Wiki source code of Requête en langage naturel

Last modified by Aurelie Bertrand on 2026/07/20 11:08

version	line-number	content
23.10	1	{{ddtoc/}}
1.1	2
29.1	3	----
	4
12.2	5	(% class="wikigeneratedid" %)
58.1	6	Natural language queries enable users to explore data by converting a query into a graphical visualisation. This allows users to interact with the data without any specialist knowledge.
12.2	7
58.1	8	= Prerequisites =
1.1	9
58.1	10	Cubes in your recently refreshed portfolios.
1.1	11
58.1	12	= Creating charts using natural language queries =
56.1	13
58.1	14	Natural language queries can be run from the dashboard editor or directly within the dashboard. They are based on the data models of the currently selected role.
1.1	15
58.1	16	== From the dashboard editor ==
1.1	17
12.6	18	(% class="wikigeneratedid" %)
58.1	19	In the dashboard editor, you can add a chart created using a natural language query to the dashboard.
12.6	20
58.1	21	1. Open the Dashboard Editor and select the dashboard page to which you wish to add a chart.
	22	1. In the left-hand side panel, click ‘Create new charts’ and tick the ‘Natural language’ option.
45.1	23	[[image:Natural_language_panel_FR.png]]
58.1	24	1. Then enter your query in the search bar.
	25	See the section [[Formulating a natural language query>>doc:\|\|anchor="Formulation_requête"]] for further details.
	26	➡ A list of charts is displayed, sorted by relevance.
	27	See the section [[Query results>>doc:\|\|anchor="Résultats_requête"]] for further details.
45.1	28	[[image:Natural_language_request_example_FR.png\|\|alt="Exemple requête"]]
58.1	29	1. Click on the chart of your choice.
	30	➡ The ‘Create a new chart ’ box appears with a preview of the chart.
52.1	31	[[image:1738661254101-163.png\|\|alt="Création nouveau graphique"]]
58.1	32	1. Rename the chart if necessary and click on Add chart.
	33	➡ The new chart is added to the selected dashboard and to the list of Information Flows (Existing charts).
12.2	34
58.1	35	== From the dashboard ==
1.1	36
58.1	37	It is also possible to add an additional content element – //‘Data Query’// – to your final dashboard so that you can create new charts directly within the dashboard.
1.1	38
58.1	39	1. Open the Dashboard Editor and select the dashboard page to which you wish to add the query box.
	40	1. In the left-hand side panel, click on ‘Additional Content’ to display the list of elements.
	41	1. Drag and drop the ‘Data Query’ element onto the dashboard page.
12.7	42
12.8	43	(% class="box infomessage" %)
	44	(((
58.1	45	💡 You may find that, when running queries, you filter by members of a dimension or measure. It is therefore advisable to add the //‘Filtered Items’//element to the dashboard so that you can remove them for future queries.
12.7	46	)))
	47
13.1	48	(% start="4" %)
58.1	49	1. Save and view the dashboard.
	50	➡ You can enter a query in the search bar of the ‘Data Query’ box. See the section [[Formulating a query in natural language>>doc:\|\|anchor="Formulation_requête"]] for further details.
	51	➡ The results are displayed with a score indicating their relevance. See the section [[Query results>>doc:\|\|anchor="Résultats_requête"]] for further details.
	52	[[image:1738662183414-447.png]]
	53	1. Click on the desired chart to view a preview.
	54	[[image:1738662401588-131.png\|\|alt="Aperçu du flux"]]
	55	1. You can save it by clicking the [[image:1705681243815-758.png\|\|queryString="width=21&height=18" height="18" width="21"]] and then Save.
	56	Rename it if necessary and click OK.
	57	➡ It is added to the {{glossaryReference glossaryId="Glossary" entryId="Flux"}}Flow{{/glossaryReference}} portfolio.
1.1	58
58.1	59	= Formulating a natural language query{{id name="Formulation_requête"/}} =
1.1	60
58.1	61	Natural language queries rely primarily on keywords to suggest the most relevant charts, as well as on the column names in the data models.
1.1	62
58.1	63	A query will therefore basically consist of measure names from your model and/or dimension names, followed or not by chart types and/or sorting operations.
1.1	64
58.1	65	We will see later in this document just how important the choice of terms in a query is.
1.1	66
58.1	67	== Chart type ==
1.1	68
58.1	69	For a given query, you will be presented with a list of results comprising various charts, depending on the content of the query.
1.1	70
58.1	71	However, it is possible to specify a particular chart type, provided that the query remains coherent (the query “Communication costs as a line chart” is not coherent).
1.1	72
58.1	73	The number of entries in the query results also has an impact. Indeed, in the case of a coherent query but with too many results, you will be offered a chart suited to the number of results to be displayed.
1.1	74
58.1	75	Here are the keywords to use in the query when selecting charts:
1.1	76
58.1	77	\|Chart names\|Keywords
	78	\|Pie chart\|‘sector’
	79	‘pie chart’
	80	‘ratio’
	81	\|Gauge\|‘gauge’
	82	\|Progress bar\|‘progress bar’
	83	\|Energy bars\|(((
	84	‘energy’
1.1	85
58.1	86	‘consumption’
1.1	87	)))
58.1	88	\|Arrow indicator\|‘arrow’
	89	\|Bar chart\|(((
	90	‘bar’
1.1	91
58.1	92	‘histogram’
1.1	93	)))
58.1	94	\|Bar chart\|‘bar’
	95	\|Map\|‘map’
	96	\|Scatter plot\|(((
	97	‘scatter’
1.1	98
58.1	99	‘correlation’
1.1	100	)))
58.1	101	\|Bubble chart\|‘bubble’
	102	\|Line chart\|(((
	103	‘line’
1.1	104
58.1	105	‘curve’
1.1	106	)))
58.1	107	\|Area chart\|(((
	108	‘area’
1.1	109
58.1	110	‘area’
1.1	111	)))
58.1	112	\|Radar chart\|‘radar’
	113	\|Table\|(((
	114	‘table’
1.1	115
58.1	116	‘table’
1.1	117	)))
58.1	118	\|Indicator\|‘indicator’
	119	\|{{glossaryReference glossaryId="Glossary" entryId="Tableau croisé"}}Cross table{{/glossaryReference}}\|‘Cross table’
	120	\|Text\|‘text’
1.1	121
58.1	122	== Sort ==
1.1	123
58.1	124	You can sort your search results using the following keywords:
1.1	125
58.1	126	\|Sort\|Keywords\|Query examples
	127	\|Ascending\|(((
	128	‘sort’ (ascending)
1.1	129
58.1	130	“sorted” (ascending)
1.1	131
58.1	132	‘sorts’ (ascending)
1.1	133
58.1	134	‘sorted’ (ascending)
1.1	135
58.1	136	‘sort’ (ascending)
1.1	137
58.1	138	‘order’ (ascending)
1.1	139
58.1	140	‘ascending’
1.1	141	)))\|(((
58.1	142	‘Costs by region in France in 2006, sorted by cost in a table’
1.1	143
58.1	144	‘Costs by region in France in 2006, sorted in a table’
1.1	145
58.1	146	‘Costs by region in France in 2006, in ascending order in a table’
1.1	147
58.1	148	‘Costs by region in France in 2006, sorted in ascending order in a table’
1.1	149	)))
58.1	150	\|Descending\|(((
	151	‘sorted’ (descending)
1.1	152
58.1	153	“sorted” (descending)
1.1	154
58.1	155	“sorts” (descending)
1.1	156
58.1	157	“arranged” (descending)
1.1	158
58.1	159	‘sort’ (descending)
1.1	160
58.1	161	‘order’ (descending)
1.1	162
58.1	163	‘descending’
1.1	164	)))\|(((
58.1	165	‘Costs by region in France in 2006, sorted in descending order by cost in a table’
1.1	166
58.1	167	“Costs by region in France in 2006, in descending order in a table”
1.1	168
58.1	169	‘Costs by region in 2006 sorted in descending order in a table’
1.1	170	)))
	171
58.1	172	== Trend of a metric ==
1.1	173
58.1	174	A metric has a trend. It can be stable (default trend), increasing or decreasing:
1.1	175
58.1	176	\|Trend\|Meaning
	177	\|Stable\|Default trend: The higher, the better
	178	\|Increasing\|(((
	179	The larger, the better
1.1	180
58.1	181	//Example: for a margin//
1.1	182	)))
58.1	183	\|Decreasing\|(((
	184	The smaller, the better
1.1	185
58.1	186	//Example: for a cost//
1.1	187	)))
	188
58.1	189	You can edit a metric’s trend via Digdash Studio, in the advanced configuration of a data source, within the metric’s properties. See the section [[Editing the metric’s trend>>\|\|anchor="Tendance_mesure"]] for further details.
1.1	190
28.1	191	(% class="wikigeneratedid" id="HImpactdelatendancesurletri" %)
58.1	192	Impact of the trend on sorting
1.1	193
58.1	194	The trend affects sorting. If the sort order (ascending or descending) is not explicitly specified, it will be based on the trend of the metric being sorted. Consequently, a measure with a stable or rising trend will be sorted in descending order, whilst a measure with a falling trend will be sorted in ascending order.
1.1	195
58.1	196	\|Trend\|Examples of queries\|Sort order obtained
23.4	197	\|Stable\|(((
58.1	198	‘Revenue by region, sorted’
1.1	199
58.1	200	Turnover is a metric with a stable trend
	201	)))\|//The ‘Revenue’ measure will be sorted in descending order by the ‘Region’ dimension//
	202	\|Ascending\|(((
	203	“Sort by region: Margin”
1.1	204
58.1	205	Margin is a measure with an increasing trend
	206	)))\|//The ‘Margin’ measure will be sorted in descending order by region//
	207	\|Descending\|(((
	208	‘Cost by region, sorted’
1.1	209
58.1	210	Cost is a measure with a decreasing trend
	211	)))\|//Sorting the ‘Cost’ measure by the ‘Region’ dimension will be in ascending order//
1.1	212
58.1	213	== Worst / Best ==
1.1	214
58.1	215	You can identify the top X best/worst members from your query results using the following keywords:
1.1	216
58.1	217	\|Cases\|Keywords\|Example
	218	\|The X best\|(((
	219	‘Top’
1.1	220
58.1	221	‘best’
1.1	222
58.1	223	‘biggest’
1.1	224
58.1	225	‘most important’
1.1	226	)))\|(((
58.1	227	The best value for money in France
1.1	228
58.1	229	The 5 best profit margins in 2016
1.1	230
58.1	231	The 2 highest turnover figures in Europe
1.1	232
58.1	233	Top 3 costs in France in 2016
1.1	234	)))
58.1	235	\|The X worst\|(((
	236	‘Worst’
1.1	237
58.1	238	“not as good”
1.1	239
58.1	240	“worse”
1.1	241
58.1	242	‘smaller’
1.1	243	)))\|(((
58.1	244	The worst cost in France
1.1	245
58.1	246	The 5 worst profit margins in 2016
1.1	247
58.1	248	The 2 worst turnover figures in Europe
1.1	249	)))
	250
58.1	251	== Aggregation method ==
1.1	252
58.1	253	You can define an aggregation function for the metrics in your query by specifying the following keywords:
1.1	254
58.1	255	\|Aggregation\|Keywords\|Query examples
	256	\|Sum\|‘sum’\|“Total communication costs”
	257	\|Average\|‘average’\|‘Average communication cost’
	258	\|Minimum\|‘min’\|‘Minimum communication cost’
	259	\|Maximum\|‘max’\|“Max communication cost”
1.1	260
58.1	261	== Target ==
1.1	262
58.1	263	You can apply targets to metrics by including the following keyword in the query:
1.1	264
58.1	265	\|Keywords
	266	\|“objective”
1.1	267
58.1	268	You can also specify the names of the objectives you wish to apply directly.
1.1	269
58.1	270	Example
23.4	271
58.1	272	Consider a data model with the following columns
23.4	273
	274	(((
58.1	275	\|Dimensions\|Measures
	276	\|Date\|Communication quality
	277	\|Line type\|Communication cost (with ‘Obj’ target)
1.1	278	)))
	279
58.1	280	* Example 1: ‘Communication cost in gauge with target’
	281	➡ All targets for all measurements are applied (one target per measurement).\\
	282	* Example 2: ‘Communication cost in gauge with Obj’
	283	➡ The ‘Obj’ target is applied to the associated measure ‘Communication cost’.
1.1	284
58.1	285	== (% style="color:inherit; font-family:inherit; font-size:26px" %)Using synonyms(%%) ==
1.1	286
58.1	287	{{id name="Synonymes"/}}Natural language search supports synonyms for the terms in your queries.
1.1	288
58.1	289	You can enter synonyms or import them from a dictionary. See the page [[Using synonyms>>doc:Digdash.user_guide.studio.managers.translation.synonyms_use.WebHome]] for further details.
1.1	290
58.1	291	=== Enabling synonym dictionaries ===
1.1	292
58.1	293	You must then ensure that the use of synonym dictionaries is enabled for the Natural Language Query feature in the server configuration.
	294	From the home menu, go to Configuration -> Server Settings -> Dictionaries & Languages -> Synonym Dictionariesand tick the box labelled ‘Use synonym dictionaries for natural language queries’.
1.1	295
45.1	296	[[image:Activate_synonym_dictionary_FR.png]]
1.1	297
	298
58.1	299	=== Example of use ===
20.1	300
58.1	301	Consider a data model with the following columns
23.4	302
	303	(((
58.1	304	\|Dimensions\|Measures
	305	\|Date\|Communication quality
	306	\|Line type\|Call charges
1.1	307	)))
	308
58.1	309	In this case:
	310	The query//“Call charges by line type”//is equivalent to the query//“Call costs by line// type”
	311	(“prix” is a synonym for “coût” and “genre” is a synonym for “type”).
1.1	312
23.4	313	(% class="box warningmessage" %)
1.1	314	(((
58.1	315	❗This only applies if your thesaurus contains these synonyms.
1.1	316	)))
	317
58.1	318	= Query results{{id name="Résultats_requête"/}} =
12.2	319
23.10	320	[[image:Requête_direct_TDB2.png]]
12.2	321
58.1	322	The results of a query provide a list of charts ranked by relevance, with an associated score out of 5. The higher the score, the more relevant the associated chart is considered to be. The name of the associated data model is shown, along with a description of the chart.
12.2	323
58.1	324	* Query terms
12.2	325
58.1	326	A query result will be ranked higher if the query terms contain the exact names of the columns in your data models.
1.1	327
58.1	328	A query will therefore be considered less effective if its terms contain partial names of your columns, or synonyms for the column names in your data model.
1.1	329
58.1	330	\|(% colspan="3" %)Example
	331	\|(% colspan="3" %)Consider a data model with the following columns(((
	332	\|Dimensions\|Measures
	333	\|Date\|Communication quality
	334	\|Line type\|Call cost
1.1	335	)))
	336	\|(((
58.1	337	Request 1:
1.1	338
58.1	339	With exact names
1.1	340	)))\|(((
58.1	341	Query 2:
1.1	342
58.1	343	With partial names
1.1	344	)))\|(((
58.1	345	Query 3:
1.1	346
58.1	347	With synonyms
1.1	348	)))
58.1	349	\|‘Communication costs by line type’\|“Cost by type”\|“Price by line type”
	350	\|If only exact terms are used in this query, the results may be well-ranked.\|If partial terms are used in this query, the results may be of lower quality.\|(((
	351	‘price’ is synonymous with ‘cost’, ‘line category’ is synonymous with ‘line type’.
1.1	352
58.1	353	This query differs from the original query; the scores will be low.
1.1	354	)))
58.1	355	\|Score: 5/5\|Score: 3/5\|Score*: 2/5
	356	\|(% colspan="3" %)* The scores shown are for illustrative purposes only in this document
1.1	357
58.1	358	* Type of chart suggested
1.1	359
58.1	360	The list of results for a query includes charts that are more or less relevant to what is expected. Given the nature of the search terms, some charts will be ranked lower than others, resulting in a lower score for the latter.

Wiki source code of Requête en langage naturel

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