Index

  • 80/20 rule, 48

A

  • Abstraction level, 27–28

  • Actionable knowledge, 22, 23, 25, 35–40

    • complements, 35–37

    • Independents, 38–39

    • Key Performance Indicators, 39–40

    • randomness versus, 43

    • Substitutes, 37

  • Affinity, 19–20, 55

    • causality versus, 28–29

    • complementary Objects, 21–22, 28, 35–37

    • consumer intent versus, 29

    • hypothesis testing, 35

    • Independent Objects, 22, 28, 38–39

    • Key Performance Indicators, 39–40

    • levels of abstraction, 27–28

    • logic of Market Basket Analysis, 23–24, 26

    • monitoring, 30, 39–40, 84–85

    • monitoring, KPI exception reporting, 94

    • object groups, 84

    • statistical associations, 20

    • Substitute Objects, 22, 37

  • Alerts, 6–7

  • Analysis versus reporting, 13–15, 30, 40

  • Analyst-driven analysis, 88, 91

  • Analytic skills, 34–35

  • Arrays, 50

  • Attributes, 62, 82–83, 202–204

    • discontinued row, 212

B

  • Bacon, Sir Francis, 34

  • Batch reporting, 30, 39–40

  • “Beer and diapers,” 21–22, 28

  • BETWEEN statement, 206

  • Business Intelligence (BI) Reporting

    • analysis versus reporting, 13–15, 40

    • query performance optimization, 49

C

D

E

F

G

  • “Garbage in...garbage out,” 49, 220

  • Gravitational pull of data, 4–6, 91

  • GROUP BY operations, 50, 75, 87, 90, 94, 98, 110, 185, 229

  • Groups within enterprises, 46, 47

H

I

J

K

  • Key definitions, 60–63

    • attribute, 62

    • Itemset key, 61

    • Itemset metric, 63

    • Market Basket Analysis BI View, 79–82

    • Object key, 61–62

  • Key Performance Indicators (KPIs), 30, 39–40, 84–85

    • KPI exception reporting, 94

  • Kimball, Ralph, 7, 11, 144, 196, 234

L

  • Large data volumes, 44–46

  • Left Outer Join, 143

  • Level of abstraction, 27–28

  • Load function, ETL, 204, 214–215

  • Log of ETL activity, 104, 217

  • Lookup operation tool, ETL, 223

  • “Lube and oil,” 84

M

  • Manufactured products, 17

  • Manufacturing example, 221–229

  • Manufacturing foreman, 150

  • Market Basket, 15–16, see also Itemsets

  • Market Basket Analysis, 11

    • analyst-driven analysis, 88, 91

    • decision-making enhancement, 26

    • deliverables, expectations versus reality, 30–31

    • general approval of, 33

    • gravitational pull of data, 91

    • high level review, 233–234

    • as search, 34

    • as search, flashlight analogy, 13–15

    • ultimate goal, 21

  • Market Basket Analysis, common misperceptions, 28, 87

    • causal relationships, 28–29

    • consumer intent discernment, 29

    • need for expensive solutions, 33

  • Market Basket Analysis, delivery within Time Variant context, see Time Variant Market Basket Analysis

  • Market Basket Analysis, difficulties of, 41–42

  • Market Basket Analysis, elements of, 15, 55, see also specific elements

    • Affinity, 19–20

    • Itemsets, 15–18

    • Market Basket Scope Statement, 56

    • Objects, 18–19

    • Statistics, 20–23

  • Market Basket Analysis, logic of, 23–28

    • affinity applications, 23–24

    • outside the basket thinking, 26–28

    • patterns and enterprise relationships, 24–26

  • Market Basket Analysis architecture, 88–89

  • Market Basket Analysis BI View, 73–79, 90, 115–120, 233–234

    • additional group by data elements, 82–83

    • correlation hierarchy, 124, 249

    • driver hierarchy, 254–255

    • with entities, 259–260

    • keys and metrics, 74–75, 79–82

    • metrics, 74–75, 79, 115

    • SQL, 74, 116

    • Time Variant Market Basket Analysis, 245–250

  • Market Basket Analysis ROI, 33–34, 40

  • Market Basket BI Table, 71–73, 87–88, 90–91, 233–234

    • adding and stratifying attributes, 82–83

    • construction of, 98

    • datamart, 73, 88, 99–100, 107

    • exception reports, 93–94

    • iteration terminus, 115

    • “junk drawer” syndrome, 90–91

    • loading using correlation and driver hierarchies, 130–133

    • loading using correlation hierarchy, 120–123, 247–249

    • loading using driver hierarchy, 123–130, 252–254

    • loading with entities, 257–258

    • Market Basket Reporting architecture, 93–94

    • Market Basket Table recursive join and load into, 111–115

    • query, 65–66, 90

    • Time Variant Market Basket Analysis examples, 242–244

  • Market Basket BI Table, ETL design, 106–107

    • arithmetic juxtaposition Driver and Correlation Objects, 115–120

    • extract from Fact Table, load to Market Basket Table, 107–110

    • load table using correlation hierarchy, 120–123, 247–249

    • load table using driver and correlation hierarchies, 130–133

    • load table using driver hierarchy, 123–130

    • recursive join and load table, 111–115, 252–254

    • requirements, 101–106

  • Market Basket Data Definition Language, see Data Definition Language

  • Market Basket ETL design, 106–107, see also Extract, Transform, and Load (ETL) applications; Market Basket BI Table, ETL design

    • arithmetic juxtaposition Driver and Correlation Objects, 115–120

    • extract from Fact Table, load to Market Basket Table, 107–110

    • load table using correlation hierarchy, 120–123, 247

    • load table using driver hierarchy, 123–130, 252–254

    • recursive join and load table, 111–115

    • table populating requirements, 101–106

      • data quality, 105–106

      • Itemset completeness, 102–103

      • metadata, 104–105

      • singular Itemset representation, 102

      • unique identity, 103–104

  • Market Basket KPI Exception Reporting, 94

  • Market Basket Query, 65–66, 95, 233, see also SQL

  • Market Basket Reporting architecture, 91–92

    • ETL operations, 91–92

    • KPI exception reporting, 94

    • Market Basket Analysis BI View, 120

    • Market Basket BI Table, 93–94

    • Market Basket Table, 93

    • most recent data, 92–93

  • Market Basket Reporting batch environment, 30, 39–40, 84–85, 91–93

  • Market Basket Scope Statement, 55–56, 66, 234

    • Correlation Object definition, 59–60

    • Itemset definition, 56–58

  • Market Basket Table, 63–65, 87, 233

    • construction of, 95–98

    • Correlation Objects, 67–68

    • datamart architecture, 99–100

    • Driver and Correlation Object recursive join, 68–69

    • ETL operations, 89–90, 107–110

    • index strategy, 65, 89–90, 95–98

    • loading with entities, 256

    • manageable batches of data, 98

    • Market Basket Analysis architecture, 89–90

    • Market Basket Reporting architecture, 93

    • query, 65–66

    • recursive join function, 87

    • Single Object Itemset, 69–71

    • staging table function, 89–90, 100, 112

    • Time Variant Market Basket Analysis examples, 240, 242

  • Marketing, Four P’s of, 18, 22

  • Metadata, 104–105, 221

  • Metric measurements, 52–53

    • Correlation Object, 68, 81

    • Driver Object, 67, 79–80

    • Itemset definition, 63

    • Market Basket Analysis BI View, 74–75, 79, 115

    • Market Basket BI Table juxtaposition, 115

    • Market Basket Table, 64

  • Minds of the consumers, 29

  • Monitoring, 14, 30, 39–40, 84–85

  • Multiple Linear Regression, 21

N

  • Near real time alerts, 6–7

  • Noise, 42–43

  • “Nothing” object, 69

O

P

Q

  • QUANTITY_RATIO, 82

  • Query, see Market Basket Query; SQL

  • Query languages for temporal databases, 161–162

  • Query path, fact tables and dimension tables, 193, 194

  • Query performance optimization, 49

R

  • Randomness, 42–43

  • Random sampling, 49

  • Ratios, 19, 82, 115

  • Real-time and Time Variance, 188–189

  • Real-time ETL, 107

  • Recursive join operation, 68–69, 87

    • DB2, 96–97

    • and load into Market Basket BI Table, 111–115

    • Market Basket Table construction, 95–98

    • optimizing, 95

    • Oracle, 96

    • product (or Cartesian) join, 97–98, 158

    • SQL Server, 97

    • Teradata, 96

    • Time Variant Market Basket Analysis examples, 241

  • Recursive nature of Market Basket Analysis, 51–52

  • Relational Database Management System (RDMS), 87, 163–164, see also Data Definition Language

    • datamart architecture, 99–100

    • Market Basket BI Table construction, 98

    • Market Basket Table construction, 95–98

    • time variant problem, 155–160

    • Time Variant Solution Design goal, 164–165

  • Relational integrity, 9, 162

  • Relational set logic, 50, 155–156

  • Relation Variables (RelVars), 161

  • Replenishment cycle, 144

  • Reporting versus analysis, 13–15, 30, 40

  • Resource consumption periodicity, 205

  • Response time, 3

  • Restaurant applications, 17, 26–27, 57, 58, 151

  • Retail replenishment cycle, 144

  • ROI and Time Variance, see Time Variance ROI

  • ROI of data warehouse, 6–7, 10–11

    • data warehouse purpose and, 6–7, 10–11

  • ROI of Market Basket Analysis, 24–25, 33–34, see also Actionable knowledge; Market Basket Analysis ROI

S

  • Safety equipment, 38

  • Sample bias, 49

  • Sample sets, 20

  • Sample set size effects, 44–46

  • Scope Statement, see Market Basket Scope Statement

  • Seasonal products, 19

  • Service-level Driver Object, 58

  • Service-level Itemset, 57

  • Service technician, 151

  • Set logic, 50, 155–160

  • Shopping cart, 15–16

  • Shop standards, 95

  • Simple Instance Keys, 169, 192–193, 200, 208

    • cascading, 175

    • dimension update, 211

    • join to, 171–173, 193

    • mixed Simple and Compound, 208–209

    • new row of dimension data, 209

    • Type 2 dimension table, 192–195

  • Single Object Itemset, 69–71, 110, 240, 254

  • Source system transaction key, 61

  • SQL, see also Data Definition Language

    • CORR_OBJECT_KEY, 68

    • Correlation Group By, 98

    • DRIVER_OBJECT_KEY, 67

    • Driver Group By, 98

    • GROUP BY, see GROUP BY operations

    • ITEMSET_KEY, 66

    • load Market Basket BI Table, 111, 243

    • load Market Basket BI Table using correlation hierarchy, 121, 247

    • load Market Basket BI Table with driver hierarchy, 126, 252

    • load Market Basket BI Table with entities, 257

    • load Market Basket Table, 109, 242

    • load Market Basket Table with entities, 256

    • load Market Basket Table with hierarchies, 130

    • Market Basket Analysis BI View, 74, 116, 245

    • Market Basket Analysis BI View with correlation hierarchy, 124, 249

    • Market Basket Analysis BI View with driver hierarchy, 128, 254–255

    • Market Basket Analysis BI View with entities, 259

    • Market Basket Analysis high level review, 233

    • Market Basket BI Table, 65

    • query languages for temporal databases, 161–162

    • recursive join, 68

    • relational set logic, 50, 155–156

    • set logic, 50

    • SUM, 230–231, see also SUM operations

  • Staging table, 89–90, 100, 112

  • Standards, 95

  • Static Metadata, 104

  • Statistical models, 42

  • Statistical significance, 44–46

    • 80/20 rule, 48

  • Statistics, 20–23

    • data sampling, 47–49

  • Stock trade, 16

  • Stored procedure, 50, 52, 160

  • Strategic data, 188–189

  • Substitute Objects, 22, 37, 42

  • Summary ETL, 229–231

  • Summary reports, 6

  • Summary tables, 183–186, 192, 199

    • Type 1/Type 2 hybrid, 199, 230

  • Summation query, 185, 229, 230, see also SUM operations

  • SUM operations, 50, 87, 90, 94, 98, 110, 229

  • Surrogate keys, 168–169

T

U

V

  • Validation of data, 10

  • Valid Time, 161

W

  • Water pressure and TV viewing patterns, 43

  • Weather effects, 19

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