Press

Press

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CBA’s Laundering Compliance Problems Aren’t Unique

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.

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Using Graphs and Machine Learning to Find Needles in a Haystack

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?

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Inside Big Data

The insideBIGDATA IMPACT 50 List for Q3 2018

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.

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What Enterprises Should Know About Selecting a Graph Query Language

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.

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How Graph Analytics Is Powering E-Commerce

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.

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Modern Graph Query Language – GSQL

Today, enterprises use graph technology as a competitive edge for customer analyticsfraud detectionrisk 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.

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Combating fraud and money laundering with graph analytics

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.

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Datanami

TigerGraph Offers Graph Database Test Drive

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.

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SD Times news digest: BrowserStack’s open-source program, Tableau acquires AI startup, and TigerGraph’s free graph database

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.

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TigerGraph Announces Free Developer Edition of Graph Database

TigerGraph produces a graph analytics platform for developers to create their own big data graph applications.

This technology stores all data sources in a single, unified multiple-graph store that can scale out and up to explore, discover and predict relationships. Unlike traditional graph databases, TigerGraph can scale real-time multi-hop queries to trillions of relationships.

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