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Meaning-First Data Modeling, A Radical Return to Simplicity | Semantic Arts
Meaning-First Data Modeling, A Radical Return to Simplicity | Semantic Arts
Meaning-First data modeling for #semantic #knowledgegraphs: a replacement to Structure-First modeling. The relational model was a great start for #datamanagement, but it is time to embrace a radical return to simplicity: Meaning-First data modeling #data
·semanticarts.com·
Meaning-First Data Modeling, A Radical Return to Simplicity | Semantic Arts
Memorizing vs. Understanding (read: Data vs. Knowledge)
Memorizing vs. Understanding (read: Data vs. Knowledge)
up the value of e anytime I need it (figure 1);Figure 1. A data dictionary with key and value of arithmetic expressions.(ii) if I do not have that option then the only other alternative to get the value of e is to actually compute the arithmetic expression and get the corresponding value. The first method, let’s call it the data/memorization method, which does not require me to know how to compute e while the second does. That is, in using the second method I (or the computer!) must know the procedures of addition and multiplication, shown in figure 2 below (where Succ is the ‘successor’ function that returns the next natural number).Figure 2. Theoretical definition of the procedures/functions of addition and multip
·medium.com·
Memorizing vs. Understanding (read: Data vs. Knowledge)
Metadata Recycling into… by Thomas Frisendal [PDF/iPad/Kindle]
Metadata Recycling into… by Thomas Frisendal [PDF/iPad/Kindle]
Recycle, Reuse and Reduce also for data models! Why waste time remodeling the same data, just because you go to graph? Learn how to auto-generate graph data models (for Neo4j) from legacy data models in UML, XML, ERD, concept maps and other formats. Missing something on the list? Let the author know! New: FileMaker db's as Graph Data Models!
·leanpub.com·
Metadata Recycling into… by Thomas Frisendal [PDF/iPad/Kindle]
Michael Pollmeier on Twitter
Michael Pollmeier on Twitter
This is so cool, this is the first time for me to ship a #Scala REPL based product. The @apachetinkerpop based DSL allows to query your own codebase for security vulnerabilitries, data leaks etc. https://t.co/1xSOwCnhI3— Michael Pollmeier (@pollmeier) December 11, 2018
·twitter.com·
Michael Pollmeier on Twitter
Microsoft to introduce a free tier of its Cosmos DB NoSQL database
Microsoft to introduce a free tier of its Cosmos DB NoSQL database
at Build 2017. Azure Cosmos DB was designed t be a superset of Microsoft's existing NoSQL Document DB database. Its codename was "Project Florence," and Microsoft execs consider it a "born in the cloud/cloud native" database that's designed to be scalable and usable by customers of any size.Microsoft currently charges by provisioned throughput and consumed storage by the hour for Azure Cosmos DB. Before the introduction of the free tier, M
·zdnet.com·
Microsoft to introduce a free tier of its Cosmos DB NoSQL database
Mind the Semantic Gap
Mind the Semantic Gap
How "talking semantics" can help you perform better #DataScience. @palexop #StrataData talk in New York slides now available #dataModeling #semantics
·slideshare.net·
Mind the Semantic Gap
mm-adt
mm-adt
A blueprint for the next @apachetinkerpop and an analysis on #opensource. @twarko @_mmadt #presentation from @DataDayTexas #GraphDB #data #tech #cloud #insight #softwareengineering #softwaredevelopment #AWS
·slideshare.net·
mm-adt
Modeling Schema.org JSON-LD in TypeScript: A Story in Four Parts - DEV Community 👩‍💻👨‍💻
Modeling Schema.org JSON-LD in TypeScript: A Story in Four Parts - DEV Community 👩‍💻👨‍💻
some TypeScript type system features, such as discriminated type unions, powerful type inference, nullability checking, and type intersections, present an opportunity to both model what Schema.org-conformant JSON-LD looks like, while also providing ergonomic completions to the developer
·dev.to·
Modeling Schema.org JSON-LD in TypeScript: A Story in Four Parts - DEV Community 👩‍💻👨‍💻
Modeling Sets of Data - DATAVERSITY
Modeling Sets of Data - DATAVERSITY
Today there are also strong user stories in the contexts of investigative work flows based on sets of suspects in graph-based analysis of crime, intelligence, fraud, churn, recommendations and other behavior / networking analytical areas.
·dataversity.net·
Modeling Sets of Data - DATAVERSITY
Most Influential Swapper on Anzo | LinkedIn
Most Influential Swapper on Anzo | LinkedIn
Google applied the PageRank algorithm to rank pages to prioritize search results. The algorithm is applicable to networks in other domains, for example, interest rate swap transaction networks in banking, real estate transaction networks, and social networks to find the most influential members of t
·linkedin.com·
Most Influential Swapper on Anzo | LinkedIn
Moving Toward Smarter Data: Graph Databases and Machine Learning - DZone Database
Moving Toward Smarter Data: Graph Databases and Machine Learning - DZone Database
When we used to think about data, it was most often in regard to where data was going to be stored and how we would manage it. Yes, files worked for a while, but when manipulating data became an important business priority across industries, the “file” solution didn’t work so well anymore. To meet these increasing demands, applications were designed and developed, addressing data storage and manipulation needs simultaneously — thus, the “database” was born. Today, data is viewed quite differently. Beyond data manipulation, organizations are focusing on mining their data for more visibility into and a deeper understanding of the intelligence within that data. Utilizing the insights acquired from their data to help make informed business decisions is a key priority for business leaders and a major concern in the development, evolution, and adoption of database solutions. A new term emerged in the industry — digital assets. That data the world has been obsessing over.
·dzone.com·
Moving Toward Smarter Data: Graph Databases and Machine Learning - DZone Database
MPP Data Virtualization
MPP Data Virtualization
prem or both. The discovery and integration layer in the fabric integrates and blends data, drawing on subsets of data from across the underlying data landscape as required.
·blog.cambridgesemantics.com·
MPP Data Virtualization
Muhammad Saleem on Twitter: ""How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks" has been accepted as full paper to #www2019 #webconf2019. @DiceResearch @akswgroup @NgongaAxel"
Muhammad Saleem on Twitter: ""How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks" has been accepted as full paper to #www2019 #webconf2019. @DiceResearch @akswgroup @NgongaAxel"
"How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks" has been accepted as full paper to #www2019 #webconf2019. @DiceResearch @akswgroup @NgongaAxel— Muhammad Saleem (@saleem_muhamad) January 21, 2019
·twitter.com·
Muhammad Saleem on Twitter: ""How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks" has been accepted as full paper to #www2019 #webconf2019. @DiceResearch @akswgroup @NgongaAxel"
My list of 7 great 2018 advancements in Enterprise Knowledge Graphs (and 2019 recommendations) | LinkedIn
My list of 7 great 2018 advancements in Enterprise Knowledge Graphs (and 2019 recommendations) | LinkedIn
While the term “Knowledge Graph” is relatively new (Google 2012) the concept of “representing knowledge as a set of relations between entities - forming a “graph” has been around for much longer. 2019 marks, for example, the 20th anniversary of the publication of arguably the first open standard for
·linkedin.com·
My list of 7 great 2018 advancements in Enterprise Knowledge Graphs (and 2019 recommendations) | LinkedIn