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malization than
A
2
. In this paper, the value of the integration of the full time of
A
i
normalizes to 1. A more effective normalization system will be in the future work.
Next, we compare the degree of dependence. From a formula, the degree of de-
pendence becomes higher, so that the rate of the area of full time and the area after
t
B
is higher. Therefore, we can see that
A
2
and
B
are more appropriate anteroposterior
correlated than
A
1
and
B
.
However, in the case of
A
4
, although there is a dependency of phenomenon
Y
,
the degree of dependence is a low value. Therefore, we have to calculate the degree
of dependence in the case of at the same time.
5
Preliminary Experiment
5.1
Experiment System Setting
We focus on the Great East Japan Earthquake. Our purpose is an extraction of ap-
propriate anteroposterior correlation of the Great East Japan Earthquake.
We use Google Trends as query log. The Google Trends provides the number of
times for a keyword given by a user in each week or each month. Although this data
is secondary data, it can be seen as Big data. A query log is an important Big data.
In this paper, we only use Google Trends as a query data. Therefore, our method can
use the actual query log such as other search engine or retrieval system in intranet.
Phenomenon
A
i
: The number of
occurrence times of a query
X
A
3
A
2
A
1
A
4
time
t
B
(phenomenon
Y
occurs)
Fig. 4
A simple example of the degree of dependence in the case of applying to query log.
First, we have to normalize
A
1
,
A
2
,
A
3
,and
A
4
. The value of the integration of the full time of
A
i
normalizes to 1. Next, we compare the degree of dependence. From a formula, the degree
of dependence becomes higher, so that the rate of the area of full time and the area after
t
B
is higher. However, in the case of
A
4
, although there is the dependency of phenomenon
Y
, the degree of dependence is a low value. Therefore, we have to calculate the degree of
dependence in the case of ”not
A
i
” at the same time.
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