FedDW Global Schema Architect
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1 UML based Design Tool for the Integration of Data Mart Schemas Dr. Stefan Berger Department of Business Informatics Data & Knowledge Engineering Johannes Kepler University Linz ACM 15 th DOLAP 12 November 2, 2012
2 Outline 1 FedDW Approach 2 Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
3 1 FedDW Approach General overview of FedDW Integrating heterogeneous multidimensional schemata 2 Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
4 General overview of FedDW Problem definition; our contribution Problem: similar autonomous data marts/dws, but heterogeneous schemata and/or data Business collaboration Mergers and acquisitions Preexisting DW data across autonomous organizations Contribution: comprehensive tool suite for integration of autonomous data marts/dws Visual integration of multidimensional schemas OLAP front-end prototype, based on SQL-MDi [Berger and Schrefl, 2006] Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
5 General overview of FedDW Problem definition; our contribution Problem: similar autonomous data marts/dws, but heterogeneous schemata and/or data Business collaboration Mergers and acquisitions Preexisting DW data across autonomous organizations Contribution: comprehensive tool suite for integration of autonomous data marts/dws Visual integration of multidimensional schemas OLAP front-end prototype, based on SQL-MDi [Berger and Schrefl, 2006] Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
6 General overview of FedDW Motivating example Telecommunications sector sample, heterogeneous conceptual data mart schemas: year month date date/hr red blue date month quarter year date products product prod_name regular_fee connections duration tn_tel tn_misc customer customer p_name contract_type base_fee category products product prod_name connections dur_min turnover promotion promo promo_type dates customers customer cust_name age_grp contract_type base_fee Dimensionality (extra dimensionblue.promotion) Hierarchy ofdate dimensions Decorations of product dimensions Measures of connections facts Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
7 General overview of FedDW Motivating example Telecommunications sector sample, heterogeneous conceptual data mart schemas: year month date date/hr red blue date month quarter year date products product prod_name regular_fee connections duration tn_tel tn_misc customer customer p_name contract_type base_fee category products product prod_name connections dur_min turnover promotion promo promo_type dates customers customer cust_name age_grp contract_type base_fee Dimensionality (extra dimensionblue.promotion) Hierarchy ofdate dimensions Decorations of product dimensions Measures of connections facts Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
8 Integrating heterogeneous multidimensional schemata Conflict classification I Modeling Scope Ins stance Schema Dimension Instance ( Members ) Conflicts Dimension Schema Conflicts Schema- Instance Conflicts Cube Instance ( Cells ) Conflicts Cube Schema Conflicts Model Entity Dimension Cube Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
9 Integrating heterogeneous multidimensional schemata Conflict classification II Facts: conflicts Relevant operator of FedDW Schema-instance Dimensionality Different measures Merge measures: PIVOT MEASURES (Fact) Split measures: PIVOT SPLIT MEASURES (Fact) Choose attributes: add DIM reference (Cube) Choose measures: add MEASURE reference (Cube) Domain (measures) Convert domain: CONVERT MEASURES APPLY... (Measure) Naming of attributes Rename attributes: operator >... (Measure, Dimension) Base levels Roll-up dimension attributes: ROLLUP TO LEVEL... (Dimension) Cube cells (fact extensions) Join cubes: MERGE CUBES (n-ary) Derive measure values: AGGREGATE MEASURE (n-ary) Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
10 Integrating heterogeneous multidimensional schemata Conflict classification III Dimensions: conflicts Relevant operator of FedDW Hierarchies Domain (levels / decorations) Naming (levels) Naming (decorations) Members (dim. extensions) Roll-up functions Decoration values Map corresponding levels: add level reference [...] (Dimension) Convert domain: CONVERT ATTRIBUTES APPLY... (Dimension) Rename attributes: operator >... (Level) Map decorations: MATCH ATTRIBUTES (under Merge Dimensions n-ary) Merge sets of members: MERGE DIMENSIONS (n-ary) Overwrite hierarchies: RELATE Expression (under Merge Dimensions clause n-ary) Correct values: add RENAME function (under Merge Dimensions clause n-ary) Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
11 Integrating heterogeneous multidimensional schemata Integration workflow Establish a federation of autonomous data marts: 1 Import data mart schemas (CWM supported) (Optional: enrich roll-up hierarchies minimum match integration strategy) 2 Design global multidimensional schema (canonical model) 3 Define semantic mappings both-as-view paradigm [see McBrien and Poulovassilis, 2003] (a) Resolve schema instance conflicts (b) Intensional integration map conceptual schemata Fact tables Dimension tables + hierarchies (c) Extensional integration consolidate data Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
12 1 FedDW Approach 2 FedDW Query Tool Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
13 Overview of FedDW tool support I Java- and Eclipse-based interactive tool suite (EMF, GMF, UML2) Visual data mart integration: (GSA) OLAP front-end prototype: FedDW Query Tool [Berger and Schrefl, 2009] Auxiliary components: Metadata Dictionary, Dimension Repository Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
14 Overview of FedDW tool support II Global Schema Architect? User Query (SQL) OLAP Application Federated DW System Query Tool Global schema Meta-data dictionary SQL-MDi Parser Mappings (SQL-MDi) Import Schemas Dimension repository SQL-MDi Processor DM 1 DM 2 DM n Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
15 Overview of FedDW GSA Visual design environment for multidimensional schemas Schema Editor nested UML diagrams Import schemas Global schema Mapping Editor graphical, high-level code editor (Master Detail layout) Import mappings: unary operators (Fact, Dimension entities) intensional Global mappings: n-ary operators extensional Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
16 Sample GSA Workflow 1 Import local, autonomousconnections schemas 2 Design global connections schema 3 Create import mappings 4 Create one global mapping file 5 Export the mappings to metadata repository 6 Export fact and dimension metadata Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
17 GSA: Step 1, Import Wizard I Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
18 GSA: Step 1, Import Wizard II Wizard suggests appropriate UML stereotypes (based on PK/FK constraints): Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
19 GSA: Step 1, Import Wizard III Initialized class diagram ofred.connections: Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
20 GSA: Step 2, Global Schema Editor Global Schema wizard: Comfortably create global schema as copy of one import schema Edit the schema later on Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
21 GSA Schema Editors: edit dimension User-friendly editing of UML diagram possible (context menus, UML palette): Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
22 GSA: Step 3, Import Mappings I Recall the heterogeneities amongred andglobal: year month date date/hr red global date month year date category products product connections duration tn_tel tn_misc customer customer p_name category products connections dur_min turnover dates customers customer cust_name prod_name regular_fee contract_type base_fee product prod_name contract_type base_fee Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
23 GSA: Step 3, Import Mappings II Repair red.connections import schema: Dimensionality: add references to all three dimensions Date hierarchy: roll-up to LEVEL [date] Product decorations: delete regular_fee from Red s import schema Measures (schema instance conflict): PIVOT MEASURES tn_tel, tn_misc Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
24 Import Mappings: dimensionality Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
25 Import Mappings: hierarchy Prerequisite: delete level [date/hr] fromred.date (see Schema Editor, slide 21) Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
26 Import Mappings: pivot measures Merge measures tn_tel, tn_misc into turnover, extracting values tn_tel, tn_misc as members of the new red.category dimension: Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
27 GSA Step 4, Global Mapping I Merge Dimensions: Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
28 GSA Step 4, Global Mapping II Merge Cubes: Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
29 GSA Step 5 6, export project metadata Step 5: Export mapping file export wizard Starts generation of SQL-MDi code Static syntax check Interface to FedDW Query Tool: file system Step 6: populate Metadata Dictionary Facts + dimensions conceptual and physical metadata Later accessed by FedDW Query Tool Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
30 GSA Step 5 6, export project metadata Step 5: Export mapping file export wizard Starts generation of SQL-MDi code Static syntax check Interface to FedDW Query Tool: file system Step 6: populate Metadata Dictionary Facts + dimensions conceptual and physical metadata Later accessed by FedDW Query Tool Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
31 GSA: Summary Intelligent features: Import heuristics: analyzes PK/FK constraints in import schemas to suggest adequate UML stereotypes Create global schema as copy of one import schema User-friendly and intuitive UML notation Visual conversion modeling avoids cheap SQL-MDi syntax errors Automatically populates Dimension Repository from the exported metadata Supports the CWM standard [Poole, 2003] Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
32 FedDW Query Tool Query Tool in a Nutshell Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
33 Literatur Thanks for your attention! Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
34 Literatur References Berger, S. and Schrefl, M. (2006). Analysing multi-dimensional data across autonomous data warehouses. In Tjoa, A. M. and Tho, N., editors, DaWaK, pages Berger, S. and Schrefl, M. (2009). FedDW: A tool for querying federations of data warehouses. In ICEIS (1). McBrien, P. and Poulovassilis, A. (2003). Data integration by bi-directional schema transformation rules. In Dayal, U., Ramamritham, K., and Vijayaraman, T. M., editors, ICDE, pages IEEE Computer Society. Poole, J. M. (2003). Common Warehouse Metamodel Developer s Guide. John Wiley & Sons, Inc., New York, NY, USA. Stefan Berger (Univ. Linz) DOLAP Nov. 2, / 31
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