This document discusses key concepts in dimensional modeling and online analytical processing (OLAP) using cubes. It describes facts and measures as the most detailed information for analysis, and dimensions as providing context for facts. Dimensions are used to filter, aggregate, order, and define relationships between facts. The document also outlines features of the Cubes OLAP server, including defining logical models, aggregating and browsing data, slicing and dicing queries, and localization of data and metadata.

1. Cubes
Light-weigth Online Analytical Processing
Stefan Urbanek
stefan.urbanek@gmail.com April 2011
@Stiivi

2. Features
■ logical model (metadata)
■ aggregated data browsing
■ OLAP HTTP server with json interface
■ data and metadata localization
■ multiple backends

3. Introduction
cubes, facts and dimensions

4. data cell
data cube

5. most detailed
information measurable
Fact examples: measure examples:
• contract • contract amount
• donation • revenue
• spending fact • duration
• invoice • price with VAT
• project
• ...
data cell

6. location
type
time
dimensions

7. dimensions type
location
time
■ provide context for facts
■ used to filter queries or reports
■ control scope of aggregation of facts
■ used for ordering or sorting
■ define master-detail relationships
☛ [star]

8. region
2010 May 1st
date
hierarchies

9. subject area
data mart

10. Summary
■ fact – most detailed information for analysis
■ measure – an attribute for computation
■ dimension – context of facts
■ hierarchy – master-detail relationship

11. ✂
✂ Slicing and Dicing

12. 08
06
07
09
10
20
20
20
20
20
location
type
time
spending in 2010

13. contracts in
location Estonia
type Czech Republic
Slovakia
Hungary
Poland
Estonia
time

14. ✂
✂
Estonia 2010
contracts in
location Estonia in 2010
type
time

15. location
type IT contracts
time

16. ✂
✂ ✂ IT
Estonia 2010
IT contracts in
Estonia in 2010
location
type
time

17. spending in 2010
revenue from IT
projects
measures can be aggregated
top 10
contractors

18. Drilling down

19. 250
250
∑ amount
125
0
All years
drill down by date
∑
70
70
amount 35 50
40
60
30
0
2006 2007 2008 2009 2010

20. looking at more detailed level

21. 250
250
top level 125
0
All years
70
70
60
35 50
year level 30
40
0
2006 2007 2008 2009 2010
7
7
5
month level 3,5
3
4
3
2
0 1 1
Jan Feb Mar Apr March April May ...

22. Logical Model
description based on how you analyze data

23. Logical Model
user’s or analyst’s perspective:
how data are being
measured, aggregated and reported
amount
∑ amount
24% 12%
28%
20%
16%

24. abstraction over physical data
✶ star
❄ snowflake

25. Model Legend:
"#localizable
name
!#required during cube creation
label "
description "
and denormalisation
$#required by browser
locale
Cube
name Dimension
label " name Hierarchy
description " label " name
measures description " label "
details default hierarchy
fact table !
Level
name
label "
key attribute
label attribute
!
Join ! Attribute
Mapping !
master name
master
detail label "
detail
alias description "
locales !$

26. Slicer
Cubes OLAP server

27. Application
model
HTTP request
JSON reply
Slicer
Cell
(point of view)
∑ aggregates
facts
(details)
Aggregation Browser
model

28. GET /model
{
"cubes": {
"contracts": {
"measures": [
{
"name": "amount",
"label": "Contract amount"
}
],
"dimensions": ["date", "supplier", "process_type", "cpv"]
}
},
"dimensions": {
"supplier": {
...
}
}
...
}

29. GET /aggregate
aggregate measures

30. ∑ amount
GET /aggregate

31. {
"drilldown": {},
"summary": {
"record_count": 19278,
"zmluva_hodnota_sum": 11222821530.12966
}
}
GET /aggregate

32. cut=...
slice and dice with cut parameter

33. ∑ amount
✂
2010
GET /aggregate?cut=date:2010

34. {
"drilldown": {},
"summary": {
"date.year": 2011,
"record_count": 64,
"zmluva_hodnota_sum": 78717997.108
}
}
✂
2010
GET /aggregate?cut=date:2010

35. ✂
Estonia
∑ amount
✂
2010
/aggregate?cut=date:2010|region:ee

36. cut=date:2010|region:es
dimension points
cut

37. date:2010
path
dimension
region:ee
dimension points

38. month level
year level
date:2010,12
path
dimension
hierarchies

39. date:2010,12|
category:it,sw|
region:ee
contracts in December 2010
in Estonia for IT – Software
more hierarchies

40. pipe separates cuts
date:2010,12|category:it,sw|region:ee
comma separates levels
cut

41. PUT /report
if you want multiple tables and charts with single
request

42. drilldown=
get more details

43. 250
250
∑ amount
125
0
All years
drilldown=date
∑
70
70
amount 35 50
40
60
30
0
2006 2007 2008 2009 2010

44. 250
250
125
0
All years
drilldown=date
70
70
60
35 50
40
30
0
cut=date:2010 2006 2007 2008 2009 2010
&drilldown=date
implicit 7
7
3,5 5
4
hierarchy 0
3
2
1
3
1
Jan Feb Mar Apr March April May ...

45. report by year:
drilldown=date
report by month:
drilldown=date&cut=date:2010
report by day:
drilldown=date&cut=date:2010,11

46. more dimensions:
drilldown=date,supplier
for cross-tables

47. Atributes and Measures
naming

48. date.year
date.month
region.region_name
region.city_name
region.region_code
category.description
attribute
dimension

49. hierarchical attribute
dependencies are implicit
defined in model

50. there is no “date” data type
date components are normal attributes of
date dimension
year month day

51. received_amount_sum
measure aggregation
record_count

52. page=...&order=
ordering and pagination

53. page=3&pagesize=20
3rd page with 20 results per page

54. order=region.name
order by region name
order=amount:desc
order by amount descending

55. Natural Ordering
attributes can have default order
specified in model
order=year:asc
... might be omitted if model contains:
{“name” = “year”, “order” = “asc”}

56. lang=
transparent localization

57. Localization
1
2
3

58. + +
master
translations
model

59. drilldown=type&lang=en
drilldown=type&lang=sk
report query is language independent

60. Cubes
online analytical processing
github/bitbucket: Stiivi

61. ☛ References
and further reading
[star] Christopher Adamson: Star Schema, 2010