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Wiki source code of Requête en langage naturel

Last modified by Aurelie Bertrand on 2025/02/07 10:09

version	line-number	content
23.10	1	{{ddtoc/}}
1.1	2
53.1	3	= Prerequisites =
29.1	4
53.1	5	* Cubes in your wallets recently synchronized
12.2	6
53.1	7	= Creating flows with text query =
1.1	8
53.1	9	Digdash offers the possibility to create flows in some clicks with natural language.
1.1	10
53.1	11	* Via the dashboards editor
1.1	12
53.1	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.
1.1	25
53.1	26	* Via the dashboard
1.1	27
53.1	28	It is also possible to add a « Text query » element in your dashboard in order to create flows on the fly.
12.6	29
53.1	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.
12.2	37
53.1	38	You would better have a « Filtered elements » element in your dashboard to remove these filters for the potential next queries.
1.1	39
53.1	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.
1.1	44
53.1	45	{{{
	46	}}}
12.7	47
53.1	48	= Composition of a text query =
12.7	49
53.1	50	Digdash allows you to create flows from data models in natural language.
1.1	51
53.1	52	Digdash mainly rests on keywords to suggest you the most relevant flows and the names of your data models’ columns.
1.1	53
53.1	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.
1.1	55
53.1	56	We shall later explain how important query terms are important.
1.1	57
53.1	58	= Results for a text query =
1.1	59
53.1	60	[[image:text_query_en_html_c7eda020d25db863.png\|\|height="117" width="550"]]
	61
1.1	62
53.1	63	//__Screenshot: Results for a text query__//
1.1	64
53.1	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.
1.1	66
53.1	67	We shall later see in this document which are the criteria that could have an influence on the score of a result.
1.1	68
53.1	69	= Choices of graphics =
1.1	70
53.1	71	For a given query, you will be suggested a list of results of multiple graphics, depending on your query members.
1.1	72
53.1	73	Nevertheless, you can require a particular graphic, as long as it remains coherent (the query « Cost in line » has no sense).
1.1	74
53.1	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.
1.1	76
53.1	77	Here are the keywords to use for the choice of graphics:
1.1	78
53.1	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 »
1.1	87
53.1	88	« histogram »
1.1	89	)))
53.1	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 »
1.1	97
53.1	98	« zone »
1.1	99	)))
53.1	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 »
1.1	105
53.1	106	//__Table presenting keywords for the different flows__//
1.1	107
53.1	108	= Sorting =
1.1	109
53.1	110	It is possible for you to use sorting operations with the following keywords :
1.1	111
53.1	112	\|Sorting\|Keywords\|Queries examples
	113	\|Ascending\|(((
	114	« sort » (ascending)
1.1	115
53.1	116	« sorted » (ascending)
1.1	117
53.1	118	« ascending »
	119	)))\|(((
	120	« Cost by region in France in 2006 sorted by cost in table »
1.1	121
53.1	122	« Cost by region in France in 2006 sorted in table »
1.1	123
53.1	124	« Cost by region in France in 2006 ascending in table »
1.1	125
53.1	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 »
1.1	134	)))\|(((
53.1	135	« Cost by region in France in 2006 sorted by cost in table »
1.1	136
53.1	137	« Cost by region in France in 2006 descending in table »
1.1	138
53.1	139	« Cost by region in France in 2006 sorted in descending order in table »
1.1	140	)))
	141
53.1	142	//__Table presenting keywords for sorting operations__//
1.1	143
	144
53.1	145	= Trend of a measure =
1.1	146
53.1	147	A measure has a trend. It can be stable (trend by default), increasing or decreasing:
1.1	148
53.1	149	{{{
	150	}}}
1.1	151
53.1	152	\|Tendance\|Signification
	153	\|STABLE\|Default trend: The bigger the better
	154	\|INCREASING\|(((
	155	The bigger the better
1.1	156
53.1	157	//Example : for a quality//
	158	)))
	159	\|DECREASING\|(((
	160	The lowest the better
1.1	161
53.1	162	//Example : for a cost//
1.1	163	)))
	164
53.1	165	__//Table presenting possible trends for a measure//__
1.1	166
53.1	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.
1.1	168
	169
53.1	170	//__Definition of a measure’s trend in Digdash’s Studio Enterprise__//
1.1	171
53.1	172	* Impact of a measure’s trend
1.1	173
53.1	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.
1.1	175
	176
	177
53.1	178	\|Trend\|Queries examples\|Sort
	179	\|STABLE\|(((
	180	« Duration by state sorted »
1.1	181
53.1	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 »
1.1	186
53.1	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 »
1.1	191
53.1	192	Cost is a measure with a decreasing trend
	193	)))\|//The sorting of measure Cost on dimension state will be ascendant//
1.1	194
53.1	195	//__Table with examples presenting impact of a measure’s trend on sorting orders__//
1.1	196
53.1	197	{{{
	198	}}}
1.1	199
53.1	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\|(((
1.1	206	« Top »
	207
53.1	208	« biggest »
1.1	209
53.1	210	« best »
1.1	211	)))\|(((
53.1	212	The best cost in France
1.1	213
53.1	214	The 5 best costs in 2016
1.1	215
53.1	216	The 2 biggest costs in Europe
1.1	217
53.1	218	Top 3 of costs in France in 2016
1.1	219	)))
53.1	220	\|The X worst\|(((
	221	« Worst »
1.1	222
53.1	223	« bottom »
1.1	224
53.1	225	« smallest »
1.1	226	)))\|(((
53.1	227	The worst cost in France
1.1	228
53.1	229	The 5 worst costs in 2016
1.1	230
53.1	231	The 2 smallest costs in Europe
1.1	232	)))
	233
53.1	234	//__Table presenting keywords for best/worst, top/bottom__//
1.1	235
	236
53.1	237	= Aggregation =
1.1	238
53.1	239	You can define an aggregation method for the measures of your query using these keywords :
1.1	240
53.1	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 »
1.1	246
53.1	247	//__Table presenting keywords for aggregation__//
1.1	248
	249
53.1	250	= Targets =
23.4	251
53.1	252	It is possible to apply targets on measures mentioning the following keyword :
23.4	253
53.1	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 »)
1.1	269	)))
53.1	270	\|(((
	271	Example 1 : « Cost in gauge with target »
1.1	272
53.1	273	* Every measure has one target applied on it.
1.1	274
53.1	275	Example 2 : « Cost in gauge avec Obj »
1.1	276
53.1	277	* The target « Targ » is applied on the associated measure « Cost ».
	278	)))
1.1	279
53.1	280	__//Table with example presenting the use of target in text query//__
1.1	281
	282
53.1	283	= Use of synonyms =
1.1	284
53.1	285	Text query takes in charge synonyms of your query members.
1.1	286
53.1	287	== Creating synonyms dictionaries ==
1.1	288
53.1	289	To use synonyms in text query, you first need to import a synonyms dictionary in Digdash.
1.1	290
53.1	291	Please refer to the documentation called « synonyms_dictionary_en.pdf » to import a synonyms dictionary in Digdash.
1.1	292
53.1	293	== Activating synonyms dictionaries ==
20.1	294
53.1	295	You then need to check if the use of synonyms dictionaries is activated for text query in the server configuration.
23.4	296
53.1	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
1.1	317	)))
53.1	318	\|(((
	319	In that case, the query
1.1	320
53.1	321	« Price by sort of line »
1.1	322
53.1	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.
1.1	330	)))
	331
53.1	332	//__Table with example presenting a use case of text query with synonyms__//
12.2	333
53.1	334	= Scores for a result of a text query =
12.2	335
53.1	336	Queries results are ordered according to their relevance and the best score can reach a 5 out of 5.
12.2	337
53.1	338	The score for a result may vary for different reasons.
12.2	339
53.1	340	* Importance of the query members
1.1	341
53.1	342	Indeed, a result will be better marked if the query is composed of the exact columns’ names of your data models.
1.1	343
53.1	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
1.1	353	)))
	354	\|(((
53.1	355	Query 1 :
1.1	356
53.1	357	With the exact names
1.1	358	)))\|(((
53.1	359	Query 2 :
1.1	360
53.1	361	With partial names
1.1	362	)))\|(((
53.1	363	Query 3 :
1.1	364
53.1	365	With synonyms
1.1	366	)))
53.1	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 ».
1.1	370
53.1	371	This query is different from the original query, the score will be low.
1.1	372	)))
	373	\|Score* : 5/5\|Score* : 3/5\|Score* : 2/5
53.1	374	\|(% colspan="3" %)* indicated scoes are just to illustrate our example
1.1	375
53.1	376	//__Table with example presenting how important query members are important__//
1.1	377
53.1	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

Wiki source code of Requête en langage naturel

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