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Und das ist noch lange nicht alles, was hier im Angebot ist. Registrierung, Suchfunktionen und Navigation auf der Seite - alles einwandfrei. Trotz ihres unspektakulären Erscheinungsbildes ist die Harzlift-Community ein beliebtes Kontaktanzeigen-Portal unter den niedersächsischen Singles. Im Laufe der Jahre seit Gründung haben sich bei Harzflirt mehr als Neben der Flirt-Suche ist die Singlebörse dazu gedacht, nette Leute zu treffen und sich für gemeinsame Aktivitäten in der Freizeit zu verabreden.
Die Veranstaltungstipps waren bei unserem letzten Testbesuch etwas mau bestückt, und ein Relaunch wäre auch mal nicht übel Die Kontaktanzeigen-Site Hannover-Singles ist mit Auf dem Portal gibt es auch eine Rubrik speziell für Tanzkurse mit Tanzpartnerbörse. Auch alle Single-Profile sind persönlich freigeschaltet worden vom Anbieter.
HarzDating ist ein unscheinbares, aber dennoch sehr beliebtes Single-Portal für den Harz, das ähnlich wie Harz-Flirt überraschend viele Anmeldungen aufweist über Klassisches Kontaktanzeigen-Portal mit über Diese echt norddeutsche Singlebörse ist nach einem fulminanten Revival im März wieder so richtig durchgestartet und hat nach eigenen Angaben schon Neben den klassischen Funktionen verfügt die Seite auchüber einen Chat und Blog. Nicht das Non-plus-Ultra, aber einen Besuch wert Bei der liebevoll-jecken, kölschen Singlebörse Koelner-Single.
Ein Liebesschloss von der Hohenzollernbrücke prangt im Hintergrund und auch sonst kommen einem viele Dinge gleich bekannt vor. Fotovoting, Blog, Videos und der Kölner Event-Kalender sind gut gemacht und laden ebenfalls dazu ein, auch mal ein bisschen Zeit auf dieser Seite zu verbringen. Mittlerweile sind schon fast Wer Partner für Aktivitäten sucht, ist hier bestens aufgehoben. Neben der Suche nach dem oder der Richtigen gibt es auch die Möglichkeit, gemeinsam Sportevents zu besuchen, einen schönen Tag zu verbringen und so neue Menschen zu treffen.
Das Portal für die Idealpartner-Suche hat über Singles aus Rheinland-Pfalz können sich hier finden - und das recht schnell und einfach. Man muss unter den deutschlandweit 1,5 Millionen registrierten Profilen nur die Augen aufhalten. Die regionale Community-Seite www. Zu den zahlreichen Zusatzservices gehören Forum, Blogs, Gruppen und Fotoalben, weshalb die Partnersuche hier manchmal nicht komplett an erster Stelle steht. Jetzt ist erst eine Registrierung nötig, damit man sich im Kontakt-Board umsehen kann.
Dafür ist das Board auch kostenlos. Bei unserem Test konnten wir allerdings nur Mitgliederprofile finden. Besonders beliebt auf dieser Seite ist unter anderem das Fotoduell, bei dem zwei Fotos im Vergleich bewertet werden. Während unseres letzten Tests war jedenfalls auf der Seite eine ganze Menge los HHier haben sich im Laufe der Jahre mehr als Die Seite ist recht werbelastig, dafür aber auch kostenfrei.
Eine Seite, die man sich als Chemnitzer ruhig mal ansehen kann Flirtportal für Sachsen-Anhalt mit über registrierten Singles , sehr einfach gehalten. Ansonsten herrscht ebenfalls leider total tote Hose: Nach einem fulminanten Revival im März ist die Singlebörse wieder so richtig durchgestartet und hat nach eigenen Angaben schon Etwas in die Jahre gekommene Singlebörse für Thüringen mit angeblich über Auch wenn die Optik nicht gerade der Megaburner ist, lohnt sich aber dennoch ein kleiner Testblick ins Innere.
Die Seite ist leider von uns gegangen. Hier sind die letzten Infos zur "verstorbenen" Seite Bei dieser einladend gestalteten, aber sehr auf die Wahrung der Anonymität bedachten Singlebörse müssen Sie zunächst kostenpflichtig Mitglied werden, um den Service zu nutzen.
Dadurch gibt's keine Karteileichen und nur seriöse Profile! Einige Singles haben sich schon registriert. Singlebörsen mit Schwerpunkt auf die regionale Partnersuche gibt es mittlerweile reichlich.
Einige Singles haben dabei mehr Glück z. Singles in Hamburg, München, dem Harz , andere Singles weniger z. Singles im Saarland oder den Neuen Bundesländern. Henning Wiechers beobachtet seit die Welt der Singlebörsen und gilt in den Medien als führender Fachmann zum Thema. Wir haben haufenweise gute Singlebörsen für bestimmte Zielgruppen für Sie katalogisiert. Für Sie im Test: Für welches Bundesland suchen Sie eine Regio-Singlebörse?
Anmeldung, Partnervorschläge und Kontaktanfragen kostenlos. Anmelden und Umschauen sind kostenlos bei www. Kostenlos umschauen und 2 Mails täglich schreiben wenn Profilfoto vorhanden. Registrierung und Basis-Mitgliedschaft sind kostenlos. Anmeldung und Umschauen sind bei den Berliner Singles kostenlos. Der Grundservice ist kostenlos. Der Community-Kreis ist klein, aber er hält sich.
Im Kontaktanzeigen-Portal der Tageszeitung "20cent Lausitz" sind rund 3. Die Grundfunktionen sind komplett kostenlos inkl. Die freiwillige "Goldfisch"-Mitgliedschaft erweiterte Singlesuche etc. Verliebt-im-Norden kann komplett kostenlos genutzt werden. Anmeldung, Stöbern in Profilen und Favoriten speichern ist kostenlos.
Längere Laufzeiten sind günstiger. Die Basis-Dienste sind bei hanseflirt. Anmeldung und sich umschauen ist kostenlos. Use aggregated visualizations as a starting point and use details visualizations for smaller, filtered portions of data only. Many graphical elements in the analysis will take some time to render. This is especially important on the web player which does not allow hardware acceleration.
Can you use a different visualization type? Or partly aggregate the data? For example, binning can be used to aggregate markers in a scatter plot and still allow you to see a distribution. Using the bin sliders you can increase the number of markers shown until it takes too long time to make changes. Analysis files which have previously been saved in version 4. Hide or delete unused filters or do not create filters for external columns unless you have to.
Use the list box filter or the text filter rather than the item filter when working with columns with a lot of unique values. Item filters are costly to display, even when they are not used. If you have old analysis files using item filters for these type of columns it is recommended to manually change the filter type to a list box or text filter and save the file again. Some types of aggregations are more time consuming than others. For example, use average rather than median, if possible.
Use the data type real rather than currency. The currency formatter can be applied to the real data type. It is recommended to use the filters panel instead of adding a lot of filters to text areas. Filters in text areas can make the analysis seem unresponsive. The more filters you add to the text area, the less responsive the application becomes. Calculated values labels and sparklines in text areas may also give rise to unresponsive analyses.
Use post-aggregation expressions for all expressions including OVER since these calculations are faster when done on an already aggregated view.
Do not run other applications on the same machine when working with large data volumes. If the data is in a tall and skinny format rather than a short and wide you may obtain better performance. Avoid visualizations with many graphical elements no hardware acceleration will make the rendering time very long.
With lower limits, the allowed complexity of marking queries is reduced. This is important when working with external data sources. See Preferences Descriptions in the Administration Manager help for more information. Prefer iterator based data access over random access.
Be careful when using custom comparers - depending on usage they may become a bottleneck. Consider if the problem cannot be solved in other ways. If things are slow and you are using old custom extensions, see if they can be refactored or if some time-consuming steps can be removed. When you are working with data from an external data source in-database or in-db data there are a number of features that you can use with in-memory data that are unavailable.
See below for more information. One thing to think about when working with in-db data is that changes to the underlying database schema will not automatically be reflected in the Spotfire analysis. Not all users will have the sufficient database privileges to perform a full schema refresh. Before you can work with in-db data in Spotfire there are few prerequisites that need to be met see the System Requirements at http: You may need to have drivers for the data source of interest installed on your machine.
Some connectors require additional packages to be deployed on the Spotfire server. You need to have been granted access to the licenses by your Spotfire administrator. When data is located in Microsoft SQL Server Analysis Services cubes it behaves rather different compared to data in the relational databases traditionally accessed via Spotfire. In relational databases the facts are directly available in the database tables. In the cube, all facts are already aggregated by the cube administrator.
A cube is built from several predefined combinations of hierarchies, which could be either attribute hierarchies or user hierarchies defined using dimension attributes, see below. In the schematic image above, the sides of the cube could be said to represent different hierarchies. When a connection to an external source is set up with in-database data tables, all calculations are done by the external data source and not by Spotfire.
You reach the different ways to load data via the File menu or using Add Data Tables. With Add Data Tables, you can add more than one data table to your analysis.
When the data source contains large amounts of data, it may take a long time to retrieve all data and the application could also be perceived as less responsive to different actions. You may also want to restrict some data from certain users. When you are working with information links it is possible to limit what data to open in different analyses in a number of different ways combinations are also possible:.
When you want the data in your analysis to dynamically change with some predefined condition. For example, when setting up a details visualization dependent on the marking or filtering in another data table. Another example is when you want one information link to return different data for different analysis files, in which case you could use the on-demand data table as the only data table in the analysis with a document property as input. You can only specify a single fixed value as input to on-demand loading, so if you need to retrieve multiple values from a certain column you will have to make sure that an information link is set up to use a multiple selection prompt rather than using it as an on-demand data table.
When you are analyzing in-database data using a connection to an external data source you only load the requested data. By setting up visualizations based on the in-db data as details visualizations limited by the marking or filtering in a master visualization you can make sure that the actual loaded data is limited to a subsection of the available data only.
Set up the new details visualization to use the in-db data table. When the source data amount is huge, but the end users of the information link are allowed to determine what data to bring in for analysis themselves. When you want the data source to return only information applicable for a certain user name via a lookup table or for a specified group or user domain. When you want the data source to return only information applicable for a certain user or group in a more flexible way than with personalized information links.
Parameters are created in Information Designer for example, as a part of an expression set on a column or filter but their properties and definitions are defined using the API. By using a parameterized information link and a configuration block, it is possible to create an analysis with different input parameters e.
If a colleague has created an analysis file a DXP file and either sent it to you in an email, or, given you a link to the Library where the file is located, double-clicking on the file will open it. Note that SFS files cannot be opened from the Library.
If the content of a delimited text file is pasted into Spotfire, the Import Settings dialog will not be displayed. The default settings will be used during import. The library provides publishing capabilities for your analysis materials, so you can share data with your colleagues. You can save both complete analyses and raw SBDF files in the library. The files in the library can be used directly from Spotfire by anyone who has at least read privileges.
Analysis files published in the library can also be accessed directly by users of Spotfire Web Player by clicking on a link to the analysis in an email or on a website. Right-click in the library tree to display a pop-up menu where you can delete or edit the properties of previously added files and folders. You can also copy the URL to an analysis and open the analysis in the Web Player or send the link to a colleague.
Information links are predefined database queries, specifying the columns to be loaded, and any filters needed to reduce the size of the data table prior to visualization. They are organized into different folders in the library. Which folders in the library are available to you depends on how your permissions have been set by the administrator. You can also search for an item in the library by entering its name, or part of the name in the search field in the upper right corner in the dialog, and then pressing enter.
All the files, information links and folders matching your search string will then be listed. See Searching the Library for detailed information about library search. You can search for library items in the Open from Library dialog, in the Library Administration tool and in Information Designer. Searching for a text string will by default look for matching text in the title and keywords of the items in the library. You can use wildcards and boolean operators to search for parts and combinations of words.
Data can be added to the analysis in several different ways: Adding data as separate data tables is useful if the new data is unrelated to the previously opened data table or if the new data is in a different format pivoted vs. If you have a visualization made from a particular data table which has filtering and marking that you would like to apply to visualizations made from another data table, then you must define a relation between the two tables.
For a relation to be useful, you need to have one or more key columns identifier columns available in both data tables, and use these to define which rows in the first data table will correspond to rows in the second data table. If you need more than one key column to set up a unique identifier, you must add one relation for each identifier column. To combine data from different data tables in one visualization, the data tables are loaded as usual, but at least one column must match between the data tables in order to combine the data from the data tables.
Columns match if they have the same data type. If two columns are of the same data type and have the same name, Spotfire will match them automatically during loading.
You can view and edit column matches in the Data Table Properties dialog. To learn more about using many data tables in the same visualization, see Multiple Data Tables in One Visualization. In some cases when you need to bring in-memory data from different data sources together in any other single visualization, it may be more suitable to use the Insert Columns or Insert Rows tools. And with in-database data tables you can often join several database tables into a single virtual data table before adding it to Spotfire.
For a simple line from a different data table in a scatter plot, see Details on Line from Data Table. You can always go back and edit relations as well as create new ones using the Data Table Properties dialog.
A data connection is used when you are analyzing massive amounts of data and you need to keep the underlying data in the database in-db rather than bringing it into Spotfire's internal data engine. However, you can also select to import data tables from relational data connections. On-demand is not available for cube connections. Both the connection data source and the data connection itself can be shared in the library, if desired. A connection data source is an important part of a data connection.
It can be set up in advance by your administrator, using the Manage Data Connections tool, and shared in the library, but it can also be set up within the context of a data connection, "embedded in connection", if you have access to the login credentials to the underlying database. The information required to set up a connection data source varies between different types of connections, but it typically includes a server name, port number, database name and credentials information.
See the login dialog for the interesting connection type for details. Which connectors you have access to is determined by the licenses set up by your Spotfire administrator. Some connectors also require that a driver is installed on the machine running Spotfire. See the system requirements at http: You can also create relations between different data tables in Spotfire without actually joining them.
This will form a looser connection between the tables but it can be used if you want to set up a details visualization using one of the data tables, limited by selections in the other.
See Details on Manage Relations for more information. Connectors are used to set up data connections. The connectors can be seen as adapters which can translate data queries between Spotfire and an external system. Which connectors you have access to depends on several different things:. What licenses the groups you belong to have been granted access to by the Spotfire administrator.
Whether or not your computer has the required drivers for a certain connector. Your company may also have deployed additional connectors on the server that are not embedded in the standard Spotfire distribution.
In Spotfire it is possible to reuse the visualizations, calculations and setup from a previously created document with new data, as long as the new data is reasonably similar to the old data. This is useful when creating an analysis for, say, sales figures for a certain month. You create a full analysis using the data from January, set up visualizations, calculations, etc. When the sales figures for February are available, you can open the same file again, and replace the data from January with the data from February, and the visualizations will be updated.
This of course requires that the data table for February is structured in the same way as for January, using the same column names and format. Sometimes the data you want to analyze in Spotfire is not in the most appropriate format, or may contain errors. It can therefore be useful to perform transformations on the data in order to get the best results from the analysis. Transformations can be applied either when data is loaded, or later on, when the data has already been loaded into Spotfire.
You can perform transformations on most of the "regular" column types that are loaded into Spotfire, but not on certain column types whose content changes depending on selections you make in the analysis.
Calculated columns, columns that were created by adding tags to the analysis, and columns created by using tools like K-means Clustering and Line Similarity are some examples of column types that you cannot apply transformations to. Columns that cannot be transformed will not be available for selection in any of the settings dialogs used for transformations.
Also, data that is located in an external data source in-database data cannot be transformed. However, if you add data from an external data source, and select to import the data table into Spotfire, you can apply transformations to the data after it has been loaded, by using Insert Transformations, as described below. The data is distributed into columns usually aggregating the values.
This means that multiple values from the original data end up in the same place in the new data table. When the data types of source columns differ, the varying data is converted to a common data type so the source data can be part of one single column in the new data set.
A number of normalization methods can be written as expressions, or used when transforming data. See the links at the end of this topic for a description of the theory behind each method. This dialog is shown when you open a linked analysis file in which the file path to one or more of the source files is no longer correct.
Occasionally, the columns included in a data table do not allow you to perform all necessary operations, or to create the visualizations needed to fully explore the data table. However, in many cases the necessary information can be computed from existing columns by using the mathematical and logical expressions provided by the Insert Calculated Column tool.
A calculated column is treated like any other column and its contents are static during all further analysis. If you want to use expressions that change during filtering of your data table, you should instead use custom expressions that are defined where you need them for example, select Custom Expression Insert Calculated Column, which creates a new column in the data table, and Custom Expression, which is used to dynamically modify the expression used on an axis or to define a setting.
Both types of expressions are created with a similar user interface. Many of the expression or script editing fields available in Spotfire allow you to use the following keyboard shortcuts:. An expression is considered valid if it is syntactically correct and all function, operator and column references can be resolved. If an expression is not valid, it cannot be evaluated.
This will be indicated in the Sample result field of the Insert Calculated Column dialog as " Error", Empty , or similar.
When generating a result data table from the expression, errors are converted to null. Wrap the expression with a call to the SN Arg1 , Arg2 function to override this behavior. The SN Arg1 , Arg2 function can be used to substitute null with a certain value, for example, 0. Empty values are generated whenever a column value from the data table is missing, when a calculation involves an invalid value, or by explicitly writing null in the expression. Results that are null, are displayed as " Empty " or simply left blank.
When aggregating within a column, the invalid value will be ignored, whereas row-wise calculations between columns will result in invalid values each time one of the involved columns contains an invalid value. This dialog lets you format values on column level. If you change settings for a specific column or hierarchy in this dialog the new settings will be used for that specific column or hierarchy everywhere in the analysis from then on. If the format you want to use cannot be created with the given settings, the custom format string allows you to create your own formats using a code explained in the examples below.
The special characters allow you to multiply, divide, separate numbers, etc. Other characters are printed out in the resulting data. Binning is a way to group a number of more or less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals.
Numeric columns can also be temporarily grouped by right-clicking on a column selector and clicking Auto-bin Column. Right-click on a column selector and select Auto-bin Column to create a temporary, automatic binning on an axis. Binning functions can also be used in custom expressions. When using a numeric column for the X-axis in a visualization the category axis in a bar chart , you sometimes may want to bin the values to compare segments of the data to each other. One very handy tool to help you dynamically do this is the binning slider.
There are more binning functions available in the Custom Expression dialog. You can insert columns from many different sources. Below are some examples of how to add columns from some of the most common sources.
Type the name of the data source directly at the top of the Select menu to search for a data source. The results are grouped after source origin to help you find the source from the correct location. You can insert rows from many different sources. Below are some examples of how to add rows from some of the most common sources. Below is a short description of the different concepts used when handling multiple data tables. A data table is either fetched from a data source, or created within the application.
Data loaded from a data source can be handled either in-memory or in-database depending on how it is added to the analysis. In-memory data tables have one or more columns and zero or more rows, whereas in-database data tables technically do not contain any data but simply fetch the requested data directly from the source. See Data Overview for more information. In-memory data tables can be linked or embedded.
Linked data tables can be loaded completely into the application, but if the source is an information link they can also be configured to load data on demand only. When data tables are related, any marking or filtering in one data table may be propagated to the other related data tables. Data tables can have column matches between them if columns are of the same data type. A column match is used to aggregate data correctly when the data from different data tables is combined in a single visualization.
Column matches are much looser in nature than the relation mentioned above, because they do not join the data tables together. Marking and filtering is still individual for each data table even if they have columns matching between them. To learn more about column matches, see Matching Columns. On-demand data tables are data tables to which only rows related to a defined input are loaded. The input could be something like the marked rows in another, related, data table, the filtered rows of another data table or a property value selected in a text area.
Changing the input means changing the "demand", i. On-demand data tables can be used by Details Visualizations, and only data from information links or data connections can be loaded on demand. While data from in-database data tables is retrieved only when needed, the use of an in-database data table as a details visualization may also be seen as a type of on-demand visualization. As a means of helping you keep track of which data tables are related, a stripe of color will be added to the left of the filters in the filters panel when more than one data table is available.
Filters from related data tables which may affect each other when they are manipulated all have the same color. Also, the visualizations that use related data tables will show the same color in the title bar, if it is displayed. You can specify whether or not filtering in a data table should affect what is shown in visualizations used by other, related data tables. The default setting is to ignore filtering in related data tables. See Filtering in Related Data Tables for more information.
To see which column matches exist in the visualization, open the Column Matches tab in Data Table Properties. You can also see which column matches are used in a specific visualization by opening the Visualization Properties dialog on the Data page for the visualization of interest. When you set up an analysis in TIBCO Spotfire, you may want to be able to visualize data from more than one data table.
See How to Add Data Tables for more information. However, if you choose to bring in a lot of data tables, you may find it difficult to keep track of which data tables are related and which are not. Spotfire will add some extra visual hints when more than one data table is available to help you see which data tables are related.
You may also want to view which data tables have matching columns and therefore can be combined in one visualization. Or simply view which data tables have already been combined in a certain visualization.
Sometimes the data you want to analyze in Spotfire is located in different data tables. Working with visualizations combining data from multiple data tables is not very different from working with data from a single data table. You can choose the visualization that best suits your data, you can filter, mark, and drill down in your data, just as with a visualization using data from a single data table.
However, a couple of concepts are important to be familiar with when setting up and working with a visualization combining data from different data tables. This topic describes key concepts, and includes a couple of examples.
If you are unsure how to set up a visualization combining columns from different data tables, this recommended workflow can be helpful. Start by having a look at the data in the different data tables, and try to answer a couple of questions.
What data do they contain? What do you want to visualize based on that data? A data table containing categories you would like to group your visualization by is a good candidate for the main data table. For instance, you may want to group by region, department, salesperson, product type, or similar.
Create the visualization type you want to use, and then configure as much as you can of that visualization with columns only from the main data table. Select how and by which columns the visualization should be grouped, and if the main data table also contains columns that you want to show as aggregated, add those columns to the appropriate axes as well.
When the visualization has been configured as much as possible with only main data table, you can start adding aggregated columns from other data tables. This is an example of independent data tables. These two visualizations are placed on the same page, but they are not related to each other.
The visualizations correspond to separate data tables. Marking or filtering in one visualization will not affect the other when they are independent. The Details-on-Demand displays information about the marked item in the active visualization. Color stripes are used to indicate what visualization, filter and Details-on-Demand that are related. In this example, the bar chart shows the sum of sales for different types of fruits and vegetables.
The scatter plot shows the content of fructose and glucose for different types of fruits and vegetables. This is an example of multiple related data tables. The visualizations are based on different data tables that are related. Marking items in one visualization will mark the corresponding items in the related visualizations. Filtering data in one data table may filter the related data in the other data tables. Visualizations that are related share the same color in the color stripe to the left in the visualization.
Filters belonging to related data tables also share the same color stripe. Related visualizations can be placed on different pages in an analysis. This means that markings that are not visible at the moment can affect the analysis that you are looking at. In this case, two data tables with information about fruit and vegetables are related. The scatter plot shows the amount of glucose and fructose for different types of fruits and vegetables, while the bar chart shows the sum of sales for the same types of fruits and vegetables.
Marking an item in the scatter plot, in this case the one with the highest level of fructose Apples , will mark the Sum Sales for Apples in the bar chart. This is an example of multi-step Master-Detail visualizations. The visualizations in this example are based on the same data table and show different levels of detail.
However, the visualizations could just as well be based on data from different data tables. Marking in one visualization defines the data of the next visualization, making it possible to drill down in level of detail.
Related visualizations as the Master-Detail case can be placed on different pages in a visualization. This means that markings in a visualization that is not visible at the moment can affect the visualization that you are looking at. If a visualization is empty, it may be because it is based on markings from another visualization. Go to the master visualization and mark an item to display information in the details visualization. When creating the analysis, you can add a message explaining in which visualizations to mark items in order to view details.
See What is a Details Visualization? The Details-on-Demand displays information about the marked rows from the active visualization; it could be either the master or the details visualization.
By inserting columns or rows, it is possible to combine data from different sources into a single data table that can be used in a visualization. You can copy data from Spotfire for later use within Spotfire, or in other applications. The copying options below are available from the Edit menu. You can add data to your analysis by pasting data that is currently available on the clipboard. For example, you may have copied some data from a TXT file, the content in a few cells in a spreadsheet, or an entire file that is located in a folder on your computer.
Either way, you can load the copied content into Spotfire by using one of the available pasting options. The pasting options below are available from the Edit menu.
The Data panel is used to get an overview of the columns in all data tables, in-memory as well as in-database in-db. When working with in-db data the Data panel is the starting point for configuring both visualizations and the filters panel, since no filters are created automatically for external data.
Depending on the data source, there will be different sections available for a selected data table, see below for some examples. Using the right-click menu in the Columns field, you can easily specify that a column contains geocoding information, that is, information that can be used to position data on a map.
Right-click in the data panel to bring up the pop-up menu. You will have access to different options depending on where in the data panel you click. For example, you can define which data table to use as default when creating new visualizations, set up sharing routines, or define how data should be stored when saving the analysis.
To learn more about data tables, see Data Tables Overview. This dialog is used to define key columns for a data table in an analysis. The key columns are used to uniquely identify all rows in the data table. You should specify key columns if you want to be able to see the markings that were active when saving the file, or if you want any specified tags or bookmarks to be able to be reapplied when reopening the analysis file.
However, there is no guarantee that a selection always can be reapplied even if key columns are specified since a selection of a visualization item might include references to other columns than the key columns. Column properties are any type of metadata available for the columns and, in some cases, also for hierarchies in your data table.
For example, this could be the name or number of decimals of a column, the data type, an optional description of the column content, or, a customized sort order for a string column. Predefined hierarchies can be set up when two or more columns are somehow related to each other. For example, a hierarchy can add structure to columns containing Country, State and City. The predefined hierarchies allow you to quickly change the level of detail in a visualization by using the hierarchy sliders, or, when you wish to combine two or more filters to a more structured hierarchy filter.
The number of allowed nodes in a hierarchy with more than one level is limited to If you try to create a hierarchy with more nodes, you will simply receive a hierarchy with one value, All.
If this should happen, edit the hierarchy and remove the column with too many unique values from the hierarchy. It presents the data as a table of rows and columns, and is used to see details and compare values. By clicking on a row you mark it, and by dragging the mouse pointer over several rows you can mark more than one row.
You can sort the rows in the table according to different columns by clicking on the column headers, or filter out unwanted rows by using the filters.
All visualizations can be set up to show data limited by one or more markings in other visualizations only details visualizations. Tables can also be limited by one or more filterings. You can also set up a table without any filtering at all. When working with in-db data the table visualization cannot show more than 10 rows. If not all data can be shown, you will get a notification about this in the legend.
A primary key must be specified in the external database table to enable highlighting and markings in the table visualization. A cross table is a two-way table consisting of columns and rows. It is also known as a pivot table or a multi-dimensional table. Its greatest strength is its ability to structure, summarize and display large amounts of data. Cross tables can also be used to determine whether there is a relation between the row variable and the column variable or not. Optionally, the cross table can display grand totals for columns, rows, or for the whole measure.
It can also display subtotals for columns,. The aggregated value for subtotals and grand totals is not calculated on the values shown in the cross table, but on the underlying row values. Cross tables can also be limited by one or more filterings. Another alternative is to set up a cross table without any filtering at all. See Limiting What is Shown in Visualizations for more information. A graphical table is a summarizing visualization designed to provide a lot of information at one glance.
It can be set up to show columns with dynamic items such as sparklines, calculated values, conditional icons, or bullet graphs. One value is shown for each row as specified on the Rows axis. In the example below, the graphical table shows sales performance for different regions.
You can add any number of dynamic items to a graphical table. Each dynamic item column uses its own axis expression and it can also be filtered and limited by markings separately. This way, you can show both the total values for some calculated value and the currently filtered values simultaneously. When a hierarchical structure is used on the Rows axis, the graphical table is grouped into sections and sorting can be performed within each section by clicking on a column header.
Sparklines are small, simple line graphs traditionally used for displaying trends or variations of some variable:. They can be displayed in the context of a graphical table or, separately, in a text area.
The general idea of sparklines is that they can be included directly where they are needed, in tables or text, in order to provide context to a value. Sparklines can be shown both separately in a text area or be included as a column in a graphical table. The behavior of the sparkline is quite similar in both places, but some differences exist. Therefore, this list of step instructions has been split into three different sections: Graphical table specific information, Text area specific information, and General information applicable to both instances.
Calculated values are values derived from some kind of aggregated expression, similar to the data shown in cross tables. The general idea of calculated values is that they can be included directly where they are needed, in tables or text, in order to provide information at a glance.
By adding rules that control the color and font style you can make sure that a value stands out when it falls outside the specified limits:. Icons are small, simple images traditionally used for displaying trends or variations of some variable. In the example below, the icons are used in a graphical table to show the top, bottom and intermediate sales region of some fictive product:. The general idea of icons is that they can be included directly where they are needed, in tables or text, in order to provide information at a glance.
Icons can be shown both separately in a text area or be included as a column in a graphical table. The behavior of the icon is quite similar in both places, but some differences exist. Bullet graphs are used to compare one value, represented by a horizontal bar, to another value, represented by a vertical line, and relate those to qualitative ranges.
The general idea of bullet graphs is that they can be included directly where they are needed, in tables or text, in order to provide information at a glance. Bullet graphs can be set up to change with filtering like any traditional Spotfire visualization or they can be locked to show fixed values, using the Data page in the Bullet Graph Settings dialog.
Bullet Graphs can be shown both separately in a text area or be included as a column in a graphical table. The behavior of the bullet graph is quite similar in both places, but some differences exist. A bar chart is a way of summarizing a set of categorical data continuous data can be made categorical by auto-binning. The bar chart displays data using a number of bars, each representing a particular category. The height of each bar is proportional to a specific aggregation for example the sum of the values in the category it represents.
The categories could be something like an age group or a geographical location. It is also possible to color or split each bar into another categorical column in the data, which enables you to see the contribution from different categories to each bar or group of bars in the bar chart.
Line charts are ideal for showing trends over time. A standard example would be how the stock value for a certain company develops over time on the stock market.
However, it does not necessarily need to be time along the X-axis. Any data that behaves like a function with respect to the variable on the X-axis can be plotted. Line charts emphasize time flow and rate of change rather than the amount of change. You can select parts of a line by clicking and dragging with the mouse.
If one node in the line is included when you drag, that node will be marked. If two or more adjacent nodes are included, the line between the nodes will also be marked, but if there are nodes in between which are not included, only the separate nodes will be marked.
You can select several nodes in different parts of the line by pressing Ctrl and clicking and dragging with the mouse. Press Alt and click and drag to use lasso-marking to encircle the nodes of interest. The line chart can also be used as a step chart, where the lines are drawn in incremental steps rather than as straight lines between each value.
Step charts are especially useful when changes occur at certain times but the values remain more or less stable between changes. The combination chart is a visualization that combines the features of the bar chart and the line chart.
A combination of bars and lines in the same visualization can be useful when comparing values in different categories, since the combination gives a clear view of which category is higher or lower.
An example of this can be seen when using the combination chart to compare the projected sales with the actual sales for different time periods. Similarly to the function of Color by in other visualizations, Series by in the combination chart is a way to divide the data into slices. The difference is that the slices in the combination chart, called series, can be defined as bars or lines as well as being colored separately.
That is, each series in the combination chart will be represented by a line or a set of bars in the visualization. Pie charts are circle graphs divided into sectors, each pie sector displaying the size of some related piece of information. Pie charts are used to show the relative sizes of the parts of a whole. Scatter plots are used to plot data points on a horizontal and a vertical axis in the attempt to show how much one variable is affected by another.
Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X and Y axes. A third variable can be set to correspond to the color or size of the markers, thus adding yet another dimension to the plot.
The relationship between two variables is called their correlation. If the markers are close to making a straight line in the scatter plot, the two variables have a high correlation. If the markers are equally distributed in the scatter plot, the correlation is low, or zero. However, even though a correlation may seem to be present, this might not always be the case. Both variables could be related to some third variable, thus explaining their variation, or, pure coincidence might cause an apparent correlation.
Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X, Y, and Z axes. A fourth variable can be set to correspond to the color or size of the markers, thus adding yet another dimension to the plot. The relationship between different variables is called correlation. If the markers are close to making a straight line in any direction in the three-dimensional space of the 3D scatter plot, the correlation between the corresponding variables is high.
If the markers are equally distributed in the 3D scatter plot, the correlation is low, or zero. The variables could be related to some fourth variable, thus explaining their variation, or pure coincidence might cause an apparent correlation. You can change how the 3D scatter plot is viewed by zooming in and out as well as rotating it by using the navigation controls located in the top right part of the visualization.
It is still possible to open an analysis with a 3D scatter plot in the web player, but the 3D scatter plot will not be shown. Map charts allow you to position your data in a context, often geographical, using different layers. The layers can be either data layers, such as marker layers or feature layers, or reference layers such as map layers or image layers.
If multiple data layers are included in the map chart, you must always specify which layer should be the interactive layer. The interactive layer is the only layer in which you can mark items, but you can easily switch the interactive layer using the Layers control. The layers control can also be used to hide or show the different layers. Feature layers contain map features or shapes of the type polygon, line, or point. A feature layer can be used either as a data layer or as a reference layer only displaying items of visual interest.
Below is an example of a map chart with an interactive feature layer showing shapes, where each shape represents a state in the United States. Each shape in the map is a separate item, and you can interact with those items the same way you do with items in any other visualization.
As mentioned above, the shapes in the feature layer can be one of three geometry types: When polygons are used, as in the example above, the shapes constitute different areas in the map, and these areas will be filled with color. When lines or points are used, the interactive shapes are the actual lines or points.
The color you define in the Colors will be the color of the lines or points. Examples of when maps with lines as interactive shapes could be useful are maps showing highways or a street grid. Below is an example of a map chart with interactive shapes, where each shape represents a highway. Which geometry type is used in a map is defined in the map data before you load it into your analysis, and this cannot be changed in Spotfire. In a marker layer, markers or pies are positioned in the different areas.
In the example below, the map shows the same geographical area as in the first example, and is also divided into states. But instead of the states being interactive, a marker is placed in each of the states, and you can interact with the markers just as you do with markers in other visualizations.
If the data table for markers or pies has columns containing coordinates, you can use these to position the markers or pies in their correct locations on the map, but you can also map a certain hierarchy e. The markers are also well suited to be displayed on an online map using a map layer, see below.
Map layers are always used as a reference layer and cannot be interacted with directly. The available default maps can either be compound standard maps which include both borders, labels and roads, or you can use separate layers for each type of information and select only the information of interest. More details may become visible when you zoom in the map. You also have the option to specify at which zoom level a certain layer should become visible.
You can zoom and pan in a map using the navigation controls to the right of the map. Click on the small arrow icon on the map chart title bar shown on mouse over to show or hide the navigation controls.
The example below shows a map chart with states in North America as in the first example, but it has been zoomed in to show only some of the states. Labels can be used in the map chart to identify and describe markers or interactive shapes. In the example above, a label with the state name has been added to the marked shape in the feature layer. Open the Labels page of the layer settings if you want to modify the labels settings for a certain layer.
When this projection reference system is selected you can zoom out to view more than one version of the earth, so that you can show data using any location as the center of the earth and view parts of the earth outside the data range. If you want to restrict the shown part of the world to the data range, either use auto-zoom or switch the projection reference system to None.
A third way to set up a map chart is to use a background image and then position markers or pies on top of that image. This works similarly to the map with markers or pies, but with the difference that you do not need to have map data in a data table in order to set it up. However, for the markers to be placed correctly in geographical positions, the data table must contain X and Y coordinates.
Below is an example of a map chart where the background is a map image of a part of North America. On top of the background image are markers pointing out cities in the United States. A map chart can be used to show other than geographical data. The example below displays different types of failures on a wafer, a semi-conductor material used to manufacture microchips.
The background is an image representing the wafer. The markers in the visualization represent the chips on the wafer, and are placed on the background the same way they are placed on the actual wafer.
The colors and labels indicate the six different types of manufacturing failures that have occurred on this wafer. Copying the actual layout of the wafer is a way to enhance the readability of the data. To be able to view the data this way, you need to use tiled markers. This means that all the markers have the same size, and are displayed in a grid-like layout. Go to the Shape page in the Marker Layer Settings to change to tiled markers.
A ll visualizations can be set up to show data limited by one or more markings in other visualizations only details visualizations. Map charts can also be limited by one or more filterings. Another alternative is to set up a map chart without any filtering at all. A map chart is normally built by several different layers. Each layer can be configured separately with regards to coloring, labels and appearance.
The order of the visible layers and the transparency of each layer determines what will be visible in the final map chart. For example, place transparent layers containing labels at the top. The key to positioning different layers relative to each other is in most cases based on geocoding, and the geocoding is in turn based on a column matching between the data table containing the data of interest and a geocoding hierarchy. If any issues should arise, it is often the column matching that needs to be reviewed.
Since the column matching allows you to view data from multiple data tables in one visualization, you can use data from one data table to display information in a layer based on a different data table.
For example, if you create a feature layer based on a geocoding data table to show different regions in your country, you can use your own sales data table to color the regions. For some information about converting 5. The navigation controls for zooming and panning are located at the top right of the visualization:. In order to display data on a map, the data needs to be geocoded. These coordinates or features are then used for correctly positioning the data in a map context.
If your data contains simple geographic elements such as country names, states, or similar, then Spotfire will attempt to automatically geocode your data. If no automatic geocoding can be performed, you can set up the geocoding manually instead. Many parts of the user interface allow you to specify that a column in a data table should be used to match against a specific geocoding hierarchy.
This will make the process of setting up a map chart with that data easier. TIBCO Spotfire comes with a selection of geocoding hierarchies that normally should be added to the library, but you can also define your own geocoding tables and save them in the library.
This is accomplished by setting a few document and column properties on the geocoding table in TIBCO Spotfire and then exporting the file to the library. When Shape files are opened in TIBCO Spotfire they are automatically configured so that they can be used as feature layers in map charts.
However, there may be times when some manual work is needed before the data can be used in a feature layer. See What is a Map Chart?
However, for compatibility reasons, you can go back to use the 5. However, there may be times when some manual work is needed before the data can be used in a map with interactive shapes.
Treemaps are ideal for displaying large amounts of hierarchically structured tree-structured data. The space in the visualization is split up into rectangles that are sized and ordered by a quantitative variable.
The levels in the hierarchy of the treemap are visualized as rectangles containing other rectangles. Each set of rectangles on the same level in the hierarchy represents a column or an expression in a data table.
Each individual rectangle on a level in the hierarchy represents a category in a column. For example, a rectangle representing a continent may contain several rectangles representing countries in that continent. Each rectangle representing a country may in turn contain rectangles representing cities in these countries. You can create a treemap hierarchy directly in the visualization, or use an already defined hierarchy.
To learn more, see the section To Create a Treemap Hierarchy. A number of different algorithms can be used to determine how the rectangles in a treemap should be sized and ordered.
The treemap in Spotfire uses a squarified algorithm. The rectangles in the treemap range in size from the top left corner of the visualization to the bottom right corner, with the largest rectangle positioned in the top left corner and the smallest rectangle in the bottom right corner.