Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F40/00—Handling natural language data
  • G06F40/10—Text processing
  • G06F40/166—Editing, e.g. inserting or deleting
  • G06F40/177—Editing, e.g. inserting or deleting of tables; using ruled lines
  • G06F40/18—Editing, e.g. inserting or deleting of tables; using ruled lines of spreadsheets

Definitions

• the present invention relates to methods for combining and viewing data from data sources, and in particular new types of retrieval, combining and visualization services capable of combining complementary or competing information available in particular on the Web. or in the enterprise, and to navigate easily in combinations made even when they represent large volumes of data
• the so-called “Mashup” tools allow combining data extracted from websites or other multidimensional data sources. For example, data extracted from a website giving hotel addresses can be combined. with data extracted from a site giving schedules of planes, with data extracted from a meteo site, etc.
• mashup tools can be used to combine information sources which are inherently competing. For example, if we consider extractors providing, from a book sales website, a set of data. multidimensional (typically presented in rows) composed of dimensions (typically columns) such as “Lead Author”, “Title” and “Price”, a join can be performed on the columns “Lead Author” and "Title”, to compare price of books supplied by different sellers on different sites
• SUBSTITUTE SHEET • Take advantage of indicators (keys), if any already associated with the data itself (in the example above only the columns “Lead Author” and “Title” should typically be used as keys and thus be used as join columns , although the user has also matched the "Price” column) to automatically combine the data in the most appropriate way, and this could be determined automatically;
• each offer has necessarily been the most recent one time (at least at the moment of its first appearance), and since it has not been withdrawn since then (it is still valid), it will be displayed .
• the user could display the offers of different sellers at the same time or even put his own books for sale using the same process.
• a difficulty with existing table data visualization tools is that the layout of the tree structure (left-to-right connections) is pre-established and the layout of the columns in this presentation may be different from that of the table. starting data.
• a method of automatic combination of multidimensional data as a function of manipulations in their dimensions in a computer environment comprising computer equipment capable of accessing multidimensional data sources, characterized in that it comprises the following steps:
• the invention proposes a method for automatically combining multidimensional data from a plurality of data sources, characterized in that it comprises a succession of cascaded implementations of the method as defined above, the combined data of a given implementation of said method constituting a data source for a subsequent implementation of said method.
• the arrangement of non-key values added with pre-existing values for the same combined multidimensional data includes the selection of a non-empty value among the values from the different sources.
• the arrangement of non-key values added with pre-existing values for the same combined multidimensional data includes the selection of a value among the values from different sources according to a given decision-making process.
• Each data is associated with a validity period, and the decision-making process includes the selection of a value belonging to a valid data item on a given date.
• Each data is associated with a date of first appearance, and the decision process includes the selection of a value from a data most recently appeared at a given date possibly adjustable.
• the method comprises, for the data of a source to which no date of first appearance is associated, a step of creating a first appearance date equal to the date on which the data was put into play in a combination of data for the first time.
• At least one of the multidimensional data sources comprises at least two upstream data sources and information defining a combination previously made according to claim 1.
• said actions are manipulations on a graphical interface of a representation of at least one dimension of the second source in order to match it with a representation of at least one dimension of the first source or to insert it between two dimensions of the first source, the dimensions corresponding to the manipulated representations determining either said associations between data, or said non-key value arrangements of the second source with values of the first source, depending on whether the dimension of the second source corresponding to the representation manipulated contains or not key values.
• a multi-dimensional data combining method comprising the following steps:
• the correspondence information also includes correspondence information between the dimensions of said sources and the method furthermore comprises, when accessing a data source that has already been the subject of combinations with other data sources, signaling also the correspondences between dimensions.
• the method further comprises the default execution of combining operations by the method of claim 1 between the data source to which it is accessed and said other data sources.
• the correspondence information is stored for a plurality of users, and the signaling step is performed according to preponderance rules among the correspondence information.
• a fourth aspect of the invention is a method for combining multidimensional data, comprising the following steps:
• a fifth aspect of the invention is directed to a process of enriching multidimensional data by automatic combination according to manipulations in their dimensions in a computing environment comprising computer equipment capable of accessing multidimensional data sources, characterized in that it understands, after applying to a previous source of data a selection function to obtain a previous selection of data, the following steps:
• mapping of dimensions between the data of the two sources is carried out during the implementation of the method.
• the method further comprises a synthesis step for displaying said selections in their graphical environment and associating values with means for dragging and dropping.
• the method is implemented repeatedly when accessing a succession of data sources and, when accessing a current data source for which there is no mapping of dimensions with the source previous, we seek the existence of a mapping of dimensions between the current source and a previous source, and the enrichment method is applied on pairs of sources constituted by said previous source and any source consulted more recently with which there is a mapping of dimensions, then on a pair of sources constituted by said anterior source thus enriched and the current source.
• the method is implemented when accessing a succession of SN-2, SN-I and SN data sources, and comprises the following steps: if a mapping between SN-2 and SN-I sources on the one hand, and SN-I, SN on the other, exists, implement the process between the SN and SN-I sources using source SN-I the result of the method according to claim 15 implemented on sources SN-I and SN-2, if no mapping between sources SN-2 and SN-I exists, determine if there is a mapping between the dimensions of the sources SN-2 and SN and, if so, implement the method according to claim 15 on the one hand on the sources SN and SN-I and on the other hand on the sources SN and SN-2, and if no mapping between SN-I and SN sources exists, determine if there is a mapping between the SN-2 and SN source dimensions and between SN-2 sources and SN-I and, if so, implement the method according to claim 15 on the one hand on sources SN-I and SN
• the method comprises a step consisting, as a function of actions exerted with the aid of an input user interface, associated with said other column at said single line, to cause the display of the different values taken by the dataset group in this other column.
• the method further comprises, in response to an action using an input user interface on an indicator, the deployment of the line in question into a sub-array.
• each row of said sub-table contains a different value in said other column and represents a subgroup of sets of data all having this value in said other column.
• the method comprises the repetition of steps (b) to (d) for at least one of the lines constituting said subarray, a repetition applied to at least one other column in which there are at least two values for the sub-table. group of data sets corresponding to said line at least. * there is an additional virtual type "line", the table being initially presented as a single line grouping all the rows, an indicator being displayed in association with each column in which there are at least two values.
• each row of the sub-table represents, for the columns from the indicators of which the sub-table was formed, a specific combination of values different.
• the method comprises displaying an indicator associated with said other column after deployment and on which an action using an input user interface causes the grouping (reduction) of said sub-array into said single line.
• the indicator associated with said other column comprises a symbol that can be oriented downwards or upwards.
• the method comprises the display in said other column of one of the values taken by the group of data sets in this column.
• the method comprises displaying in said other column a combination of the values taken by the group of data sets in this column.
• the method comprises displaying in said other column a property such as the cardinality of all the values taken by the group of data sets in this column.
• the method comprises a step of determining a value set selection key, determining a group of sets of values to which a change of value in a column at a displayed line will apply collectively.
• said selection key is constituted by the values displayed in the column or columns from the indicators of which a sub-table has been formed.
• said selection key is constituted by the values displayed in all the columns, including the value before change for the column in which the change is made.
• said selection key is constituted by the value before change in the column in question.
• the method comprises a step of adding to the selection key a value displayed in a column by the indicator from which no sub-array has been formed, by a specific action using an interface input user.
• the method comprises a step of deleting the selection key of a value displayed in a column by the indicator from which no sub-array has been formed, by a specific action using a user interface input.
• the method is able to display in association with the resource an indicator to allow direct access to said data source.
• the invention proposes a method of presenting data, comprising the following steps:
• the values of the "Valid From” and “Valid To” time columns come from an automatic detection of the first (or last) appearance of the data in the respective data sources, which the "null” values indicate. a value not known, and that the vertically repeated values are not presented 3 (in the following figure “Author2" is repeated in two lines but mentioned in one).
• multidimensional data sources and “data sources” are used interchangeably, and the term “data source” is sometimes used instead of “data source table”.
• the sources “Seller2” and “Seller3” contribute values on the one hand together in the column “Price” and on the other hand respectively in the “Number of pages” and “Rating” columns. It is considered here that the sources presented are in fact “selections” and that one applies the method of enhancement selections described below.
• Each missing value represents a repetition of the value above. This happens when from an already deployed value of a column of one row, a value of another column of the same row is deployed, this is described later.
• Vendor3 source does not provide a "Number of Pages” column
• the user now sees the value "350".
• this value is obtained by combining with the Vendor2 source 7 .
• Vendor's offer for ⁇ Author2, Title3 ⁇ is always 8 .
• the fourth line of Figure 1, which presented an offer for ⁇ Author3, Title4 ⁇ is not presented since it appeared after March 22, 2007 10:10 and there are no sources other offer data for this book that appeared before this date and is still valid at the time "Now".
• Vendor's offer for the book ⁇ Author2, Title2 ⁇ is more advantageous than Seller2's, and although less recent, it is still valid now. But are there even more advantageous offers (and
• the user can also request to display price differences over time for each book, as shown in Figure 6, or any other aggregation function (such as Min, Max, Average, etc.) applied. on a reduced cell, as will be described later.
• any other aggregation function such as Min, Max, Average, etc.
• the main interest of the process is to unify the vocabularies of the combined sources.
• FIG. 9 shows that Table B being combined with Table A, and that Col5 column of B being slid-dropped between Col2 and Col3 columns of A, the corresponding Col5 values of B are displayed in the resulting array A + B within a new column Col5 placed between Col2 and Col3 n .
• FIG. 10 shows that since table B is combined with table A, and the Col5 column of B is slid-deposited on column Col2 of A, the latter two are matched and thus, as a result of combining, the appropriate values of Col5 of B are displayed in the resulting array A + B within the same column Col2 (called "Col2 (Col5)" in the figure).
• the figures represent by means of broken lines the regions making it possible to distinguish (when detecting on-drop events) these two cases of dragging.
• the figure also shows a set of time cursors, by a start date of validity (date of first appearance) present in the sample.
• Setting multiple time cursors would mean (for the user) to display the union of the rows of the table corresponding to said placed cursors.
• Deploying the entire column "Valid from” (this can be done by clicking on a symbol """not shown in the figure, but used later) is indeed to display the union of the lines representing the offers respectively the most recent ones with respect to these time sliders.
• mappings of data source tables and table columns are counted, and this helps to determine which mappings to suggest (or apply by default) to users automatically 13 .
• Weights are associated with the mappings during their counting so that the preponderance rules used privilege matching by "close” users, for example users working in the same domain. And, of course, the user-made matching is offered first.
• new data sources can be automatically combined by default, provided that they have already been combined previously.
• a user himself creates a "Seller5" data source (for example from an already existing source, in this case from "Sellerl”) and presents the offer to sell a book “Authorl” "Titrel” (eg a secondhand book he would like to resell).
• Another user who accesses "Seller1” reads the offer of "Seller5" simply because a relatively large number of other users have already combined “Seller5" with “Sellerl” and matched their respective columns. .
• the implementation may consist of, for each pair among the columns in the current combination of tables, considering all the users having combined the tables in question (where are the columns forming this pair) and have kept this combination as a stored version (ie in the form of "views"), and count the number of times that this pair is matched in said stored version (take by user the average for all stored versions where this combination is kept) as well as possibly the number of times a suggestion of said matching has been denied by a user.
• access rights can be associated with the combinations and matched, so that for example the combinations made by a user can be reserved for him alone.
• said counts can also take into account data 15 visualized by the user when combinations.
• An extractor provides a "Yamazuki" data source from the website of the great Yamazuki motorcycle manufacturer, which presents all the motorcycles of this brand, with all their characteristics.
• An individual publishes a "I sell" data source containing a line showing the type of motorcycle (as a key value), the details, the price and the place of sale of a recent Yamazuki motorcycle that it sells.
• search engine provides, in a column “Domain”, the domain (in this case “fly fishing") corresponding to the keyword ("fly") given.
• domain in this case "fly fishing”
• search Engine source "Sellerl”
• ellerl is a seller specialized book in the field “Fly fishing”
• Each data source 20 is associated with the degree of fineness of the information to be taken into account during the counts.
• An "array of changes” is an array of modification lines and a “simple array” is an array of simple lines.
• any array of changes can be seen as an array of simple lines. This is done by seeing each line of modification as a line composed of the non-key cohones given there and completed, for the cohval not included in the set of non-key cohones given there, by key cohones given there .
• the first table presented above represents a table of simple lines drawn from the table of modifications that follows it. 27
• each pass: val is a value val in a column col of a row of the table - note that each row implicitly includes a pass: val with the value "null For each column not mentioned)
• a simple array (called the first array) can be combined with an array of changes (called the second array) using the key values of each of the rows in the second array to achieve, based on actions performed using an interface user on representations of the columns of the first table and the second table, associations between the rows of the first table and the rows of the second table, by combining the values of the first table with at least one part, also determined according to said actions, non-key values of the second table, and arranging the non-key values combined with pre-existing values also according to said actions.
• Said actions are manipulations (such as drag-and-drop, as already described) of a representation of at least one column of the second array to match it with a representation of at least one column of the first array (or for insert it between two columns of the first table), the columns corresponding to the manipulated representations determining either said associations between lines, or said non-key value arrangements of the second table with values of the first table, according to whether the column of the second table corresponding to the manipulated representation contains or not key values.
• manipulations such as drag-and-drop, as already described
• Conditions can be associated with key columns and stored as metadata.
• table "Seller4" given previously (and reproduced below 29 ) including the columns “Number of pages Min”, “Number of pages Max”, “Rating”, “Seller” and “Price” both first columns was associated with a condition expressing that the number of pages must be between the values given in these first two columns.
• the user who tries to map a column (which would be for example labeled "#pages") of a first table with a column of the table "Seller4" is then asked to match it with the number of columns "Number" of pages Min » « Number of pages Max »instead of a single column.
• the metadata may include global indications and conditions on the data sources to be combined.
• the first source able to provide an array of simple lines (or a table of changes seen as an array of simple lines)
• the second source able to provide a table of changes, a correspondence being established between at least one column of the second table (ie the table provided by the second source) and at least one column of the first table (ie the table supplied by the first source)
• the rows of said tables can be combined in cases where all the values key of the second table are thus mapped to columns of the first table (even in the absence of values, that is to say even if they have a zero value in the first table) and, if values are missing in the first table for these matched columns, in the case where key columns are given for the whole of the first array 31 , all the key cohentials of the first array have been its correspondence.
• This verification can be performed by a preprocessor, before the implementation of the method of combining the tables itself described below.
• each value is associated a beginning of validity (ie time of first appearance or beginning of belief of this value).
• Each line has an associated validity period: the beginning of validity of the line is equal to the greater validity start time associated with a value of the line, and the end of validity of the line is its last appearance time 32 (or belief end time of this data).
• An end of null validity means that the data is always valid (ie value always published by its source or value always raw).
• the rows are filtered relative to the time slider positioned by the user (as illustrated by the examples given at the beginning): only the lines having a lower validity start time and a valid end time greater than or equal to time indicated by the cursor are retained (the cursor time indicates the belief time and only the raw data at the set time are considered).
• the implementation of the method of combining a second table with a first table consists in adding to the first table the result of a relational join (operator known per se) between key tables corresponding to the first and second tables (respectively called first and second key tables). This join is performed on the key values in the columns of second key table 33 mapped by the user 34 taking into account the conditions 35 and / or actions if
• key table column means the corresponding columns in the corresponding table
• key table column means the corresponding columns in the corresponding table
• 3 4 or by accepting automatic suggestions for column mappings associated with the metadata (as described above); by providing said key values with the highest value of validity start 36 each, and for the other values of the matched columns, supplying the existing values having the largest validity start 37 , the values of the beginning of validity associated with said provided values being those they had before combination; the rows of said key tables being filtered with respect to the time set (time slider, as described above).
• the deployment / reduction method described later allows the user to present only the most recent data (by reducing the column "Start of validity", as illustrated above in the examples).
• the first selection will be "enriched with the second and first sources", namely: it will be enriched by the "combination” of the second source with she and she will be further enriched by adding the "combination" of the second selection with the first source taken entirely except the first selection (since the latter has already been combined with the second source taken in full); by said "combinations” is meant the combination method already described above.
• mapping 4 3 to enrich the third selection from the first and third sources 44 that if the previous selection, namely the first, had not been enriched with one before the previous one, in the second case, and a setting of at least one dimension correspondence was made with the latter, the same method should also have been applied to enrich the third selection with the second and third sources), if not, if a mapping of at least one dimension was made with that before the previous one, in this case the second, the same method is applied to enrich the third selection from the second and third sources, otherwise as in this example no other source was accessed in the current session, the third selection is not enriched.
• the current selection is enriched from the previous and current sources (see below the definition of these terms) and optionally, if the user has in the same session accessed a source before the previous one and a selection was presented to it, in case the previous selection had not itself been enriched with that before the previous one, if a matching of 'at least one dimension has been made between the latter and the current source, the current selection is enriched with the sources before the previous and current, and so on until the beginning of the session, ELSE
• the current selection is enriched from said source before the previous and the current source and optionally, if the user has in the same session accessed a source before 'the source before the previous' and a selection him in was presented, in the case where the selection before the previous one had not been enriched with that before 'before the previous', if a mapping of at least one dimension was made between the latter and the current source, the current selection is enriched with the sources before the previous and current, and so on until the beginning of the session,
• Said enrichment of the current selection from a previous source and the current source is to add to the current selection
• Per session is a succession of user access to data sources whose combination is potentially relevant. Typically, we will consider that close access in time
• Figure 11 shows schematically on the left a page of results of a site of sale of books, grouped by authors and on the right the table resulting from its extraction 47 .
• the user who creates an extractor associates him with meta-data in which he can notably indicate which are the key columns 48 of the extracted array. It can indicate several options. Thus for the example of figure 11 it can indicate optionl: the column "ISBN", and option2: the pair of columns "Author” and "Title".
• optionl the column "ISBN”
• option2 the pair of columns "Author” and "Title”.
• the system will then choose the first option (in the order of the given options) that forms part of the column (s) put in correspondence. For example, if the end user puts "Author” and "Title” in correspondence during a combination, this is the second option that will be chosen.
• An extractor provides a table (simple or modifications) from the data coming from a web page. It must therefore indicate on the one hand the request (url, GET or POST parameters) and on the other hand how to extract the data from the page. It can also manage paging and automatically download multiple pages of results.
• the method of creating an extractor from a web page containing a multidimensional data set is semi-automatic.
• the user selects in the web page one or more objects each corresponding to a row of the table, and indicates which object of the page corresponds to which row of the table to generate.
• the system compares the paths of these objects and conventionally constructs a generic path (Xpath) covering at least all the objects indicated by the user. 49
• the system can determine the values for each object, and present the table thus obtained to the user.
• the synthesizer is the inverse of the extractor, it is created automatically at the moment of the creation of the corresponding extractor, and allows to display the data of a table in the presentation style of the Web page, graphic areas being placed at the location of the objects containing the array values to allow them to be rolled out or collapsed and dragged and dropped to match columns of different tables corresponding to different webpages (ie to different combined sites as we describe further). It is created as follows: The user chooses a model object corresponding to a row of table 50 . All objects corresponding to other rows of the table are removed from the page and all objects referenced by objects corresponding to rows in the table but not the model object are deleted. The values in the template object are changed to
• all objects corresponding to the constructed path are highlighted and the user can refine the path by specifying additional objects or deselecting highlighted objects.
• the system refines the Xpath to respect these constraints.
• the user specifies for one of these objects (the "model object") all the attributes that will correspond to the columns of the array.
• the attribute an object in the page, a column name and, if necessary, the attribute HTM L to extract (for example, for links, it has the choice between the value of the attribute href or the text of the link).
• the system establishes, for each attribute, a pair (column name, Xpath), the path being relative to the model object, and stores this information in the extractor.
• 5 0 (the one that served as a template at the time of the extraction of the extractor, as described in the previous note) match the first row of the table, and one copy of the object is inserted afterward with the values of each other line to be displayed. 51
• a copy of ol (and thus also of oJ for all J> l) is created, its attributes objects are modified to reflect the current line, and it is inserted as a result (as brother) of the last copy of ol to have been placed in the document.
• the user can request to modify a synthesizer.
• the same procedure above is then applied based on a one-row array containing column names instead of values, with special markings to distinguish them from normal text (eg, "$ ⁇ author ⁇ " in the author column, and so on).
• the model object is marked by special marks (for example ⁇ model-object> ... ⁇ / model-object>).
• the user can modify the resulting document as he wishes, for example using a text editor, and return it to the system.
• the above method now uses this new structure (provided that there is exactly one area bounded by the model object markers). Note, however, that it is allowed to delete or duplicate attribute markers.
• the method of enriching selections obtained respectively from the data sources accessed in the same session can be applied to selections viewed in web pages acting as data sources (via extractors). Column mappings can be done by the user directly on presentations of these data sources (via synthesizers) in the form of web pages.
• the deployment / reduction process will now be described in detail.
• the methods described in this part assume the existence of a device providing an interface similar to a database server and giving access to the table displayed in the interface.
• this device the data source.
• a typical example is an extractor layer.
• the data source stores a "table” which is a data structure having a number of “columns” and “lines”, and each line of which has some content for each column.
• the lines represent information entities and the property columns of these entities, and it frequently happens that for a column, the same value is found in several lines, for example in the case where a property of the same entity can by nature have several values (it is said that the property in question is "multivalued”).
• the "table” refers to the table provided by the data source.
• the interface will provide a way to apply a number of filters to the rows (or, in other words, to search the table).
• a filter selects rows with a specific value in a certain column, the value of that column is said to be "specified”. More generally, it may also be possible to impose constraints ("Specifying" a column value then becomes a special case of constraint). For example, a filter can select rows containing a given word in a column. 52
• the method of the invention makes it possible, in the presence of multivalued fields, to replace the lines having the same values in a set of columns (the "deployed” columns) by a single “reduced” line.
• the display is essentially as follows: it contains a line (hereinafter called “displayed lines" as opposed to the rows of the table) for each combination of values in the columns deployed that exists in the table. For a displayed row and a reduced column data, if there is only one possible value according to the table, this value
• Each displayed line represents the subset of tuples (in the table) that have the values shown in the respective deployed columns 54 .
• each undeployed column in each undeployed column a "deploy" button is displayed if in the table there is at least one other tuple that has a. a different value for this column b. and the same values for the respective deployed columns.
• buttons "deploy” appearing in each column for which in the array at least one other tuple having a different value for that column exists.
• the user can choose an aggregation function to represent the reduced cells.
• Means can optionally be offered to select several buttons “deploy” or “reduce” and operate with one click.
• Column headers have associated user interface elements to reorder columns relative to one another and delete columns.
• the user also has a way to provide a "filter" on the lines to be displayed in the table. For example to show only the lines that have a certain value in a certain column, or that have two equal data columns, or even corresponding to an arbitrary SQL expression (ie any valid expression as parameter of WHERE or HAVING, taking good sure the usual precautions to prevent prohibited access) provided by the user.
• a "filter" any valid expression as parameter of WHERE or HAVING, taking good sure the usual precautions to prevent prohibited access
• sub-array means the set of newly displayed cells as described in point 5 above.
• a column ci uses an aggregation function a and it is not deployed, then the query contains a (ci) instead of ci.
• a new rotation of a column r to a value w in a displayed line L is treated as follows: The pair r-> w is added at the end of the list of rotations, to obtain r1-> w1, r2-> w2, ..., rn-> wn, r-> w. Then we add this sequence to the specified columns, as well as the association of the deployed columns dl, d2, ... to the values they take in line L. If at least one line is found, its values are displayed for L. In the opposite case, the first association (rl-> wl) is removed from the table T, and the process starts again, until at least one line is found.
• the columns to be shown are cl, c2, ..., the same as T; the specified values of the sub-table are f-> v1, f2-> v2, .. v d1-> L (d1), d2-> L (d2), ..., those of T plus the values of L for the deployed columns; the extended columns of T 'are dl, d2, ..., C, the same as those in the table containing L plus column C.
• the rotations indicated by T for the line L are removed from T, and placed in T, for the same line, with the exception, where appropriate, of a rotation of the column C for the line L, which is recorded as an overall rotation parameter for C.
• T 'therefore represents all the rows of the table corresponding to L.
• the system queries the data source to obtain only information not yet visible to the user.
• T ' it is not necessary for T 'to contain all the columns: in fact, the values of the specified columns are already visible in the parent table, and can be omitted in the sub-table, in order to lighten the display.
• the values of the specified columns are already visible in the parent table, and can be omitted in the sub-table, in order to lighten the display.
• the columns shown in the sub-tables be a contiguous interval of the columns shown in the root table. contain a deployment button just browse the corresponding column in the sub-table to reduce, and check if it contains cells with a deployment button or if two cells have a different value.
• the state of the line before deployment is recorded so that it can be restored at the time of the reduction.
• the spin reduction operation will at most change the values in the line, not the existence or not of deployment buttons.
• a query is sent to the data source, containing the properties of the sub-array about to be created (columns, specified values, and rotation information).
• the data source determines the contents of the subarray (the values to be shown, and all cells to contain a deployment button), and returns it to the user.
• the received data replaces the line containing the button that was clicked by the user.
• each associated deployment button has a reference to the sub-table to be visible when this button is activated.
• a button is pressed for the first time, a sub-table is created as described above, and a reference to it is saved in the deployment button.
• the sub-array is minimized, it is simply rendered invisible, and the reduced line is made visible. If the user uses the deployment button again, the sub-array reference is found, and the sub-array is simply made visible again.
• Figure 16 shows the case where all the columns are reduced. Note that in this example the user interface has in each reduced cell a value (rather than for example how many different values this cell represents) and that the set of values (01, El, Pl) shown in the different columns corresponds to a line that actually exists in the data source.
• Figure 17 shows the sub-table T 'presented following the deployment of the Organization column (the user having clicked the button associated with 01) in the single line that was presented in the previous example. Note that this button is then replaced by a reverse button that reduces Ol to new (and thus return to the situation of the previous figure).
• the Organization column all existing values (ie, Ol and 02) are then presented, each with an associated value presented in each other column, the values presented in each row together forming an existing tuple in the data source 58 .
• Figure 18 shows the sub-table T "that appears following the click on the button associated with El in the previous example (to deploy the employees of the organization 01) .It is noted that there is no need to repeat Ol in the second line, allowing a more pleasant presentation to read 59.
• Figure 19 highlights the sub-table T "which appears after the click on the button associated with the project Pl of the first line in the previous example (to deploy the projects of El of 01) El is implicit in the second line 60
• Figure 20 highlights the sub-table that appears after clicking the button associated with E3 in the previous example. We see that we now see the 5 rows of the data source table and that there is no longer a cell to deploy.
• Figure 21 shows the status of the displayed table, following the click on the button associated with Pl (deploy projects).
• Pl deployment projects
• all existing values P1 and P2 are then presented, each with an associated value presented in each other column.
• Figure 22 shows the sub-table T "that appears following the click on the button associated with El in the first line of the previous example (this click is intended to deploy the employees participating in the project Pl). directly deployed in full and, as the second tuple of the data source is already presented, the button deploy from Ol to the first line is no longer necessary.
• the user can then arrive directly at the fully deployed table by clicking the button associated with P1 in the first row, as shown in Figure 27.
• each column can also have a combination or aggregation or cardinality of the existing values or indeed any other relevant information, or nothing at all.
• the interface thus has a tree (a hierarchical structure) whose root is 01, El and E2 form two branches, and where P1 and P2 are the two leaves of El.

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