Wiki source code of Requête en langage naturel

Last modified by Aurelie Bertrand on 2025/02/07 10:09
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1 {{ddtoc/}}
2
3 = Prerequisites =
4
5 * Cubes in your wallets recently synchronized
6
7 = Creating flows with text query =
8
9 Digdash offers the possibility to create flows in some clicks with natural language.
10
11 * Via the dashboards editor
12
13 |[[image:text_query_en_html_f2d69e963b6dfe7b.png||height="177" width="275"]]
14 |**Dashboards editor **>** Creating new graphics **>** In natural language**
15 |[[image:text_query_en_html_d3b55a8e10123fdd.png||height="207" width="266"]]
16 |In the showing dialog, you can enter you query in the search bar.
17 |[[image:text_query_en_html_98fd1f57c633562.png||height="184" width="318"]]
18 |Choose a flow in the results list (we are going to pick the first one)
19 |[[image:text_query_en_html_c8e99a6b1e0bc9e9.png||height="170" width="355"]]
20 |And save the flow in the current wallet.
21 |[[image:text_query_en_html_cddc52d001b3a1e9.png||height="213" width="355"]]
22 |Give a name to your flow.
23 |[[image:text_query_en_html_f70ed4175e1093f9.png||height="175" width="355"]]
24 |The new flow will be added to your dashboard editor and you will find it in the existing graphics list.
25
26 * Via the dashboard
27
28 It is also possible to add a « Text query » element in your dashboard in order to create flows on the fly.
29
30 |[[image:text_query_en_html_21e80dc5c36d4220.png||height="468" width="202"]]
31 |**Dashboards editor **>** Additional content **>** Elements **>** Text query**
32 |[[image:text_query_en_html_f940b7f7ead63723.png||height="223" width="325"]]
33 |Add the element in your dashboard editor.
34 |[[image:text_query_en_html_81f6639240d4c264.png||height="193" width="288"]]
35 |(((
36 According to your query, you may filter on some measures/dimensions.
37
38 You would better have a « Filtered elements » element in your dashboard to remove these filters for the potential next queries.
39
40 **Dashboard editors **>** Additional content **>** Elements **>** Filtered elements**
41 )))
42 |[[image:text_query_en_html_758db70155096f91.png||height="123" width="325"]]
43 |You will find those elements dashboard side.
44
45 {{{
46 }}}
47
48 = Composition of a text query =
49
50 Digdash allows you to create flows from data models in natural language.
51
52 Digdash mainly rests on keywords to suggest you the most relevant flows and the names of your data models’ columns.
53
54 So a query is basically composed of measures names from your data model and/or dimensions names, followed (or not) by flows names and/or sorting operations.
55
56 We shall later explain how important query terms are important.
57
58 = Results for a text query =
59
60 [[image:text_query_en_html_c7eda020d25db863.png||height="117" width="550"]]
61
62
63 //__Screenshot: Results for a text query__//
64
65 Results for a text query are presented in list, with its associated flow, sorted by its relevance and a score. The more the score is high, the more the suggested flow is considered as relevant. Also, the cube name is mentioned so is a description of the flow.
66
67 We shall later see in this document which are the criteria that could have an influence on the score of a result.
68
69 = Choices of graphics =
70
71 For a given query, you will be suggested a list of results of multiple graphics, depending on your query members.
72
73 Nevertheless, you can require a particular graphic, as long as it remains coherent (the query « Cost in line » has no sense).
74
75 The numbers of members for a cube has an incidence. Indeed, when a query, even coherent, has too many members to show, you will be suggested a more adapted graphic.
76
77 Here are the keywords to use for the choice of graphics:
78
79 |**Charts name**|**Keywords**
80 |Pie chart|« pie »
81 |Gauge|« gauge »
82 |Progress bar|« progress bar »
83 |Energy bars|« energy »
84 |Arrow indicator|« arrow »
85 |Column chart|(((
86 « column »
87
88 « histogram »
89 )))
90 |Bar chart|« bar »
91 |Map chart|« map »
92 |Scatter chart|« scatter »
93 |Bubble chart|« bubble »
94 |Line chart|« line »
95 |Zone chart|(((
96 « area »
97
98 « zone »
99 )))
100 |Radar chart|« radar »
101 |Tab chart|« table »
102 |Indicator|« indicator »
103 |{{glossaryReference glossaryId="Glossary" entryId="Tableau croisé"}}Cross table{{/glossaryReference}}|« cross table »
104 |Text chart|« text »
105
106 //__Table presenting keywords for the different flows__//
107
108 = Sorting =
109
110 It is possible for you to use sorting operations with the following keywords :
111
112 |**Sorting**|**Keywords**|**Queries examples**
113 |Ascending|(((
114 « sort » (ascending)
115
116 « sorted » (ascending)
117
118 « ascending »
119 )))|(((
120 « Cost by region in France in 2006 sorted by cost in table »
121
122 «  Cost by region in France in 2006 sorted in table »
123
124 «  Cost by region in France in 2006 ascending in table »
125
126 «  Cost by region in France in 2006 sorted in ascending order in table »
127 )))
128 |Descending|(((
129 « sort » (descending)
130
131 « sorted » (descending)
132
133 « descending »
134 )))|(((
135 «  Cost by region in France in 2006 sorted by cost in table »
136
137 «  Cost by region in France in 2006 descending in table »
138
139 «  Cost by region in France in 2006 sorted in descending order in table »
140 )))
141
142 //__Table presenting keywords for sorting operations__//
143
144
145 = Trend of a measure =
146
147 A measure has a trend. It can be stable (trend by default), increasing or decreasing:
148
149 {{{
150 }}}
151
152 |**Tendance**|**Signification**
153 |STABLE|Default trend: The bigger the better
154 |INCREASING|(((
155 The bigger the better
156
157 //Example : for a quality//
158 )))
159 |DECREASING|(((
160 The lowest the better
161
162 //Example : for a cost//
163 )))
164
165 __//Table presenting possible trends for a measure//__
166
167 You can change the trend of a measure via the Digdash Enterprise Studio, in the data source advanced configuration, in the properties part of a measure.
168
169
170 //__Definition of a measure’s trend in Digdash’s Studio Enterprise__//
171
172 * Impact of a measure’s trend
173
174 Trend has an impact on the sorting order. Indeed, if the type of sorting is not explicitly mentioned, the kind of sorting will be based on the measure’s trend. Consequently, we will get an ascending sort for a stable or increasing trend and a descending sort for a decreasing trend.
175
176
177
178 |**Trend**|**Queries examples**|**Sort**
179 |STABLE|(((
180 « Duration by state sorted »
181
182 Duration is a measure with a stable trend
183 )))|//The sorting of measure Duration on dimension state will be descendant//
184 |INCREASING|(((
185 « Quality by state sorted »
186
187 Quality is a measure with an increasing trend
188 )))|//The sorting of measure Quality on dimension state will be descendant//
189 |DECREASING|(((
190 « Cost by state sorted »
191
192 Cost is a measure with a decreasing trend
193 )))|//The sorting of measure Cost on dimension state will be ascendant//
194
195 //__Table with examples presenting impact of a measure’s trend on sorting orders__//
196
197 {{{
198 }}}
199
200 = The best / the worst, the top / bottom =
201
202 It is possible to get the X best/worst members of the results of your query using these keywords:
203
204 |**Cases**|**Keywords**|**Example**
205 |The X best|(((
206 « Top »
207
208 « biggest »
209
210 « best »
211 )))|(((
212 The best cost in France
213
214 The 5 best costs in 2016
215
216 The 2 biggest costs in Europe
217
218 Top 3 of costs in France in 2016
219 )))
220 |The X worst|(((
221 « Worst »
222
223 « bottom »
224
225 « smallest »
226 )))|(((
227 The worst cost in France
228
229 The 5 worst costs in 2016
230
231 The 2 smallest costs in Europe
232 )))
233
234 //__Table presenting keywords for best/worst, top/bottom__//
235
236
237 = Aggregation =
238
239 You can define an aggregation method for the measures of your query using these keywords :
240
241 |**Aggregation**|**Keywords**|**Queries examples**
242 |Sum|« sum »|« Sum of cost »
243 |Average|« average »|« Average of cost »
244 |Minimum|« min »|« Min of cost  »
245 |Maximum|« max »|« Max of cost  »
246
247 //__Table presenting keywords for aggregation__//
248
249
250 = Targets =
251
252 It is possible to apply targets on measures mentioning the following keyword :
253
254
255 |**Keyword**
256 |« target »
257
258 __//Table presenting the keyword for targets//__
259
260
261 You can also directly mention the name of the targets you want to apply.
262
263
264 |**Example**
265 |Given a data model with the following columns(((
266 |**Dimensions**|**Measures**
267 |Date|Quality
268 |Type of line|Cost (with target « Targ »)
269 )))
270 |(((
271 Example 1 : « Cost in gauge with target »
272
273 * Every measure has one target applied on it.
274
275 Example 2 : « Cost in gauge avec Obj »
276
277 * The target « Targ » is applied on the associated measure « Cost ».
278 )))
279
280 __//Table with example presenting the use of target in text query//__
281
282
283 = Use of synonyms =
284
285 Text query takes in charge synonyms of your query members.
286
287 == Creating synonyms dictionaries ==
288
289 To use synonyms in text query, you first need to import a synonyms dictionary in Digdash.
290
291 Please refer to the documentation called « synonyms_dictionary_en.pdf » to import a synonyms dictionary in Digdash.
292
293 == Activating synonyms dictionaries ==
294
295 You then need to check if the use of synonyms dictionaries is activated for text query in the server configuration.
296
297 [[image:text_query_en_html_d057beaef5d1f988.png||height="238" width="444"]]
298 \\
299
300 [[image:text_query_en_html_f1d5ad7376929373.png||height="241" width="413"]]
301 \\
302
303 In the server configuration page, at the bottom of the page > **Advanced >>** > Category **Synonyms dictionaries** > Checkbox **Use synonyms dictionaries for text query**
304
305 [[image:text_query_en_html_f1e4eedd6b1df4.png||height="114" width="508"]]
306 \\
307
308 //__Screenshot: Activation of the use of synonyms dictionaries for text query__//
309
310 == Use case ==
311
312 |**Example**
313 |Given a data model with the following columns(((
314 |**Dimensions**|**Measures**
315 |Date|Quality
316 |Type of line|Cost
317 )))
318 |(((
319 In that case, the query
320
321 « Price by sort of line »
322
323 is equivalent to th query
324
325 « Cost by type of line »
326
327 (« price » is a synonym of « cost » and « sort » is a synonym of « type »).
328
329 __**NB**__ : This is valid only if your synonyms dictionaries contains these synonyms.
330 )))
331
332 //__Table with example presenting a use case of text query with synonyms__//
333
334 = Scores for a result of a text query =
335
336 Queries results are ordered according to their relevance and the best score can reach a 5 out of 5.
337
338 The score for a result may vary for different reasons.
339
340 * Importance of the query members
341
342 Indeed, a result will be better marked if the query is composed of the exact columns’ names of your data models.
343
344 A query is then considered as less good if it contains partial names of your columns, or synonyms.
345
346 |(% colspan="3" %)**Example**
347 |(% colspan="3" %)Given a data model with these columns(((
348 |**Dimensions**|**Measures**
349 |Date|Quality
350 |Type of line|Cost
351
352
353 )))
354 |(((
355 **Query 1 :**
356
357 **With the exact names**
358 )))|(((
359 **Query 2 :**
360
361 **With partial names**
362 )))|(((
363 **Query 3 :**
364
365 **With synonyms**
366 )))
367 |« Cost by type of line »|« Cost by type »|« Price by sort of line »
368 |With only exact terms, the results can be well-marked.|With partial names, the results can be less well-marked.|(((
369 « price » is a synonym of « cost », « sort of line » is a synonym of « type of line ».
370
371 This query is different from the original query, the score will be low.
372 )))
373 |Score* : 5/5|Score* : 3/5|Score* : 2/5
374 |(% colspan="3" %)* indicated scoes are just to illustrate our example
375
376 //__Table with example presenting how important query members are important__//
377
378 * Importance of the suggested type of graphics
379
380 The results list for a query suggests graphics more or less relevant for what is expected. Given the nature of the query members, some flows will be considered less relevant, hence a lower score for them.
381
382
383