required to operate on compressed bitmap is proportional to the total size of the bitmap ā—¼ Optimal on attributes of low cardinality as well as those of high cardinality. ā—¼ WAH out performs BBC by about a factor of two

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Data Mining Concepts and Techniques Chapter 4 Summary

  • 1. 11 Data Mining: Concepts and Techniques (3rd ed.) ā€” Chapter 4 ā€” Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University Ā©2011 Han, Kamber & Pei. All rights reserved.
  • 2. 2 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
  • 3. 3 What is a Data Warehouse? ā—¼ Defined in many different ways, but not rigorously. ā—¼ A decision support database that is maintained separately from the organizationā€™s operational database ā—¼ Support information processing by providing a solid platform of consolidated, historical data for analysis. ā—¼ ā€œA data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of managementā€™s decision-making process.ā€ā€”W. H. Inmon ā—¼ Data warehousing: ā—¼ The process of constructing and using data warehouses
  • 4. 4 Data Warehouseā€”Subject-Oriented ā—¼ Organized around major subjects, such as customer, product, sales ā—¼ Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing ā—¼ Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process
  • 5. 5 Data Warehouseā€”Integrated ā—¼ Constructed by integrating multiple, heterogeneous data sources ā—¼ relational databases, flat files, on-line transaction records ā—¼ Data cleaning and data integration techniques are applied. ā—¼ Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources ā—¼ E.g., Hotel price: currency, tax, breakfast covered, etc. ā—¼ When data is moved to the warehouse, it is converted.
  • 6. 6 Data Warehouseā€”Time Variant ā—¼ The time horizon for the data warehouse is significantly longer than that of operational systems ā—¼ Operational database: current value data ā—¼ Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) ā—¼ Every key structure in the data warehouse ā—¼ Contains an element of time, explicitly or implicitly ā—¼ But the key of operational data may or may not contain ā€œtime elementā€
  • 7. 7 Data Warehouseā€”Nonvolatile ā—¼ A physically separate store of data transformed from the operational environment ā—¼ Operational update of data does not occur in the data warehouse environment ā—¼ Does not require transaction processing, recovery, and concurrency control mechanisms ā—¼ Requires only two operations in data accessing: ā—¼ initial loading of data and access of data
  • 8. 8 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
  • 9. 9 Why a Separate Data Warehouse? ā—¼ High performance for both systems ā—¼ DBMSā€” tuned for OLTP: access methods, indexing, concurrency control, recovery ā—¼ Warehouseā€”tuned for OLAP: complex OLAP queries, multidimensional view, consolidation ā—¼ Different functions and different data: ā—¼ missing data: Decision support requires historical data which operational DBs do not typically maintain ā—¼ data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources ā—¼ data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled ā—¼ Note: There are more and more systems which perform OLAP analysis directly on relational databases
  • 10. 10 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
  • 11. 11 Three Data Warehouse Models ā—¼ Enterprise warehouse ā—¼ collects all of the information about subjects spanning the entire organization ā—¼ Data Mart ā—¼ 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 ā—¼ Independent vs. dependent (directly from warehouse) data mart ā—¼ Virtual warehouse ā—¼ A set of views over operational databases ā—¼ Only some of the possible summary views may be materialized
  • 12. 12 Extraction, Transformation, and Loading (ETL) ā—¼ Data extraction ā—¼ get data from multiple, heterogeneous, and external sources ā—¼ Data cleaning ā—¼ detect errors in the data and rectify them when possible ā—¼ Data transformation ā—¼ convert data from legacy or host format to warehouse format ā—¼ Load ā—¼ sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions ā—¼ Refresh ā—¼ propagate the updates from the data sources to the warehouse
  • 13. 13 Metadata Repository ā—¼ Meta data is the data defining warehouse objects. It stores: ā—¼ Description of the structure of the data warehouse ā—¼ schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents ā—¼ Operational meta-data ā—¼ 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) ā—¼ The algorithms used for summarization ā—¼ The mapping from operational environment to the data warehouse ā—¼ Data related to system performance ā—¼ warehouse schema, view and derived data definitions ā—¼ Business data ā—¼ business terms and definitions, ownership of data, charging policies
  • 14. 14 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
  • 15. 15 From Tables and Spreadsheets to Data Cubes ā—¼ A data warehouse is based on a multidimensional data model which views data in the form of a data cube ā—¼ A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions ā—¼ Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year) ā—¼ Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables ā—¼ 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.
  • 16. 16 Cube: A Lattice of Cuboids 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
  • 17. 17 Conceptual Modeling of Data Warehouses ā—¼ Modeling data warehouses: dimensions & measures ā—¼ Star schema: A fact table in the middle connected to a set of dimension tables ā—¼ 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 ā—¼ Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation
  • 18. 18 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
  • 19. 19 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
  • 20. 20 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
  • 21. 21 A Concept Hierarchy: Dimension (location) all Europe North_America MexicoCanadaSpainGermany Vancouver M. WindL. Chan ... ...... ... ... ... all region office country TorontoFrankfurtcity
  • 22. 22 Data Cube Measures: Three Categories ā—¼ Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning ā—¼ E.g., count(), sum(), min(), max() ā—¼ Algebraic: if it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function ā—¼ E.g., avg(), min_N(), standard_deviation() ā—¼ Holistic: if there is no constant bound on the storage size needed to describe a subaggregate. ā—¼ E.g., median(), mode(), rank()
  • 23. 23 View of Warehouses and Hierarchies Specification of hierarchies ā—¼ Schema hierarchy day < {month < quarter; week} < year ā—¼ Set_grouping hierarchy {1..10} < inexpensive
  • 24. 24 Multidimensional Data ā—¼ Sales volume as a function of product, month, and region Product Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day
  • 25. 25 A Sample Data Cube Total annual sales of TVs in U.S.A.Date Country sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum
  • 26. 26 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
  • 27. 27 Typical OLAP Operations ā—¼ Roll up (drill-up): summarize data ā—¼ by climbing up hierarchy or by dimension reduction ā—¼ Drill down (roll down): reverse of roll-up ā—¼ from higher level summary to lower level summary or detailed data, or introducing new dimensions ā—¼ Slice and dice: project and select ā—¼ Pivot (rotate): ā—¼ reorient the cube, visualization, 3D to series of 2D planes ā—¼ Other operations ā—¼ drill across: involving (across) more than one fact table ā—¼ drill through: through the bottom level of the cube to its back-end relational tables (using SQL)
  • 28. 28 Fig. 3.10 Typical OLAP Operations
  • 29. 29 A Star-Net Query Model Shipping Method AIR-EXPRESS TRUCK ORDER Customer Orders CONTRACTS Customer Product PRODUCT GROUP PRODUCT LINE PRODUCT ITEM SALES PERSON DISTRICT DIVISION OrganizationPromotion CITY COUNTRY REGION Location DAILYQTRLYANNUALY Time Each circle is called a footprint
  • 30. 30 Browsing a Data Cube ā—¼ Visualization ā—¼ OLAP capabilities ā—¼ Interactive manipulation
  • 31. 31 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
  • 32. 32 Design of Data Warehouse: A Business Analysis Framework ā—¼ Four views regarding the design of a data warehouse ā—¼ Top-down view ā—¼ allows selection of the relevant information necessary for the data warehouse ā—¼ Data source view ā—¼ exposes the information being captured, stored, and managed by operational systems ā—¼ Data warehouse view ā—¼ consists of fact tables and dimension tables ā—¼ Business query view ā—¼ sees the perspectives of data in the warehouse from the view of end-user
  • 33. 33 Data Warehouse Design Process ā—¼ Top-down, bottom-up approaches or a combination of both ā—¼ Top-down: Starts with overall design and planning (mature) ā—¼ Bottom-up: Starts with experiments and prototypes (rapid) ā—¼ From software engineering point of view ā—¼ Waterfall: structured and systematic analysis at each step before proceeding to the next ā—¼ Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around ā—¼ Typical data warehouse design process ā—¼ Choose a business process to model, e.g., orders, invoices, etc. ā—¼ Choose the grain (atomic level of data) of the business process ā—¼ Choose the dimensions that will apply to each fact table record ā—¼ Choose the measure that will populate each fact table record
  • 34. 34 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 refinementModel refinement
  • 35. 35 Data Warehouse Usage ā—¼ Three kinds of data warehouse applications ā—¼ Information processing ā—¼ supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs ā—¼ Analytical processing ā—¼ multidimensional analysis of data warehouse data ā—¼ supports basic OLAP operations, slice-dice, drilling, pivoting ā—¼ Data mining ā—¼ knowledge discovery from hidden patterns ā—¼ supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools
  • 36. 36 From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM) ā—¼ Why online analytical mining? ā—¼ High quality of data in data warehouses ā—¼ DW contains integrated, consistent, cleaned data ā—¼ Available information processing structure surrounding data warehouses ā—¼ ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools ā—¼ OLAP-based exploratory data analysis ā—¼ Mining with drilling, dicing, pivoting, etc. ā—¼ On-line selection of data mining functions ā—¼ Integration and swapping of multiple mining functions, algorithms, and tasks
  • 37. 37 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
  • 38. 38 Efficient Data Cube Computation ā—¼ Data cube can be viewed as a lattice of cuboids ā—¼ The bottom-most cuboid is the base cuboid ā—¼ The top-most cuboid (apex) contains only one cell ā—¼ How many cuboids in an n-dimensional cube with L levels? ā—¼ Materialization of data cube ā—¼ Materialize every (cuboid) (full materialization), none (no materialization), or some (partial materialization) ā—¼ Selection of which cuboids to materialize ā—¼ Based on size, sharing, access frequency, etc. )1 1 ( +ļƒ• = = n i iLT
  • 39. 39 The ā€œCompute Cubeā€ Operator ā—¼ Cube definition and computation in DMQL define cube sales [item, city, year]: sum (sales_in_dollars) compute cube sales ā—¼ Transform it into a SQL-like language (with a new operator cube by, introduced by Gray et al.ā€™96) SELECT item, city, year, SUM (amount) FROM SALES CUBE BY item, city, year ā—¼ 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)
  • 40. 40 Indexing OLAP Data: Bitmap Index ā—¼ Index on a particular column ā—¼ Each value in the column has a bit vector: bit-op is fast ā—¼ The length of the bit vector: # of records in the base table ā—¼ The i-th bit is set if the i-th row of the base table has the value for the indexed column ā—¼ not suitable for high cardinality domains ā—¼ A recent bit compression technique, Word-Aligned Hybrid (WAH), makes it work for high cardinality domain as well [Wu, et al. TODSā€™06] Cust Region Type C1 Asia Retail C2 Europe Dealer C3 Asia Dealer C4 America Retail C5 Europe Dealer RecID Retail Dealer 1 1 0 2 0 1 3 0 1 4 1 0 5 0 1 RecIDAsia Europe America 1 1 0 0 2 0 1 0 3 1 0 0 4 0 0 1 5 0 1 0 Base table Index on Region Index on Type
  • 41. 41 Indexing OLAP Data: Join Indices ā—¼ Join index: JI(R-id, S-id) where R (R-id, ā€¦) ļ€¾ļ€¼ S (S-id, ā€¦) ā—¼ Traditional indices map the values to a list of record ids ā—¼ It materializes relational join in JI file and speeds up relational join ā—¼ In data warehouses, join index relates the values of the dimensions of a start schema to rows in the fact table. ā—¼ E.g. fact table: Sales and two dimensions city and product ā—¼ A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city ā—¼ Join indices can span multiple dimensions
  • 42. 42 Efficient Processing OLAP Queries ā—¼ Determine which operations should be performed on the available cuboids ā—¼ Transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g., dice = selection + projection ā—¼ Determine which materialized cuboid(s) should be selected for OLAP op. ā—¼ Let the query to be processed be on {brand, province_or_state} with the condition ā€œyear = 2004ā€, and there are 4 materialized cuboids available: 1) {year, item_name, city} 2) {year, brand, country} 3) {year, brand, province_or_state} 4) {item_name, province_or_state} where year = 2004 Which should be selected to process the query? ā—¼ Explore indexing structures and compressed vs. dense array structs in MOLAP
  • 43. 43 OLAP Server Architectures ā—¼ Relational OLAP (ROLAP) ā—¼ Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware ā—¼ Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services ā—¼ Greater scalability ā—¼ Multidimensional OLAP (MOLAP) ā—¼ Sparse array-based multidimensional storage engine ā—¼ Fast indexing to pre-computed summarized data ā—¼ Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer) ā—¼ Flexibility, e.g., low level: relational, high-level: array ā—¼ Specialized SQL servers (e.g., Redbricks) ā—¼ Specialized support for SQL queries over star/snowflake schemas
  • 44. 44 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
  • 45. 45 Attribute-Oriented Induction ā—¼ Proposed in 1989 (KDD ā€˜89 workshop) ā—¼ Not confined to categorical data nor particular measures ā—¼ How it is done? ā—¼ Collect the task-relevant data (initial relation) using a relational database query ā—¼ Perform generalization by attribute removal or attribute generalization ā—¼ Apply aggregation by merging identical, generalized tuples and accumulating their respective counts ā—¼ Interaction with users for knowledge presentation
  • 46. 46 Attribute-Oriented Induction: An Example Example: Describe general characteristics of graduate students in the University database ā—¼ Step 1. Fetch relevant set of data using an SQL statement, e.g., Select * (i.e., name, gender, major, birth_place, birth_date, residence, phone#, gpa) from student where student_status in {ā€œMscā€, ā€œMBAā€, ā€œPhDā€ } ā—¼ Step 2. Perform attribute-oriented induction ā—¼ Step 3. Present results in generalized relation, cross-tab, or rule forms
  • 47. 47 Class Characterization: An Example Name Gender Major Birth-Place Birth_date Residence Phone # GPA Jim Woodman M CS Vancouver,BC, Canada 8-12-76 3511 Main St., Richmond 687-4598 3.67 Scott Lachance M CS Montreal, Que, Canada 28-7-75 345 1st Ave., Richmond 253-9106 3.70 Laura Lee ā€¦ F ā€¦ Physics ā€¦ Seattle, WA, USA ā€¦ 25-8-70 ā€¦ 125 Austin Ave., Burnaby ā€¦ 420-5232 ā€¦ 3.83 ā€¦ Removed Retained Sci,Eng, Bus Country Age range City Removed Excl, VG,.. Gender Major Birth_region Age_range Residence GPA Count M Science Canada 20-25 Richmond Very-good 16 F Science Foreign 25-30 Burnaby Excellent 22 ā€¦ ā€¦ ā€¦ ā€¦ ā€¦ ā€¦ ā€¦ Birth_Region Gender Canada Foreign Total M 16 14 30 F 10 22 32 Total 26 36 62 Prime Generalized Relation Initial Relation
  • 48. 48 Basic Principles of Attribute-Oriented Induction ā—¼ Data focusing: task-relevant data, including dimensions, and the result is the initial relation ā—¼ Attribute-removal: remove attribute A if there is a large set of distinct values for A but (1) there is no generalization operator on A, or (2) Aā€™s higher level concepts are expressed in terms of other attributes ā—¼ Attribute-generalization: If there is a large set of distinct values for A, and there exists a set of generalization operators on A, then select an operator and generalize A ā—¼ Attribute-threshold control: typical 2-8, specified/default ā—¼ Generalized relation threshold control: control the final relation/rule size
  • 49. 49 Attribute-Oriented Induction: Basic Algorithm ā—¼ InitialRel: Query processing of task-relevant data, deriving the initial relation. ā—¼ PreGen: Based on the analysis of the number of distinct values in each attribute, determine generalization plan for each attribute: removal? or how high to generalize? ā—¼ PrimeGen: Based on the PreGen plan, perform generalization to the right level to derive a ā€œprime generalized relationā€, accumulating the counts. ā—¼ Presentation: User interaction: (1) adjust levels by drilling, (2) pivoting, (3) mapping into rules, cross tabs, visualization presentations.
  • 50. 50 Presentation of Generalized Results ā—¼ Generalized relation: ā—¼ Relations where some or all attributes are generalized, with counts or other aggregation values accumulated. ā—¼ Cross tabulation: ā—¼ Mapping results into cross tabulation form (similar to contingency tables). ā—¼ Visualization techniques: ā—¼ Pie charts, bar charts, curves, cubes, and other visual forms. ā—¼ Quantitative characteristic rules: ā—¼ Mapping generalized result into characteristic rules with quantitative information associated with it, e.g., .%]47:["")(_%]53:["")(_ )()( tforeignxregionbirthtCanadaxregionbirth xmalexgrad =ļƒš= ļƒžļƒ™
  • 51. 51 Mining Class Comparisons ā—¼ Comparison: Comparing two or more classes ā—¼ Method: ā—¼ Partition the set of relevant data into the target class and the contrasting class(es) ā—¼ Generalize both classes to the same high level concepts ā—¼ Compare tuples with the same high level descriptions ā—¼ Present for every tuple its description and two measures ā—¼ support - distribution within single class ā—¼ comparison - distribution between classes ā—¼ Highlight the tuples with strong discriminant features ā—¼ Relevance Analysis: ā—¼ Find attributes (features) which best distinguish different classes
  • 52. 52 Concept Description vs. Cube-Based OLAP ā—¼ Similarity: ā—¼ Data generalization ā—¼ Presentation of data summarization at multiple levels of abstraction ā—¼ Interactive drilling, pivoting, slicing and dicing ā—¼ Differences: ā—¼ OLAP has systematic preprocessing, query independent, and can drill down to rather low level ā—¼ AOI has automated desired level allocation, and may perform dimension relevance analysis/ranking when there are many relevant dimensions ā—¼ AOI works on the data which are not in relational forms
  • 53. 53 Chapter 4: Data Warehousing and On-line Analytical Processing ā—¼ Data Warehouse: Basic Concepts ā—¼ Data Warehouse Modeling: Data Cube and OLAP ā—¼ Data Warehouse Design and Usage ā—¼ Data Warehouse Implementation ā—¼ Data Generalization by Attribute-Oriented Induction ā—¼ Summary
  • 54. 54 Summary ā—¼ Data warehousing: A multi-dimensional model of a data warehouse ā—¼ A data cube consists of dimensions & measures ā—¼ Star schema, snowflake schema, fact constellations ā—¼ OLAP operations: drilling, rolling, slicing, dicing and pivoting ā—¼ Data Warehouse Architecture, Design, and Usage ā—¼ Multi-tiered architecture ā—¼ Business analysis design framework ā—¼ Information processing, analytical processing, data mining, OLAM (Online Analytical Mining) ā—¼ Implementation: Efficient computation of data cubes ā—¼ Partial vs. full vs. no materialization ā—¼ Indexing OALP data: Bitmap index and join index ā—¼ OLAP query processing ā—¼ OLAP servers: ROLAP, MOLAP, HOLAP ā—¼ Data generalization: Attribute-oriented induction
  • 55. 55 References (I) ā—¼ S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDBā€™96 ā—¼ D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. SIGMODā€™97 ā—¼ R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDEā€™97 ā—¼ S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997 ā—¼ E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July 1993. ā—¼ 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. ā—¼ A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and Applications. MIT Press, 1999. ā—¼ J. Han. Towards on-line analytical mining in large databases. ACM SIGMOD Record, 27:97-107, 1998. ā—¼ V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. SIGMODā€™96 ā—¼ J. Hellerstein, P. Haas, and H. Wang. Online aggregation. SIGMOD'97
  • 56. 56 References (II) ā—¼ C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design: Relational and Dimensional Techniques. John Wiley, 2003 ā—¼ W. H. Inmon. Building the Data Warehouse. John Wiley, 1996 ā—¼ R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2ed. John Wiley, 2002 ā—¼ P. Oā€™Neil and G. Graefe. Multi-table joins through bitmapped join indices. SIGMOD Record, 24:8ā€“ 11, Sept. 1995. ā—¼ P. O'Neil and D. Quass. Improved query performance with variant indexes. SIGMOD'97 ā—¼ Microsoft. OLEDB for OLAP programmer's reference version 1.0. In http://www.microsoft.com/data/oledb/olap, 1998 ā—¼ S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. ICDE'94 ā—¼ A. Shoshani. OLAP and statistical databases: Similarities and differences. PODSā€™00. ā—¼ D. Srivastava, S. Dar, H. V. Jagadish, and A. V. Levy. Answering queries with aggregation using views. VLDB'96 ā—¼ P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987. ā—¼ J. Widom. Research problems in data warehousing. CIKMā€™95 ā—¼ K. Wu, E. Otoo, and A. Shoshani, Optimal Bitmap Indices with Efficient Compression, ACM Trans. on Database Systems (TODS), 31(1): 1-38, 2006
  • 58. 58 Compression of Bitmap Indices ā—¼ Bitmap indexes must be compressed to reduce I/O costs and minimize CPU usageā€”majority of the bits are 0ā€™s ā—¼ Two compression schemes: ā—¼ Byte-aligned Bitmap Code (BBC) ā—¼ Word-Aligned Hybrid (WAH) code ā—¼ Time and space required to operate on compressed bitmap is proportional to the total size of the bitmap ā—¼ Optimal on attributes of low cardinality as well as those of high cardinality. ā—¼ WAH out performs BBC by about a factor of two