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Google Uses Big Data to Fight Human Trafficking | CIO Today

Google Uses Big Data to Fight Human Trafficking | CIO Today | Big Data Technology, Semantics and Analytics | Scoop.it
Big Data is being used to obtain intelligence on virtually every transaction conceivable. Now, Google is taking Big Data analytics into a new arena -- the fight against human trafficking.
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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 Announces Strategic Hires for Ontotext USA - Ontotext

Ontotext Announces Strategic Hires for Ontotext USA - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Strategic hires for Ontotext USA indicates Ontotext's expansion in the North American marketplace.
Tony Agresta's insight:

Ontotext has long had a presence in North America but recently expanded operations for a number of reasons:  support for the growing install base in this region, expanding into key US markets and building out alliances.   Success in EMEA and wide adoption of Ontotext has driven this growth.    Recently, Ontotext released 6.0 of its native RDF triplestore, GraphDB.  GraphDB is widely regarded as the most powerful RDF triplestore in the industry and has support for inferencing, optimized support for data integration through owl:sameAs, enterprise replication cluster, connectors to Lucene, SoLR & Elasticsearch, query optimization, SPARQL 1.1 support, RDF rank to order query results by relevance or other measures, simultaneous high performance loading, queries and inference and much more. 


Free versions of the Lite edition have been available for quite some time.   But Ontotext recently also started making the Standard and Enterprise versions available for testing (http://www.ontotext.com/products/ontotext-graphdb/)


Organizations have gravitated toward Ontotext more so than other NoSQL vendors and pure triplestore players because of the broad portfolio of semantic technology Ontotext provides that goes beyond GraphDB.  This includes Natural Language Processing, Semantic Enrichment, Semantic Data Integration, Curation and Authoring tools.    Experience Ontotext has working with Linked Open Data sets extends back to the beginning of the LOD movement.    When these tools and technologies are blended with GraphDB, they offer a powerful combination of semantic technologies that deliver a solution using a single vendor while lowering maintenance costs, shortening time to delivery and delivering proven deployment options.  

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Semantic Technology Training Courses - Ontotext

Semantic Technology Training Courses - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Semantic technology training provides instruction in broadly applicable semantic technology concepts. Courses can be customized to meet your needs.
Tony Agresta's insight:

Graph databases, also known as RDF Triplestores, have unique benefits over other databases allowing users to store linked data, query the graph and a NoSQL database simultaneously and infer new meaning using reasoning engines thereby crating facts that can be used to answer questions very quickly and enhance the search and discovery user experience.  


The underlying technology (RDF, Ontologies and SPARQL as the query language) are often not well understood by everyone.    Here are a set of classes that users interested in this topic can take.     The link takes you to a description of the classes. 

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Gregory Piatetsky: Overfitting Is the Cardinal Sin of Data Science | DataReview.info

Gregory Piatetsky: Overfitting Is the Cardinal Sin of Data Science | DataReview.info | Big Data Technology, Semantics and Analytics | Scoop.it
Data scientists are some of the most demanded apecialists in the IT-market. What tasks the solve?What challenges they face? DataReview has addressed these

Via Carla Gentry CSPO
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Carla Gentry CSPO's curator insight, September 4, 4:22 AM

In 1960-s, statisticians have used terms like «Data Fishing» or «Data Dredging» to refer to what they considered a bad practice of analyzing data without a prior hypothesis. The term «Data Mining» appeared around 1990′s in the database community. I coined the term «Knowledge Discovery in Databases» (KDD) for the first workshop on the same topic (1989) and this term became popular in academic and research community. KDD conference, now in its 21 year, is the top research conference in the field and there are also KDD conferences in Europe and Asia.

However, the term «data mining» is easier to understand it became more popular in the business community and the press.

 

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Not All Graph Databases Are Created Equally - An Interview with Atanas Kiryakov - Ontotext

Not All Graph Databases Are Created Equally - An Interview with Atanas Kiryakov - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Graph databases help enterprise organizations transform the management of unstructured data and big data.
Tony Agresta's insight:

Atanas Kiryakov is a 15 year veteran of semantic technology and graph databases.   He will be interviewed on September 30th at 11 AM EDT.   I would suggest you sign up for this webinar which will focus on the following:


  • Significant use cases for semantic technology - How are they transforming business applications today?
  • The importance of graph databases - What makes them unique?
  • Creating text mining pipelines - How are they used in conjunction with graph databases?
  • The Semantic Platform - What other tools make up a complete semantic platform and how are they used?


You can review the webinar using the link above and sign up.  Details about the webinar itself will be e-mailed to you around the middle of September.


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194 Million Linked Open Data Bibliographic Work Descriptions Released by OCLC - Semanticweb.com

194 Million Linked Open Data Bibliographic Work Descriptions Released by OCLC - Semanticweb.com | Big Data Technology, Semantics and Analytics | Scoop.it

Most significantly, OCLC has now released 194 Million Linked Open Data Bibliographic Work descriptions. According to Wallis, “A Work is a high-level description of a resource, containing information such as author, name, descriptions, subjects etc., common to all editions of the work.” In his post, he uses the example of “Zen and the Art of Motorcycle Maintenance” as a Work.


Via Irina Radchenko
Tony Agresta's insight:

Yet another source of Linked Open Data (LOD) that can now be used for enrichment purposes.  Among other uses, LOD is often used to semantically enrich entities extracted from free flowing text.  It can be very important when identifying entities and disambiguating one from another.  The world of linked open data continues to grow creating demand for graph databases and applications to search, discover and analyze.

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Why are graph databases hot? Because they tell a story... - Ontotext

Why are graph databases hot? Because they tell a story... - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Graph databases, text mining and inference allow you extract meaning from text, perform semantic analysis and aid in knowledge management and data discovery
Tony Agresta's insight:

Inference is the ability to infer new facts using existing facts.  For example, if you know that Susan lives in Texas and Texas is in the USA, you can infer that Susan lives in the USA.   Inference can take on much more complex scenarios the results of which can be stored inside a graph database.  As these new facts are "materialized" they can inform websites, search applications and various forms of analysis.  This is where the real power of inference comes into play.


Do this "at scale" requires a high performance graph database that can infer new facts while users are simultaneously querying the database and new facts are being loaded - all within an enterprise resilient environment. This blog post explains more about graph databases, inference and how the semantic integration of data can improve productivity and results.

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Linked data. Connecting and exploiting big dataLinked data


Via Irina Radchenko
Tony Agresta's insight:

Great ideas on how to collect and exploit linked data.

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Fàtima Galan's curator insight, July 2, 3:42 AM

"A functional view on big data"

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Ontotext Improves Its RDF Triplestore, GraphDB™ 6.0: Enterprise Resilience, Faster Loading Speeds and Connectors to Full-Text Search Engines Top the List of Enhancements

Ontotext Improves Its RDF Triplestore, GraphDB™ 6.0:  Enterprise Resilience, Faster Loading Speeds and Connectors to Full-Text Search Engines Top the List of Enhancements | Big Data Technology, Semantics and Analytics | Scoop.it
Sofia, Bulgaria (PRWEB) August 20, 2014 -- Today, Ontotext released GraphDB™ 6.0 including enhancements to the high availability enterprise replication cluster, faster loading speeds, higher update rates and connectors for Lucene, SOLR and Elasticsearch. GraphDB™ 6.0 is the next major release of OWLIM – the triplestore known for its outstanding support for OWL 2 and SPARQL 1.1 that already powers some of the most impressive RDF database showcases.
Tony Agresta's insight:

This press release from PRWEB summarizes the latest enhancements to GraphDB from Ontotext including improvements in load speeds, enterprise high availability replication cluster and connectors to Lucene SoRL and Elasticsearch.  

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The Truth About Triplestores & GraphDB, the Meaningful Database

The Truth About Triplestores & GraphDB, the Meaningful Database | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

There are two free white papers that I recommend you download.  The Truth About Triplestores discusses the top 8 things you need to consider when evaluating a triplestore.   The Meaningful Database is a product watch written by the Bloor Group about the most scalabe RDF triplestore.  

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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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Part 2: Investigating the Investigations - X Marks the Spot

Part 2: Investigating the Investigations - X Marks the Spot | Big Data Technology, Semantics and Analytics | Scoop.it
Posted by Douglas Wood, Editor.  Most of the financial crimes investigators I know live in a world where they dream of moving things from their Inbox to their Outbox. Oh, like everyone else, they a...
Tony Agresta's insight:

Here's another good post from Doug Wood of www.fightfinancialcrimes.com.   Advances in technology are revolutionizing how fraud investigations are being done today.

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Ellie Kesselman Wells's curator insight, April 29, 2:24 AM

The field is enterprise fraud detection. Investigating is the starting point. Adjudication is the final outcome of fraud detection and analysis.


Data (repositories such as enterprise data warehouses)  +

Technology (secure sharing across jurisdictions, automated link discovery, non-obvious relationship detection and identity resolution) are used to uncover insights which result in adjudication and closure of a complex incident investigation.

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Importance of NoSQL to Discovery - A Data Analysis Road Map You Can Apply Today

Importance of NoSQL to Discovery - A Data Analysis Road Map You Can Apply Today | Big Data Technology, Semantics and Analytics | Scoop.it
When you use the analytical process known as discovery, I recommend that you look for tools and environments that allow you connect to NoSQL platforms
Tony Agresta's insight:

The convergence of data visualization and NoSQL is becoming a hotter topic every day.  We're at the very beginning of this movement  as organizations integrate many forms of data with technology to visualize relationships and detect patterns across and within data sets.  There aren't many vendors that do this well today and demand is growing.  Some organizations are trying to achieve big data visualization through data science as a service.   Some software companies have created connectors to NoSQL (and other) data sources to reach this goal.  As you would expect, deployment options run the gamut. 


Examples of companies that offer data visualization generated from a variety of data sources including NoSQL are Centrifuge Systems who displays results in the form of relationship graphs, Pentaho who provides a full array of analytics including data visualization and predictive analytics and Tableau who supports dozens of data sources along with great charting and other forms of visualization.   Regardless of which you choose (and there are others), the process you apply to select and analyze the data will be important.  


In the article, John L Myers discusses some of the challenges users face with data discovery technology (DDT).  Since DDT operates from the premise that you don’t know all the answers  in advance, it’s more difficult to pinpoint the sources needed in the analysis.    Analysts discover insights as they navigate through the data visualizations.  This challenge isn’t too distant from what predictive modelers face as they decide what variables they want to feed into models.  They oftentimes don’t know what the strongest predictors will be so they apply their experience to carefully select data.  They sometimes transform specific fields allowing an attribute to exhibit greater explanatory power.   BI experts have long struggled with the same issue as they try and decide what metrics and dashboards will be most useful to the business.  


Here are some guidelines that may help you solve the problem.   They can be used to plan your approach to data analysis.


  • Start by writing down a hypothesis you want to prove before you connect to specific sources.  What do you want to explore?  What do you want to prove?  In some cases, you'll want to prove many things. That's fine.   Write down your top ones.
  • For each hypothesis create a list of specific questions you want to ask the data that could prove or disprove the hypothesis.   You may have 20 or 30 questions for each hypothesis.
  • Find the data sources that have the data you need to answer the questions.  What data will you need to arrive at a conclusion? 
  • Begin to profile each field to see how complete the data is.   In other words, take an inventory of the data checking to see if there are a missing values, data quality errors or values that make the specific source a good one. This may point back to changes in data collection needed by your current systems or processes. 
  • Go a layer deeper in your charting and profiling beyond histograms to show relationships between variables you believe will be helpful as you attempt to answer your list of questions and prove or disprove your hypothesis.  Show some relationships between two or more variables using heat maps, cross tabs and drill charts.
  • Reassess your original hypothesis.  Do you have the necessary data?  Or do you need to request additional types of data?
  • Once you are set on the inventory of data and you have the tools to connect to those sources, create a set of visualizations to resolve the answers to each of the questions.  In some cases, it may be 4 or 5 visualizations for each question.  Sometimes, you will be able to answer the question with one visualization.
  • Assemble the results for each question to prove or disprove the hypothesis.    You should arrive at a nice storyboard approach that, when assembled in the right order, allows you to articulate the steps in the analysis and draw conclusions needed to run your business.     


If you take these steps upfront and work with a tool that allows you to easily connect to a variety of data sources, you can quickly test your theory, profile and adjust the variables used in your analysis and create meaningful results the organization can use.  But if you go into the exercise without any data planning, without any goals in mind, you are bound to waste cycle times trying to decide what to include in your analysis and what not to include.    Granted, you won't be able to account for every data analysis issue your department or company has.   The purpose of this exercise is to frame the questions you want to ask of the data in support of a more directed approach to data visualization. 


Intelligence-led-decisions should be well received by your cohorts and applied more readily with this type of up front planning.  The steps you take to analyze the data will run more smoothly.   You will be able to explain and better defend the data visualization path you've taken to arrive at conclusions.  In other words, the story will be more clear when you present it. 


Consider the types of visualizations supported by the analytics technology when you do this. Will you need temporal analysis?   Will you require relationship graphs that show connections between people, events, organizations and more?    Do you need geospatial visualizations to prove your hypothesis?  A little bit of planning when using data discovery and NoSQL technology will go a long way in meeting your analytical needs. 


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Big Oil Drills Into Big Data - Wall Street Journal (blog)

Big Oil Drills Into Big Data - Wall Street Journal (blog) | Big Data Technology, Semantics and Analytics | Scoop.it
Big Oil Drills Into Big Data
Wall Street Journal (blog)
Big Oil is the latest industry to turn to Big Data software to shave costs.
Tony Agresta's insight:

Now here's a compelling reason to apply big data technology  - Down for 2 days, down $1 million.  Big Oil needs to collect and analyze massive amounts of data generated by equipment to reduce down time and anticipate outages.  It's  critical in Big Oil - both offshore or onshore. 


A related application that is not referenced in the post is the use of data visualization to identify the locations and connection points for replacement parts.   By analyzing the inventory of parts using network graphs and maps, analysts can identify locations and the shortest paths that need to be taken to optimize delivery times. 

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Northern Va. organizations form committee focusing on big data - Loudoun Times-Mirror

Northern Va. organizations form committee focusing on big data - Loudoun Times-Mirror | Big Data Technology, Semantics and Analytics | Scoop.it
Northern Va. organizations form committee focusing on big data Loudoun Times-Mirror “Industry, academic and research leaders in this region, and the NVTC's leadership in particular, truly believe that we have a unique set of powerful resources,...
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With 70 % of the world's daily internet traffic coming through Loudon every day, one of the most beautiful counties in the US is also a center for big data.
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Why Linked Data Is A Major Theme At APIcon In London

Why Linked Data Is A Major Theme At APIcon In London | Big Data Technology, Semantics and Analytics | Scoop.it

Via Ian Sykes
Tony Agresta's insight:

Great article on the benefits of Linked Data - An excerpt is below


Berners-Lee envisioned a Web where all sites inherently included this capability. A Web that involved far less effort, time, and expense for both Web site producers and users. A Semantic Web. On social networks, this idea of starting with one piece of data (i.e.: a user of Facebook) and finding your way to other data (that user's friends, and then their friends, and who they work for and where they live) is often referred to as a social graph. A social graph is an example of a data graph and the foundational element of a data graph is something called a triple. "David is a friend of Wendell" is a triple. It involves two objects (David and Wendell) and the explanation of the relationship. In true Semantic Web vernacular, "David" is the subject, "is a friend of" is the predicate, and "Wendell" is the object. When linked together (David knows Wendell who knows Kevin and so on..), triples form the basis of graphs.

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The Fall of Intuition-Based Decisions and Rise of Little Data

The Fall of Intuition-Based Decisions and Rise of Little Data | Big Data Technology, Semantics and Analytics | Scoop.it
Companies have more data than they think, they need less data than they think, and predictive models consistently outperform human decision-making abilities.
Tony Agresta's insight:

All of the model types described in this article will help smaller companies predict outcomes, deploy resources more effectively and save time.   Given some of the less expensive data mining tools on the market today, you would be surprised at how low costs are to create them given good input data and someone familiar with predictive analysis.  


Small organizations interested in analytic techniques to detect unusual patterns in data are more likely exposed using data visualization technology, specifically link analysis.  Interested parties should look at low cost tools like Key Lines or possibly Centrifuge Systems which performs interactive analysis tools and allows users to easily connect to data quickly. 


Predicting churn, retention, up-sell, or money spent should be left to predictive analytic products that focus on response or performance models.  Visualizing networks of activity through connection points are best done with data visualization tools and link analysis.

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The Social Network of Alexander the Great: Social Network Analysis in Ancient History

The Social Network of Alexander the Great: Social Network Analysis in Ancient History | Big Data Technology, Semantics and Analytics | Scoop.it
The Social Network of Alexander the Great: Social Network Analysis in Ancient History
Tony Agresta's insight:

There are some important concepts of social network analysis covered in this presentation.   

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Ontotext Delivers Semantic Publishing Solutions to the World’s Largest Media & Publishing Companies

Ontotext Delivers Semantic Publishing Solutions to the World’s Largest Media & Publishing Companies | Big Data Technology, Semantics and Analytics | Scoop.it
Washington DC (PRWEB) August 27, 2014 -- Ontotext Media & Publishing delivers semantic publishing solutions to the world’s largest media and publishing companies including automated content enrichment, data management, content and user analytics and natural language processing. Recently, Ontotext Media and Publishing has been enhanced to include contextually-aware reading recommendations based on content and user behavior, delivering an even more powerful user experience.
Tony Agresta's insight:

Semantic Recommendations are all about personalized, contextual recommendations based on a blend of search history, users profiles and, most importantly, semantically enriched content.  This refers to content that has been analyzed using natural language processing. Entities are extracted from the text, classified and indexed inside a graph database.  When a visitor comes to a website or information portal,  "Semantic Recommendations" understands more than just the past browsing history.  It understands what other articles have relevant, contextual information of interest to the reader.  This, in turn, creates a fantastic user experience because visitors get much more than they originally thought would be available in search results.  This news release talks more about Semantic Recommendations and Ontotext Media and Publishing. By the way, this same technology can be used for any website, any information product, any search and discovery application.  The basic premise is that once all of your content has been semantically enriched, search engines deliver highly relevant results. 

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Thought Leaders in Big Data: Atanas Kiryakov, CEO of Ontotext (Part 1)

Thought Leaders in Big Data: Atanas Kiryakov, CEO of Ontotext (Part 1) | Big Data Technology, Semantics and Analytics | Scoop.it
Next»» Next»» This segment is part 1 in the series : Thought Leaders in Big Data: Atanas Kiryakov, CEO of Ontotext1 2 3 4 5
Tony Agresta's insight:

This interview was with Atanas Kiryakov, founder and CEO of Ontotext.  He is an expert in semantic technology and discusses use cases for text mining, graph databases, semantic enrichment and content curation.   This is a five part series and I would recommend this to anyone interested in taking the next step in big data - semantic analysis of text leading to contextual search and discovery applications. 

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Text Mining & Graph Databases - Two Technologies that Work Well Together - Ontotext

Text Mining & Graph Databases - Two Technologies that Work Well Together - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Graph databases, also known as triplestores, have a very powerful capability – they can store hundreds of billions of semantic facts (triples) from any subject imaginable.  The number of free semantic facts on the market today from sources such as DbPedia, GeoNames and others is high and continues to grow every day.   Some estimates have this total between 150 and 200 billion right now.   As a result, Linked Open Data can be a good source of information with which to load your graph databases. Linked Open Data is one source of data. When does it become really powerful?  When you create your own semantic triples from your own data and use them in conjunction with linked open data to enrich your database.  This process, commonly referred to as text mining,  extracts the salient facts from free flowing text and typically stores the results in some database.  With this done, you can analyze your enriched data, visualize it, aggregate it and report on it.  In a recent project Ontotext undertook on behalf of FIBO (Finanical Information Business Ontology), we enhanced the FIBO ontologies with Linked Open Data allowing us to query company names and stock prices at the same time to show the lowest trading prices for all public stocks in North America in the last 50 years.   To do this, we needed to combine semantic data sources,  something that’s easy to do with the Ontotext Semantic Platform. We have found that the optimal way to apply text mining is in conjunction with a graph database.  Many of our customers use our Text Mining to do just that. Some vendors only sell graph databases and leave it up to you to figure out how to mine the text.  Other vendors only sell the text mining part and leave it up to…
Tony Agresta's insight:

Here's a summary of how text mining works with graph databases.  It describes the major steps in the text mining process and ends with how entities, articles and relationships are indexed inside the graph database.  The blend of these two major classes of technology allow all of your unstructured data to be discoverable.  Search results are informed by much more than just the metadata associated with the document or e-mail.  They are informed by the meaning inside the document, the text itself which contains important insights about people, places, organizations, events and their relationship to other things. 

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September 30th at 11 AM EDT: Not All Graph Databases Are Created Equally - An Interview with Atanas Kiryakov - Ontotext

September 30th at 11 AM EDT: Not All Graph Databases Are Created Equally - An Interview with Atanas Kiryakov - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Graph databases help enterprise organizations transform the management of unstructured data and big data.
Tony Agresta's insight:

Graph databases store semantic facts used to describe entities and relationships to other entities.  This educational webinar will be hosted by Ontotext and will be an interview format with Atanas Kiryakov, an expert in this field.   If you want to learn about use cases for graph databases and how you can extract meaning from free flowing text and store results in the graph databases, this webinar is must. 

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Ontotext Releases Text Mining & Semantic Technology Running in the Cloud – Welcome to “S4″

Ontotext Releases Text Mining & Semantic Technology Running in the Cloud – Welcome to “S4″ | Big Data Technology, Semantics and Analytics | Scoop.it
Sofia, Bulgaria (PRWEB) August 21, 2014 -- The Self Service Semantic Suite (S4) provides a complete set of tools that developers can use to build text mining and semantic applications. Fully hosted, low cost and on demand, S4 includes proven text mining technology, Linked Open Data for enrichment, the world's most powerful RDF triplestore (GraphDB™) and a set of tools for developers to build and deploy cloud-based semantic applications.
Tony Agresta's insight:

Small and mid size business can take advantage of text mining and semantic analysis of documents using S4, the Self Service Semantic Suite.   This allows developers to build custom applications that run in  he cloud but you can upload documents and web pages for free to test it out.  The very same enterprise technology that powers some of the largest semantic applications in the world is part of S4.

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Open Policy: Knowledge Makes Document Searches Smarter - YouTube

The OpenPolicy semantic search tool is a powerful way to instantaneously locate related content across scores of complex documents. See how OpenPolicy unlock...
Tony Agresta's insight:

Here's a great use case on the use of semantic technology to discover  documents hosted in a knowledge base.  The application was built by LMI and Ontotext and recently won 2014 Northern Virginia Technology Council award.  Search results against massive page counts are returned in seconds.  The combined use of ontologies and a scalable triple store from Ontotext make this application possible.  

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Defining Big Data Visualization and Analysis Use Cases -- TDWI -The Data Warehousing Institute

Defining Big Data Visualization and Analysis Use Cases -- TDWI -The Data Warehousing Institute | Big Data Technology, Semantics and Analytics | Scoop.it
Use these five use cases to spark your thinking about how to combine big data and visualization tools in your enterprise.
Tony Agresta's insight:

One form of data visualization that is underutilized by sales and marketing professionals is a relationship graph which shows you connections between people, places, things, events...any attributes you want to see in the graph.  This form of visualization has long been used by the intelligence community to find bad guys and identify fraud networks.  But it also has practical applications in sales and marketing. 

 

Let's say you're trying to improve your lead conversion process and accelerate sales cycles.   Wouldn't it be important to analyze relationships between campaigns, qualified leads created, the business development people that created the leads and how fast each lead progressed through sales stages?

 

Imagine a network graph that showed the campaigns, business development people that worked the lead pool, qualified leads and the number of opportunities created.  Imagine if components of the graph (nodes) were scaled based on the amount of money spent on each campaign, the number of leads each person worked and the value of each opportunity.  Your eye would be immediately drawn to a number of insights.  

 

You could quickly see which campaigns provided the most bang for your buck - the ones with relatively low cost and high qualified lead production.   You could quickly see which business development reps generated a high volume of qualified leads and how many turned into real opportunities.  Now imagine if you could play the creation of the graph over time.   You could see when campaigns started to generate qualified leads.  How long did it take?   How soon could sales expect to get qualified leads?   Should your campaign planning cycles change?   Are your more expensive campaigns having the impact you expected?   Is this all happening fast enough to meet sales targets?  

 

This form of data visualization is easier to apply than you think.  There are tools on the market that allow you to connect to CSV files exported from your CRM system and draw the graph in seconds.   As data visualization becomes more common in business, sales and marketing professionals will start to use this approach to measure performance of campaigns and employees while better understanding influencing factors in each stage of the sales cycles.  

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IBM’s new data discovery and visualization cloud offerings with predictive analytics

IBM’s new data discovery and visualization cloud offerings with predictive analytics | Big Data Technology, Semantics and Analytics | Scoop.it

IBM is introducing new data discovery software that enables business users to visually interact with and apply advanced analytics to their data without any specialized skills to get deeper insights about their business. The new software will help close the analytics skills gap that makes current data discovery tools inaccessible for the everyday business user and make it possible to go from raw information to answers hidden deep within structured and unstructured information in minutes. 


- See more at: http://www.vizworld.com/2013/11/ibms-data-discovery-visualization-cloud-offerings-predictive-analytics/#sthash.Ux5doBkj.dpuf


Tony Agresta's insight:

It was bound to happen and IBM seems headed in the right direction - Predictive Analytics and Data Visualization converge in the cloud.  In a post 911 era, analysts recognized that revealing insights required the human mind to explore data in an unconstrained manner.   If they had the chance to interact with disparate data sets, visualize that data in different forms and navigate to connection points directed by their experience, they could quickly pinpoint relationships that matter.

Today, groundbreaking approaches in intelligence analysis have their foundations built on unconstrained data discovery.   Insurance organizations are applying interactive data visualization to uncover patterns that clearly point to fraud and collusion.  eCommerce organizations are using these techniques to examine both employee and vendor behavior as they connect the dots highlighting networks of interest.

This revolution in analysis is only just beginning.   Imagine what can be accomplished when predictive models and rules are applied to big data in real time yielding more focused data sets for discovery.  


Four years ago I spoke with a global top 10 bank that applied predictive  models to detect fraudulent transactions.   When I asked if they had combined this approach with data visualization to pick up on any error in the models, they responded that their analysts couldn't use those tools because they were too complex.   They couldn't identify fraud networks using relationship graphs.  After nearly $2 billion in fines, I wonder if they are rethinking this approach?   The fact is, they could have detected money laundering among their account holders by joining the results of predictive analysis with human data discovery. 

Within 5 years, I would be surprised if every major bank, insurance company, retailer and healthcare organization wasn't following in the footsteps of the intelligence community.   As these analytic methods converge, the criminal's chances of hiding the truth diminish dramatically. 

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Henry Pan's curator insight, November 9, 2013 12:50 PM

Is this a better tool set?