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Gartner's basic business rules for capitalizing on big data

Gartner's basic business rules for capitalizing on big data | Big Data Technology, Semantics and Analytics | Scoop.it
A breakthrough like big data comes around only once in a blue moon. Here are some basic business rules on what executives need to do to capitalize on it.

Via Adrian Carr
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Adrian Carr's curator insight, April 25, 2013 10:00 AM

Doug Laney of Gartner continues to drive out the ghosts of "Big Data" and pin it's feet firmly to the ground (mixing metaphors ? - maybe).

A pragmatic approach to grounding objectives in business necessity, starting with internal data sources (not all Big Data projects start with collecting Tweets and blogs..) and then evolve.

 

I am turning into a big fan of Doug's.

Bryan Borda's curator insight, April 26, 2013 9:29 AM

Excellent insights from Gartner.

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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How Graph Analytics Can Connect You To What's Next In Big Data

How Graph Analytics Can Connect You To What's Next In Big Data | Big Data Technology, Semantics and Analytics | Scoop.it
Everything in our digital universe is connected. Every single day, you wake up and start a series of interactions with people, products and machines. Sometimes, these things influence you, and sometimes you play the role of the influencer. This is how our world is connected, in a network of relationships [...]
Tony Agresta's insight:

This recent article by Scott Gnau of Teradata does a great job at discussing Graph Analytics. For example, Scott writes


"The ability to track relationships between people, products, processes and other 'entities' remains crucial to breaking up sophisticated fraud rings."


He also talks about the fact that graph analytics:


"Allow companies to detect, in near real-time, the cyber-threats hidden in the flood of diverse data generated from IP, network, server and communication logs – a huge problem, as we know, that exists today."


But what's powering the analysis?  Where is the data stored that drives the visual display of the graph?  Today, graph databases are becoming more and more popular.  One type of graph database is the native RDF triplestore.


Triplestores store semantic facts in the form of subject - predicate - object using the Resource Description Framework.  These facts might be created using natural language processing pipelines or imported from Linked Open Data.  In either case, RDF is a standard model for data publishing and data interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ. RDFS and OWL are its schema languages; SPARQL is the query language, similar to SQL.


RDF specifically supports the evolution of schemas over time without requiring all of the data transformations or reloading of data. A central concept is the Universal Resources Identifier (URI). These are globally unique identifiers, the most popular variant of which are the widely used URLs. All data elements (objects, entities, concepts, relationships, attributes) are identified with URIs, allowing data from different sources to merge without collisions. All data is represented in triples (also referred to as “statements”), which are simple enough to allow for easy, correct transformation of data from any other representation without the loss of data. At the same time, triples form interconnected data networks (graphs), which are sufficiently expressive and efficient to represent even the most complex data structures.


When Scott talks about tracking "entities", one way to do this is using graph visualization (graph analytics) that sits on top of a native RDF triplestore. The data in the triplestore contains the relationships between the entities.  They can be displayed using relationships graphs (a form of data visualization).  Since graphs can get very complex very fast (and since triplestores can hold billions of RDF statements, all of which are theoretically eligible to be displayed in the visual graph), users apply link analysis techniques to filter the data, configure the graph, change the size of the entities (nodes) on the graph and the links (edges), search the graph space and interact with charts, tables, timelines and geospatial views. 


SPARQL, the powerful query language that can be used with triplestores, is more than adequate to create subsets of RDF statements which can then be stored in smaller, more nimble triplestores that make graph analysis easier. 


So, while graph analysis is getting hot, the power is in the blend of the visual aspect and the underlying RDF triplestore.   It's also fair to say that creating the triples by analyzing free flowing text is also an important part of this solution.


To read more about native triplestores, graph visualization and text mining, get the  white paper published by Ontotext called "The Truth About Triplestores" which outlines all of this and goes deeper into text mining and other semantic technology.


To learn more about graph visualization, you can go to to Cambridge Intelligence or Centrifuge Systems.


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Ontotext Releases GraphDB™ 6.1 RDF Triplestore - Ontotext

Ontotext Releases GraphDB™ 6.1 RDF Triplestore - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
GraphDB 6.1 is the latest version of Ontotext's flagship RDF Triplestore product.
Tony Agresta's insight:

GraphDB 6.1, a native triplestore, is now available from Ontotext.  You can get a free copy of Lite, Standard or Enterprise here:  http://www.ontotext.com/products/ontotext-graphdb/  This is worth trying, especially since it comes with the Knowledge Path Series which guides you through the entire evaluation:  http://www.ontotext.com/graphdb-knowledge-path-series/

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Semantic Biomedical Tagger - S4 - Ontotext Wiki

Semantic Biomedical Tagger - S4 - Ontotext Wiki | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

Take a look at the types of entities that the semantic biomedical tagger (SBT) can identify from complex text.   The biomedical tagger has a built-in capability to recognize 133 biomedical entity types and semantically link them to a knowledge base system.  In this case it is Linked Life Data (LLD). The SBT can load entity names from the LLD service or any other RDF database with a SPARQL endpoint.


What does this mean for you?  You can analyze free flowing text that has complex biomedical terms.   Ontotext can analyze the text, identify entities and match those entities to our Linked Life Data service.  By doing so, we enrich the terms identified in the Biomedical Tagger.  Entity names can then be loaded into GraphDB (an RDF database) or any other RDF database in support of search and discovery or analytics applications.  Your documents are discoverable at the ENTITY LEVEL allowing analyst and researchers to find precisely what they are looking for - instantly.  


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On-demand Metadata Management with Ontotext S4 - Ontotext

On-demand Metadata Management with Ontotext S4 - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Last week I had a chance to present the Self Service Semantic Suite (S4) at the LT-Accelerate conference in Brussels. LT-Accelerate is a new event focusing on language technology and its applications in various domains: social media…
Tony Agresta's insight:

The presentation by Marin Dimitrov from Ontotext is worth a review.   Ontotext has been delivering solutions across verticals that share some common themes:


  1. Uncovering insight from text using search and discovery applications that pinpoint specific parts of free flowing text based on semantic indexing and classification.
  2. Interlinking structured text AND structured data in the same semantic repository allowing for complete and accurate search results.
  3. Reducing the impact of schema evolution while also integrating heterogeneous data sources.
  4. Applying the vast amounts of Linked Open Data available to enrich internal data sources and also disambiguate entities.
  5. Revealing implied relationships (new facts) from existing RDF statements and then using these facts to answer queries faster and uncover new insights.


Today, these common themes have been used to deliver solutions in Media & Publishing, Life Sciences, Government, Financial Services, Healthcare, Claims Management and other areas.  


S4, the Self Service Semantic Suite allows developers to build their own applications using proven, enterprise class tools that run on demand and in the cloud.   If you are looking for a low cost alternative to Semantic Technology, try Ontotext S4 for free.  If you want to learn more about the complete suite of tools in the Ontotext portfolio, visit www.ontotext.com

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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,...
Tony Agresta's insight:
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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How Big Data Is Changing Medicine

How Big Data Is Changing Medicine | Big Data Technology, Semantics and Analytics | Scoop.it
Used to be that medical researchers came up with a theory, recruited subjects, and gathered data, sometimes for years. Now, the answers are already there in data collections on the cloud. All researchers need is the right question.
Tony Agresta's insight:

Through semantic analysis of free flowing text and the indexing of results, fine grained details about diseases, treatments, symptoms, clinical trials and current research can be made accessible to medical practitioners in real time.   How does this work?   It typically involves creating a text mining or natural language processing "pipeline" that is used to analyze the text, identify entities (even complex bio medical terms), classify them, develop relationships between them and then "index everything."


The way we have done this successfully is by using proven text mining algorithms and tuning them to highly specific domains like life sciences, healthcare and biotech.   We use curation tools and trained curators to read the text, annotate it and gain agreement on the annotations.  Then the results are used to refine the text mining algorithms, test and validate.


This process may seem cumbersome to some but the reality is, when done by trained pros, it is not.  It has the added benefit of being done one time and then being applied for long periods of time without interruption.   Results are highly accurate.  


Seeing is believing.  You can try it for yourself here: 


  1. Go to:  https://console.s4.ontotext.com/#/home
  2. Click on "Demo for Free"
  3. Paste text into the box from an article or research paper on healthcare or life sciences - make sure the article is replete with complex bio medical terms that you don't think any automated algorithm can figure out.
  4. Select Bio Medical Tagger (by the way, you can also do this for general news or Tweets)
  5. Click Execute
  6. Analyze the results


Pretty cool.


Organizations that don't semantically enrich their content are operating at a disadvantage.  The benefits are real - saving patients lives, finding new treatment strategies, developing drugs faster and much more.


If you would like to learn more about semantics, we suggest you visit www.ontotext.com where's there's a wealth of information, demos, customer stories and news about this important subject. 


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Sizing AWS instances for the Semantic Publishing Benchmark | LDBCouncil

Tony Agresta's insight:

This post is a bit technical but I would encourage all readers to look this over, especially the conclusions section.   The key takeaway has to do with the ability for a graph database (GraphDB 6.1 in this case) to perform updates (inserts to the database) at the same time queries are being run against the database.   Results here are impressive. 

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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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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, 2014 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, 2014 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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