Condor Manual 2.2

Dynamic Social Network Analysis for Windows, Mac, and Linux

Send feedback to pgloor@galaxyadvisors.com

©February 20, 2007, Galaxyadvisors

Condor Manual 2.2

Table of Contents

1 Introduction .......................................................................................................................42 System Requirements .........................................................................................................5

2.1 Windows XP .....................................................................................................................................5

2.2 Mac OS X 1.4....................................................................................................................................5

2.3 Linux Kernel 2.6.x ...........................................................................................................................5

3 Installation Notes ...............................................................................................................54 Getting Started: Loading Sample Data into MySQL using FileLoader..............................65 Experimenting with the hipforum database .....................................................................116 Loading Data into Condor................................................................................................12

6.1 Loading Eudora mailboxes...........................................................................................................12

6.2 Loading Outlook mailboxes .........................................................................................................14

6.3 Loading comma-delimited text files ................................... .........................................................14

6.4 Loading Web Pages using the Google API.................................................................................14

6.5 Loading online mailing lists and Web forums ...........................................................................16

7 Visualizing Data with Condor ..........................................................................................167.1 Dynamic View of Communications .............................................................................................18

7.1.1 Control->Select Datasets…........................................................................................................................207.1.2 Control->Dynamic Setting… .....................................................................................................................217.1.3 Control->Speed….......................................................................................................................................217.1.4 Bipartite->graph in domain ........................................................................................................................217.1.5 Bipartite->graph in organization................................................................................................................227.1.6 View->Color By->Dataset .........................................................................................................................227.1.7 View->Color By->Node.............................................................................................................................237.1.8 View->Color By->domain .........................................................................................................................237.1.9 View->Color By->organization .................................................................................................................237.1.10 View->Color By->Node’s Betweenness Centrality .............................................................................237.1.11 View->Color By->Node’s Degree Centrality.......................................................................................237.1.12 View->Draw Arrow...............................................................................................................................247.1.13 View->Content Filter->Filter…............................................................................................................247.1.14 View->Content Filter->Cancel Content Filter......................................................................................247.1.15 View->Nodes Filter…...........................................................................................................................257.1.16 View->Fisheye…...................................................................................................................................257.1.17 View->Zoom…......................................................................................................................................257.1.18 View->Show Labels->name..................................................................................................................267.1.19 View->Show Labels->domain ..............................................................................................................267.1.20 View->Show Labels->organization ......................................................................................................267.1.21 View->3 D Mapping..............................................................................................................................267.1.22 View->Reset Layout..............................................................................................................................267.1.23 View->Visibility…................................................................................................................................277.1.24 View->Stroke….....................................................................................................................................27

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7.1.25 Measure->Actor Contribution Index.....................................................................................................277.1.26 Measure->Core/Periphery->Calculate ..................................................................................................277.1.27 Measure->Core/Periphery->Cancel ......................................................................................................287.1.28 Measure->Link Prediction->Draw Out.................................................................................................287.1.29 Measure->Link Prediction->Show Score… .........................................................................................287.1.30 Measure->Link Prediction->Adjust…..................................................................................................287.1.31 Measure->Link Prediction->Cancel Link Prediction...........................................................................287.1.32 Measure->Group Centrality and Group Density ..................................................................................287.1.33 Measure->Export Group Density..........................................................................................................297.1.34 Measure->Export Actor’s Betweenness Centrality..............................................................................297.1.35 Measure->Export Actor’s Degree Centrality .......................................................................................297.1.36 Measure->Export Actor Contribution Index ........................................................................................297.1.37 Measure->New Users Plot.....................................................................................................................297.1.38 Measure->Social Structure....................................................................................................................30

7.2 Static View of Communications...................................................................................................31

7.3 Netgraphs........................................................................................................................ ................31

7.4 Processing Contents for the Term Views....................................................................................32

7.5 Dynamic View of Terms................................................................................................................337.5.1 Control->Remove Terms............................................................................................................................347.5.2 Control->Add new terms............................................................................................................................347.5.3 Control->Filter by Term Rank… ...............................................................................................................347.5.4 View->Proximity Search............................................................................................................................34

7.6 Static View of Terms .....................................................................................................................35

7.7 Static and Dynamic Document View...........................................................................................36

Condor Manual 2.2

1 INTRODUCTION

Condor allows you to create visual maps, movies and many graph metrics of relationships.Relationships can come from social networks, Web site link structures, and concept maps ofunstructured documents, online forums, phone archives, e-mail networks, and many more.

Condor takes as input outlook mailboxes, Eudora mailboxes, Web mailing lists and onlineforums, Web links, and flat files. It parses those documents and incrementally stores them in adatabase. It then offers the option to visualize and analyze this data in manifold interactive andvisual ways.

textfiles

WebHTMLpageslinks

E-mailOutlook

converter

Condor

WebCollector

Blogposts

RSSCollector

Onlineconverter

MailingLists

forums

Database

Fileloader

Struc-tured(csv)files

text parser

Eudora parser

Dynamic View of Communications

Dynamic View of Concepts

Dynamic View of Network Statistics

Condor System Architecture

Condor Manual 2.2

2 SYSTEM REQUIREMENTS

2.1 Windows XP

CPU : Pentium 1.5G or fasterMemory: 1GB (2GB Recommended, works with 512MB)Java JRE Version 1.5.0Java 3D libraries version 1.3.1 http://java.sun.com/products/java-media/3D/download.htmlMySQL 5.0 or later www.mysql.com

2.2 Mac OS X 1.4

Memory: 1GB (2GB Recommended, works with 512MB)Java 1.5.0 Support http://www.apple.com/support/downloads/java2se50release1.htmlMySQL 5.0 or later www.mysql.com

2.3 Linux Kernel 2.6.x

CPU : Pentium 1.5G or fasterMemory: 1GB (2GB Recommended)Java JRE Version 1.5.0Java 3D libraries version 1.3.1 http://java.sun.com/products/java-media/3D/download.htmlMySQL 5.0 or later www.mysql.com

3 INSTALLATION NOTES

Step 1. Download and Install all the required third party software for your chosen platform Make sure MySQL server is running and accessible

Step 2. Check to see if you have following files in your Condor directory

• Condor.jar – Main Application• Condor.bat -- Starts Condor application (for WindowsXP)• Converter.zip -- An utility to convert Outlook .pst files to Eudora .mbx format

Sample Data:• fileloader_sample.txt -- sample input file for FileLoader.jar

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• hipforum.sql -- Condor test database to experiment with the system

4 GETTING STARTED: LOADING SAMPLE DATA INTO MYSQL USING FILELOADER

First you need to create a new test data file in the MySql database. On Windows, start theCondor main application by doubleclicking Condor.bat.On the Mac, open a Mac terminal window and type “java -Xmx1G -jar Condor.jar” in thedirectory where Condor.jar is stored.Alternatively, you could also doubleclick Condor. jar, but starting Condor form the terminalwindow or with a .bat file will allocate it 1 GB of memory (-Xmx1G), and will display errormessages in the terminal window.Then enter the monthly authorization key provided to you. You will only need to enter it onceper month.

Your screen will look like this:

Then choose “Tools->FileLoader”. In the window, that pops up, choose “Database->new”. Loginto the MySQL database, set the name of the new database to “loadertest”

Condor Manual 2.2

You will now have to create a simple MySQL database to load the fileloader_sample.txt file intoit. The fileloader_sample.txt file consists of three parts, looking as follows://datasetFileloader_test//charuser0074@internal0001,internal0001,1user0086@external0023,external0023,2user0108@internal0001,internal0001,1…//comuser0385@internal0001,user0551@internal0001,2/15/2005 15:42,,touser0385@internal0001,user0551@internal0001,2/15/2005 15:43,,touser0385@internal0001,user0551@internal0001,2/15/2005 15:44,,to…The “//dataset” part defines the name of the dataset, the “//char” part defines the actors(“=characters”) and their attributes, and the “//com” part defines the links or ties between theactors including timestamps plus attributes of the link.

After having registered the new database “loadertest”, you are prompted to create the actor tablein MySQL to store the attributes of the actors. In response to the question “Which type is youractor”, just click “ok”, which will generate an actor of type “String”.

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You will now be prompted to create attribute fields in the actor table.

Answer the question “Do you want to add new attributes?” with “Yes”. In the dialog that popsup, name the first attribute “organization”, and click on the “+” sign

Then create a second attribute “intORext” , by overtyping the string “organization” and clickingagain the “+” sign

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Now click on the “Set” button to actually create the database with all the fields:

After having registered and created the new database, choose the file “fileloader_sample.txt” byclicking on “Browse”, and selecting “fileloader_sample.txt” with the dialog box.

Then click on “Parse”. The dialog should confirm having imported 22 records into the database,displaying something like:

Work with dataset fileloader_testChar records are read.Wait for insert 22 Char records into database...Comm records are read.Wait for insert 21 Comm records into database...Parse is finished. You could close window now

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Now close the Fileloader window, and log into the loadertest database with “File->OpenDatabase…”. In the pop up behind “Database Name” you should now be able to choose“loadertest”.

Click on “Register”. The status message in the Condor status window will now look like this:

Select the dataset name “fileloader_test” in the window, and click on “View->CreateCommunication->Create Communication Views…”.On the first question “Do you want to view all selected datasets? click on “yes”Then, on the next question” Want to set time constraints on communication data for the view?”,answer with “no”Then, on the next question, “Do you want to refine charName?”, answer with “no”. “no”.Then, on the next question “Do you want to refine data by Actor’s Communication Frequency?”,answer with “no”.The display should now read:

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Database localhost \ fileloader is connected. Current communication type is For Others type

use dataset "fileloader_test".21 records .Wait for create index on comm_id, this will take timeWait for create temp table, this will take timeTemp table is created.

Now click on “View->Communication->Dynamic View of Communications”, click on “Set”,after some calculations, the movie window should pop up.

Now click on “Run” to play the animation. Experiment with the options.

5 EXPERIMENTING WITH THE HIPFORUM DATABASE

Now you are ready to load the hipforum mysql database directly into MySQL. Copy the filehipforum.sql into the mysq/bin directory, which is usually called something like “C:/ProgramFiles/mysql/mysql-standard-5.0.12/bin.Get a mysql command prompt using the menu, then enter:mysql>create database hipforum;mysql>use hipforum;mysql>source hipforum.sqNow you should get a long listing of all the records loaded into the hipforum database.

Now you can start Condor.bat again, and log into the “hipforum” database, and select the datasethttp://hipforums.com/forums/forumdisplay.php?f=120. Now you can experiment with all thedifferent views and options of Condor.

Condor Manual 2.2

6 LOADING DATA INTO CONDOR

Users have the choice of loading data directly from Eudora .mbx files, loading Outlook .pst files,loading comma-delimited text files, loading data from Web sites using the Google API, andloading data from online mailing lists and forums.Depending on where the data comes from, Condor stores data in four different formats:mailboxes, Web pages, document files, other (from flat files using FileLoader).

6.1 Loading Eudora mailboxes

The simplest way to load new data into the Condor database is loading Eudora mailboxes in the.mbx format. Condor can load single mailboxes, or entire folders of .mbx files.

First, you need to create a new database (Menu “File->New->eMail Database”)

Now, you can add a new dataset to the database “Edit->New Dataset:”

In this example, the dataset “testdataset” has been created.

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Clicking the “Yes” button brings now up a “Process Mails” dialog where the user can select amailbox or an entire mailfolder to be loaded into the Condor mysql database

In this example the mailfolder “KMCluster is selected to be parsed. The user has the option toinclude either subject line and/or message contents for later analysis and display, they can alsoanonymize the names of the senders and recipients of mails, in this case a table “lookup” iscreated in the mysql database “agni” where names of the form charname_1 to charname_n aremapped to the real names. The lookup table can then either be deleted, or be given only to aselected group of users for later mapping of anonymous identifiers to real names.

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Clicking on the button “process” will start the parsing process. Once parsing is done, users caneither choose more .mbx files for inclusion into the same dataset, or close the “Process mail”dialog and proceed to visualizing a dataset.

6.2 Loading Outlook mailboxes

Outlook mailboxes in .pst format can be converted into Eudora .mbx format using the"Converter" tool. The Converter is a Visual Basic application that calls the Outlook API and onlyworks on Windows.In order for Converter.exe to run, the two files Interop.Microsoft.Office.Core.dll andInterop.Outlook.dll need to be placed in the same directory. Converter also needs to previouslyhave the .NET framework (version 1.1) from Microsoft installed.

6.3 Loading comma-delimited text files

This is done using the File Loader menu in (Tools->File Loader) as described in the section“Getting Started: Loading Sample Data into MySQL using FileLoader”

File Loader works with comma-delimited data files. It expects an input format where, forexample, the order of the fields "FROM", "TO", "TIME", "SUBJECT"... must match with theorder of these corresponding fields inside the MySQL table.

6.4 Loading Web Pages using the Google API

First you need to get one or more Google API keys from www.google.com/api. Then you need tostart the WebCollector choosing the menu “Tools->Web Collector”.

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Warning: As of January 1, 2007 Google stopped giving out new SOAP API keys. If youalready have one, it still works fine (as of end of Feb 2007) . We are working on asubstitution.

First you need to create a new database “Dabase->New”, or log into an existing database.Afterwards, you need to add your Google API keys, which will be of the form“JSIQ2ixQFHLAgSXiHJkMJRnavN+OfaXY”.

Afterwards you have to option of collecting a web of URLs either originating at designatedURLs (Website Queries), or links between URLs in response to a Google query.

Either way, you can either do a bounded search crawl, where only links between the URLsreturned by the original Google query are collected, or do a degree-of-separation crawl, whereyou can also collect the URLs linked to the URLs returned by Google. You can do this up to adepth of 5, but beware, you will get lots or URLs very quickly.

You can also specify, how many search results from Google you want to include into yoursearch, typical values are 10 to 20.

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You can also collect the contents of each Web site (URL) for later content analysis.

6.5 Loading online mailing lists and Web forums

This is done using an auxiliary tool, the online process tool.

7 VISUALIZING DATA WITH CONDOR

The first step in visualizing one or more datasets in Condor is to login into a database, and loadsome data, if none has been loaded yet.Afterwards, users need to select a dataset in the scrolling window by clicking it (command-clickto select multiple datasets for integrated, parallel analysis)

Aftwards, menu (View->Communication->Create Communication Views…) will bring up theoption to store the new visualization in a playback file (select the check box “Record loadingprocess into file” and specify a file name). As the precalculation process to precompute alllocations of a all actors over the entire time interval can be quite time-consuming, particularly forlarge datasets, this gives the user the option to store a visualization file for rapid playback, e.g.for demonstrations.

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Clicking on the “Yes” button now gives the user the option to specify a time interval or filtermessages/documents/Web pages by contents within which the relationships will be analyzed.

The user has also the option to only get the actors who are both senders and receivers (choose“Only From & To”), only senders, or only receivers.

(If there are multiple datasets to be analyzed, the “Set” and “Next” buttons have to be clickedrepeatedly. In the end the “Finish” button needs to be clicked.

As next option the user can now select to combine multiple e-mail addresses into virtual persons,by answering yes to the question “do you want to refine mailAddress?”

in the refine mailAddress dialog, clicking a name brings it in the middle window. Per default,“Auto Select by Name” is checked, which means that similar names of the structure

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names@domain.organization are suggested to be merged by being moved in the middle window.Clicking on the “Rename” button will now give the option to merge collaborate@colabria.comand collaborate@kmcluster.com into the name collaborate@kmcluster.com permanently, or justfor this visualization session.

If the “Yes and Remember” button is clicked, the merged names will be stored in a separatemyslq table.In future analysis sessions of the dataset “testdataset” users will then be able to just click thebutton “Auto Change”, which will execute all predefined mail-address-merges.

In the final visualization step, users have the option to only include actors with communicationfrequency larger than a predefined threshold. That is, only people exchanging more than n e-mailmessages totally will be included in the visualization.

In this example, the most active person (which might also be a mailing-list address) hasexchanged 269 messages.

Now we are ready to look at a visualization.

7.1 Dynamic View of Communications

Selecting menu (Views->Communication->Dynamic View of Communications) brings up ashort dialog to set the options to create the interactive movie.

Window size permits to specify the active time window, ranging from 1 to 50 days.“With history” permits to incrementally include all communication records from day one to theend of the visualization.“Fast Decay” only works in combination with “With history”, to exponentially “forget” oldrecords.

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For most memory efficient calculation, select small time window size, and don’t check “Withhistory”. This might lead to jerkier, less informative displays, but loads significantly faster.After preprocessing, a frame like the one below will come up. This frame can be dynamicallyresized by dragging it at the lower right corner.

Clicking on the red “Run” button will start the animation, which can be stopped at any time byclicking on the “Stop” button (“Run” changes to “Stop”). Alternatively, users can also drag theslider directly along the time axis, to jump to a chosen day. Dragging the mouse over a nodedisplays the name of the node (enterprisesna@comcast,net in this example.)An individual node can be dragged around with the mouse to change its location. It will then stay“glued” to this location for the remainder of the animation. This function can be reset by menu(View->Reset Layout).Clicking on a node brings up the links coming and originating from a particular node.

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It also pops up a scrolling list of all the messages exchanged between this actor and the otherparticipants. Clicking in the scrolling message list displays the contents of this message, whichcan be search for single keywords by entering a text string into the search box and clicking the“Search” button.

Similarly, clicking on a link (i.e. an edge) brings up the messages exchanged between two actors.

Right-clicking on a node brings up a pop-up menu to set single node attributes.

Now the different menu options of the dynamic view of communication will be explained

7.1.1 Control->Select Datasets…If more than one dataset has been selected for this visualization, users can interactively displaywhich ones they want to display.

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7.1.2 Control->Dynamic Setting…This brings up the animation parameters dialog to interactively recalculate animation settings.

Warning: This may take a long time depending on the size of the dataset!

7.1.3 Control->Speed…Allows to set the playback speed of the animation.

7.1.4 Bipartite->graph in domainBrings up a network view of the actors clustered by organization name in their e-mail address.

Right-clicking on a node brings up a pop up menu

Clicking on “Expand Abstract Node” brings up a “bipartite graph.” It shows the single actorsbelonging to the clicked-on domain, with the clicked-on domain displayed in gray and linked tothe domain.

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7.1.5 Bipartite->graph in organizationDisplays same view as above for organizations.Parameters of the bipartite view are calculated on the fly based on the dataset used for thevisualization. For example, for an phone archive, attributes of actors might include business unit,role, and age group instead of organization and domain.

7.1.6 View->Color By->DatasetPermits users to change colors of actors and links of individual datasets.

If there are multiple datasets, coloring precedence can be set by choosing a dataset name, andmoving it up or down clicking the “Move up” and “move down” buttons.

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7.1.7 View->Color By->NodePermits to change node color of individual actors by selecting them in a scrolling list of actornames. (Click on the name of an actor, then the “Select color for” dialog will pop up)

7.1.8 View->Color By->domainPermits to change node color of individual domains by selecting them in a scrolling list ofdomain names.

7.1.9 View->Color By->organizationPermits to change node color of individual organizations by selecting them in a scrolling list oforganization names.

7.1.10 View->Color By->Node’s Betweenness CentralityPermits to visually identify the actors with highest betweenness centrality. A list with thenumerical values of the highest individual betweenness centralities pops up.

Clicking the “Set” button at the bottom of the list will change the size of the nodes in the view(for the actual time!) in relation to their betweenness centrality.

Dragging around of nodes, and clicking and right-clicking of nodes still work. Clicking the“Run” button resets this function.

7.1.11 View->Color By->Node’s Degree CentralityPermits to visually identify the actors with highest betweenness centrality (similar to above).

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7.1.12 View->Draw ArrowShows directionality of links by drawing arrows.

7.1.13 View->Content Filter->Filter…This function permits to track the diffusion of new ideas through the social network.

Users can set Boolean expressions of keywords (e.g. “knowledge&management”), which arethen tracked in all message content. Whenever an actor is “infected”, i.e. the expression orkeyword is contained in a message she/he receives, his/her node is turned bright. He/she thenremains in the bright color (“stays being Content-Related”) until the infection is “cured”.In this example all people that talk about knowledge management within the last 20 days arecolored blue.

All usual functions (Run, Stop, clicking on a node, etc.) will work in Conent-filter mode.

7.1.14 View->Content Filter->Cancel Content FilterCancels the Content filter and switches back into normal display mode of nodes.

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7.1.15 View->Nodes Filter…This function permits to dynamically identify the top n actors that have exchanged more than xmessages, or have the highest degree or betwenness centrality.

7.1.16 View->Fisheye…The fish eye view function permits to more easily navigate in large graphs in a non-linear way(objects close to the viewer are zoomed the most)

Normal View Fish Eye View

Drag the black rectangle in the view to interactively change the focus (center) of the fish eye.

7.1.17 View->Zoom…This is a linear zoom function. The zoom controller permits to set the zoom level, the light bluerectangle in the snapshot window can be dragged around with the mouse to set the viewing area.

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7.1.18 View->Show Labels->nameshow all names of actors

7.1.19 View->Show Labels->domainshow all domain names of actors

7.1.20 View->Show Labels->organizationshow all organization names of actors

7.1.21 View->3 D MappingMaps the entire network on a 3D sphere. The sphere can be rotated with the mouse. Option-dragging grows and shrinks the sphere.

7.1.22 View->Reset LayoutThis function resets the network layout, if individual nodes have been dragged around with themouse, which also sticks them to a permanent location for the entire animation.

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7.1.23 View->Visibility…Sets the level of visibility for “decayed” nodes in with-history mode, i.e. nodes which are outsideof the current time window.

7.1.24 View->Stroke…Sets the width of the edges (links) of the network

7.1.25 Measure->Actor Contribution IndexShows contribution index of all actors in group context (defined in book “SwarmCreativity”).This window is synchronized with main communication view and plays back in synch.

Clicking on a node brings up all messages send/received by actor, right-clicking permits to colornode.

7.1.26 Measure->Core/Periphery->CalculateMeasures the degree of how much the network has a recognizable core group. Marks the “mostcore” members with a black dot, diameter of dot is proportional to “coreness” of actor.

The float number shown in the dialog is the Core/Periphery property. The value is from 0 to 1,the higher the value is, the more perfect the Core/Periphery property of the network.

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7.1.27 Measure->Core/Periphery->CancelCancels the Core network display.

7.1.28 Measure->Link Prediction->Draw OutPredicts potentially emerging links between actors which are not yet drawn, based on pastlinking behavior of actors.

Predictive lines are drawn in brown color, the wider the line, the higher the probability that thelink will actually happen (this is based on an adaptation of the Adamic/Adnar algorithm)

7.1.29 Measure->Link Prediction->Show Score…Shows the prediction list of potential links between actors, sorted by probability of occurrence.

7.1.30 Measure->Link Prediction->Adjust…To adjust the prediction score for drawing the lines. Only the links with probability predictionscore higher than the threshold set will be drawn.

7.1.31 Measure->Link Prediction->Cancel Link PredictionCancels link prediction mode.

7.1.32 Measure->Group Centrality and Group DensityBrings up the centrality plot. This window is synchronized with the main dynamiccommunication window, and plays back in synch.

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It selectively displays betweeness centrality evolution over time, degree centrality evolution overtime, and density evolution over time.

7.1.33 Measure->Export Group DensityExports space delimited group densities for each day as a vector as a text file for input intoExcel, Matlab, etc.

7.1.34 Measure->Export Actor’s Betweenness CentralityExports space delimited actor betweenness centralities for each day as a matrix as a text file forinput into Excel, Matlab, etc.

7.1.35 Measure->Export Actor’s Degree CentralityExports space delimited actor degree centralities for each day as a matrix as a text file for inputinto Excel, Matlab, etc.

7.1.36 Measure->Export Actor Contribution IndexExports space delimited actor contribution index for each day as a matrix as a text file for inputinto Excel, Matlab, etc.

7.1.37 Measure->New Users PlotShows the number of new users per day or per month visually and as a list

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This is very useful to measure relative and absolute growth of a community, and identify peakperiods of growth.

7.1.38 Measure->Social StructureShows evolution of group relationship (communication) as a temporal social surface.

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X-axis is time, y-axis is actors, z-axis is actor betweenness centrality. Identifies for example.different phases in the lifetime of an online community

7.2 Static View of Communications

The static view of communication is similar to the dynamic view, but it only shows a staticsnapshot of the entire time period, with the different colors of the links (i.e. edges) representingthe different months when messages have been exchanged. Functionality of menu commands isthe same, wherever applicable.

7.3 Netgraphs

Netgraphs permit to visualize communication between actors in an adjacency matrix. In the firststep, the datasets are selected. In the second step the actor attributes by whom the actors will besorted are selected and in the third step the sorting order is defined. In the fourth step thecoloring is set, in the fifth and final step the adjacency matrix is shown (see below).

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7.4 Processing Contents for the Term Views

Besides the communication view, Condor also permits to visualize relationshipsbetween terms. In the first step, a full-text analysis of contents is done (View->Term->Content Process)

Users can also add their own term files for clustering documents. A term file is simply alist of terms occurring in the documents, for which the user wants to have nodes in theview of terms, for example:

jobeuropecountryAfghanistanUSAterroristplanchemical

Clicking on the “Start” button builds the index.

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Users can again narrow down the number of documents included into the index bycontents and time. After lengthy processing, users have the option to also build adocument view (see below), and to define their own stopword lists.

Stopwords (= stop terms) will not be included into the index and the term view.

In the view of terms, each node is a term. Links between terms are common documents.The more two terms share common documents, that is, the more two keywords occur inthe same documents, the stronger the link between them will be, and the closer theywill be drawn together.

7.5 Dynamic View of Terms

The dynamic view of terms has the same functions as the dynamic view of communications, onlythis time the nodes represent terms (=concepts) instead of people.

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Some menu commands are term-specific.

7.5.1 Control->Remove TermsAllows to remove terms from the term view.

7.5.2 Control->Add new termsAllows to add new terms to the term view.

7.5.3 Control->Filter by Term Rank…Allows to show only the n most relevant terms in the term view. Unchecking the check box“Constant terms each day” will take the at most n terms for each day which are most significantfor each particular day.

7.5.4 View->Proximity SearchWill search the documents for particular terms, and display the most significant terms containingthe most significant documents linked to that term. NOTE: the term does NOT have to occur inthe relevant documents.

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7.6 Static View of Terms

The static view of terms has the same functionality as the dynamic view of terms. In addition, itpermits to load a taxonomy file to cluster documents into predefined bins. A sample taxonomyfile looks like:

<level0>utopia<level1>government<level2>economy<level2>politics<level2>leaders[President;Leader;commander]<level1>foreign partnerships<level2>regions<level2>countries<level1>infrastructure<level2>utilities<level2>transportation<level3>land<level3>water<level3>air[plane;jet;helicopter]

Each word after a <levelX> keyword is will be a bin. Documents including the particularkeyword will be put into this bin. The words in brackets are synonyms. If they occur, they willalso be put into the same bin.

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7.7 Static and Dynamic Document View

In this view, links between similar documents are drawn. Each node represents a document.Links between documents are drawn based on common keywords. The more common keywordstwo documents have, they stronger the link between them will be.