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What's New?
Data Sharing Initiative Demonstrations at the IACP Conference
VAI will be demonstrating our new DIG Server-to-Server capabilities as part of our live Data Sharing demo at the
IACP show in Philadelphia on October 22 - 24. This demo is designed to show how data can be shared between law
enforcement agencies to aid in the investigative process while protecting data ownership and addressing privacy concerns.
VAI offers law enforcement personnel a robust, integrated suite of tools to look at data from a variety of sources,
including databases, documents, e-mails, .pdf documents, web pages, etc., and identify patterns, networks, links,
associations and anomalies in that data.
This live demo will showcase DIG's ability to facilitate data sharing between the Philadelphia/Camden HIDTA,
Washington/Baltimore HIDTA and the Bergen County, NJ Police Department in an easy-to-use, secure manner.
Bob Dintino, Special Agent at the Philadelphia/Camden HIDTA, will be at the booth to demo DIG's Server-to-Server
capabilities and answer questions about the Data Sharing Initiative.
The demos are scheduled during the following times at the Denali Solutions booth (#851, Exhibit Hall C, Blue Level):
- Wednesday, October 22 at 2 p.m. and 6 p.m.
- Thursday, October 23 at 11:30 a.m., 1 p.m. and 4 p.m.
- Friday, October 24 at 10:30 a.m.
If you are unable to attend these sessions, please stop by the booth to sign up for a one-on-one demo.
Be sure to ask us about how you might qualify to receive a VisuaLinks/DIG system (and become part of the Data Sharing Initiative) for FREE!
We look forward to seeing you there!
VAI Implements DIG Server-to-Server at Multiple Law Enforcement Sites
This week, DIG's new Server-to-Server capabilities are being implemented at a number of locations on the East Coast. This new capability allows multiple DIG applications, each installed on different servers, to operate as if they were one application. Prior to implementing this functionality, users that wanted to share data required data replication processes, a series of data communication stove pipes, a custom-developed application or in some cases, a combination of all three. DIG Server-to-Server functionality supports multiple distinct user domains with disparate databases and data sources, all sharing data across a single communication link to the end user.
As an example, assume that a Virginia State Trooper wants to determine if there are any outstanding warrants or wants on a car with Florida tag number AAA-321. In addition, he is interested in knowing if there are any wants or warrants in any state that borders Virginia. With DIG Server-to-Server, the trooper can execute one request on a radio frequency-based computing device, and have wants and warrants information returned from Maryland, West Virginia, Virginia and North Carolina in a matter of seconds.
In addition, the West Virginia data could be retrieved from an Access database, while the North Carolina data retrieved from a Microsoft SQL database, the Virginia data from an Oracle database and the Maryland data from a Sybase database. DIG Server-to-Server allows each “data owner” to secure their data, make their own decisions as to whom they share it with, and does not require data replication which can be costly and cause data synchronization problems.
DIG Server-to-Server is a powerful information retrieval tool designed to search large quantities of data from multiple data sources, and turn the data into useful end-user information. Moreover, DIG Server-to-Server preserves data security and privacy by allowing each data owner to determine to whom and how much access should be granted.
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Here are a few tips and tricks to help you use DIG quickly and more efficiently.
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Grouping Data
The Group By Field feature lets you merge results with the same field values together into a single row so that you can easily see results containing the same value.
To accomplish this, you group the results by the column containing the value you want to group. Simply click on the column heading and drag it above the grid (to where it says "Drag a column header here to group by that column").
You can group the results up to four levels deep. Once the results are grouped, you can sort the rows in an ascending or descending order. To sort the results, click the heading of the column you want to sort by.
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Walking Data
You can use the Object Walk to search data sources for records matching a value in your search results.
- Right-click the value you want to search (to search multiple values, use Ctrl + Click).
- Choose Object Walk from the pop-up menu.
- Select the sources you want to search and click OK.
DIG loads the new results into a separate results tab.
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Exporting DIG Search Results to Microsoft Excel
You can export DIG search results to a number of formats, including Microsoft® Excel. Following is an example of exporting DIG search results to an Excel Pivot Table.
First, execute a DIG search request. In most cases, the source DBs (as opposed to text or e-mail data sources) will return the type of data that is best suited for export to Microsoft Access or Microsoft Excel. An example of data returned from a DB search is shown below.
The data selection column (the 1st column with the check box) is used to identify which rows of data you would like to export. A right-click of the mouse lets you select or deselect “ALL ROWS.”
Once the data is selected, you can export it. From the Tools menu, choose Export. The sub-menu shows your export options. It is important to note that the destination application (Access/Excel) must be installed on the local PC (where data is being exported to).
In the example below, we chose the “Excel Pivot Table” option and the following Microsoft Excel and DIG application windows are displayed.
Notice that the column names from the DIG search results are listed as “Pivot Table Fields.” You can select all or some of the columns available for subsequent inclusion in the Excel Worksheet. In addition, DIG lets you import the items from the list as rows or columns as shown below.
The DIG “Export” feature is extremely valuable for:
- Creating data sources that can be used to build temporary or custom databases
- Distributing DIG search results to a wide range of users
- Reformatting data so it can be reviewed or examined from a different perspective
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