Data Warehouses and OLAP Slides for Textbook Chapter
Data Warehouses and OLAP — Slides for Textbook — — Chapter 2 — ©Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http: //www. cs. sfu. ca Han: Dataware Houses and OLAP 1
What is Data Warehouse? n n n Defined in many different ways, but not rigorously. n A decision support database that is maintained separately from the organization’s operational database n Support information processing by providing a solid platform of consolidated, historical data for analysis. “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 Data warehousing: n The process of constructing and using data warehouses Han: Dataware Houses and OLAP 2
Data Warehouse—Subject-Oriented n Organized around major subjects, such as customer, product, sales. n Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing. n Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process. Han: Dataware Houses and OLAP 3
Data Warehouse—Integrated n n Constructed by integrating multiple, heterogeneous data sources n relational databases, flat files, on-line transaction records Data cleaning and data integration techniques are applied. n Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources n n E. g. , Hotel price: currency, tax, breakfast covered, etc. When data is moved to the warehouse, it is converted. Han: Dataware Houses and OLAP 4
Data Warehouse—Time Variant n The time horizon for the data warehouse is significantly longer than that of operational systems. n n n Operational database: current value data. Data warehouse data: provide information from a historical perspective (e. g. , past 5 -10 years) Every key structure in the data warehouse n n Contains an element of time, explicitly or implicitly But the key of operational data may or may not contain “time element”. Han: Dataware Houses and OLAP 5
Data Warehouse—Non-Volatile n A physically separate store of data transformed from the operational environment. n Operational update of data does not occur in the data warehouse environment. n Does not require transaction processing, recovery, and concurrency control mechanisms n Requires only two operations in data accessing: n initial loading of data and access of data. Han: Dataware Houses and OLAP 6
Data Warehouse vs. Heterogeneous DBMS n Traditional heterogeneous DB integration: n Build wrappers/mediators on top of heterogeneous databases n Query driven approach n n n 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 Complex information filtering, compete for resources Data warehouse: update-driven, high performance n Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis Han: Dataware Houses and OLAP 7
Data Warehouse vs. Operational DBMS n OLTP (on-line transaction processing) n n Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) n Major task of data warehouse system n Data analysis and decision making Distinct features (OLTP vs. OLAP): n User and system orientation: customer vs. market n Data contents: current, detailed vs. historical, consolidated n Database design: ER + application vs. star + subject n View: current, local vs. evolutionary, integrated n Access patterns: update vs. read-only but complex queries Han: Dataware Houses and OLAP 8
OLTP vs. OLAP Han: Dataware Houses and OLAP 9
Why Separate Data Warehouse? n n High performance for both systems n DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery n Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation. Different functions and different data: n missing data: Decision support requires historical data which operational DBs do not typically maintain n data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources n data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled Han: Dataware Houses and OLAP 10
From Tables and Spreadsheets to Data Cubes n n 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 n n n 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. Han: Dataware Houses and OLAP 11
OLAP Terminology n n A data cube supports viewing/modelling of a variable (a set of variables) of interest. Measures are used to report the values of the particular variable with respect to a given set of dimensions. A fact table stores measures as well as keys representing relationships to various dimensions. Dimensions are perspectives with respect to which an organization wants to keep record. A star schema defines a fact table and its associated dimensions. Han: Dataware Houses and OLAP 12
Cube: A Lattice of Cuboids all time, item 0 -D(apex) cuboid item time, location item, location time, supplier location, supplier item, supplier time, location, supplier time, item, location time, item, supplier 1 -D cuboids 2 -D cuboids 3 -D cuboids item, location, supplier 4 -D(base) cuboid time, item, location, supplier Han: Dataware Houses and OLAP 13
Conceptual Modeling of Data Warehouses n Modeling data warehouses: dimensions & measures n Star schema: A fact table in the middle connected to a set of dimension tables n 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 n Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation Han: Dataware Houses and OLAP 14
Example of Star Schema time item time_key day_of_the_week month quarter year Sales Fact Table time_key item_key branch_key branch_name branch_type location_key units_sold dollars_sold avg_sales item_key item_name brand type supplier_type location_key street city province_or_street country Measures Han: Dataware Houses and OLAP 15
Example of Fact Constellation time_key day_of_the_week month quarter year item Sales Fact Table time_key item_key item_name brand type supplier_type location_key branch_name branch_type units_sold dollars_sold avg_sales Measures Han: Dataware Houses and OLAP time_key item_key shipper_key from_location branch_key branch Shipping Fact Table location to_location_key street city province_or_street country dollars_cost units_shipped shipper_key shipper_name location_key shipper_type 16
A Data Mining Query Language, DMQL: Language Primitives n n n Cube Definition (Fact Table) define cube <cube_name> [<dimension_list>]: <measure_list> Dimension Definition ( Dimension Table ) define dimension <dimension_name> as (<attribute_or_subdimension_list>) Special Case (Shared Dimension Tables) n First time as “cube definition” n define dimension <dimension_name> as <dimension_name_first_time> in cube <cube_name_first_time> Han: Dataware Houses and OLAP 17
Defining a Star Schema in DMQL define cube sales_star [time, item, branch, location]: dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*) define dimension time as (time_key, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier_type) define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city, province_or_state, country) Han: Dataware Houses and OLAP 18
Defining a Fact Constellation in DMQL define cube sales [time, item, branch, location]: dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*) define dimension time as (time_key, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier_type) define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city, province_or_state, country) define cube shipping [time, item, shipper, from_location, to_location]: dollar_cost = sum(cost_in_dollars), unit_shipped = count(*) define dimension time as time in cube sales define dimension item as item in cube sales define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type) define dimension from_location as location in cube sales define dimension to_location as location in cube sales Han: Dataware Houses and OLAP 19
Measures: Three Categories n 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. n n 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. n n E. g. , count(), sum(), min(), max(). E. g. , avg(), min_N(), standard_deviation(). holistic: if there is no constant bound on the storage size needed to describe a subaggregate. n E. g. , median(), mode(), rank(). Han: Dataware Houses and OLAP 20
A Concept Hierarchy: Dimension (location) all Europe region country city Germany Frankfurt office Han: Dataware Houses and OLAP . . Spain North_America Canada Vancouver. . . L. Chan . . . Mexico Toronto M. Wind 21
View of Warehouses and Hierarchies Specification of hierarchies n Schema hierarchy day < {month < quarter; week} < year n Set_grouping hierarchy {1. . 10} < inexpensive Han: Dataware Houses and OLAP 22
Multidimensional Data Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths gi on n Re Industry Region Year Product Category Country Quarter Product City Office Month Week Day Month Han: Dataware Houses and OLAP 23
Pr od TV PC VCR sum 1 Qtr 2 Qtr Date 3 Qtr 4 Qtr Total annual sales sum of TV in U. S. A Canada Mexico Country uc t A Sample Data Cube sum Han: Dataware Houses and OLAP 24
Cuboids Corresponding to the Cube all 0 -D(apex) cuboid product, date country product, country 1 -D cuboids date, country 2 -D cuboids product, date, country Han: Dataware Houses and OLAP 3 -D(base) cuboid 25
Browsing a Data Cube n n n Han: Dataware Houses and OLAP Visualization OLAP capabilities Interactive manipulation 26
Typical OLAP Operations n Roll up (drill-up): summarize data n n Drill down (roll down): reverse of roll-up n n project and select Pivot (rotate): n n from higher level summary to lower level summary or detailed data, or introducing new dimensions Slice and dice: n n by climbing up hierarchy or by dimension reduction reorient the cube, visualization, 3 D to series of 2 D planes. Other operations n n drill across: involving (across) more than one fact table drill through: through the bottom level of the cube to its backend relational tables (using SQL) Han: Dataware Houses and OLAP 27
A Star-Net Query Model Customer Orders Shipping Method Customer CONTRACTS AIR-EXPRESS ORDER TRUCK Time PRODUCT LINE ANNUALY QTRLY DAILY CITY Product PRODUCT ITEM PRODUCT GROUP SALES PERSON COUNTRY DISTRICT REGION Location Each circle is called a footprint Han: Dataware Houses and OLAP DIVISION Promotion Organization 28
Three Data Warehouse Models n n Enterprise warehouse n collects all of the information about subjects spanning the entire organization Data Mart n 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 n n Independent vs. dependent (directly from warehouse) data mart Virtual warehouse n A set of views over operational databases n Only some of the possible summary views may be materialized Han: Dataware Houses and OLAP 29
Data Warehouse Development: A Recommended Approach Multi-Tier Data Warehouse Distributed Data Marts Data Mart Model refinement Enterprise Data Warehouse Model refinement Define a high-level corporate data model Han: Dataware Houses and OLAP 30
OLAP Server Architectures n n Relational OLAP (ROLAP) n Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces n Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services n greater scalability Multidimensional OLAP (MOLAP) n Array-based multidimensional storage engine (sparse matrix techniques) n fast indexing to pre-computed summarized data Hybrid OLAP (HOLAP) n User flexibility, e. g. , low level: relational, high-level: array Specialized SQL servers n specialized support for SQL queries over star/snowflake schemas Han: Dataware Houses and OLAP 31
Efficient Data Cube Computation n Data cube can be viewed as a lattice of cuboids n The bottom-most cuboid is the base cuboid n The top-most cuboid (apex) contains only one cell n n How many cuboids in an n-dimensional cube with L levels? Materialization of data cube n n Materialize every (cuboid) (full materialization), none (no materialization), or some (partial materialization) Selection of which cuboids to materialize n Based on size, sharing, access frequency, etc. Han: Dataware Houses and OLAP 32
Cube Operation n Cube definition and computation in DMQL define cube sales[item, city, year]: sum(sales_in_dollars) compute cube sales n 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 n CUBE BY item, city, year Need compute the following Group-Bys (city) () (item) (year) (date, product, customer), (city, item) (city, year) (item, year) (date, product), (date, customer), (product, customer), (date), (product), (customer) () (city, item, year) Han: Dataware Houses and OLAP 33
Cube Computation: ROLAP-Based Method n Efficient cube computation methods n n ROLAP-based cubing algorithms (Agarwal et al’ 96) Array-based cubing algorithm (Zhao et al’ 97) Bottom-up computation method (Bayer & Ramarkrishnan’ 99) ROLAP-based cubing algorithms n n n Sorting, hashing, and grouping operations are applied to the dimension attributes in order to reorder and cluster related tuples Grouping is performed on some subaggregates as a “partial grouping step” Aggregates may be computed from previously computed aggregates, rather than from the base fact table Han: Dataware Houses and OLAP 34
Views and Decision Support n n OLAP queries are typically aggregate queries. n Precomputation is essential for interactive response times. n The CUBE is in fact a collection of aggregate queries, and precomputation is especially important: lots of work on what is best to precompute given a limited amount of space to store precomputed results. Warehouses can be thought of as a collection of asynchronously replicated tables and periodically maintained views. n Has renewed interest in view maintenance!
Query Modification (Evaluate On Demand) View CREATE VIEW Regional. Sales(category, sales, state) AS SELECT P. category, S. sales, L. state FROM Products P, Sales S, Locations L WHERE P. pid=S. pid AND S. locid=L. locid Query SELECT R. category, R. state, SUM(R. sales) FROM Regional. Sales AS R GROUP BY R. category, Modified Query R. state SELECT R. category, R. state, SUM(R. sales) FROM (SELECT P. category, S. sales, L. state FROM Products P, Sales S, Locations L WHERE P. pid=S. pid AND S. locid=L. locid) AS GROUP BY R. category, R. state R
View Materialization (Precomputation) n Suppose we precompute Regional. Sales and store it with a clustered B+ tree index on [category, state, sales]. n Then, previous query can be answered by an index-only scan. SELECT R. state, SUM(R. sales) FROM Regional. Sales R WHERE R. category=“Laptop” GROUP BY R. state SELECT R. state, SUM(R. sales) FROM Regional. Sales R WHERE R. state=“Wisconsin” GROUP BY R. category Index on precomputed view is great! Index is less useful (must scan entire leaf level).
Issues in View Materialization n What views should we materialize, and what indexes should we build on the precomputed results? Given a query and a set of materialized views, can we use the materialized views to answer the query? How frequently should we refresh materialized views to make them consistent with the underlying tables? (And how can we do this incrementally? )
Top N Queries SELECT P. pid, P. pname, S. sales FROM Sales S, Products P WHERE S. pid=P. pid AND S. locid=1 AND ORDER BY S. sales DESC OPTIMIZE FOR 10 ROWS SELECT P. pid, P. pname, S. sales FROM Sales S, Products P WHERE S. pid=P. pid AND S. locid=1 AND S. timeid=3 AND S. sales > c ORDER BY S. sales DESC n n construct is not in SQL: 1999! Cut-off value c is chosen by optimizer. OPTIMIZE FOR
Indexing OLAP Data: Bitmap Index n n n Index on a particular column Each value in the column has a bit vector: bit-op is fast The length of the bit vector: # of records in the base table The i-th bit is set if the i-th row of the base table has the value for the indexed column not suitable for high cardinality domains Base table Han: Dataware Houses and OLAP Index on Region Index on Type 40
Indexing OLAP Data: Join Indices n n n Join index: JI(R-id, S-id) where R (R-id, …) S (S-id, …) Traditional indices map the values to a list of record ids n It materializes relational join in JI file and speeds up relational join — a rather costly operation In data warehouses, join index relates the values of the dimensions of a start schema to rows in the fact table. n E. g. fact table: Sales and two dimensions city and product n A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city n Join indices can span multiple dimensions Han: Dataware Houses and OLAP 41
Discovery-Driven Exploration of Data Cubes n Hypothesis-driven: exploration by user, huge search space n Discovery-driven (Sarawagi et al. ’ 98) n pre-compute measures indicating exceptions, guide user in the data analysis, at all levels of aggregation n Exception: significantly different from the value anticipated, based on a statistical model n Visual cues such as background color are used to reflect the degree of exception of each cell n Computation of exception indicator (modeling fitting and computing Self. Exp, In. Exp, and Path. Exp values) can be overlapped with cube construction Han: Dataware Houses and OLAP 42
Examples: Discovery-Driven Data Cubes Han: Dataware Houses and OLAP 43
Data Warehouse Usage n Three kinds of data warehouse applications n Information processing n n n supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs Analytical processing and Interactive Analysis n multidimensional analysis of data warehouse data n supports basic OLAP operations, slice-dice, drilling, pivoting Data mining n n knowledge discovery from hidden patterns supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools. Differences among the three tasks n Han: Dataware Houses and OLAP 44
From On-Line Analytical Processing to On Line Analytical Mining (OLAM) n Why online analytical mining? n n n High quality of data in data warehouses n DW contains integrated, consistent, cleaned data Available information processing structure surrounding data warehouses n ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools OLAP-based exploratory data analysis n mining with drilling, dicing, pivoting, etc. On-line selection of data mining functions n integration and swapping of multiple mining functions, algorithms, and tasks. Architecture of OLAM Han: Dataware Houses and OLAP 45
An OLAM Architecture Mining query Mining result Layer 4 User Interface User GUI API OLAM Engine OLAP Engine Layer 3 OLAP/OLAM Data Cube API Layer 2 MDDB Meta Data Filtering&Integration Database API Filtering Layer 1 Databases Han: Dataware Houses and OLAP Data cleaning Data integration Warehouse Data Repository 46
Summary n Data warehouse n n n A multi-dimensional model of a data warehouse n Star schema, snowflake schema, fact constellations n A data cube consists of dimensions & measures OLAP operations: drilling, rolling, slicing, dicing and pivoting OLAP servers: ROLAP, MOLAP, HOLAP Efficient computation of data cubes n n A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process Partial vs. full vs. no materialization Multiway array aggregation Bitmap index and join index implementations Further development of data cube technology n n Discovery-drive and multi-feature cubes From OLAP to OLAM (on-line analytical mining) Han: Dataware Houses and OLAP 47
References (I) n n n n S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. In Proc. 1996 Int. Conf. Very Large Data Bases, 506 -521, Bombay, India, Sept. 1996. D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 417 -427, Tucson, Arizona, May 1997. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data, 94 -105, Seattle, Washington, June 1998. R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. In Proc. 1997 Int. Conf. Data Engineering, 232 -243, Birmingham, England, April 1997. K. Beyer and R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs. In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'99), 359 -370, Philadelphia, PA, June 1999. S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26: 65 -74, 1997. OLAP council. MDAPI specification version 2. 0. In http: //www. olapcouncil. org/research/apily. htm, 1998. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab and subtotals. Data Mining and Knowledge Discovery, 1: 29 -54, 1997. Han: Dataware Houses and OLAP 48
References (II) n n n n V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. In Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data, pages 205 -216, Montreal, Canada, June 1996. Microsoft. OLEDB for OLAP programmer's reference version 1. 0. In http: //www. microsoft. com/data/oledb/olap, 1998. K. Ross and D. Srivastava. Fast computation of sparse datacubes. In Proc. 1997 Int. Conf. Very Large Data Bases, 116 -125, Athens, Greece, Aug. 1997. K. A. Ross, D. Srivastava, and D. Chatziantoniou. Complex aggregation at multiple granularities. In Proc. Int. Conf. of Extending Database Technology (EDBT'98), 263 -277, Valencia, Spain, March 1998. S. Sarawagi, R. Agrawal, and N. Megiddo. Discovery-driven exploration of OLAP data cubes. In Proc. Int. Conf. of Extending Database Technology (EDBT'98), pages 168 -182, Valencia, Spain, March 1998. E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley & Sons, 1997. Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for simultaneous multidimensional aggregates. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 159 -170, Tucson, Arizona, May 1997. Han: Dataware Houses and OLAP 49
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