The document provides information about the SmartLab research group at the University of Genoa in Italy. It discusses SmartLab's work in areas like real-time analytics for fuel prediction and skid prediction in racing cars. It also mentions past projects involving traffic forecasting and bus arrival time prediction. The document outlines SmartLab's computing resources and plans to expand its IBM cluster. It discusses potential future work in areas like process mining, condition-based maintenance using NoSQL databases, and advanced data analytics.
A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."
Big data in transport an international transport forum overview oct 2013OpenSkyData
Comprehensive Guide on the use of Big Data in Transportation Services from the International Transport Forum. OpenSky loves making big data work for organisations large and small.
http://www.openskydata.com/our-sectors/transport.html
This paper is written based on the researches of models and its applications in Real-Time Traffic Information. Firstly, this would be introduced briefly about traffic information system and some traffic sensors which are currently used to record and send data to centre. The major part will focus on explanation of models for estimation and prediction in Real-Time Traffic Information. Some standard models such as Regression Model, Bayesian Model and Probabilistic Graphical Model are applied to figure out many indicators in traffic system (the Level of Service, road network, congestion, etc.) and run processes of predictions, then, send the solutions to drivers or other relevant. Besides these models, some experiments from the project of Mobile Millennium which also helps to explain how these models apply in Real-Time Traffic Information would be introduced. Finally, some specified applications which are widely used in the world are also mentioned as the newest approaches in Real-Time Traffic Information.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."
Big data in transport an international transport forum overview oct 2013OpenSkyData
Comprehensive Guide on the use of Big Data in Transportation Services from the International Transport Forum. OpenSky loves making big data work for organisations large and small.
http://www.openskydata.com/our-sectors/transport.html
This paper is written based on the researches of models and its applications in Real-Time Traffic Information. Firstly, this would be introduced briefly about traffic information system and some traffic sensors which are currently used to record and send data to centre. The major part will focus on explanation of models for estimation and prediction in Real-Time Traffic Information. Some standard models such as Regression Model, Bayesian Model and Probabilistic Graphical Model are applied to figure out many indicators in traffic system (the Level of Service, road network, congestion, etc.) and run processes of predictions, then, send the solutions to drivers or other relevant. Besides these models, some experiments from the project of Mobile Millennium which also helps to explain how these models apply in Real-Time Traffic Information would be introduced. Finally, some specified applications which are widely used in the world are also mentioned as the newest approaches in Real-Time Traffic Information.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Applications of Artificial Intelligence in Transportation SystemsEITESAL NGO
Dr.Mohamed el Shenawy speech about applications of Artificial Intelligence in transportation systems
▪️Mentioning how can AI can change our future? And how Intelligent machines works starting with the Perception ➡️ Comprehension ➡️ Projection ➡️ Decision
▪️ AI graduation projects in Egyptian Universities
▪️ AI in self-driving cars
▪️AI in traffic management
#Artificial_Intelligence
#EiTESAL
Keynote talk by David Dietrich, EMC Education Services at ICCBDA 2013 : International Conference on Cloud and Big Data Analytics
http://twitter.com/imdaviddietrich
http://infocus.emc.com/author/david_dietrich/
ITS "Intelligent Transportation System" Guided Vehicle using IOT ProjectMohamed Abd Ela'al
Our project is design and implementation for ITS technology integrated with partial autonomous vehicle using internet of things to make the vehicle controlled according to the surrounding data
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Presentation on applications of AI in the geospatial domain at the Fourth Edition of AI in Practice (6th November 2019, Startup Village, Amsterdam, The Netherlands)
Erik Van Der Zee, Enterprise Architect, Geodan
Big Data to avoid weather related flight delaysAkshatGiri3
This topic generally belongs to weather forecasting, how we will implement Big Data computing for future weather prediction so that weather Related Flight Delays get minimized.
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
Text Mining is an Important part of data mining and it is used nowadays on a large scale. This mining technique is used to find patterns in text data collected from many online sources , and to gain some interestings insights from the patterns observed. Since text is basically everywhere on the internet, it becomes quite difficult to get the data in structured format, which is why text mining plays a huge role. It uses NLP(Natural Language Processing Techniques) to automate the text mining and this concept is used in Machine Learning.
Applications of Artificial Intelligence in Transportation SystemsEITESAL NGO
Dr.Mohamed el Shenawy speech about applications of Artificial Intelligence in transportation systems
▪️Mentioning how can AI can change our future? And how Intelligent machines works starting with the Perception ➡️ Comprehension ➡️ Projection ➡️ Decision
▪️ AI graduation projects in Egyptian Universities
▪️ AI in self-driving cars
▪️AI in traffic management
#Artificial_Intelligence
#EiTESAL
Keynote talk by David Dietrich, EMC Education Services at ICCBDA 2013 : International Conference on Cloud and Big Data Analytics
http://twitter.com/imdaviddietrich
http://infocus.emc.com/author/david_dietrich/
ITS "Intelligent Transportation System" Guided Vehicle using IOT ProjectMohamed Abd Ela'al
Our project is design and implementation for ITS technology integrated with partial autonomous vehicle using internet of things to make the vehicle controlled according to the surrounding data
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Presentation on applications of AI in the geospatial domain at the Fourth Edition of AI in Practice (6th November 2019, Startup Village, Amsterdam, The Netherlands)
Erik Van Der Zee, Enterprise Architect, Geodan
Big Data to avoid weather related flight delaysAkshatGiri3
This topic generally belongs to weather forecasting, how we will implement Big Data computing for future weather prediction so that weather Related Flight Delays get minimized.
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
Text Mining is an Important part of data mining and it is used nowadays on a large scale. This mining technique is used to find patterns in text data collected from many online sources , and to gain some interestings insights from the patterns observed. Since text is basically everywhere on the internet, it becomes quite difficult to get the data in structured format, which is why text mining plays a huge role. It uses NLP(Natural Language Processing Techniques) to automate the text mining and this concept is used in Machine Learning.
Transport and logistics business pains and UNIT4 solutionsUNIT4 UK
Optimising capacity, managing costs, complying with regulations and deploying resources efficiently are constant challenges for transport and logistics managers.
UNIT4's solutions have been helping major players in the sector deal with these pains and more and decades. This presentation sets out some of the challenges transport and logistics businesses face and how UNIT4's solutions can help you deal with them.
TechnoFleet is a team of engineers with a rich experience in Information Technology, Tyre and Transport industry.
We understand that your fleet is at the heart of your business and your daily challenge is to deliver your goods, your passengers and your services to your clients.
Our mission is to support you in your day-to-day challenges; maximizing your vehicle’s up-time so that your fleet is always mobile.
Jubatus: Realtime deep analytics for BIgData@Rakuten Technology Conference 2012Preferred Networks
Currently, we face new challenges in realtime analytics of BigData, such as social monitoring, M2M sensor, online advertising optimization, smart energy management and security monitoring. To analyze these data, scalable machine learning technologies are essential. Jubatus is the open source platform for online distributed machine learning on the data streams of BigData. we explain the inside technologies of Jubatus and show how jubatus can achieve realtime analytics in various problems.
Charith Perera, Arkady Zaslavsky, Peter Christen, Ali Salehi, Dimitrios Georgakopoulos, Capturing Sensor Data from Mobile Phones using Global Sensor Network Middleware, Proceedings of the IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, Australia, September, 2012
How to expand the Galaxy from genes to Earth in six simple steps (and live sm...Raffaele Montella
FACE-IT is an effort to develop a new IT infrastructure to accelerate existing disciplinary research and enable information transfer among traditionally separate fields. At present, finding data and processing it into usable form can dominate research efforts. By providing ready access to not only data but also the software tools used to process it for specific uses (e.g., climate impact and economic model inputs), FACE-IT allows researchers to concentrate their efforts on analysis. Lowering barriers to data access allows researchers to stretch in new directions and allows researchers to learn and respond to the needs of other fields. FACE-IT builds on the Globus Galaxies platform, which has been developed over the past several years at the University of Chicago. FACE-IT also benefit from substantial software development undertaken by the communities who have developed most of the domain-specific tools required to populate FACE-IT with useful capabilities. The FACE-IT Galaxy manages earth system datatypes (as NetCDF), new tool parameters (dates, map, opendap), aggregated datatypes (RAFT), service providers and cool map visualizers.
A late upload. This slide was presented on Aug 31, 2019, when I delivered a talk for AIoT seminar in University of Lambung Mangkurat, Banjarbaru. It's part of Republic of IoT 2019 event.
Data Summer Conf 2018, “Architecting IoT system with Machine Learning (ENG)” ...Provectus
In this presentation, the speaker will share his experiences from building successful IoT systems. He will also explain why many IoT systems fail to get traction and how Machine Learning can help in that. Finally, he will talk about the right system architecture and touch upon some of the ML algorithms for IoT systems.
A brief overview of Real-Time Analytics at Netflix and the challenges we've faced in designing and deploying production ready products based on real-time data.
WSO2 Machine Learner takes data one step further, pairing data gathering and analytics with predictive intelligence: this helps you understand not just the present, but to predict scenarios and generate solutions for the future.
HPC traditionally handles data at rest. The acquisition of streaming data presents a different set of challenges that, at scale, can be difficult to tackle. The approach to building data ingestion infrastructure at ARC-TS involves treating every service as a swappable building block. With this pluggable design using Docker containers you are free to choose which component is best. We will use an example use case to show how data is being generated, ingested, and how each component in the stack can be replaced.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
IOT/IOE Elastically Scalable Architecture for Smart City and Industry 4.0Paolo Nesi
Snap4 has been created as an open, standardized, data-driven, service-oriented, user-centric platform enabling large-scale co-creation IOT/IOE applications and services for Helsinki, Copenhagen and Antwerp. Snap4 is a fully open source, robust, scalable, easy to use solution, provides tools for co-creation of mixt data driven, stream and batch processing, extending the powerful semantic reasoner of Km4City https://www.km4city.org, with IOT/IOE, GDPR, and city dashboards. Snap4 for Smart city is Snap4City (Https://www.snap4city.org ) is an open platform and solution for setting up Living Labs engaging different all kinds of stakeholders (city operators, researchers, city users, in house, industries) in contributing to the city evolutions, with a platform providing online tools for developing IOT applications, web and mobile Apps, data analytics, micro Applications, external services, KPI, POI, dashboards, IOT edge, etc.
Snap4city has been validated in multiple devices (PC, Android, Raspberry, IOT Button, Arduino, ..), and domains: mobility and transport, tourism, health, welfare, social and cities such as Florence, Pisa, Arezzo, and large area of millions on inhabitants as Tuscany and millions of data per day. The innovation is mainly related to semantic reasoning, IOT interoperability, microservices, automated dashboard production, end-2-end encrypted secure communications, GDPR, .. thus setting up in a Snap smart city solutions.
The solution is fully complient with NodeJS with nodex published on JS foundation, is powered by Fi-Ware, compliant with LoraWan, SigFox, Mqtt, AMQP, Coap, NGSI, OMAM2M, WSs, Https, powered by Km4City, TensorFlow NVIDIA, Hadoop, etc. etc.
slides and demos: the platform includes full stack, any format, any protocol, from IOT Device, IOT Edges, Data Analytics, and Dashboards.
Presentació a càrrec de Maria Isabel Gandia, cap de Comunicacions del CSUC, duta a terme al 10è SIG-NOC Meeting, celebrat els dies 13 i 14 de novembre de 2019 a Praga.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
Big data analytics for transport
1. DITEN - University of Genoa - Italy
www.smartlab.ws
(Big) Data Analytics and Intelligent Systems
(for Transport)
Davide.Anguita@unige.it
SmartLab
2. DITEN - University of Genoa - Italy
www.smartlab.ws
University of Genoa
Polytechnic School
2
Polytechnic
School
Established
in
1870
–
~1000
students
/year
Genuense
Athenaeum
Established
in
1481
35000
students
Italian
Rank:
2nd
(CENSIS
2010
-‐
among
medium-‐large
UniversiMes)
DITEN
Dept.
of
InformaMon
Technology,
Electrical
and
Naval
Engineering
3. DITEN - University of Genoa - Italy
www.smartlab.ws
SmartLab People
SMARTLAB 3
Prof.
Sandro
Ridella
SmartLab
ScienMfic
Advisor
Prof.
Davide
Anguita
SmartLab
Coordinator
Dr.
Alessandro
Ghio
Postdoc
Research
Assistant
Luca
Ghelardoni
Postdoc
Research
Assistant
Luca
Oneto
Ph.D.
Student
Isah
Abdullahi
Lawal
ICE
Ph.D.
Student
(with
Univ.
of
London,
UK)
Jorge
Luis
Reyes
Or@z
ICE
Ph.D.
Student
(with
Univ.
Politec.
de
Catalunya,
Spain)
Giuseppe
Ripepi
Ph.D.
Student
(now
Postdoc
@
CNR)
+
Master
students
in:
• Industrial
Engineering
• Electronic
Engineering
• Computer
Engineering
• RoboMcs
Engineering
Mehrnoosh
Vahdat
ICE
Ph.D.
Student
(end
of
2013)
4. DITEN - University of Genoa - Italy
www.smartlab.ws
Teaching and training
• Master Course in Industrial Engineering (SV)
– Business Intelligence
• Istituto Superiore di Studi in Tecnologie dell'Informazione
e della Comunicazione
– Business Intelligence & Analytics
• Master Course in Electronic Engineering
– Computational Intelligence
• Corporate training
SMARTLAB 4
5. DITEN - University of Genoa - Italy
www.smartlab.ws
(Big) Data Analytics
• Present
– What can be done
• Past
– What we have learned to do
• Future
– What we intend to do
SMARTLAB 5
6. DITEN - University of Genoa - Italy
www.smartlab.ws
(Big) Data Analytics
• Present
– What can be done
• Past
– What we have learned to do
• Future
– What we intend to do
SMARTLAB 6
7. DITEN - University of Genoa - Italy
www.smartlab.ws
7
Analytics: a process
AbstracMon
InformaMon
storage
InducMon
DeducMon
AcMon
Learning
from
Data
8. DITEN - University of Genoa - Italy
www.smartlab.ws
Big Data
8
Source: UC Berkeley School of Information
9. DITEN - University of Genoa - Italy
www.smartlab.ws
9
(Big) Data
Servers
Running
Hadoop
at
Yahoo.com
10. DITEN - University of Genoa - Italy
www.smartlab.ws
Big Data Analytics: V3
• Volume: The increase in data volumes within enterprise systems is caused
by transaction volumes and other traditional data types, as well as by new
types of data. Too much volume is a storage issue, but too much data is
also a massive analysis issue.
• Variety: IT leaders have always had an issue translating large volumes of
transactional information into decisions — now there are more types of
information to analyze — mainly coming from social media and mobile
(context-aware). Variety includes tabular data (databases), hierarchical
data, documents, e-mail, metering data, video, still images, audio, stock
ticker data, financial transactions and more.
• Velocity: This involves streams of data, structured record creation, and
availability for access and delivery. Velocity means both how fast data is
being produced and how fast the data must be processed to meet demand.
(Gartner – 2011)
10
11. DITEN - University of Genoa - Italy
www.smartlab.ws
(Big) Data Analytics
11
Data
storage
/
Data
warehouse
/
OLAP
Visual
AnalyMcs
Data
Mining
Machine
Learning
…
11
12. DITEN - University of Genoa - Italy
www.smartlab.ws
(Big) Data Analytics
• Present
– What can be done
• Past
– What we have learned to do
• Future
– What we intend to do
SMARTLAB 12
13. DITEN - University of Genoa - Italy
www.smartlab.ws
Real-time analytics
Ferrari 13
Fuel
predicMon
Skid
predicMon
15. DITEN - University of Genoa - Italy
www.smartlab.ws
Fuel prediction - solution
Ferrari 15
Gaussian
Kernel
Support
Vector
Regressor
with
Cross-‐validated
Model
SelecMon
DB
Offline
Online
16. DITEN - University of Genoa - Italy
www.smartlab.ws
Fuel prediction - results
Ferrari 16
Brazil
06-‐Jun-‐03
Lap
21-‐28
OK
Alert
No
fuel
18. DITEN - University of Genoa - Italy
www.smartlab.ws
Skid prediction - solution
Ferrari 18
Gaussian
Kernel
Support
Vector
Classifier
with
Cross-‐validated
Model
SelecMon
DB
Offline
Skid
No
skid
Online
19. DITEN - University of Genoa - Italy
www.smartlab.ws
Skid prediction - result
05/03/14 Prova 19
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
0 2000 4000 6000 8000 10000 12000
Analog output
Real target
M.Schumacher
-‐
Fiorano
PredicMon
20. DITEN - University of Genoa - Italy
www.smartlab.ws
SMARTLAB 20
Smart Waves
In
cooperaMon
with
MoMon
predicMon
for
Landing
Period
Designator
21. DITEN - University of Genoa - Italy
www.smartlab.ws
NeuroZenit
SMARTLAB 21
ForecasMng
of
urban
traffic
Part
of
Elsag
Zenit
system
In
cooperaMon
with
22. DITEN - University of Genoa - Italy
www.smartlab.ws
SMARTLAB 22
Smart Bus
In
cooperaMon
with
Arrival
Mme
forecasMng
for
bus
fleets
Tests
performed
on
ATM
(Milan)
bus
#90
23. DITEN - University of Genoa - Italy
www.smartlab.ws
SMARTLAB 23
Oracle Data Mining Suite
Oracle
10g
DM
Suite
–
Beta
tesMng
24. DITEN - University of Genoa - Italy
www.smartlab.ws
SMARTLAB 24
EUNITE
European Network on Intelligent Technologies
ISAAC
Internet Smart Adaptive Algorithm
Computational Server
(2002
–
2004)
25. DITEN - University of Genoa - Italy
www.smartlab.ws
… 2013…
SMARTLAB 25
(Grimilde)
4
x
Xeon
(8C)
–
64
virtual
cores
–
128
GB
Ram
(Arla)
2
x
Xeon
(4C)
–
16
virtual
cores
–
32
GB
Ram
6TB
NAS
–
Storage
1Gb/s
Ethernet
26. DITEN - University of Genoa - Italy
www.smartlab.ws
…2015
SMARTLAB 26
(IBM
Cluster
-‐
256
nodes)
27. DITEN - University of Genoa - Italy
www.smartlab.ws
Business Intelligence on Clouds
SMARTLAB 27
Courtesy:
Salesforce.com
In
cooperaMon
with:
28. DITEN - University of Genoa - Italy
www.smartlab.ws
(Big) Data Analytics
• Present
– What can be done
• Past
– What we have learned to do
• Future
– What we intend to do
SMARTLAB 28
29. DITEN - University of Genoa - Italy
www.smartlab.ws
SMARTLAB 29
Analytics for Complex Data:
Process Mining
In
cooperaMon
with:
Log
file
Process
descripMon
32. DITEN - University of Genoa - Italy
www.smartlab.ws
Advanced Data Analytics
• Hierarchichal Functionality
– Descriptive Analytics
(what happened ?)
Data fusion, correlation, association,…
– Predictive Analytics
(what will happen ?)
Modelling, forecasting,…
– Prescriptive Analytics
(what should we do ?)
Interpretation, optimization,…
32
FROM:
Shit2Rail
EC
PPP
33. DITEN - University of Genoa - Italy
www.smartlab.ws
Incremental Data
Analytics
33
Time
Incremental
Knowledge
Building
for
Decision
Support
FROM:
Shit2Rail
EC
PPP
34. DITEN - University of Genoa - Italy
www.smartlab.ws
Adaptive Data Analytics
• Domain adaptation
34
Knowledge
transfer
FROM:
Shit2Rail
EC
PPP
35. DITEN - University of Genoa - Italy
www.smartlab.ws
Contract based
knowledge exchange
35
Open
Data
FROM:
Shit2Rail
EC
PPP
36. DITEN - University of Genoa - Italy
www.smartlab.ws
Open Linked Data
36
RDF:
Resource
DescripMon
Framework
format
RDF
query
language:
SPQRQL
37. DITEN - University of Genoa - Italy
www.smartlab.ws
Open Data mashup
(example)
37
39. DITEN - University of Genoa - Italy
www.smartlab.ws
Connectivity and information
sharing for intelligent mobility
Taken
from
hvp://whaMnspiresnick.files.wordpress.com/2011/09/urban-‐density-‐11.jpg
Boost
of
polluMon
CongesMon
of
people/freight
Urban
congesMon
costs
approx.
8
B£/yr
in
the
UK
Life
span
of
UK
ciMzens
living
in
large
urban
areas
reduced
by
approx.
8
months
Source
IBM
Human,
Social,
Envornmental,
Economic
(HSE2)
sustainability
issues
encompassed
Open
data
On-‐field
sensors
WWW
…
CiMzen
centric
approach
Towards
TAVA
decision-‐
making
T iming
A ccurate
V aluable
A cMonable
HSE2
KPIs
(Big)
Data
AnalyMcs
engine
40. DITEN - University of Genoa - Italy
www.smartlab.ws
Things simply do not work (yet..)
Marassi
Stadium
Lack
of
ability
in
planning
acMviMes
by
contemplaMng
heterogeneous
available
informaMon
42. DITEN - University of Genoa - Italy
www.smartlab.ws
References
National Patents
• D.Anguita, S.Pischiutta S.Ridella, D.Sterpi, Dispositivo per l'esecuzione della fase in avanti di un
classificatore automatico, (Device for the computation of the feed-forward phase of a classifier), N.
0001371367, Dep. 10/01/2006, 08/03/2010.
• D.Anguita, S.Ridella, D.Sterpi, Procedimento e sistema per la classificazione automatica multiclasse di
dati di misura di una grandezza fisica, (Method and system for the automatic classification of multi-class
data), N. 0001352198, Dep. 23/07/2004, 19/01/2009.
Selected publications
• L.Ghelardoni, A.Ghio, D.Anguita, Energy Load Forecasting Using Empirical Mode Decomposition and
Support Vector Regression, IEEE Transactions on Smart Grids, Vol. 4, No. 1, pp. 549-556, 2013.
• L.Oneto, A.Ghio, D.Anguita, S.Ridella, An Improved Analysis of the Rademacher Data-dependent Bound
Using Its Self-Bounding Property, Neural Networks, Vol. 44, No., pp. 107-111, 2013.
• D.Anguita, A.Ghio, L.Oneto, S.Ridella, In-Sample Model Selection for Trimmed Hinge Loss Support
Vector Machine, Neural Processing Letters, Vol. 36, No. 3, pp. 275-283, 2012.
• D.Anguita, A.Ghio, L.Oneto, S.Ridella, In-Sample and Out-of-Sample Model Selection and Error
Estimation for Support Vector Machines, IEEE Trans. on Neural Networks and Learning Systems, Vol. 23,
No. 9, pp. 1390-1406, 2012.
SMARTLAB 42
43. DITEN - University of Genoa - Italy
www.smartlab.ws
Technology Transfer
SMARTLAB 43
Spin-‐off
founded
in
February
2007:
10%:
University
of
Genoa
10%:
Researchers
(University
of
Genoa)
60%:
Industry
partner
(IsoSistemi
S.r.l.)
20%:
Private
investors
Target
market:
Steel
Industry
Intelligence
BI
&
AnalyMcs
44. DITEN - University of Genoa - Italy
www.smartlab.ws
Technology Transfer
SMARTLAB 44
Start-‐up
founded
in
March
2013:
49%:
Researchers
(University
of
Genoa)
49%:
Industry
partner
(Infinity
S.p.A.)
2%:
Private
investors
In
preparaMon:
request
for
recogniMon
as
academic
Spin-‐off
Target
market:
Manufacturing
Intelligence
Real-‐Mme
AnalyMcs
Scheduling
&
Planning
45. DITEN - University of Genoa - Italy
www.smartlab.ws
Thank you !
Davide.Anguita@unige.it