 |
|
|
 |
What's New?
There have been a lot of modifications made to VisuaLinks over the past few months—mostly for improved performance, greater
understanding, or more control over the final result. Below are descriptions of some of the more prevalent changes in VisuaLinks
4.1:
Data Source Connector - The data source connectors have been separated from the model wizard for a more flexible
and convenient way of changing database parameters without having to update any of the models. For example, if a password changes
in an underlying database, users only have to update the data source and not every associated model which uses that source.
Object Creation from Text - VisuaLinks now lets you highlight any text segment within the system and
automatically create a new entity in the display. Additionally, you can also link the newly created object back to the
original text segment. For more information about this feature, see VisuaLink's Feature Spotlight.
History - The settings from any service can now be stored for future recall. For example, models, objects,
comparators, and values can be stored to let you re-create the same query at a future date. Additionally, sessions can be
stored and shared with other users using the History feature within the View.
Updated Timelines - A considerable amount of changes were made to the timelines: new header settings, unit
layouts, label formatting, and controlling the date ranges. Additional date periods can be "folded" in the display by
selecting ranges using the mouse. Individual objects can also be moved about the timeline and re-associated with alternative
dates.
Panel Placements - A number of the panels used within VisuaLinks can be moved around and customized,
depending on user preference. Under the My Environment Menu on the main interface, there are settings for the Details,
Object Tree, and Results panels. Each can be made to appear at the top, bottom, right, or left side of the display.
Service Help - Each primary analytical service within VisuaLinks has its own service help, which provides
you with a quick reference for learning the parameters, functionality, and capabilities of the service.
|
|
|
 |
 |
...that VisuaLinks automatically backs up your VBase system?
VisuaLinks automatically creates a backup of the VBase system every 24 hours using an internal schedule and control methods
set by the administrator. If a system corruption occurs, the VBase system can be quickly restored to ensure minimal disruption.
Additionally, VisuaLinks can be configured to write the backup files to an alternative location, which reduces the number of
issues with disk, hardware, or network failures that can delay a system restore. By default, the system writes the backup to
a folder called backup in the
serverresources directory.
There are many settings that can be modified for the VBase subsystem.
For example, if the Run Interval is Daily, the Auto VBase Backup feature (located under the VBase Admin
tab within the administrative interfaces) is enabled by default.
These settings can be easily changed or updated by simply adjusting the parameters in the interface.
Check your VBase settings today to make sure your backups are occurring on a regular basis.
|
|
|
 |
Creating Objects from a Text Segment
There has been an overwhelming response from members within the community for more support of text-mining capabilities.
VAI, along with several other companies, perform document categorization and entity extraction. To further expand on this
emerging marketplace, VAI has equipped the latest version of VisuaLinks with the ability to highlight text and create objects
automatically.
To create a new object from any text area (e.g., the Reports View in the Details Report Memo window), right-click on the
highlighted text (or choose the  Create Node button)
and select the appropriate options in the pop-up window provided. The newly created objects are automatically linked back
to the original object. The new text-mining feature is simple, easy-to-use, and provides an integrated solution without
having to purchase additional software.
In the following law enforcement example, we see that Bob Pearson has a number of connections to criminal activities stored
in the underlying database. All of these objects and associations are created from the standard VisuaLinks model wizard,
which took the information from arrest reports to create the data field content used for the diagram.
Drilling down to view the details of Bob Pearson, we can quickly view all of the supporting information contained in the
database. One attribute that stands out is the REMARKS, which contains a text narrative describing the nature of Pearson's
narcotics conviction.
It is not unusual to find useful, relevant, embedded data from text fields, memos, narratives, or remarks contained in a
database that has not been graphically represented in the relational schema. If we read through Bob Pearson's remarks, the
address 2500 Block of Bunker Hill Road is clearly referenced. Viewing the previous diagram, we can see that the only address
information related to our suspect is in Atlanta, GA.
Since this new address is important to show in the display, we will create a new node by highlighting the text in the Details
Report Memo window and choosing the Create Node button to
automatically open an object-definition window. By default, it comes
up with the model (Tracker-All) used to create the original reference object (e.g., Bob Pearson). Any model can be selected by
using the pull-down menu to choose an alternative model with different object types and structures.
Since the highlighted text represents an address, the user chose an ADDRESS object icon to reflect the new data.
Additionally, at the bottom of this menu is a Link New Node To field. This field has a pull-down menu that contains
all the active objects in the current information set. By default, the original reference object is automatically selected,
however, any other object can be easily chosen from the list. When all of the settings have been configured, click on the
Create button to finalize the process.
The diagram below shows the newly created object, which is displayed with the appropriate icon and the highlighted text as its
label. It is also linked back to the original object. This new entity acts just like any other object in the view and can be
copied, pasted, walked, or displayed using any of the VisuaLinks functionalities.
Finally, in the original text segment where the object was first created, the text is
automatically highlighted (using a different color) to indicate that it is now an object.
References to any object-keys will automatically be highlighted to help emphasize the
objects in the display with the same values.
This new text mining feature within VisuaLinks comes standard as part of the Professional and Enterprise versions of the software.
Check it out today and see how easy it is to identify, mark, and tag text as an object.
|
|
 |
Money Laundering: The Exception
There are many factors that must be considered when analyzing data, including the reliability of sources, quality of data,
different formats, security levels, creation date, misspellings, inconsistencies, and more. What may appear to be a
great pattern can actually be less than ideal due to data discrepancies. Please remember, there are always exceptions to
the pattern and there are often exceptions to the exceptions. The goal is to detect and expose potential targets of interest
and then drill-down and interpret the results before making a final decision.
The following example is based on Suspicious Activity Reports (SARs) that are filed by worldwide banking and finance systems
to their respective Financial Intelligence Units (FIUs). The database was queried to show all Social Security Numbers (SSNs)
that are connected to multiple SUSPECTS. The initial target of interest, shown below, represents a single SSN with two
different SUSPECTS. Occasionally, a SSN will be shared by a husband and wife in certain types of financial transactions.
In this case, the names are not similar, so the investigators consider these people unrelated.
One important factor to note about this diagram is that the SSN label depicts a "NO," which means it failed to be properly
validated using the Social Security Administration's authentication algorithms. Ultimately, this means the SSN is a fake number
(for more information on the algorithm used, visit the white paper section of our support site).
At this point, the investigators need to consider the validity and certainty of the pattern. From here, they want to know why
both SUSPECTS are using the same SSN. The network is expanded to show the ID NUMBER for each, as shown below.
As suspected, their driver's licenses are different. Next, the investigators want to check the PHONE numbers listed on the SARs.
The premise being that a common phone number or a shared driver's license in conjunction with the SSN would guarantee a strong
connection between the two SUSPECTS. The results are shown in the diagram below.
Yet again, there is no additional overlap. The next step is to look at the ADDRESSES of these SUSPECTS. Addresses are perhaps
the most widely varying data encountered in any system. There are many abbreviations, spellings, and formats used to encode an
address. It is not unusual to see 3, 4, or 5 variations of the same ADDRESS—often differentiated only by extra periods,
commas, or directional encoding (e.g., NW, N., or North).
For the two ADDRESSES shown in the diagram, the investigators quickly see they are not even close to one another. If they
were in the same CITY or STATE, there would be more of a chance that the SUSPECTS were related. Unfortunately, these two
addresses are more than 1,300 miles apart from one another—which dramatically diminishes the likelihood they are related.
Finally, the financial transactions are displayed in the network. As shown in the final diagram, each SUSPECT has only
a single, unique transaction (SAR). This tells the investigators that the SUSPECTS are not actively engaged in multiple
transactions. The investigator can then safely determine that the common SSN is most likely a data entry problem and the
entire network can be discounted. If the SUSPECTS each had more than one transaction, it would be highly unlikely that the
same transposition would occur for every transaction. If that were the case, the investigators would aggressively pursue
these SUSPECTS.
What looked like a promising pattern quickly deteriorated into a review of "bad" data. Often times, especially with numbers,
they can be easily misrepresented where 2's look like 5's, 4's like 9's, or 1's like 7's. All too often, these simple
transpositions can result in more complicated analytics.
In this example, all of the details for the SUSPECTS can be presented in one step; however, the interpretation of each entity
was important, therefore each was introduced one at a time.
|
|
|
 |
|
