Computational Intelligence for Decision SupportIntelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making. Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest. The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable. |
Contents
DECISION SUPPORT AND COMPUTATIONAL INTELLIGENCE | 1 |
13 COMPUTERIZED DECISION SUPPORT MECHANISMS | 2 |
14 COMPUTATIONAL INTELLIGENCE FOR DECISION SUPPORT | 3 |
16 DATA INFORMATION AND KNOWLEDGE | 5 |
17 ISSUES TO BE DISCUSSED IN THIS BOOK | 6 |
SUMMARY | 8 |
REFERENCES | 9 |
SEARCH AND REPRESENTATION | 11 |
722 BASICS OF ANALOGICAL REASONING | 165 |
73 REASONING AS QUERYINVOKED MEMORY REORGANIZATION | 166 |
733 DOCUMENT STORAGE AND RETRIEVAL THROUGH RELATIONAL DATABASE OPERATIONS | 167 |
734 GENERATING SUGGESTIONS | 177 |
74 SUMMARY | 184 |
REFERENCES | 185 |
COMPUTATIONAL CREATIVITY AND COMPUTER ASSISTED HUMAN INTELLIGENCE | 187 |
822 THEORETICAL FOUNDATION FOR STIMULATING HUMAN THINKING | 188 |
222 APPLICATIONS | 14 |
23 DEFINITION OF COMPUTATIONAL INTELLIGENCE | 16 |
TURING TEST | 17 |
24 BASIC ASSUMPTIONS OF COMPUTATIONAL INTELLIGENCE | 18 |
242 SEQUENTIAL OR PARALLEL | 19 |
243 LOGICBASED APPROACH | 20 |
245 SUMMARY | 21 |
LISTS STACKS QUEUES AND PRIORITY QUEUES | 22 |
254 INDEX STRUCTURES FOR DATA ACCESS | 23 |
257 REMARKS ON SEARCH OPERATION | 24 |
262 STATE SPACE SEARCH | 25 |
263 REMARKS ON SCALING UP | 26 |
272 USING ABSTRACT LEVELS | 27 |
273 PROGRAMMING LANGUAGES FOR COMPUTATIONAL INTELLIGENCE | 28 |
28 STATE SPACE SEARCH | 29 |
282 HEURISTIC SEARCH | 32 |
29 REMARK ON CONSTRAINTBASED SEARCH | 36 |
210 PLANNING AND MACHINE LEARNING AS SEARCH | 37 |
2102 SYMBOLBASED MACHINE LEARNING AS SEARCH | 38 |
SUMMARY | 39 |
SELFEXAMINATION QUESTIONS | 40 |
REFERENCES | 41 |
PREDICATE LOGIC | 43 |
322 PROPOSITIONAL CALCULUS | 44 |
323 PREDICATES | 45 |
324 QUANTIFIERS | 47 |
326 INFERENCE RULES | 48 |
327 SUBSTITUTION UNIFICATION MOST GENERAL UNIFIER | 49 |
33 PROLOG FOR COMPUTATIONAL INTELLIGENCE | 53 |
332 SAMPLE PROLOG PROGRAMS | 58 |
333 SUMMARY OF IMPORTANT THINGS ABOUT PROLOG | 62 |
34 ABDUCTION AND INDUCTION | 63 |
343 ABDUCTION | 64 |
352 COMMONSENSE REASONING | 65 |
353 CIRCUMSCRIPTION | 66 |
354 SUMMARY OF NONMONOTONIC REASONING | 67 |
SELFEXAMINATION QUESTIONS | 68 |
RELATIONS AS PREDICATES | 69 |
43 OVERVIEW OF RELATIONAL DATA MODEL | 70 |
432 DECLARATIVE AND PROCEDURAL LANGUAGES | 71 |
44 RELATIONAL ALGEBRA | 72 |
442 HOW TO FORM A RELATIONAL ALGEBRA QUERY FROM A GIVEN ENGLISH QUERY | 73 |
FUNDAMENTAL OPERATORS | 74 |
445 COMBINED USE OF OPERATORS | 76 |
446 EXTENDED RA OPERATIONS | 77 |
452 INTEGRITY CONSTRAINTS | 78 |
46 FUNCTIONAL DEPENDENCIES | 79 |
461 DEFINITION OF FUNCTIONAL DEPENDENCY | 80 |
ARMSTRONG AXIOMS | 81 |
465 ALGORITHMS FOR FINDING KEYS FROM FUNCTIONAL DEPENDENCIES | 82 |
466 REFERENTIAL INTEGRITY | 83 |
472 BOYCECODD NORMAL FORM BCNF AND THIRD NORMAL FORM 3NF | 85 |
473 REMARKS ON NORMAL FORMS AND DENORMALIZATION | 86 |
474 DESIRABLE FEATURES FOR DECOMPOSITION GLOBAL DESIGN CRITERIA | 87 |
475 DECOMPOSITION ALGORITHMS | 88 |
48 MULTIVALUED DEPENDENCIES | 90 |
482 MULTIVALUED DEPENDENCIES | 91 |
483 FOURTH NORMAL FORM 4NF | 92 |
49 REMARK ON OBJECTORIENTED LOGICAL DATA MODELING | 93 |
410 BASICS OF DEDUCTIVE DATABASES | 94 |
4103 DEDUCTIVE QUERY EVALUATION | 97 |
411 KNOWLEDGE REPRESENTATION MEETS DATABASES | 99 |
SUMMARY | 100 |
SELFEXAMINATION QUESTIONS | 101 |
RETRIEVAL SYSTEMS | 103 |
52 DATABASE MANAGEMENT SYSTEMS DBMS | 104 |
523 SCHEMA VERSUS INSTANCES | 105 |
525 DATABASE LANGUAGES | 106 |
532 BASIC STRUCTURE OF SQL QUERY | 107 |
534 WRITING SIMPLE SQL QUERIES | 108 |
GENERAL STEPS | 109 |
537 AGGREGATE FUNCTIONS | 110 |
54 BASICS OF PHYSICAL DATABASE DESIGN | 111 |
542 FILE STRUCTURES AND INDEXING | 112 |
543 TUNING DATABASE SCHEMA | 113 |
552 BASICS OF TRANSACTION PROCESSING | 114 |
56 INFORMATION RETRIEVAL IR | 115 |
563 WEB SEARCHING DATABASE RETRIEVAL AND IR | 117 |
57 DATA WAREHOUSING | 118 |
572 DATA WAREHOUSING AND DECISION SUPPORT | 120 |
573 MIDDLEWARE | 121 |
58 RULEBASED EXPERT SYSTEMS | 122 |
582 DEDUCTIVE RETRIEVAL SYSTEMS | 123 |
583 RELATIONSHIP WITH KEY INTERESTS IN COMPUTATIONAL INTELLIGENCE | 124 |
586 KNOWLEDGE ENGINEERING | 128 |
587 BUILDING RULEBASED EXPERT SYSTEMS | 129 |
588 SOME OTHER ASPECTS | 132 |
A BRIEF OVERVIEW | 133 |
59 KNOWLEDGE MANAGEMENT AND ONTOLOGIES | 134 |
592 INFORMATION TECHNOLOGY FOR KNOWLEDGE MANAGEMENT | 135 |
593 DATA AND KNOWLEDGE MANAGEMENT ONTOLOGIES | 136 |
SUMMARY | 137 |
REFERENCES | 138 |
CONCEPTUAL DATA AND KNOWLEDGE MODELING | 141 |
622 A SIMPLE EXAMPLE | 142 |
623 MAJOR CONSTRUCTS | 143 |
625 DESIGN ISSUES IN ER MODELING | 144 |
626 MAPPING ER DIAGRAMS INTO RELATIONS | 145 |
A BANKING ENTERPRISE | 146 |
629 EXTENDED ER FEATURES AND RELATIONSHIP WITH OBJECTORIENTED MODELING | 147 |
63 REMARK ON LEGACY DATA MODELS | 148 |
64 KNOWLEDGE MODELING FOR KNOWLEDGE REPRESENTATION | 149 |
65 STRUCTURED KNOWLEDGE REPRESENTATION | 150 |
652 BASICS OF STRUCTURED KNOWLEDGE REPRESENTATION SCHEMES | 151 |
662 CLASSES SUBCLASSES AND INSTANCES | 152 |
67 CONCEPTUAL GRAPHS | 153 |
672 USING LINEAR FORM TO REPRESENT CONCEPTUAL GRAPHS | 155 |
674 LOGICRELATED ASPECTS | 156 |
675 REMARKS ON SYNERGY OF FRAME SYSTEMS CONCEPTUAL GRAPHS AND OBJECT ORIENTATION | 159 |
SUMMARY | 160 |
SELFEXAMINATION QUESTIONS | 161 |
REASONING AS EXTENDED RETRIEVAL | 163 |
823 CREATIVITY IN DECISION SUPPORT SYSTEMS | 189 |
83 IDEA PROCESSORS | 190 |
832 COMMON COMPONENTS IN IDEA PROCESSORS | 192 |
834 THE NATURE OF IDEA PROCESSORS | 193 |
84 RETROSPECTIVE ANALYSIS FOR SCIENTIFIC DISCOVERY AND TECHNICAL INVENTION | 195 |
842 RETROSPECTIVE ANALYSIS FOR KNOWLEDGEBASED IDEA GENERATION OF NEW ARTIFACTS | 197 |
843 A PROLOG PROGRAM TO EXPLORE IDEA GENERATION | 198 |
85 COMBINING CREATIVITY WITH EXPERTISE | 201 |
853 STUDYING STRATEGIC HEURISTICS OF CREATIVE KNOWLEDGE | 203 |
854 DIFFICULTIES AND PROBLEMS IN ACQUIRING STRATEGIC HEURISTICS | 204 |
855 THE NATURE OF STRATEGIC HEURISTICS | 205 |
856 TOWARD KNOWLEDGEBASED ARCHITECTURE COMBINING CREATIVITY AND EXPERTISE | 206 |
SUMMARY | 207 |
SELFEXAMINATION QUESTIONS | 208 |
CONCEPTUAL QUERIES AND INTENSIONAL ANSWERING | 211 |
922 SOME FEATURES OF QUESTION ANSWERING | 212 |
931 MEANING OF INTENSIONAL ANSWERS | 213 |
933 CONCEPTUAL QUERY ANSWERING | 215 |
934 DUALITY BETWEEN CONCEPTUAL QUERIES AND INTENSIONAL ANSWERS | 216 |
94 AN APPROACH FOR INTENSIONAL CONCEPTUAL QUERY ANSWERING | 218 |
942 CONSTRUCTING AN ABSTRACT DATABASE FOR INTENSIONAL ANSWERS | 219 |
943 GENERATING INTENSIONAL ANSWERS FOR CONCEPTUAL QUERIES | 221 |
944 METHOD FOR INTENSIONAL CONCEPTUAL QUERY ANSWERING | 222 |
SUMMARY | 223 |
FROM MACHINE LEARNING TO DATA MINING | 225 |
102 BASICS OF MACHINE LEARNING | 226 |
103 INDUCTIVE LEARNING | 227 |
1033 IDS ALGORITHM AND C45 | 228 |
104 EFFICIENCY AND EFFECTIVENESS OF INDUCTIVE LEARNING | 232 |
105 OTHER MACHINE LEARNING APPROACHES | 233 |
1052 EVOLUTIONARY ALGORITHMS FOR MACHINE LEARNING | 235 |
1053 SUMMARY OF MACHINE LEARNING METHODS | 239 |
1062 KDD VERSUS DATA MINING | 240 |
1063 DATA MINING VERSUS MACHINE LEARNING | 242 |
1064 DATA MINING VERSUS EXTENDED RETRIEVAL | 243 |
1065 DATA MINING VERSUS STATISTIC ANALYSIS AND INTELLIGENT DATA ANALYSIS | 244 |
DATA MINING FROM A DATABASE PERSPECTIVE | 245 |
107 CATEGORIZING DATA MINING TECHNIQUES | 246 |
1073 SYMBOLIC CONNECTIONISM AND EVOLUTIONARY ALGORITHMS | 247 |
108 ASSOCIATION RULES | 248 |
1082 FINDING ASSOCIATION RULES USING APRIORI ALGORITHM | 251 |
1083 MORE ADVANCED STUDIES OF ASSOCIATION RULES | 253 |
SUMMARY | 255 |
REFERENCES | 256 |
DATA WAREHOUSING OLAP AND DATA MINING | 261 |
112 DATA MINING IN DATA WAREHOUSES | 262 |
113 DECISION SUPPORT QUERIES DATA WAREHOUSE AND OLAP | 263 |
1132 ARCHITECTURE OF DATA WAREHOUSES | 264 |
1133 BASICS OF OLAP | 266 |
114 DATA WAREHOUSE AS MATERIALIZED VIEWS AND INDEXING | 270 |
1142 MATERIALIZED VIEWS | 271 |
1143 MAINTENANCE OF MATERIALIZED VIEWS | 273 |
1144 NORMALIZATION AND DENORMALIZATION OF MATERIALIZED VIEWS | 274 |
1145 INDEXING TECHNIQUES FOR IMPLEMENTATION | 275 |
115 REMARKS ON PHYSICAL DESIGN OF DATA WAREHOUSES | 277 |
116 SEMANTIC DIFFERENCES BETWEEN DATA MINING AND OLAP | 278 |
1162 AGGREGATION SEMANTICS | 279 |
117 NONMONOTONIC REASONING IN DATA WAREHOUSING ENVIRONMENT | 282 |
118 COMBINING DATA MINING AND OLAP | 283 |
1182 SOME SPECIFIC ISSUES | 284 |
119 CONCEPTUAL QUERY ANSWERING IN DATA WAREHOUSES | 288 |
1192 REWRITING CONCEPTUAL QUERY USING MATERIALIZED VIEWS | 289 |
1110 WEB MINING | 290 |
11102 DISCOVERY TECHNIQUES ON WEB TRANSACTIONS | 291 |
SUMMARY | 293 |
REASONING UNDER UNCERTAINTY | 297 |
122 GENERAL REMARKS ON UNCERTAIN REASONING | 298 |
1222 DIFFERENT TYPES OF UNCERTAINTY AND ONTOLOGIES OF UNCERTAINTY | 299 |
1223 UNCERTAINTY AND SEARCH | 300 |
123 UNCERTAINTY BASED ON PROBABILITY THEORY | 301 |
1232 BAYESIAN APPROACH | 302 |
1233 BAYESIAN NETWORKS | 303 |
1234 BAYESIAN NETWORK APPROACH FOR DATA MINING | 307 |
1235 A BRIEF REMARK ON INFLUENCE DIAGRAM AND DECISION THEORY | 310 |
1236 PROBABILITY THEORY WITH MEASURED BELIEF AND DISBELIEF | 311 |
124 FUZZY SET THEORY | 314 |
1242 FUZZY SET OPERATIONS | 317 |
1243 RESOLUTION IN POSSIBILISTIC LOGIC | 319 |
125 FUZZY RULES AND FUZZY EXPERT SYSTEMS | 320 |
1252 SYNTAX AND SEMANTICS OF FUZZY RULES | 321 |
1253 FUZZY INFERENCE METHODS | 324 |
126 USING FUZZYCLIPS | 326 |
127 FUZZY CONTROLLERS | 328 |
1272 BUILDING FUZZY CONTROLLER USING FUZZYCLIPS | 329 |
1273 FUZZY CONTROLLER DESIGN PROCESS | 332 |
128 THE NATURE OF FUZZY LOGIC | 335 |
1281 THE INCONSISTENCY OF FUZZY LOGIC | 336 |
1283 IMPLICATION TO MAINSTREAM COMPUTATIONAL INTELLIGENCE | 337 |
SELFEXAMINATION QUESTIONS | 338 |
RE APPROACHES FOR UNCERTAIN REASONING AND DATA MINING | 341 |
1322 RECONSTRUCTION AND DATA MINING | 342 |
133 SOME KEY IDEAS OF KSYSTEMS THEORY AND ROUGH SET THEORY | 343 |
1332 REDUCTIONDRIVEN APPROACH IN ROUGH SET THEORY | 344 |
1333 KSYSTEMS THEORY VERSUS AND ROUGH SET THEORY | 345 |
1342 TERMINOLOGY | 346 |
1343 AN EXAMPLE | 347 |
1344 RULE INDUCTION USING ROUGH SET APPROACH | 349 |
1345 APPLICATIONS OF ROUGH SETS | 350 |
135 KSYSTEMS THEORY | 351 |
SUMMARY | 353 |
| 354 | |
TOWARD INTEGRATED HEURISTIC DECISION MAKING | 357 |
143 HIGH LEVEL HEURISTICS FOR PROBLEM SOLVING AND DECISION SUPPORT | 359 |
1433 SUMMARY OF HEURISTICS | 364 |
1442 METAKNOWLEDGE AND METAREASONING | 366 |
1443 METAKNOWLEDGE AND METAPATTERNS IN DATA MINING | 371 |
1444 METALEARNING | 373 |
SUMMARY | 374 |
REFERENCES | 375 |
| 377 | |
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Common terms and phrases
aggregation algorithms analysis applications approach Artificial Intelligence association rules attributes basic Bayesian candidate key Chen computational intelligence conceptual graph conceptual query answering considered constraints constructed data mining data modeling data warehouse data warehousing DBMS decision support defined denotes developed discussed in Chapter document stems domain knowledge Engineering entity set example expert system Figure functional dependencies fuzzy logic fuzzy set theory goal heuristics idea processors implementation important induction information retrieval integrated intensional answers involved issues K-systems K-systems theory knowledge base knowledge discovery knowledge representation knowledge-based systems language machine learning materialized views methods neural networks node Note object object-oriented OLAP operations perspective predicate logic primary key problem solving Prolog propositional recursion referred relational database relationship represent result rough set schema Self-examination Questions solution stored student summary Table task techniques transaction tuples uncertain reasoning uncertainty variables