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Rescooped by luiy from Big Data Technology, Semantics and Analytics
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Graph Analysis Powers the Social and Semantic Spheres of Big Data - IBM - #SNA #dataviz

Graph Analysis Powers the Social and Semantic Spheres of Big Data - IBM - #SNA #dataviz | e-Xploration | Scoop.it
Why predictive modeling of human behavior demands an end-to-end, low-latency database architecture

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Tony Agresta's curator insight, May 16, 2013 10:49 AM



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:

 

http://semanticweb.com/marklogic-7-vision-world-class-triple-store-and-world-beating-information-store_b37123








Rescooped by luiy from Big Data Technology, Semantics and Analytics
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Predictive Analysis: 7 Reasons You Need It Today

Predictive Analysis: 7 Reasons You Need It Today | e-Xploration | Scoop.it
With today’s enterprise software you no longer have to take a shot in the dark at decision making. Regardless of your organization’s size, industry, or the

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Tony Agresta's curator insight, December 18, 2012 8:47 AM

I would expect predictive analytics technology to surge in growth, especially with the deluge of data arriving every day.   We are well beyond the early days when direct marketing pioneers applied predictive models to forecast response or performance (although that still happens more often than you think!).   Today, models can take advantage of more independent attributes than ever before – including structured and unstructured data    In turn, the predictive precision of the models increases.  There are more than a few ways to apply the results.   The obvious one is applying the scoring algorithms to new sets of data.   But don't lose sight of the fact that model scores can also be used as filters in queries to segment and report on your big data.   They can also be used as attributes in link analysis graphs designed to pinpoint fraud, cyber breaches, and networks of frequent buyers.  Imagine a network graph where links between people are scaled based on their predicted spend or the number of products they will buy during the holiday season.  It would easy to identify clusters of loyal customers which you could then study in more detail.  When coupled with other characteristics and contact info, targeting becomes precise and the meaning behind your big data becomes obvious.