Overview

Dataset statistics

Number of variables4
Number of observations43891994
Missing cells15532919
Missing cells (%)8.8%
Total size in memory2.6 GiB
Average record size in memory64.6 B

Variable types

Text2
Numeric1
DateTime1

Dataset

Description[unitless] Returns the name of the application (or application package) that is currently running in the foreground of the phone. To compare each sensor observation, the frequency was reduced to one minute. The first non-missing name is reported for each of the categorical variables.
CreatorAndrea Bontempelli, Matteo Busso, Roy Alia Asiku
AuthorAndrea Bontempelli, Matteo Busso, Fausto Giunchiglia
URL
Copyright(c) University of Trento - Knowledge Diversity 2023

Variable descriptions

experimentidExperiment Id
useridUser id
timestampshow month(2), day(2), hour(2), minute(2), second(2), decimals(3)
applicationnameName of the application

Alerts

experimentid has constant value "wenetItaly"Constant
applicationname has 15532919 (35.4%) missing valuesMissing

Reproduction

Analysis started2024-11-24 09:21:30.742940
Analysis finished2024-11-24 09:24:14.962838
Duration2 minutes and 44.22 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

experimentid
Text

CONSTANT 

Experiment Id

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 GiB
2024-11-24T10:24:15.028093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters438919940
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwenetItaly
2nd rowwenetItaly
3rd rowwenetItaly
4th rowwenetItaly
5th rowwenetItaly
ValueCountFrequency (%)
wenetitaly 43891994
100.0%
2024-11-24T10:24:15.278803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 87783988
20.0%
t 87783988
20.0%
w 43891994
10.0%
n 43891994
10.0%
I 43891994
10.0%
a 43891994
10.0%
l 43891994
10.0%
y 43891994
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438919940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 87783988
20.0%
t 87783988
20.0%
w 43891994
10.0%
n 43891994
10.0%
I 43891994
10.0%
a 43891994
10.0%
l 43891994
10.0%
y 43891994
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438919940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 87783988
20.0%
t 87783988
20.0%
w 43891994
10.0%
n 43891994
10.0%
I 43891994
10.0%
a 43891994
10.0%
l 43891994
10.0%
y 43891994
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438919940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 87783988
20.0%
t 87783988
20.0%
w 43891994
10.0%
n 43891994
10.0%
I 43891994
10.0%
a 43891994
10.0%
l 43891994
10.0%
y 43891994
10.0%

userid
Real number (ℝ)

User id

Distinct221
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.2149588
Minimum0
Maximum265
Zeros56185
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size669.7 MiB
2024-11-24T10:24:15.413051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q159
median119
Q3204
95-th percentile254
Maximum265
Range265
Interquartile range (IQR)145

Descriptive statistics

Standard deviation79.94863255
Coefficient of variation (CV)0.6139742568
Kurtosis-1.298453598
Mean130.2149588
Median Absolute Deviation (MAD)74
Skewness0.05448558863
Sum5715394190
Variance6391.783847
MonotonicityIncreasing
2024-11-24T10:24:15.536837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 433722
 
1.0%
118 431349
 
1.0%
8 430204
 
1.0%
163 427374
 
1.0%
20 426847
 
1.0%
19 424703
 
1.0%
80 423547
 
1.0%
31 421844
 
1.0%
79 420888
 
1.0%
5 420798
 
1.0%
Other values (211) 39630718
90.3%
ValueCountFrequency (%)
0 56185
 
0.1%
1 368784
0.8%
2 221136
0.5%
3 130051
 
0.3%
4 96621
 
0.2%
ValueCountFrequency (%)
265 98047
 
0.2%
264 77189
 
0.2%
263 385607
0.9%
262 391251
0.9%
260 210
 
< 0.1%

timestamp
Date

show month(2), day(2), hour(2), minute(2), second(2), decimals(3)

Distinct43423508
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size669.7 MiB
Minimum2020-11-16 07:00:00.028000
Maximum2020-12-11 21:59:59.988000
2024-11-24T10:24:15.658309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-24T10:24:15.774867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

applicationname
Text

MISSING 

Name of the application

Distinct1791
Distinct (%)< 0.1%
Missing15532919
Missing (%)35.4%
Memory size1.3 GiB
2024-11-24T10:24:15.934563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length97
Median length68
Mean length22.45957991
Min length5

Characters and Unicode

Total characters636932911
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)< 0.1%

Sample

1st rowit.unitn.disi.witmee.sensorlog
2nd rowit.unitn.disi.witmee.sensorlog
3rd rowit.unitn.disi.witmee.sensorlog
4th rowit.unitn.disi.witmee.sensorlog
5th rowcom.miui.home
ValueCountFrequency (%)
com.sec.android.app.launcher 5131554
18.1%
com.whatsapp 3307487
 
11.7%
com.instagram.android 2463273
 
8.7%
com.huawei.android.launcher 1901245
 
6.7%
com.google.android.youtube 1408831
 
5.0%
com.miui.home 1095311
 
3.9%
com.android.chrome 891582
 
3.1%
net.oneplus.launcher 777566
 
2.7%
it.unitn.disi.witmee.sensorlog 670585
 
2.4%
org.telegram.messenger 658394
 
2.3%
Other values (1781) 10053247
35.4%
2024-11-24T10:24:16.245446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 72732060
11.4%
o 63694382
 
10.0%
a 58629275
 
9.2%
c 49196882
 
7.7%
e 41147286
 
6.5%
n 38029299
 
6.0%
r 38002167
 
6.0%
m 37321163
 
5.9%
d 36379628
 
5.7%
i 34202862
 
5.4%
Other values (53) 167597907
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 636932911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 72732060
11.4%
o 63694382
 
10.0%
a 58629275
 
9.2%
c 49196882
 
7.7%
e 41147286
 
6.5%
n 38029299
 
6.0%
r 38002167
 
6.0%
m 37321163
 
5.9%
d 36379628
 
5.7%
i 34202862
 
5.4%
Other values (53) 167597907
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 636932911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 72732060
11.4%
o 63694382
 
10.0%
a 58629275
 
9.2%
c 49196882
 
7.7%
e 41147286
 
6.5%
n 38029299
 
6.0%
r 38002167
 
6.0%
m 37321163
 
5.9%
d 36379628
 
5.7%
i 34202862
 
5.4%
Other values (53) 167597907
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 636932911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 72732060
11.4%
o 63694382
 
10.0%
a 58629275
 
9.2%
c 49196882
 
7.7%
e 41147286
 
6.5%
n 38029299
 
6.0%
r 38002167
 
6.0%
m 37321163
 
5.9%
d 36379628
 
5.7%
i 34202862
 
5.4%
Other values (53) 167597907
26.3%