Only modelling?
Unsurprisingly, no. This approach is not limited to modelling. Humans are, by virtue of our species evolution, spatial reasoning animals.
In addition, when you think of it, it might make sense to interact with processes and their multiple aspects in this manner. Take monitoring for example, the speaker has contributed to BPM Monitoring and Profiling software like the one below.

Benefits

• It can facilitate adoption, by removing cognitive barriers to reasoning about processes
• The Business Processes are harder to ignore if they can be accessed and visualized easily
• Execution visualization. Who wants to read logs when you can see the process?
• Promotes optimization due to having less cognitive barriers

Next
On part three, we go back to the real world, but not completely. While the lines between what is real and virtual keep getting mixed, we’ll interact with …. robots?
Seems like this has nothing to do with BPM?? Stay tuned!
 
 

We learned a lot; there were many interesting talks throughout the three different track sessions dedicated to mobile, front and backend, IoT and DevOps and also to Upgrades and New Features. Unfortunately, there was no way we could attend them all but the great thing is that all this material is available to view or review at the Liferay youtube channel, as well as the agenda, otherwise I will be writing this article endlessly and it would be huge!
A very interesting talk was about Platforms of the Future: Jorge Ferrer did a great introduction on what can we expect concerning Liferay advancements and predictions. He showed a great motivational video with Brian Chan, Liferay’s Chief Architect Officer, inviting to the stage key people to discuss each point. It’s incredible the importance of APIs and how Liferay will address matters like the interaction with drones, smartwatches and other small machines!
One of the new announcements was the final release of WeDeploy, talk hosted by Zeno Rocha; it was a really good talk and I can say that in 30 minutes I was prepared to develop on WeDeploy! For example, you can launch a Liferay portal in 5 minutes or less (DXP version) or you can run docker containers, how awesome is that?! Check the presentation here.
Of course that in a conference like this something big is about to be announced: the Liferay Commerce, a talk hosted by Mark Leo and that you can listen here, is all about adding e-commerce features to the portal that will challenge developers with exciting new opportunities to enjoy easy application management on a proven platform.
The second day started with one of most important talks, not because it had some new great feature to reveal or anything like that, but it was about the Liferay Community and how much relevant it is for the Liferay roadmap and its connection with other technologies. Listen Why Liferay’s Community Matters More Than Ever and see how you can take part on some of the action!
The cherry on top of the cake was ending with Liferay’s CEO and Founder Bryan Cheung on the next steps of Liferay; this is an amazing technology that can do a lot for businesses and company’s, but if you want to know more about it, don’t hesitate to contact Polarising as we are experts in implementing these and other Liferay solutions.
So, as promised, here’s what Amsterdam is famous for:

• Canals – 165 canals with a total length of 75 kilometers;
• Museums – 51 museums like Rijksmuseum, Van Gogh and Anne Frank’s, are the most well known;
• Architecture – nearly 9,000 houses which were built in the 16th–18th century, still in use;
• Diversity – 180 nationalities in Amsterdam, only New York city has more;
• Cheese – you have multiple cheese shops where you can taste the different flavors of cheese from various regions of Holland;
• Tulips – there are parks with more than seven million tulips;
• and now, the Liferay DevCon 2017… just kidding;-)

Stay tuned for part 2 where we’ll discover a surprisingly (or maybe not) new way to model and interact with Business Processes involving… games ??!!

July is not a silly-season when it comes to sports. In fact, it’s the month when some of the greatest sport competitions take place and one of them is the Wimbledon Grand Slam. For some players, it’s where magic happens and dreams come true and those ball boys and girls for sure have plenty of stories to tell. Throughout history underdog players, romances among professional players, fights and drama have inspired grass tennis fans, so it’s not by chance that this world’s oldest tennis tournament has inspired great movies. Is there anyone that could perform better than Kirsten Dunst as an elegant tennis player?

The grass courts allow a fast playing tennis, which is very useful to quickly win matches in the best of five sets format, thus reducing the effort. I’m a tennis fan and Roger Federer is one of my top favourite players. So I was following online some tennis matches and decided to explore the stats provided by IBM SlamTracker, a scoring application with real-time scores, statistics and point-by-point analysis for each match to take a look at his odds. On Lajovic-Federer match I noticed how stats collected in real-time could help predicting the match final result:

I could see that, as soon as Federer starts hitting 77% of the points won in his first service, in 78% of the cases he will win that set. This is clearly a key to the match. Real-time analytics triggered this information, and if you find the odds interesting, you should bet on it!
The day of the match arrived, it was a warm day and this Swiss gentleman proved to be in great shape once again! Roger Federer won the tournament for the eighth time, and this time without dropping a single set! And once again, stats explained his superiority on the final (3-0): Federer winning percentage on the 1st serve for the 3 sets was 82,86,77 against Cilic’s 71,53,68.
Many other factors may explain a victory or a loss (for instance, Cilic had medical assistance during the match), but numbers represent facts, and facts are what matters in the court.[/vc_column_text][/vc_column][/vc_row]

Fig.1. Deep Learning (from http://www.atelier.net/)

Deep learning advanced so quick that is used in many aspects of modern society, from web searches to content filtering on social networks, to recommendations on e-commerce websites, and it is increasingly present in consumer products, such as cameras and smartphones. Continuous to beat records in image recognition, speech recognition, it has beaten other machine learning techniques at predicting the activity of potential drug molecules and predicting the effects of mutations in DNA and RNA on gene expression and disease.
The Deep Learning Neural Networks have many advantages:

• Are able to decompose a big problem in little ones.
• Neural networks do not make assumptions about the distribution of the data.
• No need to manually add new inputs for each different type of data that we need to analyse.
• Allow parallel processing, and because of that, failing tolerance. If an element of the neural network fails, in can continue using another path.
• Are able to auto detect features.

But it’s not easy to implement one, there are no general methods to determine optimal number of neurons and layers to solve a problem and a Deep learning model is always dependent of the quality and amount of the input data, without data is impossible to learn. Neural networks are also very similar to black boxes. After feeding the neural network with the initial parameters (weight initialisation, for example), we don’t know what the neural network is doing. It’s difficult to know how they are solving the problem and troubleshoot when they don’t work as expected.
But despite all, neural networks had to overcome so many difficulties to prove themselves with so many successes, and Deep Learning is still in its infancy and has so much potential yet to reach. In the years to come, many improvements are expected in the Deep Learning algorithms, bringing many advancements in areas as data analytics, computer vision, speech recognition, bioinformatics, natural language processing (topic classification, sentiment analysis, question answering and language translation), pharmaceuticals, health, automobile industry and the list goes on.
 

 
The neural networks experienced some deep winters in the past, but Deep Learning its today part of the world of tomorrow.
 

RICARDO SANTOS

EAI Consultant and IoT Evangelist at Polarising
Integrating the world for over 10 years and enthusiastic about the Internet of Things.
He helps to spread the word at Polarising about the future that is happening today.
Martial artist and History’s nerd, he hopes technology will help us get where we need to go.