Extending Relational Data Model with Merged Cells

February 25, 2015

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Background

(table)

783 256 26 2,719 1,213 164 154 91 130 409 199 179 203 32 1,090 1,180 141

Table: (: [5])

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Background

Problem

Avoid merged cells(TechRepublic10 ways to keep Excel from biting you in the butt)

Bad Data(Paul MurrellBad Data Handbook)

Excel( Excel, 2013)

Research purpose

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Background

Remark

col1 col2 col3 col4val1 vala valA

valval2 valbval3 valC

Figure:

col1 col2 col3 col4val1 valA

valval2

Figure:

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Background

Research process

1

2

3

4

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Previous works

Research process

1

2

3

4

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Previous works

nested relation (1NF)

name address birthdate movies

Fishercity street

Maple H'woodLocust Malibu

9/9/99

title year lengthStar wars 1977 124Empire 1980 127Return 1983 133

Hamillcity street

Oak B'wood8/3/88

title year lengthStar wars 1977 124Empire 1980 127Return 1983 133

Figure: nested relation (:DBInfoBlog[1])

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Previous works

nested relation (1)

R(name, address(city, street), birthdate, movies(title, year, length))

()

R(R1(col1, col2), col3, col4)

R(col1,R2(col2, col3), col4)

col1 col2 col3 col4val1 vala valA

valval2 valbval3 valC

Figure: ()8 / 26

Previous works

nested relation (2)

()

nested relation nest,unnest (Jaeschke et al., 1982)

H23.10.1 5 5 5 H23.10.1 - 25 25 H23.10.1 10 10 H23.10.1 - 25 25

131I 134Cs 137Cs

134Cs,137Cs

Figure: (:)

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Previous works

(OLAP)

OLAP() [2]

roll-up: 1

drill-down: roll-up

roll-up,drill-down

256 26 203 32 1,180 141

Table: roll-up

ALL 1,639 199

Table: roll-up

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Advantages and Disadvantages of merged cells

Research process

1

2

3

4

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Advantages and Disadvantages of merged cells

Advantage

()

(OK)

1,000 600 800 400

300Table: (:)

256 26 1,213 154 130 199 203 32 1,180 141Table:

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Advantages and Disadvantages of merged cells

Disadvantage

()(?)

()

()

H23.10.1 5 5 5 H23.10.1 - 25 25 H23.10.1 10 10 H23.10.1 - 25 25

131I 134Cs 137Cs

134Cs,137Cs

Figure: ()

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Advantages and Disadvantages of merged cells

DBMS

Figure:

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Advantages and Disadvantages of merged cells

Approach

roll-up, drill-down

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Proposing Datamodel

Research process

1

2

3

4

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Proposing Datamodel

Schema (R,T ,F ) 3

R

T = (VT ,ET ) (DAG)

T

F T

sum: (sum(1, 3) = 4)

pack: (pack(1, 1) = 1, pack(1, 3) = (1, 3))

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Proposing Datamodel

Figure: DAG T

(R,T ,F )

R = (, , , )

VT = {, , }

ET = { , }

+ = F = {sum }

783 256 26 2,719 1,213 164 154 91 130 409 199 179 203 32 1,090 1,180 141Table: ()

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Proposing Datamodel

Instance (R,T ,F ) I

x ()

x1, , x7 I = {xi}

x1() = 256, x1() = undened

x2() = undened, x2() = 1213

x1 783 256 26x2 2,719 1,213x3 164 154x7 1,090 1,180 141

Table: ()

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Proposing Datamodel

roll-up

f ( F )

f I = {f x x I}

f x(A) =

f (x(dom(f ))) (A = im(f ))

x(A) (otherwise)

drill-down

f ( F ) f 1

f I = {f x x I}

f x(A) =

v s.t. f ( , v,) = x(im(f )) (A dom(f ))

x(A) (otherwise)

f ( roll-up drill-down)20 / 26

Proposing Datamodel

roll-up

sum roll-up

783 256 26 2,719 1,213 164 154 91 130 409 199 179 203 32 1,090 1,180 141Table: ()

783 282 2,719 1,213 164 154 91 130 409 199 179 235 1,090 1,321

Table: sum roll-up

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Proposing Datamodel

roll-up

1

783 256 26 2,719 1,213 164 154 91 130 409 199 179 203 32 1,090 1,180 141Table: ()

783 282 2,719 1,213 164 154 91 130 409 199 179 235 1,090 1,321

Table: sum roll-up

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Proposing Datamodel

drill-down pack drill-down

1,000 600 400 800 400 300

300 200 Table: ()

1,000 600 400 800 400 300

300 300 200 Table: pack drill-down

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Classifying tables

Research process

1

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Classifying tables

C0,C1,C2 3(C2 C1 C0)

C0: (R,T ,F )

C1: (FD)( or)

C2:

H23.10.1 5 5 5 H23.10.1 - 25 25 H23.10.1 10 10 H23.10.1 - 25 25

131I 134Cs 137Cs

134Cs,137Cs

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Conclusion

roll-up, drill-down

Future tasks

1NF DDNF()

DBMS

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Purpose

783 256 26 2,719 1,213 164 154 91 130 409 199 179 203 32 1,090 1,180 141

Table: (: [5])

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Previous works

Excel Excel ( [6], 2013)

2 Cs ()

H23.10.1 5 5 5 H23.10.1 - 25 25 H23.10.1 10 10 H23.10.1 - 25 25

131I 134Cs 137Cs

134Cs,137Cs

Figure: Excel ( Bq/kg: [4])

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Previous works

Spreadsheet Algebra

Liu,JagadishA Spreadsheet Algebra for a Direct Data ManipulationQuery Interface(2009)[3]

SQL GROUP BY HAVING

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Proposing Datamodel

sum: (sum(1, 3) = 4)

avg: (avg(1, 3) = 2)

max: (max(1, 3) = 3)

cat: (cat(c, a, t) = cat)

tuple: (tuple(1, 3) = (1, 3))

pack: (pack(1, 1) = 1, pack(1, 3) = (1, 3))

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Proposing Datamodel

Instance (R,T ,F ) I

x

3 (vij , g, f ) vij g f

Group

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Proposing Datamodel

Figure: DAG T

x1() = (, null,True), x1() =(256, null,True), x1() = undened

x2() = undened,x2() = (1213, null,True)

x3, , x7I = {xi}

783 256 26 2,719 1,213 164 154 91 130 409 199 179 203 32 1,090 1,180 141Table: ()

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Classifying tables

roll-up, drill-down roll-up (RUNF): roll-up 1NF

drill-down (DDNF): drill-down 1NF

1NF()

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Reference I

DBInfoBlog. Nested Relations. 2013. url:http://www.dbinfoblog.com/post/121/nested-relations.

Jim Gray et al. Data Cube: A Relational Aggregation Operator Generalizing Group-By,Cross-Tab, and Sub-Totals. In: Data Mining and Knowledge Discovery 1.1 (1997),pp. 2953. doi: 10.1023/A:1009726021843.

Bin Liu and H.V. Jagadish. A Spreadsheet Algebra for a Direct Data Manipulation QueryInterface. In: Data Engineering, 2009. ICDE 09. IEEE 25th International Conference on.2009. doi: 10.1109/ICDE.2009.34.

. . 2011. url:http://www.mhlw.go.jp/stf/houdou/2r9852000001q51k-att/2r9852000001qjsv.pdf.

. (). 2013.url: http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo0/gijiroku/attach/__icsFiles/afieldfile/2013/10/16/1340415-9-2.pdf.

. Excel. In: SSS2013 (2013), pp. 9398. url: http://oku.edu.mie-u.ac.jp/~okumura/SSS2013.pdf.

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BackgroundPrevious worksAdvantages and Disadvantages of merged cellsProposing DatamodelClassifying tablesConclusionAppendixPurposePrevious worksProposing DatamodelClassifying tables