A view of graph data usage by Cerved

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Brief introduction to Cerved data, the role of data scientist in Cerved and how a data scientist can take advantage from graph database. Bio: Stefano Gatti: Born in 1970, has been involved for more than 15 years in several big data and technologies driven projects in leading business information companies like Lince and Cerved. He is very fond of agile metodologies, trying to apply them at all organizational levels. In last years he is strongly engaged in facilitating in Cerved the spread of innovation and the taking advantage from the new big and smart data technologies especially from a business usage perspective. datatelling, open innovation, partnership with smart actors of worldwide data driven innovation ecosystem are his actual mantra. Nunzio Pellegrino: Data Scientist in Cerved, as part of Innovation team, with focus on extract value from data and resolve problems with the latest technologies available. I’ve a degree in Statistics with background in Machine Learning. I’ve being worked primarily in Data Integration and Business Intelligence projects for 3 years. In this moment, I’m product owner of a web application based on GraphDB and involved in Italian Open Data projects. I’m a R enthusiastic, Python practitioner and fascinated of graph ecosystem.

Technology

25 Settembre 2017
With a datascience perspective
A view of graph data usage by Cerved
Stefano Gatti – Head of Innovation and data sources
Nunzio Pellegrino – Senior Data Scientist – Innovation team

3
Cerved, in a nutshell
The Italian data-driven company
CREDIT INFORMATION
Protection against credit risk
MARKETING SOLUTIONS
New business opportunities
CREDIT MANAGEMENT
Manage and collect performing and
non-performing loans.
Over 1000 a minute
ü Documents
Over 40 million
ü Lines of code
Over 30,000
ü Customers
Over 50 different
ü Data sources
Over 10million a day
ü Api call
Over 1,900
ü People
377 million Eur (2016)
ü Revenue

4
Web Data
Open Data
Proprietary data
Official data
Chamber of
Commerce
official data
C
o
m
p
l
e
x
i
t
y
Our big data

5
Cerved, in a tech view
Data
Algorithms
Solutions
Towards algorithmic economy …

6
Cerved Graph Story
2011-12 - we started from an IT problem: reengineering of beneficial owner algorithm

7
Cerved Graph Story
2014-15 - we went through a more algorithmic problem: corporate linkages algorithm

8
Cerved Graph Story
2015-16 - we go with a “full stack” solution

9
Cerved Graph thoughts
We strongly believe in …
The power of linking data
The power of analyzing data with network analysis
The power of visualizing data in a different way
To understand a little better the
increasing complexity of modern world …
also from an economic point of view

12
Key Concepts
Graph database
NoSQL database
Managing highly connected data
and complex queries
Flexible data model

13
Key Concepts
Graph database
Declarative or imperative
language
Horizontal Scaling
Graph native storage and
process

14
Where graphdb can be useful?
“Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron

15
Maybe in the future…
“Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron

16
Frame the Problem
Data Model
Simple1
Expressive2
3 Additive

17
RDBMS
RDBMS vs Graph Data Model
Graph

18
Store & Get Data
Native graph storage
Store Data Fast Write Performance
Easy Data Integration:
CSV
Jdbc
REST Api

19
Store & Get Data
Native graph storage
Store Data Fast Write Performance
Easy Data Integration:
Get Data
Native graph processing à Index free adjacency
CSV
Jdbc
REST Api
Cypher, Declarative Language
Driver:
Python
py2neo (unofficial)
R (unofficial)
Java
APOC

20
Explore Data
Transform implicit to explicitCypher (access points, pattern)

21
Explore Data
Transform implicit to explicitCypher (access points, pattern)

22
Explore Data
Transform implicit to explicitCypher (access points, pattern)

23
Prepare Data
Feature Creation with parallel Graph algorithms
Centralities
• Page Rank
• Betweenness Centrality
• Closeness Centrality
Graph Partitioning
• Label Propagation
• Connected Components
• Strongly Connected
Components
Path Finding
• Minimum Weight
Spanning Tree
• All Pairs- and Single
Source Shortest Path

24
Prepare Data
Feature Creation with parallel Graph algorithms
Centralities
• Page Rank
• Betweenness Centrality
• Closeness Centrality
Graph Partitioning
• Label Propagation
• Connected Components
• Strongly Connected
Components
Path Finding
• Minimum Weight
Spanning Tree
• All Pairs- and Single
Source Shortest Path
Graph Size (GB) nodes (M) rels (M)
PageRank
(s)
ConCom
(s)
LabelPropag (s)
StrongConCom
(s)
Pokec 7.3 2 31 10 24 12 12
DBPedia 15 11 117 46 91 51 65
Graphs500-23 7.9 5 129 19 29 18 25
Twitter-2010 49 42 1468 349 353 405 339
soc-LifeJournal1 6.3 5 69 30 34 25 23
Friendster 62 66 1806 611 619 296 483
Performance

25
Present&Launch your solution
Real time
Recommendation
Fraud Detection
Social Network Analysis
Search & Link Analysis
Knowledge Graph Natural Language
Process

Nunzio Pellegrino
Senior Data Scientist –
Innovation Team
nunzio.pellegrino@cerved.com
Stefano Gatti
Head of Innovation & Data
Sources
stefano.gatti@cerved.com

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Reinforcement Learning is a growing subset of Machine Learning and one of the most important frontiers of Artificial Intelligence. Its goal is to capture higher logic and use more adaptable algorithms than classical Machine Learning. Formally it denotes a set of algorithms that deal with sequential decision-making and have the potential capability to make highly intelligent decisions depending on their local environment. Reinforcement Learning problems can be described as an agent that has to make decisions in its environment in order to optimize a cumulative reward, and it is clear that this formalization applies to a great variety of tasks in many different fields. In this talk, the main features of the most important Reinforcement Learning algorithms will be illustrated and deepened, with some concrete and explanatory examples. Bio: Marco Del Pra Marco was born in Venice 41 years ago, has two master's degrees (Computer Science and Mathematics), and has two important publications in applied mathematics. He has been working in Artificial Intelligence for 10 years, mainly as a freelancer. Among others, he worked for the European Commission's Joint Research Center, for Cuebiq, and as Data Science Lead for Microsoft's Artificial Intelligence projects in Italy.

Time Series Classification with Deep Learning | Marco Del Pra

Data Science Milan

Today there are a lot of data that are stored in the form of time series, and with the actual large diffusion of real-time applications many areas are strongly increasing their interest in applications based on this kind of data, like for example finance, advertising, marketing, health care, automated disease detection, biometrics, retail, and identification of anomalies of any kind. It is therefore very interesting to understand the role and potential of machine learning in this sector. Many methods can be used for the classification of the time series, but all of them, apart from deep learning, require some kind of feature engineering as a separate stage before the classification is performed, and this can imply the loss of some important information and the increase of the development and test time. On the contrary, deep learning models such as recurrent and convolutional neural networks already incorporate this kind of feature engineering internally, optimizing it and eliminating the need to do it manually. Therefore they are able to extract information from the time series in a faster, more direct, and more complete way. Bio: Marco Del Pra I am 41 years old, I was born in Venice, I have 2 master's degrees (Computer Science and Mathematics). I have been working for about 10 years in Artificial Intelligence, first as Data Scientist, then as Team Leader and finally as Head of Data. Among others, I worked for Microsoft, for the European Commission (JRC of Ispra) and for Cuebiq. I am currently working as a freelancer and I am creating with 2 other cofounders an innovative AI startup. I have 2 important publications in applied mathematics. Topics: recurrent and convolutional neural networks, deep learning, time-series.

Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AI

Data Science Milan

The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures. Bio: Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs.

Audience projection of target consumers over multiple domains a ner and baye...

Data Science Milan

Traditional market research is generally conducted by questionnaires or other forms of explicit feedback, directly asked to an ad hoc panel of individuals that in aggregate are representative of a larger group of people. Unfortunately, those traditional approaches are often invasive, nonscalable, and biased. Indirect approaches based on sparse and implicit consumer feedback (e.g., social network interactions, web browsing, or online purchases) are more scalable, authentic, and more suitable for real-time consumer insights. Although those sources of implicit consumer feedback provide relevant and detailed pictures of the population, they individually provide only a limited set of observable behaviors. The Holy Grail of market research is the ability to merge different sources of consumers interests into an augmented view that connects all the dots across multiple domains. Unfortunately, user-centric "fusion" algorithms present many limitations in the case of heterogeneous datasets strongly differing in terms of size and density and when the number of sources to merge increases. We propose a novel approach of Audience Projection able to define a target audience as a subset of the population in a source domain and to project this target to a set of users into a destination dataset. We will show how libraries such as spaCy can provide Deep Learning implementations for Named Entity Recognition (NER) to match related brands and we will use Bayesian Inference to transfer knowledge from the source domain. This way, we can estimate the probability of the user to belong to the target using the source distribution of volume of interests of common entities as model evidence and the source target size as prior probability. Bio: Gianmario Spacagna is the chief scientist and head of AI at Helixa. His team’s mission is building the next generation of behavior algorithms and models of human decision making with careful attention to their potential and effects on society. His experience covers a diverse portfolio of machine learning algorithms and data products across different industries. Previously, he worked as a data scientist in IoT automotive (Pirelli Cyber Technology), retail and business banking (Barclays Analytics Centre of Excellence), threat intelligence (Cisco Talos), predictive marketing (AgilOne), plus some occasional freelancing. He’s a co-author of the book Python Deep Learning, contributor to the “Professional Manifesto for Data Science,” and founder of the Data Science Milan community. Gianmario holds a master’s degree in telematics (Polytechnic of Turin) and software engineering of distributed systems (KTH of Stockholm). After having spent half of his career abroad, he now lives in Milan. His favorite hobbies include home cooking, hiking, and exploring the surrounding nature on his motorcycle.

Weak supervised learning - Kristina Khvatova

Data Science Milan

Weakly Supervised Learning: Introduction and Best Practices In the talk we will introduce the definition of three main types of weakly supervised learning: incomplete, inexact and inaccurate; we examine how the models can be trained in case of weak supervision and view the real application of weakly supervised learning, how it can improve results and decrease the costs. Bio: Kristina Khvatova works as a Software Engineer at Softec S.p.A. Currently she is involved in the development of a project for data analysis and visualisation; it includes quantitative and qualitative analysis based on classification, optimisation, time series prediction, anomaly detection techniques. She obtained a master degree in Mathematics at the Saint-Petersburg State University and a master degree in Computer Science at the University of Milano-Bicocca.

GANs beyond nice pictures: real value of data generation, Alex Honchar

Data Science Milan

GANs beyond nice pictures: real value of data generation (theory and business applications) About the speaker, Alex Honchar: I am machine learning expert currently applying AI in medtech, fintech and other areas. I also enjoy teaching and blogging (50k+ views monthly) about deep learning applications. As an academia member, I have a track of scientific publications as well. Beside sciences, I travel, do sports and perform card magic.

Continual/Lifelong Learning with Deep Architectures, Vincenzo Lomonaco

Data Science Milan

Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge. "Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL. Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".

3D Point Cloud analysis using Deep Learning

Data Science Milan

Processing 3D images has many use cases. For example, to improve autonomous car driving, to enable digital conversions of old factory buildings, to enable augmented reality solutions for medical surgeries, etc. Also 3D images help in 3D modeling and safety evaluation of products. 3D image processing brings enormous benefits but also amplifies computing cost. The size of the point cloud, the number of points, sparse and irregular point cloud, and the adverse impact of the light reflections, (partial) occlusions, etc., make it difficult for engineers to process point clouds. Moving from using hand crafted features to using deep learning techniques to semantically segment the images, to classify objects, to detect objects, to detect actions in 3D videos, etc., we have come a long way in 3D image processing. 3D Point Cloud image processing is increasingly used to solve Industry 4.0 use cases to help architects, builders and product managers. I will share some of the innovations that are helping the progress of 3D point cloud processing. I will share the practical implementation issues we faced while developing deep learning models to make sense of 3D Point Clouds. Attendees: Beginners and Intermediate skilled in Image Processing and 3D Point Clouds Profile of the speaker: SK Reddy is the Chief Product Officer AI in Hexagon (www.hexagon.com). He is an AI and ML expert and a successful twice startup entrepreneur. He is an AI startup advisor too. Also he is a frequent speaker in conferences and is an AI blogger.

Deep time-to-failure: predicting failures, churns and customer lifetime with ...

Data Science Milan

The notebook and documentation of the original tutorial is available at https://github.com/gm-spacagna/deep-ttf. Deep Time-to-Failure: predicting failures, churns and customer lifetime using recurrent neural networks. Machineries and customers are among the most valuable assets for many businesses. A common trait of these assets is that sooner or later they will fail or, in the case of customers, they will churn. In order to catch those failure events we would ideally consider the whole history of the machine/customer available information and learn smart representations of the system status over time. Traditional machine learning and statistical models approach the prediction of time-to-failure, aka. expected lifetime, as a supervised regression problem using handcrafted features. Training those models is hard because of three main reasons: The complexity of extracting predictive features from time-series without overfitting. The difficulty of modeling uncertainty and confidence levels in the predictions. The scarcity of labeled data, failure events are by definition rare and that results in highly unbalanced training datasets. The first issue can be solved adopting recurrent neural architectures. A solution to the the last two problems could be to exploit censored data and to build survival regression models. In this talk we will present a novel technique based on recurrent neural networks that can turn any length-variable sequence of data into a probability distribution representing the estimated remaining time to the failure event. The network will be trained in presence of ground truth as well as with right-censored data. We will demonstrate using a case study regarding 100 jet engine simulated degradation provided by NASA. During the tutorial you will learn: What is Survival Analysis and what are the most popular Survival Regression techniques. How a Weibull distribution can be used as generic distribution for modeling Time-to-Failure events. How to build a deep learning algorithm in Keras leveraging recurrent units (LSTM or GRU) that can map raw time-series of covariates into Weibull probability distributions. The tutorial will also cover a few common pitfalls, visualizations and evaluation tools useful for testing and adapting this approach to generic use cases. You are free to bring your laptop if you would like to do some live coding and experiment yourself. In this case we strongly encourage to check you have all of the requirements installed in your machine. More details on the required packages can be found on the Github repository gm-spacagna/deep-ttf.

50 Shades of Text - Leveraging Natural Language Processing (NLP), Alessandro ...

Data Science Milan

50 Shades of Text - Leveraging Natural Language Processing (NLP) to validate, improve, and expand the functionalities of a product Nowadays, every company either stores or produces text data: from web logs and user queries, to translations and support tickets, yet not everyone knows how to extract valuable insights from it. In this session, we will present a practical case on how to move from raw text data to a valuable business application leveraging upon some of the major NLP methodologies (word embedding, word2vec, doc2vec, fastText, etc.) Bio: Alessandro is a data veteran. He holds two Master’s degrees in computer engineering, one from Politecnico di Milano and the other from University of Illinois at Chicago (UIC). He started his career in data consultancy, where he mastered Apache Spark for Machine Learning projects and subsequently joined WW Grainger, one of the largest MRO e-commerce companies in the United States. In September 2017, after more than 5 years in the USA, Alessandro returned to his native country, Italy, where he is now leading a team of data scientists. His current work focuses on achieving energy efficiency through the automation of energy management processes for commercial customers.

Pricing Optimization: Close-out, Online and Renewal strategies, Data Reply

Data Science Milan

“Product close-out strategy” by Ilaria Gianoli, Data Scientist, Data Reply Abstract: How to deal with products in their decline phase? Ilaria will share her experience in optimizing the close-out strategy for a multinational retail leader, with a particular focus on the price optimization. Bio: Ilaria is a Data Scientist at Data Reply, where she works as a consultant across different industries, in particular in the Retail. She uses her mathematical, statistical and machine learning background to turning data into business opportunities. She also works closely to the business to provide quantitative support for decision making, adapting the complexity of the mathematical models to customer needs. She holds a MSc in Applied Statistics - Mathematical Engineering from Politecnico di Milano. “Online pricing: from theory to application” by Giovanni Corradini, Data Scientist, Data Reply Abstract: Multi-Armed Bandit algorithms are populating the world of e-commerce. How do they work? Giovanni will share the basic of this field and an application of a state-of-the-art algorithm on real world simulation of the ticket industry. Bio: Giovanni is a Data Scientist at Data Reply. He holds a MSc in Applied Statistics - Mathematical Engineering from Politecnico di Milano. He has a background in statistics, machine learning and data mining and he provides decision making support to industries in many different fields. “Renewal Price Optimization for Subscription products” by Riccardo Lorenzon, Data Scientist, Data Reply Abstract: We are observing a huge shift in modern economy from a pay-per-product model to a subscription-based model. When it comes to pricing strategies, it is important both to close the single deal and monetize long-term relationships with the customer. Riccardo will present an application of subscription renewal pricing optimization models for a company belonging to the publishing industry. Bio: Riccardo holds a MSc in Mathematical Models for Decision Making from Politecnico di Milano. He developed hands-on experience on end-to-end data projects across multiple industries. His proactive creativity helps him be very effective in the business case design and early stages of projects.

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1. 25 Settembre 2017 With a datascience perspective A view of graph data usage by Cerved Stefano Gatti – Head of Innovation and data sources Nunzio Pellegrino – Senior Data Scientist – Innovation team

2. Cerved and its graphs in a nutshell

3. 3 Cerved, in a nutshell The Italian data-driven company CREDIT INFORMATION Protection against credit risk MARKETING SOLUTIONS New business opportunities CREDIT MANAGEMENT Manage and collect performing and non-performing loans. Over 1000 a minute ü Documents Over 40 million ü Lines of code Over 30,000 ü Customers Over 50 different ü Data sources Over 10million a day ü Api call Over 1,900 ü People 377 million Eur (2016) ü Revenue

4. 4 Web Data Open Data Proprietary data Official data Chamber of Commerce official data C o m p l e x i t y Our big data

5. 5 Cerved, in a tech view Data Algorithms Solutions Towards algorithmic economy …

6. 6 Cerved Graph Story 2011-12 - we started from an IT problem: reengineering of beneficial owner algorithm

7. 7 Cerved Graph Story 2014-15 - we went through a more algorithmic problem: corporate linkages algorithm

8. 8 Cerved Graph Story 2015-16 - we go with a “full stack” solution

9. 9 Cerved Graph thoughts We strongly believe in … The power of linking data The power of analyzing data with network analysis The power of visualizing data in a different way To understand a little better the increasing complexity of modern world … also from an economic point of view

10. Why a Graph Database?

11. 11 What is a Graph?

12. 12 Key Concepts Graph database NoSQL database Managing highly connected data and complex queries Flexible data model

13. 13 Key Concepts Graph database Declarative or imperative language Horizontal Scaling Graph native storage and process

14. 14 Where graphdb can be useful? “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron

15. 15 Maybe in the future… “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron

16. 16 Frame the Problem Data Model Simple1 Expressive2 3 Additive

17. 17 RDBMS RDBMS vs Graph Data Model Graph

18. 18 Store & Get Data Native graph storage Store Data Fast Write Performance Easy Data Integration: CSV Jdbc REST Api

19. 19 Store & Get Data Native graph storage Store Data Fast Write Performance Easy Data Integration: Get Data Native graph processing à Index free adjacency CSV Jdbc REST Api Cypher, Declarative Language Driver: Python py2neo (unofficial) R (unofficial) Java APOC

20. 20 Explore Data Transform implicit to explicitCypher (access points, pattern)

21. 21 Explore Data Transform implicit to explicitCypher (access points, pattern)

22. 22 Explore Data Transform implicit to explicitCypher (access points, pattern)

23. 23 Prepare Data Feature Creation with parallel Graph algorithms Centralities • Page Rank • Betweenness Centrality • Closeness Centrality Graph Partitioning • Label Propagation • Connected Components • Strongly Connected Components Path Finding • Minimum Weight Spanning Tree • All Pairs- and Single Source Shortest Path

24. 24 Prepare Data Feature Creation with parallel Graph algorithms Centralities • Page Rank • Betweenness Centrality • Closeness Centrality Graph Partitioning • Label Propagation • Connected Components • Strongly Connected Components Path Finding • Minimum Weight Spanning Tree • All Pairs- and Single Source Shortest Path Graph Size (GB) nodes (M) rels (M) PageRank (s) ConCom (s) LabelPropag (s) StrongConCom (s) Pokec 7.3 2 31 10 24 12 12 DBPedia 15 11 117 46 91 51 65 Graphs500-23 7.9 5 129 19 29 18 25 Twitter-2010 49 42 1468 349 353 405 339 soc-LifeJournal1 6.3 5 69 30 34 25 23 Friendster 62 66 1806 611 619 296 483 Performance

25. 25 Present&Launch your solution Real time Recommendation Fraud Detection Social Network Analysis Search & Link Analysis Knowledge Graph Natural Language Process

26. Nunzio Pellegrino Senior Data Scientist – Innovation Team nunzio.pellegrino@cerved.com Stefano Gatti Head of Innovation & Data Sources stefano.gatti@cerved.com

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