• upgraded server for improved response time and uptime
• added special category “Farm / Heavy Equipment” specially suited for auto theft investigators and SIUs
• added Make/Model/Year search specially suited for auto theft investigators and SIUs
• added account creation and account management for each user
• improved user interface and layout of features
• spamBuster technology to avoid spammy keywords in Craigslist search results

Target with Missing Last name – Download PDF

Target with Private Friends List – Download PDF

This bill has a lot of things going for it, including many proponents from private businesses, and small business.  However it will take a long time to debate the matter, another long time to get complying states to cooperate, and further to get all retailers to cooperate.  It might be years before any amount of sales or use tax is collected for each participating state.

Many enthusiasts gather on social networking sites like Purse Blog to discuss their favorite designer bags or their antiques or other hobbies that involve personal property that is purchased.  This is a tremendous opportunity to identify these individuals and contact them regarding the Use Tax obligations of their hobbies to the state.  Below is a table outlining the membership of various popular hobbies that involve high priced non-titled personal property.

Hobby Forum	Total Members	Total Postings	Postings with keywords “Sales Tax”
Antiques	448,564	1,927,435	440
Guns	148,950	10,308,111	130
Coins	73,352	2,922,977	274
Shoes	33,717	79,302	509
Bikes	484,603	18,037,720	174
Purses	386,279	23,745,362	182
Rings	10,000	2,130	286
Miscellaneous	15,946,893	145,340	151
Laptops	1,349,353	24,196,622	195
Computer Components	175,578	13,914,716	51
Furniture	226,125	236,510	22
Pianos	68,835	2,032,776	32
Jewelry	67,383	3,393,427	76
Boats	583,918	9,406,552	209
Snowmobiles	369,475	11,487,066	177
Sea Doos	55,398	304,741	848

If you are interested in working with us on identifying and discovery of Use Taxes owing in your state, please contact us.

The top 5 states in order are: West Virginia, Kentucky, Michigan, Illinois, and Wisconsin.  The main strategy is to find a boutique or retailer which does not have a physical store based in the buyer’s home state.  The most convenient method to avoid the taxes was to ship to a neighboring state, which makes West Virginia the winner.  Five different PurseBlog users are documented mentioning shipping to West Virginia based on the boutique located in Virginia.  Second prize goes to Kentucky, with 3 mentions of purchases shipped there:

• 7 of 17 purchases were made with the buyer’s state different from the state to which the item was shipped, indicating the “ship to an out of state friend” strategy is being used
• 10 of 17 purchases were made with the item shipped to the buyer’s state: a clear case where Use Tax must be collected
• Honorable mention go to New Jersey, Arizona, Texas, South Carolina, North Dakota, Minnesota, and Connecticut

Click here to download the findings in Purseblog Stats for Sales Tax.

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LTAS Technologies Inc. develops software tools to identify suspicious or fraudulent activity in online classified ads. They developed Harmari tools 2 years ago to identify unlicensed car dealers, also known as “curbstoners”, that advertise on Craigslist posing as private sellers. Harmari tools now span many different classified sites, auctions, e-commerce and social networks. Harmari tools are used by 4 state/provincial motor vehicle regulators, 1 state revenue agency, and 1 state contractor licensing board to identify curbstoners, tax scofflaws and unlicensed home renovators and contractors. It has proven to be a critical tool for enforcement and compliance by leveraging online, open source intelligence (OSINT).

LTAS’ latest product is the Web Identity Search Tool, aka “WIST”, which does a link analysis between users on Facebook. No opt-in or approval is required from a Facebook user for WIST to identify linkages. So suppose John knows Fred, but Fred’s list of friends is private. As long as John’s list of friends is publicly visible, you will be able to find Fred. WIST can analyze thousands of friends networks in minutes, possibly finding clues available nowhere else in databases.

Below is a screenshot of the network diagram from a 2 person WIST match.

Another benefit of WIST is that you can search Facebook users 100 at a time instead of 10 which is the Facebook limit on each page.  In Facebook you have to scroll and click tediously down the list to get to every 10 search results.  If you happen to be searching a common name like “John Smith”, this quick scrolling can come in handy.

WIST currently works only with Facebook, but there are plans to expand to other social media sites. It is also limited to 3 degrees of separation between any 2 Facebook users.  Downloading the free trial gives you unlimited searches, and 5 link analysis matches.  Purchase credits for link analysis for as low as $7.99 each. For more information, visit www.harmari.com and give WIST a try.  See if it discovers some new facts about your investigations that you never thought possible.

[1] See http://www.lexisnexis.com/risk/newsevents/press-release.aspx?id=1342623085481181

[2] See http://www.core77.com/blog/technology/visualizing_criminal_networks_to_help_police_solve_crime_22462.asp

• link analysis results now displayed in a social network analysis visualization (see screenshot)
• search results now provide paging for 100+ matches
• print-friendly reports of search results and link analysis
• link off results to Facebook pages easily and quickly
• overlay link analysis results together for an even larger network diagram

WIST screenshot link analysis network diagram Facebook

Where is the cut line?

Relational databases have historically been the place where digital information is kept in a structured format. Other unstructured forms of data include website traffic logs, videos, and the internet at large, but let’s just focus on databases for now. From both a software developer and data analytics standpoint, I’d say the cut line between Big Data and Small Data is about 1 million records. That’s the point where common software products start to lose their steam. Microsoft Excel abruptly cuts off at about 66,000 records. Microsoft Access starts to chug at about 100,000 records. With MySQL, a popular open source database tool, 1 million records is the point where things slow down there without some efficient indexing strategy, or server configuration tweaks. So to me, it’s a safe bet that you’ll need some specialized software, systems, or expertise to proceed beyond that 1 million mark.

Think Like the Bad Guys

If you’re doing fraud detection, tax audit discovery or other types of analysis where someone’s trying to “beat the system”, you’ll have to research it in a way where the fraud sticks out like a sore thumb. For example, if someone is trying to sell a car that he knows is a lemon, and not want to attract attention, he is going to advertise his car just below market value that it tempts his victim into buying it. That same fraudster is also simultaneously trying to avoid suspicion by putting the price of the car too low that it’s a dead giveaway of a scam. The mentality is that you have to go deeper into the analysis than the bad guys do to uncover the fraud. It requires you to think creatively and it’s a constant cat-and-mouse game, because the rules are being changed all the time.

Break down the problem

The classic and often clichéd riddle “How do you eat an elephant?” has an obvious yet profound answer “One bite at a time”. If you are stuck doing analytics with Small Data, you can still achieve reasonable results by breaking things down. Many ballistics calculations used by the Allies in World War II were made by groups of women who were good with mathematics and each had their own task to perform. Imagine how many combinations of numbers they had to crunch and they had no hard drives or RAM to do it. They also all had to share use of the ENIAC, the giant supercomputer at the time, which is still inferior to the computing power of your cell phone. It’s easy to breakdown a data set. You can split your data by the first letter, by state, by month, but make sure that you reallocate the data in a different way from time to time to avoid biasing your own perspective on the data.

Once it’s a chore, scale it up

The first set of data that you do discover something worth reporting, you’re ecstatic about it. Now your superiors want it in their monthly reports. The second set of data will still be exciting, because you didn’t think of this or that obscure possibility, maybe last month’s data was one way and this month is another way and you’ve rejigged your analysis engine to cover those possibilities. The third set is just a press of a button now and it’s turning into a chore. All the drive to discover the trend and report on it has completely passed and now it needs to be folded into a standard routine. Your managers are still just as excited about the data because it helps them make decisions, but you’re not – you’re a data scientist. You thrive on the challenge of solving new problems, discovering things, not rote mechanical actions. So at this point you can benefit from a scaled up, Big Data approach.

Big Data technologies specialize in the automation of breaking data down, applying a known predictable algorithm to that data, and recombining the outputs into a report or some conclusions. So you really need to mimic each stage of the way manually first to understand how the machine is going to do it. Don’t delude yourself into thinking there won’t be hiccups in introducing an automation, but at this point it’s worth the trade off of time spent doing tedious analysis manually or just letting the machine do what it does best, crunch data over and over and never get tired of it.   It’s key to make sure your analysis methods are sound first before proceeding to scale up, because that scale up effort will have its own formidable challenges such as tuning, resource optimizations, and bandwidth limitations.

Hopefully this has convinced you that despite the buzzwords and all the marketing, Big Data is just Small Data replicated across a network.