Multimodel-DataChallenge-AnomalieDetection

It is time for the next attack with three wonderful and different Talks:

1. Next Generation NoSQL: Multi Model Databases (Michael Hackstein)
2. SMS Digital Data Challenge (Wagner, Weimer-Hablitzel, Arnold)
3. What the heck is normal? (Christian Glatschke)

We meet at Lieferando.de (thanks!!) which is very close to Potsdamer Platz!

Here the details:

TALK 1: Next Generation NoSQL: Multi Model Databases

The recent progress in the database development has lead us a long and winding road: 

1. Using relational for everything
2. NoSQL and polyglot persistence: Introducing a ton of different technologies to learn
3. Next wave: Multi Model databases and polyglot data In this talk we will see the pros and cons of each of these stations.

We will also have a more in-depth introduction to Multi Model Databases and why they are a wonderful next step in database evolution. Finally we will talk about big data requirements and how they fit into this landscape of database technologies. How can we scale and run databases in production? We are using a whole cluster of machines, collecting and mining terabytes of data with ease with the help of Mesos and DC/OS.

Speaker: Michael Hackstein holds a master degree in computer science and is the creator of the ArangoDB graph capabilities. During his academic career he focused on complex algorithms and especially graph databases. Michael is an internationally experienced speaker who loves salad, cake and clean code.

TALK 2: SMS digital’s Data Challenge 

Data Science meets Steel Industry – an exciting match of heavy metal and high tech. SMS digital presents its first open data challenge. SMS digital is the digital laboratory of the SMS group, a worldwide leading steel plant manufacturer from Dusseldorf. Its mission is to test and develop digital business models with the help of state of the art technologies. Naturally, applied data science plays a major role in this initiative.

Casting slabs of quality steel is a demanding process that is highly sensitive to changes in its production and environmental parameters. Hence predicting casting defects is essential to provide ongoing quality assurance and indication for adjustments of the production parameters. 

The potential for optimization and the implied business opportunity are huge. So far, the primary method for prediction and detection of slab surface defects have been stochastic procedures on continuous measurements. In the future however, SMS digital hopes to improve the predictive quality of the existing procedures by applying state-of-the-art data mining and analytics methods. This will be the objective of SMS digital’s data challenge that promises a 5-figures price money for the top contributors.  At our meetup, SMS digital’s team will present this data science use case, explain the provided data sets, and answer all remaining question regarding the data challenge. 

Speaker:

Maximilian Wagner, SMS digital, CEO

Max combines extensive hands-on experience in the steel industry with an ever-curious, entrepreneurial mindset. As CEO of SMS digital he’s always looking for trends and technologies that might lead to the ‘next big thing’ in his industry and beyond.

Marc Weimer-Hablitzel, etventure, Senior Manager

Marc combines technical knowledge, creativity and business expertise. Having worked as a Data Mining Expert for several companies in the past, Marc knows the realities and raptures of data science. Now he is on a mission to revolutionize the steel industry and the B2B sector with etventure and SMS Digital.

Friedrich Arnold etventure, Project Manager

As mechanical engineer and serial entrepreneur, Friedrich is very excited about the endless opportunities of the B2B, industrial sector. He’s currently responsible for testing and scaling SMS digital’s new Data Lab. 

Talk 3: What the heck is normal?

To analyse and understand data and certain circumstances and their organizing factors, we do need a constant supervision of certain influences. In physics or information technology this could be boundary values and the definition of a ‘normal’ range.

But who defines what is normal?

Not only in psychoanalysis, also in other sciences, it is a tough challenge to tell what kind of behavior is beyond the norm.

We can answer these questions today for many applications using algorithms and machine learning. This talk will show insights, principles and tools for a succsessful anomaly detection & analytics.

Speaker: Christian Glatschke
17 years IT experience, 8 years Big Data and Analytic (Splunk, Lucidworks und Anodot)