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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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Ontotext Receives "Innovative Enterprise of the Year 2014" Award - Ontotext

Ontotext Receives "Innovative Enterprise of the Year 2014" Award  - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
The Applied Research and Communications Fund together with Enterprise Europe Network – Bulgaria and KIC InnoEnergy awarded the ‘Innovative Enterprise of the Year 2014’ to Ontotext.  The contest is supported by the Bulgarian Ministry of Economy and Energy…
Tony Agresta's insight:

The growth in unstructured data and the need to discover contextual insights in your data are fueling the growth in natural language processing, text mining, graph databases and discovery interfaces.    The vertical application of this technology is widespread.  It can include patient data, lab results, insurance claims data, clinical trials and research - all of which can be analyzed and accessible in one solution designed to improve patient outcomes, expedite claims processing or quickly find current, relevant research in support of new drug development.  


The media and publishing world applies semantic technology in a different way.  Entity extraction is still used to identify and disambiguate specific people, places, events and other attributes from within free flowing text.  But this is often combined with a digital footprint of visitor behavior and past searches to deliver highly targeted, relevant articles and facts all of which are stored within a centralized knowledge base.


Other core use cases include curating new content, automated tagging, enrichment using Linked Open Data and enhanced authoring tools designed to prompt authors with relevant content they can use to add color to their current articles.


There is no limit to the application of semantic technology including manufacturing (fast access to manuals and plans), customer service (analysis of customer call notes), financial services (targeted know-your-customer and compliance-based search) or semantic ad targeting (analyzing on line news followed by targeted ads that pinpoint places to visit, hotels, restaurants).


Ontotext has been doing this longer than anyone - 15 years and built a complete portfolio of semantic tools to analyze text, extract and classify entities, enrich the data, resolve identities, optimize the storage of tens of billions of facts and make ALL of your data discoverable.   For these reasons, Ontotext has been recognized as the Innovative Enterprise of the Year for 2014. 


To learn more about semantic technology and try it for free, visit www.ontotext.com

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Understanding The Various Sources of Big Data – Infographic

Understanding The Various Sources of Big Data – Infographic | Big Data Technology, Semantics and Analytics | Scoop.it
Big data is everywhere and it can help organisations any industry in many different ways.
Tony Agresta's insight:

If you have not had the chance to review some of the free sources of big data that can enhance your content applications, take a look at the Linked Open Data Graph.  It's updated daily and you can learn more by searching for the CKAN API.   This graph represents tens of billions of semantic facts about a diverse set of topics.   These facts have been used to enhance many content driven web sites allowing users to learn more about music, geography, populations and much more.  

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Henry Pan's curator insight, November 9, 2013 11:19 AM

Too many data source......

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Is MarkLogic the Adult at the NoSQL Party? - Datanami

Is MarkLogic the Adult at the NoSQL Party? - Datanami | Big Data Technology, Semantics and Analytics | Scoop.it
If big data is a big party, Hadoop would be at the center and be surrounded by Hive, Giraph, YARN, NoSQL, and the other exotic technologies that generate so much excitement.
Tony Agresta's insight:

MarkLogic continues to enhance it's approach to NoSQL further confirming it's the adult at the party.  MarkLogic 7 includes enhancements to enterprise search as well as Tiered Storage and integration with Hadoop. 

MarkLogic Semantics, also part of MarkLogic 7, provides organizations with the ability to enhance the content experience for users by including an even richer experience that includes semantic facts, documents and values in the same search experience.   By doing this, organizations can surface semantic facts stored in MarkLogic when users are searching for a topic or person of interest.  For example, if a user searches all unstructured data on a topic, facts about authors, publication dates, related articles and other facts about the topic would be part of the search results.

This could be applied in many ways.  Intelligence analysts may be interested in facts about people of interest.  Fraud and AML analysts could be interested in facts about customers with unusual transaction behavior.   Life Sciences companies may want to include documents, facts about the drug manufacturing process and values about pharma products as part of the search results.

Today, traditional search applications are being replaced by smarter, content rich semantic search.    This addition to MarkLogic continues to confirm that all of this can be done within a single, unified architecture saving organizations development time, money and resources while delivering enterprise grade technology used in the most mission critical applications today. 



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State Street's Chief Scientist on How to Tame Big Data Using Semantics

State Street's Chief Scientist on How to Tame Big Data Using Semantics | Big Data Technology, Semantics and Analytics | Scoop.it
Semantic databases are the next frontier in managing big data, says State Street's David Saul.
Tony Agresta's insight:

Here’s a good article on how financial institutions will use semantics to understand and manage risk.   It sounds to me that facts about people, transactions and the market, for example, can be derived from all types and sources of data including unstructured data in documents.   The relationships and connections between these facts can be stored, searched and analyzed.   Adding in the dimension of time would allow you to see when the relationships were formed.  Looking at the connection points in the form of a graph would allow analysts to identify networks that reveal individuals central to the graph that take on new importance.


Imagine if you could search a series of data sources that include information about customers for transactions over a certain level.   Imagine if you could identify all the associated people (employees and other customers) linked to the transactions.  What would you see if you could take all of these related facts and graph them in the form of a social network to visually show the connection points between people, addresses, institutions, lending officers and more?  


The combination of semantics to extract meaning from unstructured data, search, data visualization and analysis could reveal high risk transactions along with links to other individuals.  How are they connected?  Integrating data from the open web and thid party sources might reveal important insights involving past employers, educational institutions, property owned and residential addresses.  Technology to support this scenario using massive amount of consolidated intelligence is not far away. 

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Semantic Web vs. Semantic Technologies - Cambridge Semantics

Semantic Web vs. Semantic Technologies - Cambridge Semantics | Big Data Technology, Semantics and Analytics | Scoop.it
The Semantic Web is one class of Semantic Technologies, but is closely related to others, such as NLP. This Semantic University Lesson explores those relationships.
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A short summary defining semantic technologies that also includes a synopsis of what's meant when the term "semantic web" is used.

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NSA collecting phone records of millions of Verizon customers daily

NSA collecting phone records of millions of Verizon customers daily | Big Data Technology, Semantics and Analytics | Scoop.it
Exclusive: Top secret court order requiring Verizon to hand over all call data shows scale of domestic surveillance under Obama administration
Tony Agresta's insight:

In my opinion, this demonstrates one of the most important aspects of big data analysis and should continue.    Call data, including outbound call numbers, inbound call numbers, duration of call, start time and end time, are vital pieces of information necessary to analyze and protect us.  US lives are at risk.  The article by Glenn Greennwald in The Guardian states:

 

"Such metadata is what the US government has long attempted to obtain in order to discover an individual's network of associations and communication patterns. The request for the bulk collection of all Verizon domestic telephone records indicates that the agency is continuing some version of the data-mining program begun by the Bush administration in the immediate aftermath of the 9/11 attack."

 

Let's get to the heart of the matter.   This type of big data analysis has prevented attacks and has proven to work.   At this moment, all of the facts of these investigations are not disclosed (and many classified examples will probably never be revealed) but many news outlets are reporting that a planned bomb attack on the NY subway was diverted because of phone and e-mail intercept and analysis. 

 

For intelligence analysts to do this accurately and completely they need to analyze the haystack of data represented by e-mail, phone and other forms of communication.   For example, if an e-mail from someone is intercepted because that person is corresponding about bomb recipes, our government should have access to the calls this person made and received. They should be allowed to analyze ALL of the connections between other callers to discern whether or not a network of clandestine activity exists.  If warranted, our government should be able to look at the content of the unstructured data to determine if there are other people, events or places referenced in the text.  Initiating these investigations by analyzing all e-mail traffic or all phone calls made to known or suspected terrorists protects everyone of us.

 

Techniques and processes to ingest, search and manage massive amounts of data like phone records and e-mail traffic are being used today. This should not surprise anyone.  Connecting the dots between caller metadata and known or suspected terrorists is one very effective way to maintain the safety of US citizens.   If this means the NSA needs to look more closely at other calls that suspects have made, so be it.  These preventative measures are in place to safeguard our freedom and prevent lives from being lost.   Haven't we seen enough of that?

 


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LMI Named a Winner in Destination Innovation Competition - Semanticweb.com

LMI Named a Winner in Destination Innovation Competition - Semanticweb.com | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

More news about Open Policy was just published on SemanticWeb.com.    With Ontotext inside..."LMI has developed a tool—OpenPolicy™—to provide agencies with the ability to capture the knowledge of their experts and use it to intuitively search their massive storehouse of policy at hyper speeds. Traditional search engines produce document-level results. There’s no simple way to search document contents and pinpoint appropriate paragraphs. OpenPolicy solves this problem. The search tool, running on a semantic-web database platform, LMI SME-developed ontologies, and web-based computing power, can currently host tens of thousands of pages of electronic documents. Using domain-specific vocabularies (ontologies), the tool also suggests possible search terms and phrases to help users refine their search and obtain better results.”

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Semantics: The Next Big Issue in Big Data

Semantics: The Next Big Issue in Big Data | Big Data Technology, Semantics and Analytics | Scoop.it
State Street s David Saul argues big data is better when it s smart data.
Tony Agresta's insight:

Banking, like many industries, faces challenges in the area of data consolidation.  Addressing this challenge can require the use of semantic technology to accomplish the following:

 

  • A common taxonomy across banking divisions allowing everyone to speak the same language
  • Applications that integrate data including structured data with unstructured data and semantic facts about trading instruments, transactions that pose risk and derivatives
  • Ways to search all of the data instantly and represent results using different types of analysis, data visualization or through relevance rankings that highlight risk to the bank.

 

"What's needed is a robust data governance structure that puts underlying meaning to the information.  You can have the technology and have the standards, but within your organization, if you don't know who owns the data, who's responsible for the data, then you don't have good control."

 

Some organizations have built data governance taxonomies to identify the important pieces of data that need to be surfaced in rich semantic applications focused on risk or CRM, for example.  Taxonomies and ontologies understand how data is classified and relationships between the types of data.  In turn, they can be used to create facts about the data which can be stored in modern databases (enterprise NoSQL) and used to drive smart applications. 

 

Lee Fulmer, a London-based managing director of cash management for JPMorgan Chase says the creation of [data governance] standards is paramount for fueling adoption, because even if global banks can work out internal data issues, they still have differing regulatory regimes across borders that will require that the data be adapted.

 

"The big paradigm shift that we need, that would allow us to leverage technology to improve how we do our regulatory agenda in our banking system.  If we can come up with a set of standards where we do the same sanction reporting, same format, same data transcription, same data transmission services, to the Canadians, to the Americans, to the British, to the Japanese, it would reduce a huge amount of costs in all of our banks."

 

Semantic technology is becoming an essential way to govern data, create a common language, build rich applications and, in turn, reduce risk, meet regulatory requirements and reduce costs. 

 




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Social Media & Big Data in the Insurance Industry

Social Media & Big Data in the Insurance Industry | Big Data Technology, Semantics and Analytics | Scoop.it
According to a global industry survey, Insurers feel less prepared to deal with threats arising from social media and big data than they do about more familiar ones.,Insurer ,Technology
Tony Agresta's insight:



Insurance companies increased use of social media means bigger data is on the way.  In turn, the need for technology to manage this data will increase.

 

For example, insurance companies are using social to increase visibility for their brand and develop stronger customer relationships.   Chubb Insurance follows influencers and industry news on their twitter page.   They provide educational information to Chubb followers in attempt to build awareness and trust.

 

The use of social media in insurance extends beyond CRM.  Companies are listening to social media sites in an attempt to detect posts related to insurance claims.  They are detecting activities that could indicate a claimant has gone beyond what a physician would deem acceptable.

 

Traditional uses of social media to assess sentiment apply as well.   Customer service channels are better informed with real time feeds on positive and negative sentiment about their products and the industry as a whole.

 

Prospects shop for insurance products on line using communities and social networks.   Understanding when this happens helps insurance companies target their sales and marketing efforts.  Sharing bite size pieces of information directly with consumers allows insurance companies to overcome one of their main obstacles, distrust.

 

Social media has become an effective way to communicate with policy holders for events that may affect claims. Most of this is done post-catastrophic events but proactive approaches relating to health and wellness is another application of social communication in support of reduced risk and lower costs.

 

Big data technology to manage these applications allows Insurers to ingest massive volumes of data, wrap context and meaning around the unstructured content, search it in real time and deliver the facts to the right channels at the right time.

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Semantic Technologies in MarkLogic - World Class Triple Store in Version 7

Tony Agresta's insight:

This video is a fantastic overview from MarkLogic's Stephen Buxton, John Snelson and Micah Dubinko covering semantic processing, use cases for triple stores that include richer search & graph applications and the expanded architecture in MarkLogic 7.    It's an hour in length but well worth the time if you're interested in understanding how you can use documents, facts derived from text and values to build ground breaking applications.   Databases as we know them will change forever with the convergence of enterprise nosql, search and semantic processing.  This video provides you with the foundation to understand this important change in database technology.

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Updated Database Landscape map – June 2013 — Too much information

Updated Database Landscape map – June 2013 — Too much information | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:


MarkLogic is uniquely positioned on this database landscape map.    Here's what makes the position very different from other vendors:


1.  Search - MarkLogic is directly connected to all the major enterprise search vendors.   Recent recognition of this was confirmed by Gartner in its Enterprise Search Magic Quadrant.   Notice that other NoSQL technologies are nowhere close to this connection point.


2.  General Purpose - MarkLogic provides an enterprise NoSQL database and Application Services and Search.   With support for many development languages, REST and JAVA APIs, MarkLogic has clear links to SAP, Enterprise DB and a host of other database providers.


3.   Graph and Document - MarkLogic has long been recognized as a document store and used widely all over the world for this purpose.  Notice the subtle connection to Graph as well connecting MarkLogic to other vendors in this space like Neo4J.  MarkLogic 7 promises to deliver a world class triple store to index subjects, predicates and objects in XML documents or load other triples through the MarkLogic Content Pump.  For the first time, the only Enterprise NoSQL technology with search will include semantics.  Updated APIs and support for SPARQL are part of this release.


4.  Big Tables - MarkLogic's ability to handle big data has long been known.  The olive green line is designated for vendors like MarkLogic, Cassandra, HBASE, etc.   MarkLogic's partnership with Intel for Distribution of Apache Hadoop and the fact that MarkLogic ships with Hadoop connectors provide additional confirmation for this position.


5.   Key Value Stores - Data can be stored as keys without a database schema required lby relational databases.  In MarkLogic's case, huge quantities of data can be indexed in real time with data stored in memory and disk making search results instant and complete.   After a recent analysis of over 50+ MarkLogic customers, the abilty to quickly get up and running and deliver new information products to market was a business driver they mentioned over and over again.


The fact is, no one else on the list has all of these qualities.   Because of this unique position, visually you see MarkLogic distanced from other clusters or long lists of technology vendors.  


To learn more, you can go to MarkLogic Resources.






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