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Published by soedito, 2017-10-02 20:06:08

C3_Modeling_72

C3_Modeling_72

Data Warehousing
& Data Mining

Wolf-Tilo Balke
Silviu Homoceanu
Institut für Informationssysteme
Technische Universität Braunschweig
http://www.ifis.cs.tu-bs.de

Summary

• Last week:

– Architectures:Three-Tier Architecture
– Data Modeling in DW – multidimensional paradigm

• Conceptual Modeling: ME/R and mUML

• This week:

– Data Modeling (continued)

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 2

3. DW Modeling

3. Data Modeling

3.1 Logical Modeling:
Cubes, Dimensions, Hierarchies

3.2 Physical Modeling:
Array storage, Star, Snowflake

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 3

3.1 Logical Model

• Elements of the logical model

– Dimensions and cubes

• Basic operations in the multidimensional paradigm

– Cube -selection, -projection, -join

• Change support for the logical model

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 4

3.1 Logical Model

• Goal of the Logical Model

– Refine the ‘real’ facts and dimensions of the
subjects identified in the conceptual model

– Establish the granularity for dimensions
– E.g. cubes: sales, purchase, price, inventory

dimensions: product, time, geography, client

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 5

3.1 Dimensions

• Dimensions are entities Sales
chosen in the data model regarding Turnover
some analysis purpose
Purchase
– Each dimension can be used to define Amount

more than one cube Price
Unit Price
– They are hierarchically organized

Prod. Categ Prod. Family Prod. Group Article

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 6

3.1 Dimensions

• Dimension hierarchies are organized in
classification levels also called granularities
(e.g., Day, Month, …)

– The dependencies between the classification levels are
described in the classification schema by
functional dependencies

• An attribute B is functionally dependent on some attribute
A, denoted A ⟶ B, if for all a  dom(A) there exists exactly
one b  dom(B) corresponding to it

Week

Year Quarter Month Day

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 7

3.1 Dimensions

• Classification schemas

– The classification schema of a dimension D is a semi-
ordered set of classification levels
({D.K0, …, D.Kk}, ⟶ )

– With a smallest element D.K0,
i.e. there is no classification level
with smaller granularity

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 8

3.1 Dimensions

• A fully-ordered set of classification levels is
called a Path

– If we consider the classification schema of the
time dimension, then we have the following paths

• T.Day T.Week

• T.Day T.Month T.Quarter T.Year

Year Quarter Month Day

Week

– Here T.Day is the smallest element

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 9

3.1 Dimensions

• Classification hierarchies

– Let D.K0 ⟶ …⟶ D.Kk be a path in the classification
schema of dimension D

– A classification hierarchy concerning these path is
a balanced tree which

• Has as nodes dom(D.K0) … dom(D.Kk) {ALL}
• And its edges respect the functional dependencies

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 10

3.1 Dimensions

• Example: classification hierarchy for the product

dimension path

Prod. Categ Prod. Family Prod. Group Article

Category Electronics ALL Clothes

Video Audio TV …
Prod. Family
… …
Prod. Group
Video Camcorder
recorder Article


TR-34 TS-56

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 11

3.1 Cubes

• Cubes represent the basic unit of the
multidimensional paradigm

– They store one or more measures (e.g. the
turnover for sales) in raw and pre-aggregated form

• More formally a cube C is a set of cube cells
C  dom(G) x dom(M), where
G=(D1.K1, …, Dn.Kn) is the set of granularities,
M=(M1, …, Mm) the set of measures

– E.g. Sales((Article, Day, Store, Client), (Turnover))

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 12

3.1 Cubes

• Aggregates are used for speeding up queriescity

– For the 3-dim cube sales ((item, city, year), (turnover))
we have

• 3 aggregates with 2 dimensions e.g. (*, city, year)
• 3 aggregates with 1 dimension e.g. (*, *, year)
• 1 aggregate with

no dimension (*,*,*)

(*, *, *)

(*, *, year)

item (*, city, year)
13
Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig

3.1 Cubes

• In the logical model cubes (also comprising the
aggregates) are represented as a lattice of
cuboids

– The top most cuboid, the 0-D, which holds the highest
level of summarization is called apex cuboid

– The nD cube containing
non-aggregated measures
is called a base cuboid

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 14

3.1 Cubes

• But things can get complicated pretty fast (4 dim.)

all
0-D(apex) cuboid

time item location supplier

1-D cuboids

time,item time,location item,location location,supplier

time,supplier 2-D cuboids

item,supplier

time,item,location time,location,supplier

3-D cuboids

time,item,supplier item,location,supplier

time, item, location, supplier 4-D(base) cuboid

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 15

3.1 Basic Operations

• Basic operations of the multidimensional
paradigm at logical level

– Selection
– Projection
– Cube join
– Aggregation

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 16

3.1 Basic Operations

• Multidimensional Selection

– The selection on a cube C((D1.K1,…, Dg.Kg),
(M1, …, Mm)) with a predicate P, is defined as
σP(C) = {z Є C:P(z)}, if all variables in P are either:

• Classification levels K , which functionally depend on a
classification level in the granularity of K, i.e. Di.Ki ⟶ K

• Measures from (M1, …, Mm)

– E.g. σP.Prod_group=“Video”(Sales)

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 17

3.1 Basic Operations

• Multidimensional projection

– The projection of a function of some measure F(M)
of cube C is defined as

F(M)(C) = { (g,F(m))  dom(G) x dom(F(M)): (g,m)  C}

– E.g. turnover, sold_items(Sales)

Sales
Turnover
Sold_items

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 18

3.1 Basic Operations

• Join operations between cubes is usual

– E.g. if turnover would not be provided, it could be
calculated with the help of the unit price from the
price cube

• 2 cubes C1(G1, M1) and C2(G2, M2) can only be
joined, if they have the same granularity

(G1= G2 = G)

– C1⋈C2= C(G, M1∪ M2)

Sales Price
Units_Sold Unit Price

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 19

3.1 Basic Operations

• Comparing granularities

– A granularity G={D1.K1, …, Dg.Kg} is finer than
G’={D1’.K1’, …, Dh’.Kh’}, if and only if
for each Dj’.Kj’ G’ ∃ Di.Ki  G where Di.Ki ⟶ Dj’.Kj’

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 20

3.1 Basic Operations

• When the granularities are different, but we still
need to join the cubes, aggregation has to be
performed

– E.g. , Sales ⋈ Inventory: aggregate Sales((Day,Article,
Store, Client)) to Sales((Month,Article, Store, Client))

Country Region District City Client Sales
Store Turnover

Prod. Categ Prod. Family Prod. Group Article Inventory
Week Stock

Year Quarter Month Day 21

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig

3.1 Basic Operations

• Aggregation is the most important operation
for OLAP

• Aggregation functions

– Compute a single value from some set of values, e.g. in
SQL: SUM,AVG, Count, …

– Example: SUM(P.Product_group, G.City, T.Month)(Sales)

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 22

3.1 Change support

• Classification hierarchy, classification schema, cube
schema are all designed in the building phase and
considered as fixed

– Practice has proven different

– DW grow old, too

• Reasons for classification hierarchy and schema
modifications

– New requirements

– Data evolution

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 23

3.1 Classification Hierarchy

• Some electronics retail company feed data to
their DW since 2003

– Example of a simple classification hierarchy of data
until 01.07.2008, for mobile phones only:

Mobile Phone

GSM 3G

Nokia 3600 O2 XDA BlackBerry
Bold

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 24

3.1 Classification Hierarchy

• After 01.07.2008 3G becomes hip and affordable
and many phone makers start migrating towards
3G capable phones

– O2 made its XDA 3G capable

Mobile Phone

GSM 3G

Nokia 3600 O2 XDA BlackBerry
Bold

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 25

3.1 Classification Hierarchy

• After 01.04.2011 phone makers already develop
4G capable phones

Mobile Phone

GSM 3G 4G

Nokia 3600 O2 XDA BlackBerry Best phone
Bold ever

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 26

3.1 Classification Hierarchy

• Problem: Sales volume for GSM
products can be problematic

– According to the current schema, O2 XDA belongs to
the 3G category

– None of the O2XDA GSM only devices will account
for the GSM sales volume

• Solution: trace the evolution of the data

– Versioning system of the classification hierarchy
with validity timestamps

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 27

3.1 Classification Hierarchy

• Annotated change data

[01.03.2003, ∞) Mobile Phone [01.04.2011, ∞)

[01.04.2005, ∞)

GSM 3G 4G

[01.04.2005, ∞) [01.07.2008, ∞) [01.03.2006, ∞) [01.04.2011, ∞)
[01.03.2003, 01.07.2008)
BlackBerry Best phone
Nokia 3600 O2 XDA Bold ever

Relational Database Systems 1 – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 28

3.1 Classification Hierarchy

• The tree can be stored as metadata: the validity
matrix

– Rows are parent nodes and columns are child nodes

GSM 3G 4G Nokia 3600 O2 XDA Berry Bold Best phone

Mobile [01.03.2003, ∞) [01.04.2005, ∞) [01.04.2011, ∞)
phone
GSM [01.04.2005, ∞) [01.03.2003,
01.07.2008)
3G [01.03.2006, ∞)
4G [01.07.2008, ∞)

Nokia 3600 [01.04.2011
O2 XDA , ∞)

Berry Bold
Best phone

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 29

3.1 Classification Hierarchy

• Flexibility gain: Having the validity information,
queries like as is versus as was are possible

– Even if in the latest classification hierarchy GSM
products would not be provided anymore one can still
compare sales for O2XDA as GSM vs. 3G

Mobile …
Phone

……

GSM 3G 4G

… … … …

Nokia 3600 O2 XDA BlackBerry Best phone
Bold ever

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 30

3.1 Schema Versioning

• No data loss

• All the data corresponding to all the schemas are
always available

• After a schema modification the data is held in
their belonging schema

– Old data - old schema

Prod. Categ Prod. Family Prod. Group Article Sales
Turnover
– New data - new schema Sales
Turnover Purchase
Prod. Categ Prod. Group Article Amount

Purchase Price
Amount Unit Price

…. 31

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig

3.1 Schema Versioning

• Advantages

– Allows higher flexibility e.g. querying for the product
family for old data

• Disadvantages

– Adaptation of the data to the queried schema is done
on the spot

– This results in longer
query run time

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 32

3.1 Schema Modification

• Schema evolution

Prod. Categ Prod. Family Prod. Group Article Sales
Turnover

– Modifications can be performed Purchase
without data loss Amount

– It involves schema modification and Price
data adaptation to the new schema Unit Price

– Advantage: Faster to execute queries for DW with many
schema modifications

• Because all data is prepared for the current and single schema

– Disadvantage: limited user flexibility - only queries based
on the actual schema are supported

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 33

3.2 Physical Model

• Defining the physical structures

– Define the actual storage architecture
– Decide on how the data is to be accessed and how it

is arranged

– Performance tuning strategies (next lecture) 34

• Indexing
• Partitioning
• Materialization

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig

3.2 Physical Model

• The data in the DW is stored according to the
multidimensional paradigm

– The obvious multidimensional storage model is
directly encoding matrices

• Relational DB vendors, in the market place saw
the opportunity and adapted their systems

– Special schemas respecting the
multidimensional paradigm

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 35

3.2 Multidimensional Model

• The basic data structure for multidimensional
data storage is the array

• The elementary data structures are the cubes and
the dimensions

– C=((D1, …, Dn), (M1, …, Mm))

• The storage of matrices is intuitive as arrays of
arrays i.e. physically linearized

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 36

3.2 Linearization

• Linearization example: 2D cube |D1| = 5, |D2| = 4,
cube cells = 20

– Query: Jackets sold in March?

• Measure stored in cube cell D1[4], D2[3]

• The 2D cube is physically stored as a
linear array, so

D1[4], D2[3] becomes array cell 14 1 2 35 64 157 Jan (1)
63 74 78 89 109 Feb(2)
– (Index(D2) – 1) * |D1| + Index(D1) 191 102 13 14 215 Mar(3)
– Linearized Index = 2 * 5 + 4 = 14 D2 Apr(4)

161 127 185 169 270

D1

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 37

3.2 Linearization

• Generalization:

– Given a cube C=((D1, D2, …, Dn),
(M1:Type1, M2:Type2, …, Mm:Typem)),
the index of a cube cell z with coordinates
(x1, x2, …, xn) can be linearized as follows:

• Index(z) = x1 + (x2 - 1) * |D1| + (x3 - 1) * |D1| * |D2| + …
+ (xn - 1) * |D1| * … * |Dn-1| =

= 1+ ∑ n ((xi - 1) * ∏ i-1 |Di|)
i=1 j=1

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 38

3.2 Problems in Array-Storage

• Influence of the order of the dimensions in the
cube definition

– In the cube the cells of D2 are ordered
one beneath the other
1 2 35 64 157 Jan (1)

e.g., sales of all pants involves a 36 47 78 98 109 Feb(2)
column in the cube
D2 911 102 13 14 125 Mar(3)

– After linearization, the information is 161 172 158 196 207 Apr(4)

spread among several data blocks or pages D1

– If we consider a data block to hold 5 cells, a query over all
products sold in January can be answered with just 1
block read, but a query of all sold pants, involves reading 4
blocks

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 39

3.2 Problems in Array-Storage

• Solution: use caching techniques

– But…caching and swapping is performed by the
operating system, too

– MDBMS has to manage its caches
such that the OS doesn’t perform any
damaging swaps

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 40

3.2 Problems in Array-Storage

• Storage of dense cubes

– If cubes are dense, array storage is quite efficient.
However, operations suffer due to the large cubes

• Loading huge matrixes in memory is not good

– Solution: store dense cubes not linearly but on 2
levels

• The first contains indexes and the second the data cells
stored in blocks

• Optimization procedures like indexes (trees, bitmaps),
physical partitioning, and compression (run-length-
encoding) can be used

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 41

3.2 Problems in Array-Storage

• Storage of sparse cubes

– All the cells of a cube, including empty ones, have to
be stored

– Sparseness leads to data being stored in several physical
blocks or pages

• The query speed is affected by the large number of block
accesses on the secondary memory

– Solution:

• Do not store empty blocks or pages but adapt the index
structure

• 2 level data structure: upper layer holds all possible
combinations of the sparse dimensions, lower layer holds dense
dimensions

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 42

3.2 Problems in Array-Storage

• 2 level cube storage Marketing campaign

Sparse upper level

Customer

Geo
Time

Dense low level

Product

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 43

3.2 Physical Model

• Relational model, goals:

– As low loss of semantically knowledge as
possible e.g., classification hierarchies

– The translation from multidimensional queries must
be efficient

– The RDBMS should be able to run the translated
queries efficiently

– The maintenance of the present tables should be easy
and fast e.g., when loading new data

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 44

3.2 Relational Model

• Going from multidimensional to relational

– Representations for cubes, dimensions, classification
hierarchies and attributes

– Implementation of cubes without the classification
hierarchies is easy

• A table can be seen as a cube
• A column of a table can be considered as a dimension

mapping
• A tuple in the table represents a cell in the cube
• If we interpret only a part of the columns as dimensions we

can use the rest as measures
• The resulting table is called a fact table

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 45

3.2 Relational Model

Product

818

Laptops Time
Mobile p.
13.11.2010
18.12.2010

Geography

Article Store Day Sales

Laptops Hannover, Saturn 13.11.2010 6
Mobile Phones 24
Laptops Hannover Saturn 18.12.2010 3

Braunschweig 18.12.2010
Saturn

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 46

3.2 Relational Model

• Snowflake-schema

– Simple idea: use a table for each classification level

• This table includes the ID of the classification level and
other attributes

• 2 neighbor classification levels are connected by 1:n
connections e.g., from n Days to 1 Month

• The measures of a cube are maintained in
a fact table

• Besides measures, there are also the
foreign key IDs for the smallest
classification levels

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 47

3.2 Snowflake Schema

• Snowflake?

– The facts/measures are in the center
– The dimensions spread

out in each direction and
branch out with their
granularity

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 48

3.2 Snowflake Example

Product group 1 Product 1 Week

Product_group_ID Product_ID Sales Day 1 n
n Description Description
Brand n 1 Week_ID
Product_categ_ID n Product_gro Description
up_ID Product_ID n Day_ID Year_ID
… n Day_ID
Description n
Store_ID
Product category Sales n Month_ID Month Year
Revenue Week_ID
1 1 1

Product_category_ID Month_ID Year_ID
Description Description 1 Description
Quarter_ID

n

State Store Quarter

State_ID 1 1 Store_ID 1

Region n Description n Description Quarter_ID n
Region_ID State_ID
Region_ID Description
Description 1 …
Country_ID n Year_ID

time

Country fact table

1 location dimension tables

Country_ID
Description

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 49

3.2 Snowflake Schema

• Advantage:With a snowflake schema the size of
the dimension tables will be reduced and
queries will run faster

– If a dimension is very sparse (most measures
corresponding to the dimension have no data)

– And/or a dimension has long list of attributes
which may be queried

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 50


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