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IoT is “another type of network within which we can create applications that blend what people do within that network,” said Jeff Morris, head of product marketing at Neo4j. At the same time, “it’s not enough for businesses to just accumulate data—they also have to be able to act on it,” Yu Xu, CEO of TigerGraph, pointed out. “Today, most IoT users—businesses, governments—are collecting the data but have challenges making sense of it to drive value. Imagine a city that is better able to program traffic lights to improve traffic flow based on in-the-moment feedback after an accident. Saving 30 minutes in traffic is quite meaningful.”
Using linkage and network analysis enabled by the graph model, this web can be dissected with speeds and accuracy that were impossible using a relational database. Combined with conventional AML compliance tools, we can incorporate graph analytics to uncover key insights. This involves looking deep and hard at data from all types of sources — much like how our brains learn and recognize patterns of suspicious behaviors, activities and relationships — through intelligent analytics and advanced algorithms.
Fraud detection, in many ways, resembles finding needles in a haystack. You must sort and make sense of massive amounts of data in order to find your “needles” or in this case, your fraudsters.
Let’s use the example of a phone company with billions of calls occurring in its network, all on a weekly basis. How can it identify signs of fraudulent activity from its mountain — or haystack — of call logs?
The selected companies come from our massive data set of vendors and industry metrics. Yes, we use machine learning to analyze the industry in a detailed manner to determine a ranking for this list. We’re using a custom RankBoost algorithm adapted specifically for the big data community along with a plethora of propriety data sources.
When making a decision on which graph DB in which to invest, admins must take into account the development language the enterprise is going to use. There are different nuances among the database vendors for different use cases (some are more scale-out than others, for example), so organizations must scope this out ahead of time to make sure the most effective language and DB is selected for the corporate purpose.
Recently, we have seen the next-phase in the graph database evolution, with technology fulfilling the needs of e-Commerce by providing Deep Link Analytics. This enables customer intelligence in real time, along with powerful relationship analysis. With these real-time capabilities, e-Commerce sites can quickly synthesize and make sense of customer behavior. The result is the capture of key Business Moments, transient opportunities where people, businesses, data and “things” work together dynamically to create value used to personalize the customer experience, which leads to more transactions.
Today, enterprises use graph technology as a competitive edge for customer analytics, fraud detection, risk assessment and other complex data challenges. The technology offers the ability to quickly and efficiently explore, discover and predict relationships to reveal critical patterns and insights to support business goals.
Graph analytics is an ideal technology to support AML. Graphs overcome the challenge of uncovering the relationships in massive, complex and interconnect data. The graph model is designed from the ground up to treat relationships as first-class citizens. This provides a structure that natively embraces and maps data relationships, even in high volumes of data, and provides maximum insight into data connections and relationships.
TigerGraph, the startup that emerged last fall with a new native parallel graph database, released a free “developer edition” this week with the goal of giving potential users a test drive and a means of comparing the graph analytics platform with competitors like Amazon Neptune and Neo4j.
TigerGraph has announced the free developer edition of its graph analytics platform. The platform features enterprise graph massively parallel processing, support for Big Data, ability to write high-performance complex analytics queries, and the ability to continuous load over 100 GB per machine per hour.