Big Data Technology, Semantics and Analytics
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Big Data Technology, Semantics and Analytics
Trends, success and applications for big data including the use of semantic technology
Curated by Tony Agresta
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Graph Analysis Powers the Social and Semantic Spheres of Big Data —

Graph Analysis Powers the Social and Semantic Spheres of Big Data — | Big Data Technology, Semantics and Analytics |
Why predictive modeling of human behavior demands an end-to-end, low-latency database architecture
Tony Agresta's insight:

Here are some key points from the article in addition to some insights about graph analysis and big data:


  • Semantic graphs map relationships among words, concepts and other constructs in the human language allowing for unstructured data to be used in a graph showing important connections.
  • Graph analysis is not new.   It has been used as a form of data visualization to explore connections and identify patterns and relationship that would otherwise have gone undetected.
  • Some vendors have taken their graph capabilities to new levels. For example, Centrifuge Systems allows users to draw the graphs, search the graph space, interact with charts and display important measures about the graph network.   Analysts can easily pinpoint portions of the graph that require additional analysis.  Hotspots of interesting activity jump out from the graph based on the number of connections and important performance measures.
  • While social graphs may be the most popular, this approach is especially useful in detecting fraud networks, cyber data breaches, terrorist activity and more. 
  • One of the most important points is that graphs can incorporate diverse streams of big data including both structured and unstructured.  Imagine the ability to analyze banking wire transfer data in the same graph with unstructured data that includes names, locations, and employers - intelligence that has been discovered through the semantic processing of unstructured data.   That's a powerful combination of sources linking data from the open web with transactional information. When done in real-time, this can be used in anti-money laundering, fraud prevention and homeland defense.
  • "Data scientists explicitly build semantic graph models as ontologies, taxonomies, thesauri, and topic maps using tools that implement standards such as the W3C-developed Resource Description Framework (RDF)."


While this may be beyond the scope of many NoSQL and Hadoop databases, MarkLogic 7 is embracing triple stores as they continue to innovate on their Enterprise NoSQL approach. No one else has values, triple store data derived from semantic processing and documents with real time indexing and search - The bar for Enterprise  NoSQL is about to be raised again.


You can read more about this on Semantic Web:

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HSBC to Pay $1.92 Billion to Settle Charges of Money Laundering

HSBC to Pay $1.92 Billion to Settle Charges of Money Laundering | Big Data Technology, Semantics and Analytics |
The announcement of a settlement on Tuesday came after state and federal authorities decided against indicting the British bank in a money-laundering case.
Tony Agresta's insight:

If you read this, notice one of the last paragraphs - 


"HSBC has since moved to bolster its safeguards. The bank doubled its spending on compliance functions and revamped its oversight, according to a spokesman. In January, HSBC hired Stuart A. Levey as chief legal officer to come up with stricter internal standards to thwart the illegal flow of cash. Mr. Levey was formerly an under secretary at the Treasury Department who focused on terrorism and financial intelligence."


Big Data Analytics is one way to do this.   But HSBC may have fallen into the trap where they focus on one form of analysis to detect money laundering.  Predictive models used to identify transactions that may be fraud or money laundering can be a useful way to detect this type of activity.   But all models contain some amount of error.  When network analysis, geospatial analysis and temporal analysis are also applied, money laundering schemes can be revealed using data visualization that show unusual patterns of behavior, linkages between people and events, fund transfers that take place at odd times and more.   Most of these institutions need to combine descriptive reporting, alerts which are triggered when outlier transactions are ready for approval, predictive models and interactive data visualization including link analysis to explore hidden relationships in data.   Without this comprehensive approach, this problem will continue to occur.  The data is all there.  Now it needs to be integrated (including unstructured data in the form of notes) and analyzed using all of the major techniques.

WebMarketingStore's comment, May 2, 2014 1:59 AM
Staggering: $1.9b is the 'settlement' amount? How much might the damage have been, full-tilt?