TE AM FL Y OLAP Solutions Building Multidimensional Information Systems Second Edition Erik Thomsen Wiley Computer Publishing John Wiley & Sons, Inc N EW YOR K • CH ICH ESTER • WEI N H EI M • B R ISBAN E • SI NGAPOR E • TORONTO OLAP Solutions Building Multidimensional Information Systems Second Edition OLAP Solutions Building Multidimensional Information Systems Second Edition Erik Thomsen Wiley Computer Publishing John Wiley & Sons, Inc N EW YOR K • CH ICH ESTER • WEI N H EI M • B R ISBAN E • SI NGAPOR E • TORONTO Publisher: Robert Ipsen Editor: Robert Elliott Developmental Editor: Emilie Herman Managing Editor: John Atkins New Media Editor: Brian Snapp Text Design & Composition: MacAllister Publishing Services, LLC Designations used by companies to distinguish their products are often claimed as trademarks In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or all capital letters Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration This book is printed on acid-free paper Copyright © 2002 by Erik Thomsen All rights reserved Published by John Wiley & Sons, Inc Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4744 Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008, E-Mail: PERMREQ @ WILEY.COM This publication is designed to provide accurate and authoritative information in regard to the subject matter covered It is sold with the understanding that the publisher is not engaged in professional services If professional advice or other expert assistance is required, the services of a competent professional person should be sought Library of Congress Cataloging-in-Publication Data: ISBN: 0-471-40030-0 Printed in the United States of America 10 Advanced Praise for OLAP Solutions, Second Edition “Erik Thomsen’s book goes in depth where other books have not In terms of completeness, readability, and merging theory and practice, I strongly recommend this book If you buy only one book on OLAP this year, it should be OLAP Solutions, Second Edition.” W.H Inmon Partner, www.billinmon.com “Erik Thomsen’s first edition of OLAP Solutions is widely acknowledged as the standard desk reference for all serious practitioners in the areas of OLAP systems, decision support, data warehousing, and business analysis All of us have benefited immeasurably from its clear, concise, and comprehensive treatment of multidimensional information systems The second edition of OLAP Solutions not only continues this great tradition, but also contains many new and profound contributions In particular, by introducing the LC Model for OLAP and providing thorough examples of its application, this book offers a logically grounded, multidimensional framework and language that overcomes the conceptual difficulties generally encountered in the specification and use of OLAP models OLAP Solutions, Second Edition, will revolutionize how we think about, build, and use OLAP technologies.” John Poole Distinguished Software Engineer, Hyperion Solutions Corporation “Erik has done it again! I found his latest work updated to reflect valuable new information regarding the fast-paced changes in OLAP tools and methods I would recommend this book to those who already have the first edition on their bookshelves for the valuable, updated content that it provides and to those who need to move beyond the beginners’ stage of working with OLAP products.” Alan P Alborn Vice President, Science Applications International Corporation “This book is a ‘must read’ for everyone that purports to be a player in the field, as well as for developers that are building today’s leading edge analytical applications Readers who take advantage of this material will form a much greater understanding of how to structure their analytical applications.” Frank McGuff Independent consultant vi Advanced Praise “This should be required reading for students and practitioners who plan to or are working in the OLAP arena In addition to having quite a bit of practical advice, it is well suited to be used as a reference for a senior-level undergraduate or graduatelevel data mining course A ‘relational algebra’ for OLAP was sorely needed, and the real-world examples make you think about how to apply OLAP technology to actually help a business.” David Grossman Assistant Professor, Illinois Institute of Technology “This book is a comprehensive introduction to OLAP analysis It explains this complex subject and demonstrates the power of OLAP in assisting decision makers.” Mehdi Akhlaghi Information Officer, Development Data Group of the World Bank To Hannah and Max and the hopefully joyous lives in which you’ll be fully immersed by the time you can understand this book Index limitations, 29 Lookup tables, 39 multidimensional guidelines, 505 organization of definitions, 43 providing OLAP functionality, 38 relational, 31–32 transposing rows and columns, 43 DBMS facilities, multidimensional guidelines, 517 DDL (Data Definition Language), 5, 31 decision contexts, OLAP model design, 298 decision cycles, 25–26 decision examples, FCI application example, 310 decision making, data visualization, 233 decision scope, 11 decision stages, 15–16 decision support processing, differences from transaction processing, 8–10 DEER cycles (Decision-ExecutionEnvironment–Representation), 25–26 default precedence, 174–175 Dependency trees, 166–169 dependent members, OLAP model design, 296 derived contents, FCI application example, 333–337 derived descriptions, decision stages, 16 derived variables, FCI application example, 365–366 designing applications, 247–248 designing OLAP models, 273–275 actual situation questions, 277 aggregations complex, 302 formulas, 300 attributes, 288 auditing, 305 calculation precedence, 301 constraint data, 279 cube structures, 285–287 decision contexts, 298 dependent members, 296 dimensions changes over time, 291 hierarchies, 292–293 members, 297 structures, 285–287 disconnected hierarchies, 295 formulas, 299 complexities, 302 function placement, 300 links, 292 logical model definitions, 280–283 logical problems, 278 members names, 298 relationships, 297 multilevel dimensions versus multiple dimensions, 294–295 multiple hierarchies, 293 nonaggregation formulas, 300 nonleaf input data, 303 physical problems, 278 qualified data referencing, 301 refining dimension number, 290–291 requirements documentation, 279 solution design, 280 sophisticated analyses, 304 sparsity, 304–305 user requirements, 275–276 deviant hierarchies, 129 dimensional analysis, space, 125 dimensional coexistence, cube attributes, 52 dimensional hierarchies, 107–108 dimensional hurdles logical versus physical dimensions, 52 647 648 Index dimensional hurdles (Continued) mapping multiple logical dimensions onto two physical dimensions, 57 visual metaphors, 49 dimensions adding to cubes, 148–150 Age, FCI application example, 356 loss variables, 363–364 quantity on hand, 361–362 quantity received, 358 quantity requested, 358 quantity sent, 359–360 Asset, FCI application example, 406 cardinal, 95 arithmetic operations, 100 coexistent, cubes, 53 Column, three-dimensional data sets, 50 combining, 58–59 with statistical ordering, 556–558 designing OLAP models changes over time, 291 hierarchies, 292–293 members, 297 multilevel dimensions versus multiple dimensions, 294–295 refining dimension number, 290–291 structures, 285–287 DSS (Decision Support Systems), Data versus Knowledge, 551 Decision Function, 543 Levels of Abstraction, 548 Media, 543 representational, 544 explicit definitions, 95 FCI application example exchange rate cube, 329–330 inventory modeling, 343–344 revenue calculation, 453 formulaic definitions, 95 generic meta data, 74 OLAP modeling, 76–78 Geography, FCI application example, 315 hierarchical, 107–108 as intrinsic components, 74 histograms, cardinalized or ordinalized, 100 hypercubes, 148 independent, cube attributes, 53 internal structures, 93 joining, multidomain schemas, 153–156 LC model, 72 leveled, 123–124 cardinally ordered instances, 98–99, 123–124 constant scaling factors, 125–126 nominally ordered instances, 119–120 ordinally ordered instances, 122 mapping two into one, 57 MTSs, 55–56 multidimensional guidelines, 503 multilevel versus multiple, 294–295 nesting across rows and columns, 64 nominal, 96 nonconformant, cube joins, 161–162 ordering, 96 multidimensional guidelines, 504 page, three-dimensional data sets, 51 reconfiguring display, 63 referentially distinguishing, 79–80 Row, three-dimensional data sets, 50 screen relevance, 64 Time, FCI application example, 315 Index treating all generically, 76–78 Variables, two-dimensional data, 48 directional referencing, leveled hierarchies, 121 disconnected hierarchies, OLAP model design, 295 Discount variables, FCI application example, 383 distributing calculations, 264–266 distribution, FCI application example, 309 Distribution application configurations, 267 Distribution Cube view, 467 DML (Data Manipulation Language), 5, 31 Dollars_Owed_FCI variables, FCI application example, 385 domains, multidimensional guidelines, 505 dot notation, ragged dimension referencing syntax, 115, 118 DRL (Data Representation Language), DSS (Decision Support Systems), fully integrated example, 561–566 integration, 541–542 conflict resolution via degrees of confidence, 546 Data versus Knowledge dimension, 551 Decision Function dimension, 543 DEER cycle, 545 knowledge definition, 551 knowledge in decision making, 552 Levels of Abstraction dimension, 548 Media dimension, 543 numeric media, 549 reconciling data and model driven predictions, 547 representational dimensions, 544 source versus derived expressions, 545 textual media, 549 visual media, 550 multidimensional guidelines, 513 unified architecture, 542 dummy members, 132 dynamic links, 202 E earnings calculations, FCI application example, 451–452 efficiency ideal model criteria, 85 OLAP specification of dimensions and calculations, 21 empty cell interpretation, 75 Enterprise Asset Utilization and Cost cube, FCI application example, 415–417 Enterprise-class data servers, ABDOP, 14 ETL systems (Extract, Transform, and Load), 266 Exchange Rate cube, FCI application example, 329–330 exchange rates, FCI application example, 315 explicit definitions, dimensions, 95 explicit referencing, leveled hierarchies, 121 exponential growth, 233 expressions, evaluating, 147 F Fact tables, 35 fast access, OLAP, 23 FCI application example (FoodCakes International), 308 649 650 Index FCI application example (Continued) activity-based management, 403–404 Age dimension, 356 loss variables, 363–364 quantity on hand, 361–362 quantity received, 358 quantity requested, 358 quantity sent, 359–360 aggregating moving percentages, 384 aggregations, 317 allocating marketing expenses to product sales, 394–395 analytical derivations, 351–352 Asset dimension, 406 auditing, 365 basic derivations, 317–320 business processes, 406 calculations, 321–328 total cost of goods sold, 409 total costs, 413–414 combined purchasing and exchange rates, 337–338 combining time-lagged flows, 385 cost-revenue analysis calculation equation, 452–453 schema steps, 485–497 costing, 367 currency exchange, 313 data sources, 314 derived contents, 333–337 derived variables, 365–366 dimensions and revenue calculation definitions, 453 Discount variables, 383 distribution, 309 Dollars_Owed_FCI variables, 385 earnings calculations, 451–452 Enterprise Asset Utilization and Cost cube, 415–417 Exchange Rate cube, dimensions, 329–330 exchange rates, 315 foodsources, 316 global business rules, revenue calculation, 454–455 input contents, 317 aggregations, 332 Inventory cube, 345 inventory modeling dimensions, 343–344 Inventory Throughput cube, 342 legal issues, 446–447 managing price changes, 379–382 marketing, 375–376 expenditures, 392–394 share, 390–391 materials inventory, 309, 438–441 analysis, 342 materials shipping, 442–444 OLAP implementation presentation, 405 product inventory cost business processes, 424–428 production, 309 business processes, 433–436 Purchases cube, 315 purchasing, 308, 314, 445 regular decisions made, 310 Retail Dollar Sales variables, 383 sales, 309, 375–376 analysis, 386–388 marketing business processes, 416 Sales and Marketing cube, 376 List Price per Package variables, 379 Qty_Sold variables, 377 Return to Qty_Sold variables, 378–379 schemas, revenue calculation, 455–458 site by time-level costing, 368–373 stock management, 353–355 systematic age tracking, 355 Index tracking batch IDs at points of sales, 409–412 transport inventory to store business processes, 419–422 production to inventory business processes, 429–431 value states, 395, 398–402 viewing basic aggregations, 346–347, 350 firewalls, data distribution, 262 flexibility of OLAP, 22 foodsources, FCI application example, 316 formal languages, OLAP, formatting, multidimensional guidelines, 511 formulaic definitions, dimensions, 95 formulas allocation, 181 axis-based, 169–170 calculation precedence, 171–173 cell-based, 169–170 chained, 199 conditional, 196 cross-cube, 189–193 cross-dimensional selection, 180 data-based, 197 hierarchies, 166–169 multidimensional, 165, 177 aggregational, 178–179 guidelines, 507 OLAP model design, 299 complexities, 302 defining, 75 ordering, 183 overriding, 198 position-based, 196–197 product, 182 ranged, 195–196 ratio, 182 selection, 180 spreadsheets, 36 triggering conditioning, 198 types, 180 weighted, 194 four-dimensional data sets, 51 friendliness features, multidimensional guidelines, 514 functions argument notation, ragged dimension referencing syntax, 116 placement, OLAP model design, 300 functional approach to LC model, 86 functional requirements of OLAP, 5, 18 functionality of machines, 261 G generic dimensions meta data, 74 OLAP modeling, 76–78 generic loops, 57 Geography dimension, FCI application example, 315 global business rules, FCI application example, 454–455 goal-challenge matrix, 20 graphic representations difference from tabular-numeric representation, 219 relationship data, 220 graphical misrepresentations, 223 graphical tokens, 222 grouping constant relationships, 236 guidelines for OLAP model design, 274 H hierarchical dimensions, 107–108 651 652 Index hierarchies deviant, 129 dimension values, spreadsheets, 37 formulas, 166 instance-metric pairs, 109 intrinsic parts of dimensions, 74 leaves, 110 leveled, 110, 118–119 maximum distance, 114 metric basis, 106 minimum distance, 114 mixed, 133 multidimensional guidelines, 503 multiples per type, 126–127 ragged, 110–111 simple, 108 high-level logical models, 284 Hilbert curves and numbering, 258 histograms, dimensions, 100 hypercubes, 47, 81–82, 137 dimensions, 148 modeling data, 75 representing onscreen, 57 I ideal model criteria, 82 analytical awareness, 85 completeness, 84 efficiency, 85 groundedness, 83 Inclusion rule, metrics, 104 independent dimensions, cube attributes, 53 indexes data storage, 251–256 multidimensional, 257 information distribution, multidimensional guidelines, 513 processing attributes, input contents, FCI application example, 317, 332 instance-metric pairs, hierarchies, 109 instances cardinally ordered, 98–99 cousins, 118 metric associations, 101 ordinally ordered, 97 siblings, 118 integrating OLAP and data mining, 553–554 interface flexibility, OLAP, 22 internal structures, dimensions, 93 interrow calculations, databases, 40 intersections, meaningless, 150 intradimensional hierarchies, 74 invalid data, 139–141 Inventory cube, FCI application example basic aggregations, 345–347, 350 input contents, 345 inventory modeling dimensions, FCI application example, 343–344 Inventory Throughput cube, FCI application example, 342 irregular records or spreadsheets, organizing in multidomain schemas, 158 iterative queries, multidimensional guidelines, 506 J–K joins, 41 cubes and nonconformant dimensions, 161–162 dimensions, multidomain schemas, 153–156 K-means algorithm, 562–563 L languages comparisons, 519 ancestor references, 526–527 APB-1 schemata, 521 Index application ranges in joined cubes, 536 basic formulas, 532–536 calculation precedence, 531 descendants, 529 missing/inapplicable instances of types, 530 ordering members, 525 references, 522–525 schema/formula calculations, 520–521 multidimensional guidelines, 512 LC model (Located Contents), 71 attributes, 72 data and meta data, 88 dimensions, 72 functional approach, 86 measures, 72 recursion, 89 schemas, 90 super symmetry, 86 symmetry, 72–73 type structures, 89–90 leaves, hierarchies, 110–111 legal issues, FCI application example, 446–447 letter flavors of OLAP, Level tables, 208 leveled dimensions cardinally ordered instances, 123–124 constant scaling factors, 125–126 nominally ordered instances, 119–120 ordinally ordered instances, 122 leveled hierarchies, 93, 110, 118–121 light indexing, data caches, 251 line segments, MTSs, 55 linear growth, 233 linear programming, 15 links, 201–202 attribute, 203–204 bidirectional, 202 content, 203–205 data to meta data, 206 dynamic, 202 multidimensional guidelines, 508 OLAP model design, 292 static, 202 structure, 203 Structure tables, 207–208 List Price per Package variables, FCI application example, 379 Locator dimensions FCI application example, 315 two-dimensional data, 48 logical grounding, ideal model criteria, 83 logical issues, OLAP model, 74, 278–283 logical models, 284 logical values, 144 Lookup tables, 34–35, 39 M M to N relations, data warehouses, 288 machines data distribution, 249–250 functionality, 261 Maintenance Inventory Batch Cube view, 474 Maintenance Inventory Cube view, 472 managing price changes, FCI application example, 379–382 mapping two dimensions into one dimension, 57 Market Sales Batch Cube view, 476–478 Market Sales Cube Distribution Cube view, 476 Market Sales Distribution Cube view, 475 653 Index minimum distance, hierarchies, 114 minimum metric leaf distance, ragged hierarchies, 114 missing data, 142 mixed hierarchies, 133 Monte Carlo simulation, 15 MS Batch times PA Cube view, 484 MTSs (Multidimensional Type Structures), 55–56 multilevel dimensions versus multiple dimensions, 294–295 multicube data modeling, 74 multidimensional aggregation formulas, 178–179 multidimensional arrays, 561 multidimensional business data, visualization, 241 multidimensional clients, 267–269 multidimensional formulas, 165, 177 multidimensional grids, 62 multidimensional guidelines, 501–502 application building, 513 batch automation, 509 calculations, 507 cardinality, 504 computations, 516 core logical features, 502–503 cubes, 504 currencies, 512 data types, 506 databases, 505 DBMS facilities, 517 dimensions, 503 orderings, 504 domains, 505 DSS, 513 formatting, 511 formulas, 507 friendliness features, 514 hierarchies, 503 iterative queries, 506 languages, 512 AM FL Y marketing, FCI application example, 375–376 expenditures, 392–394 share, 390–391 Marketing Sales Batch Purchasing Activity Cube view, 483 materials inventory, FCI application example, 309, 341–342, 438–441 Materials Inventory schema costrevenue analysis calculation, 490 materials shipping, FCI application example, 442–444 Materials Shipping schema costrevenue analysis calculation, 492 mathematical grounding, ideal model criteria, 83 maximum distance, hierarchies, 114 maximum metric leaf distance, ragged hierarchies, 114 meaning, schemas, 138 meaningless data, 143 meaningless intersections, 150 measures, LC model, 72 members, OLAP model design, 297–298 meta data generic dimensions, 74 LC model, 88 visualization, 561 methods of ordering, 184 metric root distance, ragged hierarchies, 114 metrics basis for hierarchies, 106 inclusion rule, 104 instance associations, 101 ordering, 94 permissable operations, 104 scaling factor, 105 translation aspect, 104 TE 654 Team-Fly® Index links, 508 models, 505 multitier distribution, 516 navigation, 510 optimization, 510 platform issues, 517 queries, 506 regularity, 505 representation, 510 security, 517 storage access, 515 time, 511 versioning, 504 views, 510 multidimensional indexes, 257 multidimensional server/clients, 268–269 multidomain schemas, 152 integrating irregular spreadsheets, 158 joining dimensions, 153–156 organizing irregular records, 158 multilevel correlations, 559 multiple hierarchies hierarchies per type, 126–127 OLAP model design, 293 multitier distribution, multidimensional guidelines, 516 N named orderings, 97 navigation, multidimensional guidelines, 510 negation of terms, 141 nesting dimensions across rows and columns, 64 network bandwidth, 261 nominal dimensions, 96 nominal series, 96 nominally ordered instances, 96, 119–120 nonaggregation formulas, OLAP model design, 300 nonconformant dimensions, joins with cubes, 161–162 nonhierarchical structuring, 95 nonleaf input data, OLAP model design, 303 nonvarying variables, 157–158 normalization-based ordering, 184 not-applicable (N/A) data, 141, 144 not-applicable (N/A) variables, 157–158 numerical tokens, 222 O objects, 31 OLAP (Online Analytical Processing), attributes of general information processing, 6–7 combining dimensional and statistical ordering, 556–558 concepts, core requirements, 21–23 data mining distinctions, 17 integration, 553–554 database limitations, 29 efficient specification of dimensions and calculations, 21 flexibility, 22 formal languages, full products, functional requirements, 5, 18 functionality issues with spreadsheets and databases, 32–34 generic dimension modeling, 76–78 goal-challenge matrix, 20 letter flavors, logical issues, 74 655 656 Index OLAP (Continued) product layers, providing functionality, 36–38 rich dimensional structuring with hierarchical referencing, 21 separation of structure and representation, 23 spreadsheets functional evolution, 30–31 limitations, 29 OLAP model design, 273–275 actual situation questions, 277 aggregation formulas, 300 attributes, 288 auditing, 305 calculation precedence, 301 complex aggregations, 302 constraint data, 279 cube structures, 285–287 decision contexts, 298 dependent members, 296 dimensions changes over time, 291 hierarchies, 292–293 members, 297 structures, 285–287 disconnected hierarchies, 295 formulas, 299, 302 function placement, 300 links, 292 logical model definitions, 280–283 logical problems, 278 members, 297–298 multilevel versus multiple dimensions, 294–295 multiple hierarchies, 293 nonaggregation formulas, 300 nonleaf input data, 303 physical problems, 278 qualified data referencing, 301 refining dimension number, 290–291 requirements documentation, 279 solution design, 280 sophisticated analyses, 304 sparsity, 304–305 user requirements, 275–276 OLTP (On-Line Transaction Processing), 12 operations-oriented analysis, 10 optimization, multidimensional guidelines, 510 Order Cube view, 462 ordering, 130 aggregate, 187 dimensions, 96 formulas, 183 methods, 184 metrics, 94 named, 97 normalization-based, 184 ordinal, 98 rank-based, 184 RP transformation, 185 ordinal batches, 413 ordinal orderings, 98 ordinal series, 96 ordinally ordered instances, 97, 122 organization of definitions, databases, 43 overriding formulas, 198 P page dimensions, three-dimensional data sets, 51 parallel coordinates, 229 parent-child tables, 207 parent/child hierarchical relationships, 109 patterns data visualization, 236–238 understood behavior, 233 permissable operations, metrics, 104 physical design of applications, 247–248 Index physical problems, OLAP model design, 278 Pivot tables, 31 platform issues, multidimensional guidelines, 517 position, 109 position-based formulas, 196–197 positional ratios, 182 preaggregation, 212 precedence, 171–175 predictions, decision stages, 15–16 product formulas, 182 product inventory cost business processes, FCI application example, 424–428 Product Inventory Cube view, 465 Product Inventory schema cost–revenue analysis calculation, 487 Product Inventory Throughput Cube view, 466 product language comparisons, 519 ancestor references, 526–527 APB-1 schemata, 521 application ranges in joined cubes, 536 basic formulas, 532–536 calculation precedence, 531 descendants, 529 missing/inapplicable instances of types, 530 ordering members, 525 references, 522–525 schema/formula calculations, 520–521 product layers of OLAP, product sales analysis, data visualization, 243 Product Throughput Cube view, 464 production business processes, FCI application example, 433–436 Production schema cost-revenue analysis calculation, 489 pseudo-levels, 129 Purchases cube, FCI application example, 315 purchasing, FCI application example, 308, 314 Purchasing Activity Cube view, 481–482 Purchasing Cost Cube view, 480 purchasing costs, FCI application example, 445 Purchasing schema cost-revenue analysis calculation, 495 Q Qty_Sold variables, FCI application example, 377 qualified data referencing, OLAP model design, 301 queries, multidimensional guidelines, 506 questions and OLAP model design, 276–277 R Ragged dimensions, referencing syntax, 115, 118 Ragged hierarchies, 93, 110–111, 114 Ranged formulas, 195–196 Rank-based ordering, 184 Ratio formulas, 182 Rearranging views, spreadsheets, 37 Reconfiguring dimensional displays, 63 Recursion, LC model, 89 Referencing syntax, ragged dimensions, 115, 118 Referentially distinguishing dimensions and variables, 79–80 Refining dimension number, OLAP model design, 290–291 657 658 Index Regularity, multidimensional guidelines, 505 Relational data marts, multidimensional clients, 267 Relational databases, 32 Relational model, SQL databases, 31 Relational storage, star schemas, 262 Relational warehouses, 269 Relationship data, graphic representation, 220 Repeating patterns, data visualization, 242 Representation multidimensional guidelines, 510 structural attributes, 52 Representing hypercubes on computer screen, 57 Requirements documentation, OLAP model design, 279 Resolution, 109 Resolutional ratios, 182 Retail Dollar Sales variables, FCI application example, 383 Return to Qty_Sold variables, FCI application example, 378–379 Rich dimensional structuring with hierarchical referencing, 21 Roots, ragged hierarchies, 111 Row dimensions, three-dimensional data sets, 50 Row-member links, data tables, 209 RP transformation ordering (rankpreserving), 185 S S&M Cube view, 460 SABRE airline reservation system, 13 sales, FCI application example, 309, 375–376 analysis, 386–388 business processes, 416 Sales & Marketing schema costrevenue analysis calculation, 485 Sales and Marketing cube, FCI application example, 376 List Price per Package variables, 379 Qty_Sold variables, 377 Return to Qty_Sold variables, 378–379 sampling, 265 scaling factor, metrics, 105 schemas application ranges, 143 LC model, 90 meaning, 138 multidomain, 152 revenue calculation, 455–458 semantic spaces, 138 single domain, 151 snowflake, 262 sparsity, 139 star, 262 scope of decisions, 11 screen relevance, dimensions, 64 security firewalls, 262 multidimensional guidelines, 517 selection formulas, 180 semantic spaces, 137–138 semantics of visualization, 219 Series cubes, 79–80 servers calculations, client applications, 267 multidimensional, 268 multidimensional midtier, 269 shape representations, 222, 225 siblings, instances, 118 simple hierarchies, 108 single domain schemas, 151 single hypercube approaches, 81–82 Index site by time-level costing, FCI application example, 368–373 six-dimensional data sets, 59, 62 snowflake schemas, 262 solution design, OLAP model design, 280 sophisticated analyses, OLAP model design, 304 source descriptions, decision stages, 16 space, dimensional analysis, 125 sparse cell interpretation, 75 sparse four-dimensional matrices, 139 sparsity invalid data, 139 OLAP model design, 304–305 schemas, 139 zero values, 140 spreadsheets aggregate views, 37 aggregating base data, 36 base data form, 36 common source data set definition difficulty, 34 difficulty in providing OLAP functionality, 32–34 formulas, 36 hierarchies for dimension values, 37 irregular, integrating into multidomain schemas, 158 limitations, 29 OLAP functional evolution, 30–31, 36 rearranging views, 37 SQL databases, 32 aggregate tables, 38 difficulty in providing OLAP functionality, 32–34, 38 lookup tables, 39 relational model, 31 star schemas, 34, 262 static links, 202 statistical ordering, combining with dimensions, 556–558 stock management, FCI application example, 353–355 storage access, multidimensional guidelines, 515 storage locations, data distribution, 261 storage of data access methods, 257–258 indexes, 251–256 stored functions, user-defined, 43 structural attributes, representations, 52 structure links, 203 Structure tables, links, 207–208 subsetting, data visualization, 241 subtypes, 32 super symmetry, LC model, 86 supertypes, 32 symbolic representation, 218 symmetric hierarchies, 118 symmetry of LC model, 72–73 systematic age tracking, FCI application example, 355 T table-member links, Data tables, 211 tables Fact, 35 level tables, 208 Lookup, 34 parent-child, 207 star schema, 34 third normal form, 62 tabular-numeric representation, 219–220 temporal calculation distribution, 265–266 tesseracts, 51 659 660 Index testing for applicability, 147 text analysis systems, 561 thematic maps, 222 theoretic groundedness, ideal model criteria, 83 Third normal form, tables, 62 three-dimensional data sets, 49–51 tiled patterns, data visualization, 242 time, multidimensional guidelines, 511 Time dimension, FCI application example, 315 time-to-value relationships, tabularnumeric representation, 220 tokens, representing or conveying meanings, 222 tracking batch IDs at points of sales, FCI application example, 409–412 transaction processing, differences from decision support processing, 8–10 translation aspect, metrics, 104 transport from inventory to store business processes, FCI application example, 419–422 Transport from Inventory to Stores schema cost-revenue analysis calculation, 486 transport from production to inventory business processes, FCI application example, 429–431 Transport from Production to Product Inventory schema cost-revenue analysis calculation, 488 Transport Method Cube view, 468 transposing rows and columns, databases, 43 Travel Cube view, 461–463 trigger conditioning formulas, 198 two-dimensional data, 47–48 Type One tables, 62 type structures LC model, 89–90 visual forms, 229 Type Zero tables, 62 types, 136 constraints on instances, 103 definitions, 102 formulas, 180 multiple hierarchies, 126–127 U understood behavior and patterns, 233 unified architecture, DSS integration, 542 conflict resolution via degrees of confidence, 546 Data versus Knowledge dimension, 551 Decision Function dimension, 543 DEER cycle, 545 knowledge definition, 551 knowledge in decision making, 552 Levels of Abstraction dimension, 548 Media dimension, 543 numeric media, 549 reconciling data and model-driven predictions, 547 representational dimensions, 544 source versus derived expressions, 545 textual media, 549 visual media, 550 unified decision support framework, 553 unknown values, 141 user requirements, OLAP model design, 275–276 user-defined stored functions, 43 Index V value changes over space and time, 227 value states, FCI application example, 395, 398–402 variables nonvarying, 157–158 not-applicable (N/A), 157–158 referentially distinguishing, 79–80 two-dimensional data, 48 Variables dimensions, two-dimensional data, 48 variance, data visualization, 233 Vehicle Cube view, 462 versioning, multidimensional guidelines, 504 view flexibility, OLAP, 22 viewing basic aggregations, FCI application example, 346–347, 350 views, multidimensional guidelines, 510 visual forms and type structures, 229 visual metaphors, 49 visualization, 216, 219 W Web servers, 269 Web site traffic analysis, data visualization, 244 weighted formulas, 194 Z Z-numbering, 257 zero values, sparsity, 140 661 ... NGAPOR E • TORONTO OLAP Solutions Building Multidimensional Information Systems Second Edition OLAP Solutions Building Multidimensional Information Systems Second Edition Erik Thomsen Wiley Computer.. .OLAP Solutions Building Multidimensional Information Systems Second Edition Erik Thomsen Wiley Computer Publishing John Wiley & Sons, Inc N EW YOR K • CH ICH ESTER... in technologies such as: ■■ OLAP ■■ Multidimensional information systems ■■ Data warehousing ■■ Databases ■■ Decision support systems (DSS) ■■ Executive information systems (EIS) ■■ Business intelligence