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Automatic Database Schema Navigation

Automatic Database Schema Navigation. … or Better Living Through Graphs and Trees. David Kaplan. Background. Microsoft Access 1.0, 1.1, 2.0 (1992-94) Strong sales, first-time users Wizards for novices Adequate: basic Form, Report wizards Poor: advanced Form, Report wizards

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Automatic Database Schema Navigation

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  1. Automatic Database Schema Navigation … or Better Living Through Graphs and Trees David Kaplan

  2. Background • Microsoft Access 1.0, 1.1, 2.0 (1992-94) • Strong sales, first-time users • Wizards for novices • Adequate: basic Form, Report wizards • Poor: advanced Form, Report wizards • None: Query (non-)wizards • Leverage/Constraints • Rich state machine • Add-in environment, add-in team • Visual Basic (VBA)

  3. Goals • Solve it in Access ’95 • Automate most common user tasks • Single-record form • Master-detail form • Tabular, Banded reports • SPJ query, possibly with simple aggs • Simplify our code base

  4. Solutions • Simplified user experience • Wizard unification • Common UI components • Field Picker, Style Picker • Shared layout engine

  5. Solutions • Simplified user experience • Wizard unification • Common UI components • Field Picker, Style Picker • Shared layout engine • Shared data-organization engines • IntelliJoin (I-Join) • IntelliView (I-View)

  6. I-Join • Motivation • Information crosses multiple tables • Users don’t know from JOIN clauses • Response • Automatic join engine (graph-traversal) • User picks fields from any tables • … we do the rest

  7. I-Join • Problem: Find minimal subschema (join graph) to cover user-chosen fields • Classical graph theory problem: “Steiner Problem on Vertices” • NP-Hard, so … call in Pro from Dover (Karp)

  8. I-Join • Solution: heuristic scoring-pruning method “On Finding Steiner Vertices”, V.J. Rayward-Smith and A. Claire. Networks, Vol 16 (1986) 283-294, John Wiley & Sons, Inc. • Works well on lightly-connected graphs (e.g., database schemas)

  9. I-Join Example • Query on Customers, Products, Categories

  10. I-Join Example • Fields in Customers, Products, Categories • “Empty” join tables

  11. I-Join Example • Fields in Customers, Products, Categories • “Empty” join tables

  12. I-View • Motivation • Information requires hierarchy • Users don’t know from subforms, group levels • Response • Automatic hierarchy engine (tree-from-graph) • User picks a hoist point • … we do the rest

  13. I-View • Motivation • Information requires hierarchy • Users don’t know from subforms, group levels • Response • Automatic hierarchy engine (tree-from-graph) • User picks a hoist point1 • … we do the rest 1For ‘tis the sport to have the enginer hoist with his own petard. - Hamlet, Act III, Scene iv

  14. I-View Graph Analysis • Require acyclic input graph • Tractable graph permits auto-hierarchy • “Star almost-DAG”

  15. I-View in Action • Hoist point: Customers

  16. I-View in Action • Hoist point becomes Level 1 (no u-u nodes) 1

  17. I-View in Action • Next downstream node becomes Level 2 1 2

  18. I-View in Action • Next downstream node and its un-assigned upstream nodes become Level 3 1 2 3

  19. I-View in Action • Hoist point: Products

  20. I-View in Action • Hoist point and its un-assigned upstream node become Level 1 1

  21. I-View Algorithm Inputs: graph (must be tractable) hp, the hoist point I-View • i  0 • b  hp • Loop: • i i + 1 • N(i)  Uu(b) • If (df(b) is null) terminate • b  df(b)

  22. I-View Extras • User-defined levels • Agg queries

  23. Patents

  24. Possible Future Directions • XML • Move data as graph (id/idref), infer hierarchy at destination • Run I-Join, I-View against XML schema • Move I-Join, I-View onto server • Join-less SQL • H-SQL • Graphs with multiple most-many nodes • “Parse” into tractable, semi-disjoint sub-graphs • Display as inter-locking forms or reports

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