Demonstration – TimeTransf


TimeTransf Demonstration

What is TimeTransf

TimeTransf module is used to preprocess data containing time series to be used with LISp-Miner. This is done by computing characteristics for each time series. You define your own characteristics depending on which information you need to gain from time series.

For using module TimeTransf your database must contain at least one event matrix (table with time series).

Easy guide for TimeTransf

Choosing database

The first step to use TimeTransf is to choose working database. The best way how to create database as ODBC source is using LMAdmin module. In this example we will use Stulong database and Stulong Metabase created by LMDataSource module with already defined atributes.

Choosing the database

Figure 1: Choosing the database

Main interface

Main interface

Figure 2: Main interface

Main interface is devided into five parts.

To create new task press “Add” button.

Creating new task

Adding new task

New task

Now you can set name of task (here Ukol1), group of tasks and comment to created task.

Setting parametrs of new task

Task parametrs

After adding new task you can set its parametrs. On screen you can see four parts:

There are two buttons as well “VARIABLE DEFINITION” and “EDIT SQL QUERY”.

Variable definition

Variables list enables to manage variables by

Variables serve for the calculation of characteristics. Each variable relates to one particular table containing time series. Each variable consists of four data fields describing time series. Note: Variables are defined globally within the current metabase which means you can use it in all tasks.

Variable definition

The name of the variable identifies it the whole TimeTransf module. Each variable consists of the following data fields (all four data fields relate to the entered variable matrix):

Edit SQL query

SQL query

Window for editing SQL query enables specification of selection of key items. Such specification enables elimination of needless time series in order to speed up the calculation. It is necessary to keep SQL conventions especially the 'distinct' must be included

Adding new characteristics

New characteristic

Now we have set variables for our first task. We will try to get average weight of pacient during time period.


We want to see avarage weight of people who do not smoke so we must select AVERAGE in Function Field for variable VAHA.

Here is list of all available functions in TimeTransf.


Condition variable is possible to select only within the selected matrix. Condition variable displays as the name of variable and its data type. It is possible to select from the fields value, date/time or symbol. With the field date/time it is also possible to extract only e.g. year, day in week or minute. If you don't need to set condition it is necessary to leave Condition variable set to “none”. In the Condition field it is possible to select a comparison operator (=, <>, <, >) to compare with the value entered in the field Value. It is possible to enter more than one value separated by commas in the field Value, in such a case the logical operator OR will be used in the calculation. We want people who changed their behavior in smoking (that was created Symbol variable ZMKOURENI in previous step). People who did not change their smoking behavior have atribute ZMKOURENI > 0.

Created characteristic

New characteristic

So now we have characteristic we can run TimeTransf by pressing RUN button.

Running task


You can see progress dialog of TimeTransf be patient this can take several minutes. After finishing Close the windows and select RESULT button to see results of task.

Result windows


TimeTransf shows the results of the task we see average weights of patients who changed their smoking behavior. Patiens who did not change smoking behavior or did not smoke has zero as a result of task.


Now with created task and results you can use Graph function in main interface. Press button Data, graphs & analysis.



Finally we see graph of first patient and changes during time period.

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