The Best from Two Worlds The Blue Yonder View on Data Analytics

The Best from Two Worlds The Blue Yonder View on Data Analytics Prof. Dr. Michael Feindt IEKP, Karlsruhe Institute of Technology Founder, Phi-T GmbH Founder & Chief Scientific Advisor, Blue Yonder GmbH

Personal Vita 1982-1991 DESY (PLUTO, CELLO at PETRA) 1991-1997 CERN (DELPHI at LEP) since 1997 Professor at Univ. Karlsruhe (now KIT) since 1997 CDF II at Fermilab / DELPHI at LEP / (CMS at LHC) since 2008 Belle, Belle II at KEK 1999/2000 invention of NeuroBayes algorithm 2002 foundation of Phi-T 2008 foundation of Blue Yonder, with officesvita in Karlsruhe, Hamburg, London Personal

Blue Yonder provides predictions --- based on data (scientifically sound with quantified uncertainty -- testable and falsifiable, as predictive as possible) Data Mining and conventional Business Intelligence Predictive Analytics WHAT HAS HAPPENED? WHY? WHAT WILL HAPPEN? DATA PATTERN PATTERN PREDICTIONS Influence on business performance

Blue Yonder fastest growing BI company in Germany The Business Application Research Center (BARC) found that Blue Yonder is the fastest growing BI software company in Germany. With 175% turnaround increase in 2012 Blue Yonder is leading the field compared to 250 competitors in the area of Business Intelligence and data management. Career opportunities in Karlsruhe, Hamburg and London: www.blue-yonder.com/unternehmen/ueber-blue-yonder/karriere.html www.blue-yonder.com/en/company/about-blue-yonder/careers.html

Blue Yonder: Awards for Big Science Startup DLD 2013: Best Enterprise Solution Retail Technology Award 2012 3 time winner of the Data Mining Cup Michael Feindt bwcon Hightech Award 2012 CERN OpenLab Analytics Workshop Feb 20, 2014 Finalist 2012 Finalist 2013 Special Prize Deutsche Boerse 2012

Blue Yonder is unique Predictive Analytics Suite with a combination of statistical algorithms and neural networks Experienced, award-winning physicists and computer scientists from renowned institutions such as CERN make up the development team : >80 Ph.D.s Optimal order quantity Probability densityp E(X) 100 120 Sales

General Overview Fundamental Research Insurance Premium Optimization... Sales Forecasts Sports event predictions Fraud Detection NeuroBayes Patient s treatment optimization

Our roots: 30 years of elementary particle physics, peaking at the LHC at CERN. Built to understand how exactly our universe works. Schreiben Sie hier Ihren Text CERN (1960) CERN (2005) LHC: 27km circumference Photo: CERN

Our Background: High Energy Physics Fundamental research at the forefront of science The Large Hadron Collider 100m under ground, 27km circumference Very strong worldwide competition on getting results from data à very strong statistical methods: fast & robust multivariate algos Photo: CERN

NeuroBayes - the basis of Blue Yonder Fundamental research at the forefront of science Significance control Input Preprocessing Postprocessing Output Invented in 2000 for reconstruction of b quark fragmentation in DELPHI experiment. Further development in Phi-T, later Blue Yonder. Several hundred successful applications in DELPHI, CDF II, Belle, CMS, ATLAS, LHCb, H1, AMS experiments. More than 400 men-years development. Robust and fast algorithm for reconstruction (= prediction) of conditional probability densities classifications with extreme generalization ability by means of Bayesian regularization.

NeuroBayes example: The LHCb trigger very fast intelligent decisions with NeuroBayes At the LHC (CERN) per experiment: 40 000 000 events per second, which translates into 1 PetaByte (1,000,000,000,000,000 Byte) per second raw data But only 1 PB of interesting data per year can be stored. Need online reduction by 1 : 10,000,000 At the LHCb experiment 30 000 instances of NeuroBayes running real-time 24/7 filter out the interesting events without introducing lifetime bias Photo: CERN Michael Feindt CERN OpenLab Analytics Workshop Feb 20, 2014

NeuroBayes example: Full reconstruction of B mesons at the Japanese B factory experiment Belle Fundamental research at the forefront of science Ø Belle experiment at KEK/Japan Ø 400 physicists from whole world Ø 10 years of data taking and analysis Ø World record luminosity Ø > 400 publications Ø Automatic hierarchical reconstruction system built from 72 NeuroBayes networks reconstructed about 1100 different reactions with a factor 2 larger efficiency than all analyses before Ø Much cleaner signal Ø Work performed by 2 PhD and 1 master student Ø Corresponds to 500 normal PhD theses Ø Corresponds to another 10 years of data taking

NeuroBayes from Science to Industry Predictive Analytics in High Energy Physics Energy Momentum 10 0 50 Direction 90 E(X) Type Sub-Detector Kaon Calo propabilityp Particle Property Distance 200... Use all available and relevant information as input, e.g. measurements from the various sub-detectors, NeuroBayes will extract statistically significant patterns in the data to derive the prediction. Prediction will return the best estimator for a measurement including a statistically sound estimation of the expected spread.

NeuroBayes from Science to Industry Predictive Analytics in industry Article size Picture size M 21% e.g. Retail colour red E(X) Previous sales brand 24 171 propabilityp Prediction sales price 19,9... Use all available and relevant information as input, e.g. article properties, previous sales, etc NeuroBayes will extract statistically significant patterns in the data to derive the prediction. Prediction will return e.g. the most probable sales rate including a statistically sound estimation of the expected spread. NeuroBayes allows data-driven analysis and forecasts both in science and industry

We are familiar with your questions BIG DATA PREDICTIVE ANALYTICS Each day, companies receive giant quantities of structured and unstructured data from different sources Sample recognition of data for the prediction of risks and development to provide forward-looking bases for decision-making Growth of the data volume MCKINSEY MCKINSEY Increase of operating margins + 40% + 60%

We are the pioneers SOCIAL MEDIA PRODUCT RECOMMENDATION RETARGETING PREDICTIVE MAINTENANCE AREAS OF APPLICATION PREDICTION OF RISKS SALES PREDICTIONS DYNAMIC PRICING CUSTOMER ANALYSIS MERCHANDISE PLANNING

NeuroBayes System Working principle Historic Data Record a =... b =... c =...... t =! NeuroBayes Teacher Expert System Expertise Probability density function for target quantity t Current Data Record a =... b =... c =...... t =? NeuroBayes Expert

Forecasts conditional on many features The forecast depends on several features which themselves have several manifestations Features may be arbitrarily correlated Features can be ordered / unordered sets and continuous variables This results in a complex & high-dimensional space which is impossible to treat with classical methods Example: What s the right dose for a patient, if she is 56 years old, slightly overweight, works out on 2 days a week, enjoys late dinners, has been treated for 2 other diseases already, etc, etc,

Preprocessing Automatic preprocessing is an extremely important process before training a network It involves steps like: Ø smoothing out statistical fluctuations and outliers in input variables Ø transforming variables to unified characteristics (mean, width) Ø Decorrelate variables Ø Find variables with significant impact / throw out others The benefits of a powerful preprocessing algorithm involve Ø increased robustness Ø increased network training results (minima easier to find) Ø increased training speed

NeuroBayes output interpretable as a Bayesian posterior Likelihood Prior Posterior Evidence NB1: Posterior ist the probability that the theory is right under the given data NB2: Pior distributions need to applied carefully. A non-informative prior distribution is not flat (tax authorities know about that). Bayesian regularisation at each analysis step in order to avoid overfitting and select models with good generalisation properties is essential!

Technology Overview OPERATIONAL SYSTEM (Environment) WEB UI & SERVICES NEUROBAYES SYSTEM Simulator (industry-specific) STREAM- BASED INPUT BATCH INPUT High Performance Data Transformation R > R > Teacher Expert Predictions Industryspecific Output REAL-TIME FEED OUTPUT HISTORICAL TRANSACTION DATA EXPERTISE (Industry-specific models) 7/24 operation needed - high safety Software as a Service

Sales Forecast Fashion Example: OTTO Group Per item:» Sales forecast» Two estimates on spread (68% and 95% confidence intervals) Sales [units]

Perishable goods in Supermarkets Meat, fruit & veg, bread, diary,. Around 7% of all perishable foods (e.g. meat, fruit & veg., etc) have to be disposed of in German supermarkets. That s about 89M tons of food wasted per year

Insurance e.g. Individual risk predictions for car insurances: Accident probability Claims distribution Large claim prediction Contract cancellation prediction è Successfully implemented at

Correlations to target variable Ramler II-Plot

NeuroBayes delivers precise prognoses for the customer-individual number and height of claims Premium differentiation: NeuroBayes adjusts premium to customer-individual risk Customer structure optimisation Bind your good customers and take the bad customers Rentability improvement: Simultaneously increase your total premium volume and decrease your claims rate with a more just tariff system Risiko Premium volume Anzahl Kunden Alter Tarif NeuroBayes Claims rate Bisheriger Tarif Prämie, normiert Alter Tarif NeuroBayes

Private health insurance claims per year anything but normally distributed... NeuroBayes has the solution for difficult distributions of type f (t) = (1 P) δ(t) + P f (t t > 0) Many insured persons (fraction1-p) do not generate any claim When there is at least one claim, (fraction P), these are distributed according to f(t t>0). This distribution has fat tails (extremely high claims). t Difficult to handle by classical methods

NeuroBayes calculates for each insured person x the individualised Bayesian probability density. NeuroBayes has the solution for difficult distributions of type f (t x) = (1 P( x)) δ(t)+ P( x) f (t t > 0, x ) Insured person x will have no claims with probability 1-P(x) If insured person x will have any claim, the costs will be distributed according to f(t t>0,x) t δ(t) = Dirac- delta-,,function (distribution)

Healthcare insurance long term prediction from anamnesis NeuroBayes Expert Estimation (risk premium loading) Ø Expert estimations are at best random for patients with a long history even systematically wrong. Ø NeuroBayes forecasts costs correctly and significantly beats expert estimations more than 10 years into the future

Revenue Forecast Example: dm Large German drug-store chain Forecasts for individual stores» Prediction of the full probability density function.» Precise forecast of the expected revenue including exptected spread (68% and 95% confidence intervals) Probability density function revenue

Just released: First big data predictive analytics standard software solution Precise sales forecasts for retail and CPG Access to big data predictive analytics for B2B end users Easy handling through intuitive web-ui Software-as-a-Service allows usage of Forward Demand without high inadvance investments into software, infrastructure or highly skilled personell Michael Feindt CERN OpenLab Analytics Workshop www.blue-yonder.com/forwarddemand Feb 20, 2014

Future (2015): intelligent decisions directly on sensor (Belle II pixel detector), before big data reaches any computer Goal: find (classify) all relevant pixel information Big Data challenge: process approx. 10G bit per s Solution: NeuroBayes on Hardware Blue Yonder

NeuroBayes @ hardware*: 200 million decisions per second à 5ns for one decision *features dedicated hardware board: BELLE II experiment :» NeuroBayes on FPGA» Field Programmable Gate Array: (XILINX Virtex6 VLX75T) utilizes 40 boards: à 8 billion intelligent decisions per second Blue Yonder» Clock frequency: 250 MHz 1 decision per clock cycle» Approx. (fully pipelined architecture)» Probability decision output possible

Just launched: www.datascienceacademy.eu Michael Feindt CERN OpenLab Analytics Workshop Feb 20, 2014

Blue Yonder trends in data analytics... Very important and very difficult to bridge enormous gap between nerdish superscientists and ordinary business people à European Data Science Academy Build solutions that are easy to use by non-scientists and as automatic as possible. Leave strategic decisions to customer, but automate everyday detail decisions. Not single data mining projects, but integrate automated decisions into the customer s work flow

Blue Yonder trends in data analytics: data bases Excel is standard in many departments in most companies Graphical data representations are largely unavailable / unknown SQL already is advanced for many users Many data warehouses are write-only, analysis access too slow / restricted The advanced stuff is: Vertical data bases! (columnar ntuples for 25 years already in HEP) In-memory data bases (mostly SQL-based) Not one optimal solution for all problems of the world. MAP/REDUCE (Hadoop)

Blue Yonder trends in data analytics... Fast interactive parallel data analysis / model development important for professional data scientists: PIAF/PROOF MAP/REDUCE too much forgotten not the answer to everything (good: CPU at data) Combine goodies of both... root / C++ too complicated and not flexible enough (e.g. store new columns) Simplicity (important even for super data scientists): C++ à Python Python: numpy, pandas etc very effective, CPU time is (largely) NOT a limitation Machine Learning community often thinks too deterministic, no good understanding of (no interface for) statistical errrors and weights

Blue Yonder trends in data analytics... Article Why cutting edge technology matters for Blue Yonder solutions http://www.blue-yonder.com/en/resource-center/research-papers.html gives overview on our view on algorithms. We consider as important keywords: To know what makes a good prediction Domain knowledge in different industries NeuroBayes Neural Networks Bayesian statistics Deep learning Reinforcement learning Big data

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