Big Data Technology, Semantics and Analytics
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Big Data Technology, Semantics and Analytics
Trends, success and applications for big data including the use of semantic technology
Curated by Tony Agresta
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How Google Applies Big Data To Know You | SmartData Collective

How Google Applies Big Data To Know You | SmartData Collective | Big Data Technology, Semantics and Analytics | Scoop.it
Google is, of course, an expert in big data. They have developed many open source tools and technologies that are widely used in the big data ecosystem.
Tony Agresta's insight:

Google really does understand us.    With Semantic Search supported by a Knowledge Graph, search results are getting better and better.   This info graphic helps explain how semantic search works.

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NoSQL's real-time analytics, database consolidation entice enterprises

NoSQL's real-time analytics, database consolidation entice enterprises | Big Data Technology, Semantics and Analytics | Scoop.it
At NoSQL Now conference, industry officials predict where the data management technology is going
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DBTA: Unleashing the Power of Hadoop for Big Data Analytics

Tony Agresta's insight:

Great paper that covers how you can make Hadoop really powerful.   Not all data is created equal.  Some is needed in real time.  Some requires less expensive storage options.  Some you may need to quickly migrate from HDFS to MarkLogic.  This paper is the perfect road map to understand how you can unleash the power of Hadoop.  No registration required to download  a copy. 

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The Age of Big Data - Predicting Crime After Shocks



Tony Agresta's insight:

The BBC documentary follows people who mine Big Data, including the Los Angeles Police Department (LAPD) who uses data to predict crime.  It's proven that historical patterns can be used to predict future behavior.  With a database of over 13 million crimes spanning 80 years and real time continuous updates, the LAPD has applied mathematical algorithms and pattern recognition to identify crime hotspots.   Targeted police work has resulted in a 26% decrease in burglaries and a 12% decrease in property crimes. 

 

How does this work?  In the same way that earthquake aftershocks can be predicted, data miners analyzed historical crime statistics including location and timing.  They found patterns in the big data crime landscape.  By tracking the history, timing and location of crimes, they revealed that the probability another crime would occur in certain locales was higher.  They discovered patterns in the data.  In this case, the rate of crime and geospatial distribution of events were excellent predictors of future behavior including pinpointing small geograpic areas which they used to direct police resources. 

 

Today, these predictive aftershocks are becoming more accurate through the use of real time data feeds, alerts, geospatial analysis and temporal analysis.  Over 150 cities in the US are starting to apply these techniques allowing police officers to anticipate, focus, apprehend and therefore lower risk.   


Thanks to KD Nuggets for providing the link to the BBC video which is very well done.

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Who are the Big Data Influencers?

Who are the Big Data Influencers? | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

Here's a nice list of bloggers, tweeters and general influencers in the Big Data space.    The post makes the following key points worth noting:


  • You can now reach out to the influencer using Twitter, email, phone or any other appropriate way with the increased conviction that follows from knowing that you are being highly relevant to them. In fact – in most cases they are likely to thank you for bringing the relevant material to their attention and in many cases they will share their “find” with others.
  • The net effect of this solution is that your evangelists spend time on being relevant and building relationships with influencers – rather than spending time looking for opportunities to engage; and just as a sales team that works off a steady stream of hot leads performs better than one that has to find their own leads, your evangelists will help win significantly more hearts, minds and market share.



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Big Data, Analytics And The Future Of Marketing And Sales

Big Data, Analytics And The Future Of Marketing And Sales | Big Data Technology, Semantics and Analytics | Scoop.it
By Jonathan Gordon (@JW_Gordon), Jesko Perrey, and Dennis Spillecke (@dspillecke) Big Data is the biggest game-changing opportunity for marketing and sales since the Internet went mainstream almost 20 years ago.
Tony Agresta's insight:

Digital data can translate into higher sales with the right automation and analysis in place.   Thirty five percent of pre purchase decisions in the B to B space are made through online research.   Those interactions can be used as leading indicators and potentially trigger points to engage with personalized messaging.   In the B to C world, predictive algorithms have been used for years.  For example, Banks can determine the next most likely next product purchased and retailers can prevent defection with special discounts offered in real-time.  

 

When applying these types of solutions, the dimension of time can be critical. Are transactions slowing down for a specific customer?  Is website access accelerating?   Are downloads of valuable content happening more rapidly?  Are your customers starting to visit competitive sites more frequently? 

 

There's no shortage of data available to answer these questions.  But the variety of techniques (and the complexity of some) to address these issues can be daunting at times.   Analyzing trends over time to isolate important milestones in the customer lifecycle is one way to begin.   Organizing your customers into small sets of homogenous segments is one way to begin.  Are there segments that buy from multiple product categories frequently?  Are there other segments showing a decline in transactions?   Once these segments are identified, isolating patterns of behavior within the segments could lead to a set of rules and event triggers used to personalize messages, improve customer service, introduce new products and offer discounts.  

 

Big Data platforms that allow you to ingest massive amounts of data, classify the data in real time and take action right away can be applied in game changing ways.   Sales and Marketing professionals should focus their attention on customer behavior leading to retention, upsell and improved customer service in a world where competitive pressures are not going away anytime soon.

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What's the Scoop on Hadoop?

What's the Scoop on Hadoop? | Big Data Technology, Semantics and Analytics | Scoop.it
If you are an investor in the field of Big Data, you must have heard the terms “Big Data” and “Hadoop” a million times.  Big Data pundits use the terms interchangeably and conversations might lead you to believe that...
Tony Agresta's insight:

"Hadoop is not great for low latency or ad-hoc analysis and it’s terrible for real-time analytics."


In a webcast today with Matt Aslett from 451 Research and Justin Makeig from MarkLogic, a wealth of inforrmation was presented about Hadoop including how it's used today and how MarkLogic extends Hadoop.  When the video becomes available, I'll post it but in the meantime, the quote from the Forbes article echoes what the speakers discussed today.


Today, Hadoop is used to store, process and integrate massive amounts of structured and unstructured data and is typically part of a database architecture that may include relational databases, NoSQL, Search and even Graph Databases.  Organizations can bulk load data into the Hadoop Distributed File System (HDFS) and process it with MapReduce.   Yarn is a  technology that's starting to gain traction enabling multiple applications to run on top of HDFS and process data in many ways. But it's still early stage.


What's missing?  Real Time Applications.  That's an understatement since reliability and security have also been question marks as well as limited support for SQL based analytics.   Complex configuration makes it difficult to apply Hadoop.


MarkLogic allows users to deploy an Enterprise NoSQL database into an existing Hadoop implementation and offers many advantages including:


  • Real time access to your data
  • Less data movement
  • Mixed workloads within the same infrastructure
  • Cost effective long term storage
  • The ability to leverage your existing infrastructure


Since all of your MarkLogic data can be stored in HDFS including indexes, you can combine local storage for active, real time results with lower cost tiered storage (HDFS) for data that's less relevant or needs additional processing.  MarkLogic allows you to partition your data, rebalance and migrate partitioned data interactively.


What does this mean for you?  You can optimize costs, performance and availability while also satisfying the needs of the business in the form of real time analytics, alerting and enterprise search. You can take data "off line" and then bring it back instantly since it's already indexed.  You can still process your data using batch programs in Hadoop but now all of this is done in a shared infrastructure. 


To learn more about MarkLogic and Hadoop, visit this Resource Center


When the video is live, I'll send a link out.



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Bryan Borda's curator insight, July 19, 2013 11:39 AM

Excellent information on advantages to using NoSQL technology with a Hadoop infrastructure.  Take advantage of the existing Hadoop environment by adding powerful NoSQL features to enhance the value.

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Why Big Data Will Make Semantics Feasible … and Semantics Will Make Big Data Worthwhile

Why Big Data Will Make Semantics Feasible … and Semantics Will Make Big Data Worthwhile | Big Data Technology, Semantics and Analytics | Scoop.it

Via Adrian Carr
Tony Agresta's insight:

Adding context and meaning to unstructured text is essential.  When combined with internal data and external 3rd party data, this can be a very effective approach in intelligence analysis.   Clearly there are a host of applications for the combined approach of big data and semantics.    But I find investigations to be one of the most interesting.  Connecting the dots to reveal hidden connections across data sets is a powerful way to uncover networks of clandestine activity. 

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Adrian Carr's curator insight, July 1, 2013 7:40 AM

This is a great article from Kurt at Avalon explaining why Big Data and Semantics will have a beautiful future togetehr.

Bryan Sung's curator insight, July 9, 2013 9:28 PM

Linking between  Big Data and Context.

 

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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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Big Data & Predictive Analytics | SmartData Collective

Big Data & Predictive Analytics | SmartData Collective | Big Data Technology, Semantics and Analytics | Scoop.it
Predictive analysts usually think of doing predictive modeling on structured data pulled from a database.
Tony Agresta's insight:

This article covers some important information about predictive analysis.  The definition around the "unit of analysis" and the use of unstructured data as inputs to models are two. 


The article does not touch on how big data platforms can enhance the modeleing process.  When it comes to really big data, the exercise of building and deploying predictive models may be best suited to a platform approach.  Key ingredients would includ:


  • Hadoop to preprocess the data
  • Semantic enrichment of content
  • Real time indexing and scoring allowing business users to get instant access to results
  • Search applications that use model scores displayed with other relevant data to create a richer search experience
  • In-database analytics
  • Tools to pump data in and out of the database
  • Application services to quickly build applications that use the scores.  


There’s more to predictive modeling than just the modeling technology.  Optimizing the entire process may require the use of an Enterprise NoSQL database and platform.  This could end up being a very valuable part of your technology stack.  Applying this in support of data discovery, business intelligence and predictive analytics will improve chances of success,


There's a good video on this topic here:  Analytics and Information Products

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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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New Forms of Analytics Incorporate New Forms of Data

New Forms of Analytics Incorporate New Forms of Data | Big Data Technology, Semantics and Analytics | Scoop.it
Discover how MarkLogic NoSQL database solutions help you make better decisions,
faster, with MarkLogic SolutionTracks—a series of brief, easy-to-follow, whiteboard tutorials.
Tony Agresta's insight:

In decades past analytical approaches incorporated data from transactional systems, spreadsheets and other sources.  Challenges still exist today.  The time it takes to build and udpate data warehouses sucks the air out of the analytical process, especially for those needing intelligence in realtime. Most analysts interested in exploring data at the speed of the human mind have to wait and wait...

 

Today, increased competition, demands from the business and monumental masses of new forms of data exacerbate these challenges.  Timely analysis has become even more difficult.   In theory, new information products could support new revenue streams and increased customer satisfaction but many organizations struggle to achieve analytical nirvana. They simply can't get to all of the data.

 

Fortunately, solutions do exist to consolidate documents and other forms of unstructured data with traditional data in real time.   Dashboards no longer need to be static.   Predictive analysis can include explanatory data to provide lift in your models.  Interactive analysis allows busines owners to explore data at faster speeds.

 

Enterprise NoSQL databases ingest all of your data "as is", make it available through search and real time database connectivity, derive important statistical measures and provide the ability to build user defined functions running close to the database.  Alerts inform analysts when conditions are met using pre-built queries. 

 

Enterprise hardened analytical applications won't lose your data, provide high availability, backup & recovery and government grade security.   This warrants a closer look.


Using BI Tools and In Database Analytics with MarkLogic





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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.
Tony Agresta's insight:

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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NetAppVoice: How The Semantic Web Changes Everything. Again!

NetAppVoice: How The Semantic Web Changes Everything. Again! | Big Data Technology, Semantics and Analytics | Scoop.it
The "semantic Web" is hugely important to tomorrow's business. Do not underestimate its significance: It truly changes everything. Embrace it, or risk extinction.

But what is it? And what does it mean for your business?
Tony Agresta's insight:

Semantic Search is transforming the way businesses operate.   In a short period of time, organizations will be focused on this...many already are today.   The ability to search on semantic facts (Bruce lives in New Rochelle, NY) while aslo seaching documents AND values at the same time yields search results that power rich content applications, increase visitor traffic, enhance product branding, help catch bad guys and a lot more. 


I've been spending a lot of time reviewing examples of these applications for commerical and government websites and must admit, they are very, very compelling.   They provide context-based search results that make the site sticky.  They deliver information in real time.  Look for more articles and posts on this subject to come out next week.   I'll focus on how Semantic Search is being used as a transformational technology in business and government and highlight some of the new capabilties supported in MarkLogic 7.

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Data Visualization 101: Basic Guidance | Edge of Chaos | Agile Development Blog

Data Visualization 101: Basic Guidance | Edge of Chaos | Agile Development Blog | Big Data Technology, Semantics and Analytics | Scoop.it
In my previous Visualization: Understated or Overrated? post I mentioned that sometimes people pass on the tremendous benefits of using data visualizations

Via Siarhei Mardovich
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Democratizing Data Assets: Learning From Data, Big and Small

Democratizing Data Assets: Learning From Data, Big and Small | Big Data Technology, Semantics and Analytics | Scoop.it
In talking about Big Data so much, are we neglecting the important things that you can do with Small Data? Maybe, but... probably not. Looking beyond the hype…
Tony Agresta's insight:

Great post from Kirk Borne.   I'm especially fond of "Association Discovery", an effective apporach to identify important networks of people that could be having a positive (or negative) impact on your organization while also detecting co-occurring combinations of attributes that can be used to improve rules-based trigger and alerts. 

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Predictive Analytics + Enterprise Search = Predictive Search - New Apps Anticipate What You Want to Know

Predictive Analytics + Enterprise Search = Predictive Search - New Apps Anticipate What You Want to Know | Big Data Technology, Semantics and Analytics | Scoop.it
Technology companies are rushing into predictive search, developing apps like Google Now that process digital clues to anticipate what users want to know.
Tony Agresta's insight:

Worth reading to better understand how predictive analysis is converging with search technology to create a new breed of applications that analyzes digital interaction and attributes about you and then translates the results into personalized alerts.

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The Best Database for Hadoop - Optimize Cost, Performance and Data Availability

Tony Agresta's insight:

Not all data is created equal.  Some is active and absolutely essential in solving real time problems.  And some may be needed in the future but, for now, can occupy fewer resources while keeping your costs down.   This presentation discusses how organizations can optimize cost, performance and availability using tiered storage with Hadoop and MarkLogic.  It demonstrates how you can have the best of both worlds - real time access to mission critical data AND the ability to immediately activate your long tail data stored in HDFS as needed. 


The benefits include less data movement, less ETL, the ability to index your data once, selectively mount data for real time usage and cost effective storage options. 


Today, more than ever, organizations need the flexibility to manage their data efficiently.  Why not store MarkLogic data directly in the Hadoop File System, apply Map Reduce to operate on that data through batch processing and then mount any portion of that data in MarkLogic for real time access?  Mixing real time and batch workloads allows you to manage your low density, active data in an enterprise environment with replication and high availability while also maintaining all other data in Hadoop.  This video is worth watching.


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Adrian Carr's curator insight, August 8, 2013 6:10 AM

As Hadoop comes to the top of the Gartner Hype Cycle and people and organisations begin to realise the limitations (as well as strengths) of the technology, the timing is perfect for MarkLogic to launch software to fill the gap.

Hadoop is a file store system, not a database.  Which means to use it effectively, especially for Enterprise applications, there is a lot of functionality which is required on top.

This is where MarkLogic comes in.

MarkLogic IS the database with all the 'Enterprise' functionality you would expect (High Availability, DR, ACID Transactions etc).  With the launch of Tiered Storage, MarkLogic can archive data into Hadoop storage using commodity storage whilst keeping track of it and being able to retrieve it.

Yes - MarkLogic is a proprietary software package but history has shown that to be the route to most effcetive long term support and development versus toolkit/build projects with their expensive and eventually impractical support burden and new feature drought.

Take a look at MarkLogic.com and download a free developer version.

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Oakland Crimespotting

Oakland Crimespotting | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

Here's an interesting data visualization for the city of Oakkand, CA that allows users to analyze crime by time of day, day of week, periods of time and type of crime.  This has to be useful in detecting patterns and allocating resources effectively 


There are hundreds of other data sets that are publicly available on this site for those interested.  They cover a wide variety of topics.


A big collection of sites and services for accessing data


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Gus Hunt on the importance of Network Graphs & Big Data

Gus Hunt on the importance of Network Graphs & Big Data | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

What do people care about most when trying to identify relationships in big data?  Connection points between people, places, organizations, events, things, concepts and time.  How can this be done against massive volumes of data?  Through the use of network graphs driven by semantic processing of unstructured data and consolidated information.  We are at high noon in the information age, the cusp of grasping all of the data and turning it into intelligence.  This video by Gus Hunt, the CTO of the CIA, summarizes the requirements and challenges to get this done. 



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Betting the Company: How Springer Successfully Insourced Its Flagship Content Platform

Springer, a 160 year-old scientific publisher with turnover in excess of half a billion Euro, decided to adopt an "E-First" strategy, and stop outsourcing it...
Tony Agresta's insight:

MarkLogic is powering SpringerLink which provides you with access to nearly 5 million books and articles about science, medical and technical topics.    The user experience is brilliant contributing to a 50% lower bounce rate, a 33% increase in time on the site and powerful search features to discover exactly what you are looking for.   Prototyping on this took 1 week.

 

Here's an interesting footnote.  In 2009 I was asked to write a chapter in a book that was going to be published by Springer.   Don Fergus and I focused on the use of data visualization to identify patterns in data that could indicate a cyber-data breach.   In 2010, Jennifer Bayuk and Springer published that book.   So I decided to use SpringerLink site to see if I can find it.   Searches by author, title, and editor all returned correct references to the book.

 

If you want to search for titles, here's the link:   Springer Link

 

And if you are interested in the chapter we wrote in CyberForensics, here's that link:    A Picture is Worth a Million Bytes.

 

MarkLogic continues to power some of the world's most powerful applications in the areas of Search & Discovery, Content Delivery and Analytics & Information Products.   At the core of many of these applications is the ability to consolidate all forms of data.  To learn more, you can visit our website:   www.marklogic.com

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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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The New Generation of Database Technology Includes Semantics and Search

The New Generation of Database Technology Includes Semantics and Search | Big Data Technology, Semantics and Analytics | Scoop.it
David Gorbet, VP of Product Strategy for MarkLogic, chatted with Bloor Group Principal Robin Bloor in a recent Briefing Room. This is an excerpt from the conversation.

Robin Bloor: In my view, we

Via Adrian Carr
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Adrian Carr's curator insight, June 25, 2013 7:51 AM

This is a great interview with David Gorbet of MarkLogic.

The new version of the software includes a Semantic triple store and here david explains how this will work and what benefits it will have.

This move will transform the so-called new generation of databases and move the bar further out of sight of the basic NoSQL start ups.

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Is Data the New Media?

Is Data the New Media? | Big Data Technology, Semantics and Analytics | Scoop.it
Digital data has not only gotten 'big,' as we now put it. Data has gotten fast, unstructured, and overwhelming. According to IBM, 90% of the data in the world has been produced in just the past 2 years.
Tony Agresta's insight:

From Steve Smith's article:   "Feeds from social media, offline transactions, user video and image posts, and more all converge into massive dashboards that give marketers and now publishers increasingly real-time, unmediated, and holistic views of the consumer."

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The Journey: From Print to Online

Mitchell1, a division of Snap-on Tools, is a 95-year old company that provides automotive repair, diagnostic and estimating information to the automotive rep...

Via Adrian Carr
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Adrian Carr's curator insight, June 17, 2013 5:00 AM

Great video Use Case for Mitchell1 part of Snap-On Tools.

A really nice feature here is that mechanics can annotate their own versions of the electronic manuals for later re-use without putting oil smudges on the pages.

This is a 95 year old company moving into the future with online publishing.

Steve Bosworth's comment, June 17, 2013 8:48 PM
Classic enablement case study...good stuff.