Data Warehouse Usage Three kinds of data warehouse applications Information processing supports querying, basic statistical analysis, and reporting usingcrosstabs, 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 themining results using visualization tools. Differences among the three tasks54From On-Line Analytical Processingto 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 datawarehouses ODBC, OLEDB, Web accessing, service facilities, reporting andOLAP 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
Discovery-Driven Exploration of DataCubes Hypothesis-driven exploration by user, huge search space Discovery-driven (Sarawagi, et al.’98) Effective navigation of large OLAP data cubes pre-compute measures indicating exceptions, guideuser in the data analysis, at all levels of aggregation Exception: significantly different from the valueanticipated, based on a statistical model Visual cues such as background color are used toreflect the degree of exception of each cell57Kinds of Exceptions and their Computation Parameters SelfExp: surprise of cell relative to other cells at samelevel of aggregation InExp: surprise beneath the cell PathExp: surprise beneath cell for each drill-downpath Computation of exception indicator (modeling fitting andcomputing SelfExp, InExp, and PathExp values) can beoverlapped with cube construction Exception themselves can be stored, indexed andretrieved like precomputed aggregates58Examples: Discovery-Driven Data Cubes59Complex Aggregation at MultipleGranularities: Multi-Feature Cubes Multi-feature cubes (Ross, et al. 1998): Compute complex queriesinvolving multiple dependent aggregates at multiple granularities Ex. Grouping by all subsets of item, region, month, find themaximum price in 1997 for each group, and the total sales among allmaximum price tuplesselect item, region, month, max(price), sum(R.sales)from purchaseswhere year = 1997cube by item, region, month: Rsuch that R.price = max(price) Continuing the last example, among the max price tuples, find themin and max shelf live, and find the fraction of the total sales due totuple that have min shelf life within the set of all max price tuples60Cube-Gradient (Cubegrade) Analysis of changes of sophisticated measuresin multi-dimensional spaces Query: changes of average house price inVancouver in ‘00 comparing against ’99 Answer: Apts in West went down 20%,houses in Metrotown went up 10% Cubegrade problem by Imielinski et al. Changes in dimensions changes inmeasures Drill-down, roll-up, and mutation61From Cubegrade to Multi-dimensionalConstrained Gradients in Data Cubes Significantly more expressive than association rules Capture trends in user-specified measures Serious challenges Many trivial cells in a cube “significance constraint”to prune trivial cells Numerate pairs of cells “probe constraint” to selecta subset of cells to examine Only interesting changes wanted “gradientconstraint” to capture significant changes62MD Constrained Gradient Mining Significance constraint Csig: (cnt100) Probe constraint Cprb: (city=“Van”, cust_grp=“busi”,prod_grp=“*”) Gradient constraint Cgrad(cg, cp):(avg_price(cg)/avg_price(cp)1.3)
A LiveSet-Driven Algorithm Compute probe cells using Csig and Cprb The set of probe cells P is often very small Use probe P and constraints to find gradients Pushing selection deeply Set-oriented processing for probe cells Iceberg growing from low to high dimensionalities Dynamic pruning probe cells during growth Incorporating efficient iceberg cubing method64
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