Recently, we tweet chatted with Big Data thought leaders Steve Sarsfield of Cambridge Semantics (@stevesarsfield), Joseph di Paolantonio of Constellation Research (@JAdP), Claudia Imhoff of Boulder BI Brain Trust and Intelligent Solutions (@Claudia_Imhoff), George Anadiotis of ZDNet (@linked_do), Tony Baer of dbInsight (@TonyBaer), Kurt Cagle of Semantic Data Group (@kurt_cagle), Carl Olofson of IDC (@databaseguru) and Tim King of Solutions Review (@BigData_Review) about 'The State of Graph Databases in 2020'. In this wide-ranging conversation we discuss graph database adoption, its application to COVID-19 data and which language is best. Read the full chat below to get their insights.
Don't forget to participate in our tweet chat, "The State of Graph Databases in 2020," today at 1:00 pm EST. We will be presenting questions to guide the discussion. Use #GraphDB to take part in the conversation! pic.twitter.com/EUpAa8Hso5
— Cambridge Semantics (@CamSemantics) May 28, 2020
In one hour we'll be tweet chatting with #bigdata thought leaders @stevesarsfield @linked_do @KirkDBorne @kurt_cagle @BigData_Review @databaseguru and @Claudia_Imhoff about 'The State of #GraphDatabases in 2020'! Follow #GraphDB to catch the full discussion! pic.twitter.com/Xrlf9IHmWx
— Cambridge Semantics (@CamSemantics) May 28, 2020
Looking forward to discussing Graph Databases today at 11 MDT. It should be most interesting! #graphdb
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
Excited to discuss/follow along with the @CamSemantics State of #GraphDatabases in 2020 Twitter chat shortly. Will be sharing some resources that help folks understand their options when evaluating a #GraphDB. https://t.co/8fNen1aFjj
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
Hello, everyone! Welcome to @CamSemantics Tweet Chat on #GraphDB
— Cambridge Semantics (@CamSemantics) May 28, 2020
Starting now! Our "The State of Graph Databases in 2020" tweet chat. Use #GraphDB to join the discussion now! #bigdata #graphdatabase #graphanalytics #knowledgegraph #datafabric #rdf #lpg pic.twitter.com/5yKyMI5jeL
— Cambridge Semantics (@CamSemantics) May 28, 2020
We're at the start of our tweetchat on #GraphDatabases #GraphDB Woot!
— Steve Sarsfield (@stevesarsfield) May 28, 2020
We’re planning for about an hour of lively discussion on #GraphDB
— Cambridge Semantics (@CamSemantics) May 28, 2020
Our host from Cambridge Semantics is @SteveSarsfield, VP of our #GraphDB product, AnzoGraph
— Cambridge Semantics (@CamSemantics) May 28, 2020
Hello everyone. I look forward to this discussion on graph databases and how they help with COVID-19 research. #graphdb
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
We have a series of questions that we’ll ask everyone to weigh in on. You're welcome to pose questions of your own to the group. #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Hi, @Claudi_Imhoff! #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Good to virtually be here! #GraphDB
— George Anadiotis (@linked_do) May 28, 2020
#GraphDB @CamSemantics Fire up those topics, my friend.
— Steve Sarsfield (@stevesarsfield) May 28, 2020
Hi, George! #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Why don't we get started with our first question and folks can chime in as they join #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Q1. We predicted that 2019 would be the year of the graph. What do you think about 2020? Are we ready to deploy graph? #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Often misuderstood -Graph database is a generic term. Truth: industry has developed various technologies under the graph database category. Selection is based on whether you are harmonizing diverse data sets, performing graph analytics, performing inferencing, #OWL, etc. #graphdb
— Steve Sarsfield (@stevesarsfield) May 28, 2020
— Kurt Cagle (@kurt_cagle) May 28, 2020
This should be an interesting teacher on #graphtech #graphdb https://t.co/O6Hnff91be
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
Forgot the hashtag. Graph database is a natural for COVID-19 contact tracing. #GraphDB
— Carl Olofson (@databaseguru) May 28, 2020
Ha. I'll take that, since i predicted that in 2018 actually :) I think we have been ready to deploy graph for a while now. And by we i mean the world at large here. (Just kidding) #GraphDB
— George Anadiotis (@linked_do) May 28, 2020
@CamSemantics Graph databases have shown themselves to be very beneficial. The technology's ability to connect data, handle massive volumes, and have remarkable performance makes it mandatory for any analytically inclined org. #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
A #GraphDB is designed around the concept of a mathematical graph. Unlike relational databases, they allow you to connect #data together. This enables users to take advantage of specific functions from within the graph database that help during data analysis.
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
But seriously: like #AI, the fundamental building blocks for #GraphDB have been there for a while. Like AI, it's about scale, ease of use, and maturity. Like AI, getting there takes a while. Both on the vendor and on the user side of things
— George Anadiotis (@linked_do) May 28, 2020
#graphdb In addition to straightforward contact tracing, a graph database can enable the analyst detect patterns in viral spread.
— Carl Olofson (@databaseguru) May 28, 2020
Still getting used to the https://t.co/CGFL8UQeFE platform. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
The structure of a #GraphDB enables it to map different types of relational and unstructured data. This means that it can provide a view of both simple and complex relationships between seemingly unrelated #data.
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
A1 #graphdb are in use in many areas, from #supplychsin to #healthcare with #datascience topping the interest. Directed acyclic graphs for #causalinference in #graphtech is a research interest of ours. But #IoT is oddly missing https://t.co/n5w6Ofd6iB
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
@linked_do You're on the cutting edge, George. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
#GraphDB Two main uses today: 1 Tracing connections between people when they spend time together
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
2 Drug research and repurposing. These drugs have already passed research cycles and clinical trials, so the amount of time required to vet them is greatly reduced.
Not only does a #GraphDB enable succinct #data connectivity, they also provide users with a faster path to accurate data #analytics.
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
@review_bigdata Tim, you saying that data integration is a big part of graph? #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
I'd also not discount contextualizing news articles and research data. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
You can use Twitter directly if you prefer; can be hard to follow the stream the, but you can use it direct, too #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Must-have addition = #Geospatial to #GraphDB https://t.co/Hqt0KnNqsV
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
#graphdb Not all graph analyses involve geospatial data.
— Carl Olofson (@databaseguru) May 28, 2020
@kurt_cagle So, Combining #NLP with #graphDB is a way to perform that context function. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
I'll second @stevesarsfield: #semantics are important (pun intended). Different people mean different things by #GraphDB, and that can lead to confusion. Broadly speaking, there's RDF graph DBs and property graph DBs
— George Anadiotis (@linked_do) May 28, 2020
And time-series as well as temporal #graphdb
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
No but if this tweet chat is focused on #Covid-19 as advertised, then #GraphDB initiatives must have #Geospatial aspects… Yes?
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
#knowledgegraph is a first use case for many new users. Companies need to build #analytics from a collection of structured and unstructured data. When you consider #NLP and combining its output with various sources, #graphdb is the way to go
— Steve Sarsfield (@stevesarsfield) May 28, 2020
This is a good intro to #GraphDB https://t.co/mQAWs7MkbF
— George Anadiotis (@linked_do) May 28, 2020
@stevesarsfield Yes, perhaps even more than contact tracing. Talked with one investor recently who noted that there are something #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
#graphdb OK. You have me there. Not sure that drug trial data analysis requires geospatial, but it helps with contact tracing.
— Carl Olofson (@databaseguru) May 28, 2020
like one hundred contact tracing apps out there right now, and if anything they're getting in the way. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
Good point. But still I’d want to know where the trials were taking place and whether there might be other things in that area that would effect outcomes… Just the biochemist in me :-) #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
@Claudia_Imhoff Not solely focused on COVID. Sorry we gave that impression! Just the state of graphs. COVID is a perfect use case. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
You also run into problems that contact apps have trouble discerning Bluetooth properly, so hard to tell how close someone is. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
Clinical trials often involve merging multiple sources, thus the biggest benefit of #GraphDB s
— Steve Sarsfield (@stevesarsfield) May 28, 2020
Ah — that was the subject I was told. If not, I can adjust but there are so many ways #GraphDB can help with scientific research that other DBs simply cannot!
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
From the invite: "The chat will discuss how COVID-19 is impacting the graph database market, predictions for 2020 and more." #graphdb
— Carl Olofson (@databaseguru) May 28, 2020
Yes, graphs are great for this, but I worry that they're getting tied so heavily to contact tracing. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
#knowledgegraph is trendy, and useful. Graph #analytics are also a common entry point for #GraphDB, and graph #MachineLearning is a more advanced one. Common thread - they are all done more comfortably with a #GraphDB
— George Anadiotis (@linked_do) May 28, 2020
Of course, if we are discussing how COVID-19 is affecting the graph database market, perhaps we should discuss the financial health of graph DB users... is this impacting demand? #graphdb
— Carl Olofson (@databaseguru) May 28, 2020
@kurt_cagle There is a whole area we could pursue on privacy. No doubt. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
With these things in mind, our editors have compiled a list of the most common #GraphDB use cases you need to know about so you can select the best software. https://t.co/ZjqDRcblxz
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
It is also interesting to look at the differences between native #graphtech and #graphanalytics grafted on top of RDBMS tech #GraphDB
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
With COVID-19, contact tracing would likely be the prime use case for #GraphDB
— Tony Baer (@TonyBaer) May 28, 2020
Q2. Whether COVID-related or not, what's the biggest hurdle to overcome when it comes to leveraging #GraphDB for #analytics?
— Cambridge Semantics (@CamSemantics) May 28, 2020
Absolutely. Both are useful, but they are different. Sometimes #graphanalytics can be a gateway drug for going full #GraphDB. But RDBMS + graph #analytics does not equal #GraphDB. And then we also have multi-model #database - another beast
— George Anadiotis (@linked_do) May 28, 2020
@TonyBaer Interesting to contact trace, but also ranking hotspots and almost any other analysis you can do in a RDBMS. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
#GraphDB Great quote on difficulty to study #Covid: Scientific data is typically scattered across locations. Also, for bigger organizations, & for historical reasons, data is unFAIR -- the opposite of FAIR: Findable, Accessible, Interoperable, & Reusable. https://t.co/KOrUByn59m
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
@CamSemantics Property graphs vs Semantic Graphs (KGs) muddies the water a lot. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
This is what makes #GraphDB technology so good for any scientific endeavor but especially for disease tracking and mitigation. https://t.co/P9WBCE8izn
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
Some might be reluctant to learn #cypher or #sparql, but it is very much a declarative language, like #SQL. #graphdb pic.twitter.com/WrYIRGis6b
— Steve Sarsfield (@stevesarsfield) May 28, 2020
I could see Neo4J or similar PGs being great for contact tracing - semantic graphs, perhaps not so much. @camsemantics #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
Other big usage is drug research and repurposing. Understanding how existing compounds work, -- these drugs have already passed research cycles and clinical trials, so the amount of time required to vet them is greatly reduced. #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
I see this as converging. RDF* is a property graph on RDF. But, no doubt there is confusion. #GraphDB https://t.co/Z4b7qMksAy
— Steve Sarsfield (@stevesarsfield) May 28, 2020
@Claudia_Imhoff Yes - federation is perhaps THE compelling case right now. Question is ingestion of that data into #graphdbs. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
@stevesarsfiled Sure, and you don't necessarily need even RDF* if you model it right. I was pro-RDF* initially, but ... #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
#GraphDB Another example: "Among the many challenges is the need to connect and correlate disparate data sets to make connections and derive insights. Graph data science is playing a key role in this work, enabling researchers to…
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
Agreed. From personal experience: a team of average #webdevelopers can learn to use SPARQL #GraphDB query language in a couple of weeks, with some guidance. For the simple stuff at least. So straying from SQL should not be scary. And it's worth it to be able to do #graphanalytics
— George Anadiotis (@linked_do) May 28, 2020
… create knowledge graphs of information about different research & even infection data about the coronavirus.https://t.co/GbwZjlO1Wr #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
I've found that you can model transmission vectors (and other props) as a contract just as readily #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
@kurt_cagle Let's face it, RDF had to catch up with RDF*. But properties plus OWL may be better. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
This is an important point. Thanks! #GraphDB https://t.co/SJE1DViIof
— Steve Sarsfield (@stevesarsfield) May 28, 2020
person:_A a class:_Person. person:_B a class:Person. contact:_AB a class:Contact. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
That said: there's an ongoing effort to standardize #GraphDB query languages (further, because SPARQL is already standardized) and extend SQL with #GraphDB capabilities
— George Anadiotis (@linked_do) May 28, 2020
Q3. Between #SPARQL and #RDF, the emergence of #Cypher and #GQL, and #Gremlin, what language(s) will emerge victoriously? #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
contact:_AB contact:hasPerson person:_A; contact:hasPerson person:_B. contact:hasTimeStamp "now". #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
Indeed there is #graphdb #GQL https://t.co/oFN78CqxBS https://t.co/0YkUKVmjNS
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
#GraphDB In dealing with disparate data sets, there are challenges such as lack of consistency across data sets, and potential overlap of data itself, that may be treated accidentally as discrete.
— Carl Olofson (@databaseguru) May 28, 2020
There is little difference between LPG and RDF* these days. Many vendors supporting labelled properties on RDF, allowing for graph algos and other analytics untouchable by old RDFs while giving you OWL #graphdb
— Steve Sarsfield (@stevesarsfield) May 28, 2020
@JAdP That's right, let's not forget GQL, an emerging standard that's close, but maybe not fully baked yet. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
@databaseguru Agreed. Consistency of ontologies is important here, and not really being addressed IMHO. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
Yes — the age old problems with integrating different sets of data together have not gone away — much as we would like to think there is a silver bullet! I’m feeling old… #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
My money is still on #GraphQL for web work, #SPARQL* will remain dominant. Ambivalent about #GQL. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
That's a tough one. I don't see this as a zero-sum game. It's subjective, too. I would reframe it as: which one can be the common denominator. The one that all other #GraphDB query languages an be reduced/translated to. That would be @apachetinkerpop imho https://t.co/hPEbjUKiFx
— George Anadiotis (@linked_do) May 28, 2020
There's a little more work to do OWL with URIs, but integration is faster and more reusable. Meaning is clearly expressed. The ultimate use of metadata for integration. Reusability makes up for effort. #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
I don't think that SQL access of #graphDB is really as compelling an argument as some believe. YMMV.
— Kurt Cagle (@kurt_cagle) May 28, 2020
I don't like it, and would not use it, personally. But some might. Is it worth the effort? Don't know. #GraphDB
— George Anadiotis (@linked_do) May 28, 2020
Would agree with that. It's just a bridge #GraphDB
— Tony Baer (@TonyBaer) May 28, 2020
Graph traversals are inherently recursive. SQL does not have constructs that support recursive operations, and must be extended. Not sure that's necessary. #GraphDB
— Carl Olofson (@databaseguru) May 28, 2020
@linked_do Which it are you referring to? :-) #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
I don't see SPARQL for all its strengths as dominating going forward. Too tied to RDF, which is overkill for many use cases #GraphDB
— Tony Baer (@TonyBaer) May 28, 2020
SQL-#GraphDB query language hybrids
— George Anadiotis (@linked_do) May 28, 2020
Of course, I tend to write Turtle in my sleep, so I may be biased towards SPARQL. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
@kurt_cagle It gets bad when it gets into your dreams #GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
@linked_do @stevesarfield Oh yeah. When you've been staring at an ontology you're modeling and it follows you to bed. Urrgh. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
Our editors have also begun wrapping their hands around the #GraphDB marketplace. This is our burgeoning list of the best commercial and enterprise products to consider. https://t.co/lseUn78XPv
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
Wouldn't put it that way. LPG & RDF are different in how the schema is designed. But, yes, many graph DBMSs are now bridging them, it's not rocket science to generate property from RDF. The other way around, ML could conceivably generate triples from properties #GraphDB
— Tony Baer (@TonyBaer) May 28, 2020
Thanks Tim! #GraphDB https://t.co/qOF7J2nYPL
— Steve Sarsfield (@stevesarsfield) May 28, 2020
@BigData_Review Compliance to me is the biggie. Complex, highly connected, lots of class variations. #GraphDB s are the ONLY WAY for that.
— Kurt Cagle (@kurt_cagle) May 28, 2020
Oops, hashtag error #GraphDB https://t.co/TifhoGSDrA
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
Graph vs RDBMS is not an either-or question, both are complementary. #GraphDB
— Tony Baer (@TonyBaer) May 28, 2020
This #GraphDB products directory will be filled out shortly to include representative vendors offering commercial/enterprise options. Just weeding through Authority Scores for each offering.
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
UHm, not to be divisive and all, but: this is where RDF #GraphDB really shines. No wonder - RDF was built for this. Property graphs, not really
— George Anadiotis (@linked_do) May 28, 2020
Q4. Looking ahead, what prediction(s) do you have for the #GraphDB market in 2020 and beyond?
— Cambridge Semantics (@CamSemantics) May 28, 2020
@TonyBaer Yes. Hard to convince customers/users of the differences though. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
#GraphDB Graph DB and RDBMS do different jobs.
— Carl Olofson (@databaseguru) May 28, 2020
@TonyBaer It seems like modern architecture needs multple DB types. You agree? #graphdb
— Steve Sarsfield (@stevesarsfield) May 28, 2020
Will definitely take any and all tips/experience from #GraphDB tweet chat participants here!
— Timothy "Tim" King 📊 (@BigData_Review) May 28, 2020
Re: Q4 #GraphDB s can be the perfect platform for creating a #knowledgegraph, facilitating analytics built from a collection of structured and unstructured data. This will be the onramp for many to graph databases in 2020 and beyond.
— Steve Sarsfield (@stevesarsfield) May 28, 2020
@CamSemantics I think data catalogs are next big thing, personal knowledge graphs (#PKGs), agent/avatar management. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
#GraphDB Rough going in 2H 2020 due to budget cutbacks in companies hit by the COVID-19 shutdown. Issues may extend to Q3 2021. Strong recovery after.
— Carl Olofson (@databaseguru) May 28, 2020
A4: If #GraphDB technology is shown to be the shining star it seems to be during the #covid crisis, it will have a very bright future. The vendors must continue to promote what they are doing to trace, monitor, enhance this research.
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
A4: If #GraphDB technology is shown to be the shining star it seems to be during the #covid crisis, it will have a very bright future. The vendors must continue to promote what they are doing to trace, monitor, enhance this research.
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
@databaseguru Agreed. FOSS is going to become a selling point again. Interest, but big ticket DBs could be hit hard. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
1. Growth. 50% - 100% YoY as per Gartner etc - tho in post #COVID19 world, hmm 2. Standardization - under way. 3. Consolidation. Waaaay too many #GraphDB vendors even for a growing market. Not just yet, but, inevitable
— George Anadiotis (@linked_do) May 28, 2020
It's important to have powerful use cases. We should be able to tackle many COVID analytics issues. #GraphDB https://t.co/JNUpPsWg97
— Steve Sarsfield (@stevesarsfield) May 28, 2020
Biggest use cases for #graphdb seem to include contact/relationship analysis and fraud detection. Sleeper: broad-based data structure analysis across the enterprise for data intelligence.
— Carl Olofson (@databaseguru) May 28, 2020
Also there needs to be more thought about post-Covid messaging. #graphDBs have momentum right now, but ... #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
#GraphDB I like the last one! https://t.co/06zDVgFSIX
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
Often customer-centric marketing can pay for new analytics initiatives. True. #GraphDB https://t.co/JbIYYMRQif
— Steve Sarsfield (@stevesarsfield) May 28, 2020
For all too many it's getting lost in the general AI/Analytics buzz, and most people have NO understanding about how #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
a #graphdb works or what its good forr.
— Kurt Cagle (@kurt_cagle) May 28, 2020
A4 #GraphDB continued growth as more and more organizations seek to understand the connections among their questions. We see #graphtech as important in helping #IoT implementations mature from connection through communication, collaboration and especially into… https://t.co/J17UFBwlUF
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
[2/2] connection through communication, collaboration and especially into contextualization, causation and cognition to create sensor analytics ecosystems #SensAE #GraphDB
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
@databaseguru Yes on the last. Fraud detection is tricky - still more art than science. #graphdb
— Kurt Cagle (@kurt_cagle) May 28, 2020
I have spoken with credit card firms that use #GraphDB for detection of suspicious patterns of activity. They swear by it.
— Carl Olofson (@databaseguru) May 28, 2020
@JAdP #IoT data tends to have imperfect schema, and when you need to combine it with other imperfect sources, #GraphDB might be the tool.
— Steve Sarsfield (@stevesarsfield) May 28, 2020
It's the top of the hour, but we can keep the conversation going for anyone interested & who has time! #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Gotta go but thanks for inviting me to the chat party. Most interesting. Stay healthy, all! #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
Great chat. Gotta go. #graphdb
— Carl Olofson (@databaseguru) May 28, 2020
Thanks so much for joining! Loo forward to staying in touch and stay healthy, too! #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Thanks so much for joining, Carl! Stay in touch. #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Thanks everyone!It's getting late in my timezone, but i'm always around #GraphDB #graphdatabase #graphanalytics #knowledgegraph graph #AI anyway https://t.co/MKSiCYd3jd
— George Anadiotis (@linked_do) May 28, 2020
Thanks @databaseguru @Claudia_Imhoff @kurt_cagle @JAdP @linked_do and even you @TerminusDB Fantastic chat!#GraphDB
— Steve Sarsfield (@stevesarsfield) May 28, 2020
I agree, and, from a system perspective, #GraphDB may be the way to pull together all the disparate component data as #IoT matures within organizations and their ecosystems
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
Thanks so much for joining, George! #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
Thanks for joining in, @JAdP — you always add so much to any technical conversation! #GraphDB
— Claudia Imhoff (@Claudia_Imhoff) May 28, 2020
Of course, @CZDS and I always enjoy any conversation where you are involved #GraphDB or #BBBT or otherwise 😀
— Joseph A. di Paolantonio (@JAdP) May 28, 2020
Thanks so much for joining! Looking forward to staying in touch. #graphdb
— Cambridge Semantics (@CamSemantics) May 28, 2020
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