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ITS423: Data Warehouses and Data Mining! Topics ! What Lecture 2 Data Warehouses & OLAP Technology Virach Sornlertlamvanich Thanaruk Theeramunkong Sirindhorn International Institute of Technology Thammasat University ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 1! * What is Data warehouse ? What is Data Warehouse? ! ! ! Defined in many different ways, but not rigorously. " A decision support database that is maintained separately from the organization’s operational DB " 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.” by W. H. Inmon Data warehousing: " Process of constructing and using data warehouses ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 3! is a data warehouse? ! A multi-dimensional data model ! Data warehouse architecture ! Data warehouse implementation ! Further development of data cube technology ! From data warehousing to data mining ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 2! Data Warehouse - Subject-Oriented ! ! ! Organized around major subjects, such as customer, product, sales. Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing. Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process. ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 4! 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 in naming conventions, encoding structures, attribute measures, etc. among different data sources ! " ! " " ! When data is moved to the warehouse, it is converted. Data Warehouses and OLAP! " 5! Data Warehouse - Non-Volatile A physically separate store of data transformed from the operational environment. ! Operational update of data does not occur in the data warehouse environment. Does not require transaction processing, recovery, and concurrency control mechanisms " Requires only two operations in data accessing: ! initial loading of data and access of data. " Data Warehouses and OLAP! Contains an element of time, explicitly or implicitly But the key of operational data may or may not contain “time element”. ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 6! Data Warehouse Process ! ITS423: Data Warehouses and Data Mining! 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 " e.g., Hotel price: currency, tax, breakfast covered, etc. ITS423: Data Warehouses and Data Mining! The time horizon for the data warehouse is significantly longer than that of operational systems. 7! ETL Extract, Transform, & Loading ! Data Warehouse - Time Variant ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 8! Data Warehouse vs. Heterogeneous DBMS ! Traditional heterogeneous DB integration: " Build wrappers/mediators on top of heterogeneous DBs " 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 Complex information filtering, compete for resources Data Warehouse vs. Operational DBMS ! OLTP (on-line transaction processing) " " ! Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) " " Major task of data warehouse system Data analysis and decision making Data warehouse: update-driven, high performance " Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 9! Data Warehouse vs. Operational DBMS ! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 10! Data Warehouses and OLAP! 12! OLTP vs. OLAP Distinct features (OLTP vs. OLAP): " " " " " User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 11! ITS423: Data Warehouses and Data Mining! OLTP vs. OLAP Why Separate Data Warehouse? ! High performance for both systems " " ! Different functions and different data: " " " http://datawarehouse4u.info//OLTP-vs-OLAP.html ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 13! From Tables and Spreadsheets to * A multi-dimensional data model 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 " " ! DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation. missing data: Decision support requires historical data which operational DBs do not typically maintain data consolidation: Decision support requires consolidation (aggregation, summarization) of data from heterogeneous sources data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled ITS423: Data Warehouses and Data Mining! Cube: A Lattice of Cuboids Number of Cuboid = 2 n all time item D (apex) cuboid location time,location item,location time,supplier 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. supplier D cuboids time,item 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 14! Data Warehouses and OLAP! location,supplier item,supplier D cuboids time,location,supplier time,item,location D cuboids time,item,supplier item,location,supplier D(base) cuboid time, item, location, supplier ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 15! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 16! An Example of Cuboids " ! all: SaleAmt(all) # Only one value 1-D cuboid " " " " ! time, item, location, supplier D (apex) cuboid ! ! SaleAmt(December, all, all, all) time: SaleAmt(Jan), SaleAmt(Feb), …, SaleAmt(Dec) item: SaleAmt(milk), SaleAmt(shirt) location: SaleAmt(BKK), SaleAmt(London), SaleAmt(New York) supplier: SaleAmt(company X), SaleAmt(shop Y) 2-D cuboid " " " (time, item): SaleAmt(January, milk), SaleAmt(February,shirt) (item,location): SaleAmt(milk,BKK), SaleAmt(shirt,London) … ITS423: Data Warehouses and Data Mining! 17! Data Warehouses and OLAP! Example of Star Schema time dimension table time_key day day_of_the_week month quarter year branch dimension table branch_key branch_name branch_type Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales 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 ITS423: Data Warehouses and Data Mining! item dimention table time dimension table item_key item_name brand type supplier_type time_key day day_of_the_week month quarter year location dimension table branch dimension table location_key street city province_or_street country branch_key branch_name branch_type item dimension table Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold item_key item_name brand type supplier_key location_key street city_key avg_sales 19! ITS423: Data Warehouses and Data Mining! supplier dimension table supplier_key supplier_type location dimension table Measures Data Warehouses and OLAP! 18! Data Warehouses and OLAP! Example of Snowflake Schema Measures ITS423: Data Warehouses and Data Mining! Conceptual Modeling of Data Warehouses Data Warehouses and OLAP! city dimension table city_key city province_or_street country 20! A Data Mining Query Language, DMQL: Language Primitives Example of Fact Constellation time dimension table time_key day day_of_the_week month quarter year branch dimension table branch_key branch_name branch_type Sales Fact Table time_key item_key item dimension table item_key item_name brand type supplier_type branch_key location_key units_sold dollars_sold avg_sales Measures ITS423: Data Warehouses and Data Mining! location dimension table location_key street city province_or_street country Shipping Fact Table time_key ! Cube Definition (Fact Table) define cube <cube_name> [<dim_list>]: <measure_list> item_key shipper_key ! from_location Dimension Definition (Dimension Table) define dimension <dimension_name> as <attr_or_subdim_list>) to_location ! dollars_cost Special Case (Shared Dimension Tables) First time as “cube definition” units_shipped define dimension <dimension_name> as <dimension_name_first_time> in cube<cube_name_first_time> shipper shipper_key shipper_name location_key shipper_type Data Warehouses and OLAP! 21! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! Defining a Star Schema in DMQL Defining a Snowflake 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, 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 sales_snowflake [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, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier(supplier_key, supplier_type)) define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city(city_key, province_or_state, country)) ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 23! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 22! 24! Defining a Fact Constellation in DMQL Measures: Three Categories 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, 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_loc, to_loc]: dollar_cost = sum(cost_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 ITS423: Data Warehouses and Data Mining! ! ! ! 25! Data Warehouses and OLAP! ITS423: Data Warehouses and Data Mining! A Concept Hierarchy: Dimension (location) all country city Europe Germany Frankfurt ... ... all ... Spain North_America Canada Vancouver ... ... all sum = 2100 sum = 1400 Europe region Mexico sum = 900 country Germany ITS423: Data Warehouses and Data Mining! L. Chan sum = 700 North_America sum = 500 Spain sum = 600 sum = 100 Canada Mexico city Frankfurt Berlin Aechen Segovia Madrid Vancouver Toronto Toronto 400 office 200 300 400 100 ... M. Wind Data Warehouses and OLAP! 26! Data Warehouses and OLAP! A Concept Hierarchy: Dimension (Distributive - count(), sum(), min(), max().) all region 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. ! e.g., count(), sum(), min(), max(). 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. ! e.g., avg(), min_N(), standard_deviation(). Holistic: if there is no constant bound on the storage size needed to describe a subaggregate. ! e.g., median(), mode(), rank(). 27! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 200 400 Mexico 100 28! A Concept Hierarchy: Dimension (Algebraic - avg(), min_N(), standard_deviation()) all all avg = 262.5 avg = 300 country Germany region avg = 300 avg = 100 Canada Mexico median = 300 country Germany count=2 count=2 200 ITS423: Data Warehouses and Data Mining! 300 400 100 Data Warehouses and OLAP! 200 ! ! ITS423: Data Warehouses and Data Mining! median = 200 North_America median = 250 median = 300 Spain Canada Mexico median = 100 city Frankfurt Berlin Aechen Segovia Madrid Vancouver Toronto 400 400 Mexico 100 29! View of Warehouses and Hierarchies ! median = 300 Europe count=1 city Frankfurt Berlin Aechen Segovia Madrid Vancouver Toronto 400 all median = 250 avg = 233.33 North_America count=3 avg = 250 Spain count=3 all count=8 avg = 280 count=5 Europe region A Concept Hierarchy: Dimension (Holistic - median(), mode(), rank()) 200 ITS423: Data Warehouses and Data Mining! 300 400 100 200 Data Warehouses and OLAP! 400 Mexico 100 30! An Example of OLAP tools Specification of hierarchies Schema hierarchy " day < {month < quarter; week} < year Set_grouping hierarchy " {1..10} < inexpensive Data Warehouses and OLAP! 31! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 32! A Sample Data Cube ! Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Product Industry Region TV PC VCR sum 1Qtr 2Qtr Date 3Qtr 4Qtr sum Total annual sales of TV in U.S.A. U.S.A Canada Year Mexico Country Multidimensional Data Category Country Quarter Product City Office sum Month Week Day Month ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 33! Browsing a Data Cube ! ! ! ITS423: Data Warehouses and Data Mining! ! Roll up (drill-up): summarize data " ! ! " reorient the cube, visualization, 3D to series of 2D planes. Other operations " " 35! project and select Slice (select on 1 dim.), dice (select on 2 or more dim.) Pivot (rotate): " ! from higher level summary to lower level summary or detailed data, or introducing new dimensions Slice and dice: " ! by climbing up hierarchy or by dimension reduction Drill down (roll down): reverse of roll-up " Data Warehouses and OLAP! 34! Typical OLAP Operations Visualization OLAP capabilities Interactive manipulation ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 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) ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 36! OLAP Operations on Multidimensional Data ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 37! A Star-Net Query Model A Star-Net Query Model ! The querying of multidimensional databases can be based on a starnet model, which consists of radial lines emanating from a central point, where each line represents a concept hierarchy for a dimension. Each abstraction level in the hierarchy is called a footprint. These represent the granularities available for use by OLAP operations such as drill-down and roll-up. ITS423: Data Warehouses and Data Mining! 38! Data Warehouses and OLAP! Each circle is called a footprint A Star-Net Query Model Customer Orders Shipping Method Customer Sex CONTRACTS AIR-EXPRESS MALE/FEMALE ORDER TRUCK Time PRODUCT LINE ANNUALY QTRLY DAILY PRODUCT ITEM PRODUCT GROUP Product CITY SALES PERSON COUNTRY DISTRICT REGION Location ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 39! ITS423: Data Warehouses and Data Mining! DIVISION Promotion Data Warehouses and OLAP! Organization 40! Design of a Data Warehouse: A Business Analysis Framework ! Data Warehouse Design Process ! Four views regarding the design of a data warehouse " " " " " Top-down view ! allows selection of the relevant information necessary for the data warehouse Data source view ! exposes the information being captured, stored, and managed by operational systems Data warehouse view ! consists of fact tables and dimension tables Business query view ! sees the perspectives of data in the warehouse from the view of end-user * Data Warehouse Architecture ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! Top-down, bottom-up approaches or a combination of both 41! Three-tier Data Warehousing architecture " ! From software engineering point of view " " ! Top-down: Starts with overall design and planning (mature) Bottom-up: Starts with experiments and prototypes (rapid) Waterfall: structured and systematic analysis at each step before proceeding to the next Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around Typical data warehouse design process " " " " Choose a business process to model, e.g., orders, invoices, etc. Choose the grain (atomic level of data) of the business process Choose the dimensions that will apply to each fact table record Choose the measure that will populate each fact table record ITS423: Data Warehouses and Data Mining! ! ! ! Data Warehouses and OLAP! 44! 42! 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 ! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! Independent vs. dependent (directly from warehouse) data mart Virtual Warehouse " A set of views over operational databases " Only some of the possible summary views may be materialized " Easy to build but require excess capacity on operational DB server. ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 45! DW Development: A Recommended Approach OLAP Server Architectures ! Multi-Tier Data Warehouse " (include OLAP Server) Distributed Data Marts " " ! Data Mart Relational OLAP (ROLAP) Multidimensional OLAP (MOLAP) " Enterprise Data Warehouse Data Mart " Model refinement Hybrid OLAP (HOLAP) ! Specialized SQL servers " Define a high-level corporate data model ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 46! Efficient Data Cube Computation ! " " User flexibility, e.g., low level: relational, high-level: array specialized support for SQL queries over star/snowflake schemas in read-only environment. ITS423: Data Warehouses and Data Mining! ! 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? Cube definition and computation in DMQL define cube sales[item, city, year]: sum(sales_in_dollars) compute cube sales --> compute all of 8 subsets of the set {item, city, year} ! 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 ! " 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. * Data warehouse implementation ITS423: Data Warehouses and Data Mining! () (city) Materialization of data cube " Data Warehouses and OLAP! 47! Data Warehouses and OLAP! Cube Operation Data cube can be viewed as a lattice of cuboids " Array-based multidimensional storage engine(sparse matrix techniques) fast indexing to pre-computed summarized data ! " Model refinement Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools/services greater scalability, we may keep summary in relational DB 48! (item) (year) Need compute the following Group-Bys (year, item, city), (city, item) (year,item), (year, city), (item, city), (year), (item), (city) () ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! (city, year) (item, year) (city, item, year) 49! Multi-way Array Aggregation for Cube Computation ! ! ! Multi-way Array Aggregation for Cube Computation Partition arrays into chunks (a small subcube which fits in memory). Compressed sparse array addressing: (chunk_id, offset) Compute aggregates in “multiway” by visiting cube cells in the order which minimizes the # of times to visit each cell, and reduces memory access and storage cost. C C c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 B b3 B13 b2 9 b1 5 b0 14 15 60 44 28 56 40 24 52 36 20 16 1 2 3 4 a0 a1 a2 a3 B What is the best traversing order to do multi-way aggregation? c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 B13 60 14 15 16 b3 44 28 56 b2 9 40 24 52 b1 5 36 20 2 3 4 b0 1 a0 a1 a2 a3 A The size of the dimensions A, B, and C is 6000, 1000, and 50000. A ITS423: Data Warehouses and Data Mining! 50! Data Warehouses and OLAP! Multi-way Array Aggregation for Cube Computation ITS423: Data Warehouses and Data Mining! ! B b3 b2 9 14 15 16 28 24 b1 5 b0 1 2 3 4 a0 a1 a2 a3 20 44 40 36 Data Warehouses and OLAP! Method: the planes should be sorted and computed according to their size in ascending order. " 60 56 ! 52 52! Idea: keep the smallest plane in the main memory, fetch and compute only one chunk at a time for the largest plane Limitation of the method: computing well only for a small number of dimensions " A ITS423: Data Warehouses and Data Mining! 51! Multi-Way Array Aggregation for Cube Computation (Cont.) 62 63 64 C c2 c3 61 45 46 47 48 c1 29 30 31 32 c0 B13 Data Warehouses and OLAP! If there are a large number of dimensions, “bottom-up computation” and iceberg cube computation methods can be explored. Iceberg cubes store only cube partitions where the aggregate value is above some min. support. ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 53! Multi-Way Array Aggregation (An Example) Suppose that a base cuboid has three dimensions A, B, C with the following numbers of cells: |A| = 6,000, |B| = 1,000, |C| = 50,000. Suppose that the dimensions A, B and C are partitioned into 6, 5 and 1000 portions for chunking, respectively. " If each cube cell stores one measure with 4 bytes, what is the total size of the computed cube if the cube is very dense? That is, calculate the size of a base cuboid. ! The number of cells in the computed cube is ! " ! The total size of the computed cube is " " 6 x 103 x 103 x 5 x 104 = 3x1011 cells. 4 x 3 x 1011 = 1.2x1012 bytes. State the order for computing the chunks in the cube that requires the least amount of space, and compute the total amount of main memory space required for computing the 2-D planes. ITS423: Data Warehouses and Data Mining! 54! Data Warehouses and OLAP! ! ! ! ! Index on Region ITS423: Data Warehouses and Data Mining! ! Index on a particular column Each value in the column has a bit vector: bit-op is fast 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 This technique is not suitable for high cardinality domains Base table One chunk includes A: 6000/6 = 1000 B: 1000/5 = 200 C: 50000/1000 = 50 The space we need for computing the cube is (1) All elements of plane AB: = 6000x1000 cells = 6000000 cells = 24000000 bytes (2) One row of plane BC: = 1000x50 cells = 50000 cells = 200000 bytes A B (3) One chunk of plane AC: = 1000x50 cells = 50000 cells = 200000 bytes (4) One cube of ABC: = 1000x200x50 cells = 10000000 cells = 40000000 bytes Total memory for keeping the result = 64400000 bytes = OLAP! 6.44x107 bytes. 55! Data Warehouses and Indexing OLAP Data: Join Indices Indexing OLAP Data: Bitmap Index ! Multi-Way Array Aggregation for Cube Computation (Cont.) C ! ! Index on Type Join index: JI(R-id, S-id) where R (Rid, …) joins S (S-id, …) Traditional indices map the values to a list of record ids " 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 star schema to rows in the fact table. E.g. fact table: Sales and two dimensions city and product A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city Join indices can span multiple dimensions ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 56! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 57! Efficient Processing OLAP Queries ! Determine which operations should be performed on the available cuboids: " ! ! ! Metadata Repository (1) ! " Determine to which materialized cuboid(s) the relevant operations should be applied. Exploring indexing structures and compressed vs. dense array structures in MOLAP Example 2.15 Data Warehouses and OLAP! 58! Meta data is the data defining warehouse objects. It has the following kinds " ! Data extraction: ! Data cleaning: " The algorithms used for summarization " The mapping from operational environment to the data warehouse " Data related to system performance " ! ITS423: Data Warehouses and Data Mining! " ! warehouse schema, view and derived data definitions ! Data Warehouses and OLAP! detect errors in the data and rectify them when possible convert data from legacy or host format to warehouse format sort, summarize, consolidate, compute views, check integrity, and build indices and partitions Refresh " 60! get data from multiple, heterogeneous, and external sources Load: " ! business terms and definitions, ownership of data, charging policies ITS423: Data Warehouses and Data Mining! 59! Data transformation: " Business data ! Data Warehouses and OLAP! Data Warehouse Back-End Tools and Utilities Metadata Repository (2) ! Description of the structure of the warehouse ! schema, view, dimensions, hierarchies, derived data definition, data mart locations and contents " Operational meta-data ! data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails) transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g, dice = selection + projection ITS423: Data Warehouses and Data Mining! Meta data is the data defining warehouse objects. It has the following kinds propagate the updates from the data sources to the warehouse ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 61! Discovery-Driven Exploration of Data Cubes Discovery-Driven Data Cubes (Ex.) * Further development of data cube technology ! ! Hypothesis-driven: exploration by user, huge search space Discovery-driven (Sarawagi et al.’98) " " " " Pre-compute measures indicating exceptions, guide user in the data analysis, at all levels of aggregation Exception: significantly different from the value anticipated, based on a statistical model Visual cues such as background color are used to reflect the degree of exception of each cell Computation of exception indicator (modeling fitting and computing SelfExp, InExp, and PathExp values) can be overlapped with cube construction ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 62! * From data warehousing to data mining 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 Data mining ! knowledge discovery from hidden patterns ! supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools. ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 64! ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 63! From 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, OLEDB, 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. Architecture of OLAM ITS423: Data Warehouses and Data Mining! Data Warehouses and OLAP! 65! An OLAM Architecture (Online Analytical Mining) Mining query Mining result Layer4 User Interface User GUI API OLAM Engine Layer3 OLAP Engine OLAP/OLAM Data Cube API Layer2 MDDB MDDB Meta Data Filtering&Integration Database API Multidimensional DB Filtering Layer1 Databases ITS423: Data Warehouses and Data Mining! Data cleaning Data integration Data Warehouse Data Warehouses and OLAP! Data Repository 66!