Alot of our clients use dual monitors. It’s great for comparing data and especially good when comparing rows and columns to visualisations in i2.

A few of our clients have reported that Analyst’s Notebook/iBase seemed to ‘hang’ and not respond when a user clicks on the ‘Find’ function, either through the Data Sources pane or through the right-click menu. This usually coincided with switching from dual screens to a single screen.

Analyst’s Notebook/iBase seems to assume there is another screen when there isn’t! It sometimes persists even when you go back to two screens.

There is a quick registry change to fix this. We recommend that you ask your IT to do the following tasks or back up your registry settings before attempting the below.

• Close iBase and Analyst’s Notebook

• Go to the start windows start menu and type “Regedit”. Say okay to any of the following screens until you are presented with the registry editor. You may need IT to give you access/to do this.

• Use the folder tree on the left to navigate to: Computer\HKEY_CURRENT_USER\Software\i2\iBase\8\

• Delete the folder DialogSettings

• Reopen Analyst’s Notebook and try again.

S-branch

We’ve been part of the i2 journey since 2006, either as trainers, technical consultants or marketers. Through these roles, we’ve worked with several police forces both in the UK and abroad, charities, insurance firms, law firms, government agencies, and commercial clients.

If you’re an i2 user and require technical expertise, please feel free to contact us

Who doesn’t love a good dashboard? What better way to gain a high-level understanding of your data than to see it come alive in bright colours before your eyes? They’re great for presentations but can also give valuable insights into where the peaks and troths in your data lie. From an analytical perspective, it can give you a starting point for your analysis; which with large datasets is very useful.

Dashboards need data. i2 users will store most of their data in iBase; which is very sensible. They do this because items can be quickly retrieved using the intuitive search tools, while the details and hidden connections are easily drilled down upon using i2 Analyst’s Notebook.

It is not always possible, or indeed sensible to place your entire iBase database onto an Analyst’s Notebook chart.

So i2 users have the data, they also have the ability to drill down into the detail. What they sometimes lack is the high-level statistics that a dashboard can provide.

Analyst’s Notebook – Bar Charts and Histograms

One way of doing this is to use the Bar Charts and Histograms functionality within Analyst’s Notebook. This functionality has proven itself very popular as it provides interactive statistical information. The only limitation to this functionality is: it only works with data that is on an Analyst’s Notebook Chart. Statistical information works best with lots of data and it is not always possible, or indeed sensible to place your entire iBase database onto an Analyst’s Notebook chart.

PowerBI

Power BI is a dashboard and reporting analytics tools that can connect to existing data sources and deliver insights that might otherwise have been missed. It allows its users to produce beautiful, interactive reports and then publish them for your organization to consume.

The example below shows national crime information. The data (although fake) is being held in an iBase database. The dashboard shows a location heat map (top left), number of crimes by type (top right), crimes by ward (bottom left) and crimes by date (bottom right).

PowerBI is interactive, meaning a user can drill down into a specific area of the data. In the example below a user has concentrated on pedal cycle crime.

From here the user can glean insights into this crime type. They can see the areas of the country that have the most pedal cycle crime. The time of the year which it most occurs (July to October) and which wards are most affected (Thames Valley Police and Cambridgeshire).

This can assist with strategic planning. If tasked with reducing pedal bike crime, a team could concentrate on university towns (Thames Valley and Cambridgeshire both hold large universities) between the date ranges of July to October.

What’s the catch?

In order to use iBase data with PowerBI you need a good understanding of your iBase security set-up and the underlying table structure; both of which S-branch can help you with. Regarding costs, the desktop version of PowerBI is free; yes free. This allows users to produce dashboards locally. Most organisations will choose a paid for package where they publish dashboards other colleagues within their organisation. To view a published dashboard all that is needed is a web browser.

For more information on PowerBI, visit the website here. For information on how to integrate it with your iBase implementation (new or old) please contact us

Duplicates in data is always an issue. An issue which is magnified once you start trying to load said data into a visual analysis tool like i2. The nature of tools like i2 is that they find connections between entities; in order for that to work the data must be clean a free of duplicates.

In reality, it’s not always feasible or realistic to get rid of all duplicates. For this reason, the i2 analysis suite does provide some matching functionality

Smart Matching – i2 Analyst’s Notebook

To explain it in a simple way: Smart matching in analyst’s notebook is achieved by categorising entities and applying almost human matching logic.

For example, a visualisation may contain a police officer, a victim, a suspect, a prisoner, a male and a female entity. These entities could all be categorised as People. As humans, we could look at a list of people and would instinctively apply matching logic. We’d know that date of birth was important. As are surnames, although they may change when someone marries. We know that Chris could be spelt in several different ways and (as with any data) there may be spelling mistakes.

i2 smart matching can do the same thing. It will apply a different set of logic to different categories of entities to give an almost human matching ability.

Great! Problem solved! Well yes, if you have a smallish set of data then smart matching will work well. However, for smart matching to work, the entities need to be on an analyst’s notebook chart. This may start becoming an issue once you hit the 20,000 entities mark! You could try some kind of batching process but it’s likely to be time-consuming and not entirely accurate.

 

Matching in iBase

iBase will not have this problem. Being a database it will have access to all of your records without needing to render them on a visual chart. The problem we have in iBase is it doesn’t have access to the same smart matching logic. With iBase we can match someone with exactly the same surname. Or, the exact same surname and date of birth. While useful, this will miss a lot of duplicates and therefore needs to be used with care.

Custom Matching Functionality

For some of our clients, the functionality described above didn’t meet their needs. They either had too many entities or wanted to find duplicates of completely different entity types, or maybe over different databases.

For these clients s-branch developed an external matching solution, using an existing string matching theory called Levenshtein distance. Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. What’s great about using Levenshtein distance is that it gives us a score. Meaning we can see narrow down our results to only the most relevant matches.

The external matching is computed outside of iBase. This is done to minimalise the effect on database performance but also allows us to iterate through entire datasets programmatically. This means we can compare duplicates regardless of whether they are the same entity type; or even the in the same database. The output from this is a matching score which is then imported into iBase as a ‘AutoMatch’ link. Our clients can then use existing iBase functionality, such as queries and sets to review the matches found and if applicable merge them through the normal UI.

You can read more on Levenshtein distance here.  To find out more about using Levenshtein distance with iBase or to discuss your own duplicate problem, please contact us