The Trovares Connector:
For Neo4j™ and ODBC™

Sure Trovares xGT is faster than Neo4j, but why does that matter to me? There are endless use cases where ingest speed and query time are vitally important. Especially in cases where the data analysis has a time limit (cyber, shipping and hauling, etc.) and there is a new set of data coming in right after. It can even be important when working with a very large amount of static data as Neo4j will simply “Hit a wall”.

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Below are some (but not all) fields where Trovares xGT has multiple use cases:

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• Biotech

  • There are many large-scale problems that need solving in the Biotech field. Among the many are: Genomics and genome representation, neural networks, pharmaceutical research, etc.

• Shipping and Handling

  • With so many moving parts in a shipping pipeline, some companies are juggling massive amounts of data. With so much information to keep track of such as; Retailers, customers, planes, boats, trucks, their handlers, crates and even loading machinery, many enterprises are dropping the ball along the way. Leading to full crates in the port and long lines of truckers waiting hours for shipments. We offer a dynamic graph solution to manage the data as well as run analytics to find the weak points of the pipeline.

• Fraud detection

  • Large telecom companies with e-mail services deal with bad actors trying to use their services to commit fraud. These bad actors are smart and have crafty ways to get around most systems. The only way to solve a problem like this is to look at the entire data set (very massive in most cases) and run queries to discover fraudulent activity. This is somewhere where sharding the graph could be detrimental to the result. Most of the loopholes bad actors use rely on taking advantage of the data being separated.

• Digital Forensics

  • In this case, there is a bad actor who penetrated your network and has been resident in your network for longer than you realize. Cyber intruders are known to wait months, or longer, to carry out their plans. Financial institutions face the risk of immense loss from cyber-attacks, they must carry a large reserve of cash (negative for the bottom line) to hedge against such risk. Proof that an Enterprise has invested in digital forensic searches to minimize the risk that their networks have been penetrated results in savings for the reserve (positive for the bottom line).   

• Deep Graph Search

  • Graph Search can be a simple “lookup” or it can be a deeper search.  Lookups are measured in transactions per second.  These are automated applications like checking a credit card number.  Intuitive analytical queries are measured by their response time in human time measured in seconds, minutes, and hours. Analysts need a response before they forget the question.  Then, they often wish to ask a follow-up question.  A search engine, Trovares xGT, that can provide answers in seconds when querying a 300-billion edge graph is a necessary tool for the serious data scientist.

• Advertising

  • Advertising has changed before our eyes in the last decade. Market research and available statistics previously allowed corporations to discover what a population's preference is. Now, with the recent explosion of individuals data, we can see the preferences of specific people. This problem could be small for a mom-and-pop retailer but for a larger corporation, this problem becomes massive very quickly. Not only is it important to retain all the information of specific people, but the fast analysis is necessary to deliver the right advertisements to the right people.