Data Cataloging vs. Data Modeling: Reporting from EDW2017
The Enterprise Data World 2017 Conference in Atlanta in the beginning of April was one of the best I have attended in recent years. At least 50 sessions for a guy like me interested in modeling. I chose to focus on sessions possibly related to Data Modeling in the new worlds of NoSQL, Big Data […]
Read More →2017 Trends for Semantic Web and Semantic Technologies
Are you hearing the term “Semantic Web” as often as you may have in the past? There’s no denying the importance of the technologies, standards, concepts, and collaborations that define the Semantic Web proper and all that is affiliated with it or grown out of it. These range from a dependency on RDF/OWL triple stores […]
Read More →IPTC Releases Comprehensive Controlled Vocabularies for SportsML 3.0 Standard
by Angela Guess According to a recent press release, “The IPTC has released a comprehensive set of sports controlled vocabularies as a supplement to the SportsML 3.0sports-data interchange format, which was released in July 2016. These controlled vocabularies (CVs) are in the format of NewsML-G2 Knowledge Items plus RDF variants and are available on IPTC’s […]
Read More →Banking on FIBO: Financial Institutions Turn to Semantic Standard
What is a Semantic Bank? One thing is for sure: The Financial Industry Business Ontology (FIBO) developed by the EDM Council, which semantically defines core financial industry concepts and relationships, probably plays a big role in it. A look at Deutsche Bank should help clarify the concept. The bank has leveraged FIBO as the basic […]
Read More →Triplestores 101: Storing Data for Efficient Inferencing
Relational databases are the workhorses of many corporate analytical and reporting applications, but the rise of Big Data has led to the rise of alternative, NoSQL database models. They are better suited for the nature of the data and the kinds of questions that will be asked against that data. Triplestores are a kind of […]
Read More →Applying Graph Databases and Semantic Technologies to Big Data
by Angela Guess Jans Aasman recently wrote in Information Management, “Many aspects of data management—particularly concerning big data—hinge upon the utility of graph databases. When deployed with additional semantic technologies such as ontologies, taxonomies and vocabularies, there are few analytic feats an RDF graph cannot achieve. In most instances, end users are largely unaware of […]
Read More →Linked Data Solution Advances Possibilities for Financial Analytics
Fund managers toiling in the financial research mines to discover information and make connections about the companies they cover and their ecosystems don’t have an easy time of it. The problem isn’t that there isn’t enough data out there that will help them gain valuable insights. Rather, there is so much scattered data: It lives […]
Read More →Semantic Data Lakes and the Advance of Medicine
This article charts the long path of Dr. Parsa Mirhaji and his work to bring Semantic Data Lakes and healthcare analytics together: “Mirhaji saw something in the story [from 2001] that spoke to a practical problem he had encountered in searching for and saving information on medical advances, and it set the tone for research […]
Read More →A Semantic Web Primer
by Angela Guess Mark Graham recently wrote in Slate, “In the early days of the Web, content was stuck within its containers. Early Web pages built in HTML defined both their own content and their appearance. But things started to change after Tim Berners-Lee, the inventor of the Web, along with two colleagues, James Hendler […]
Read More →How Smart Data Tools Overcome the Challenges of the Data Lake
by Angela Guess Marty Laughlin of Cambridge Semantics recently wrote for Inside Big Data, “New smart data tools are rapidly overcoming the common challenges presented by the newly emerging data lake. These tools make it easy to semantically link, analyze and manage diverse data, structured and unstructured, at big data scale and to make it […]
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