Data Mining for Business Intelligence
Ben Gurion University of the Negev
BGU Main Campus, Abraham Ben David Ohayon Behavioral Sciences Complex, Auditorium 01
May 10th, 2018
The Conference
May 10th, 2018
09:00 am
Sixth Annual Conference
Knowledge Discovery in Databases (KDD) was defined in 1996 by Fayyad, Piatetsky-Shapiro, and Smyth as “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data".
Since then, hundreds of data mining algorithms have been developed to assist data scientists in finding hidden knowledge in data. Business Intelligence (BI) is an increasingly popular term representing the tools and systems that play a key role in the strategic planning process of the corporation by turning knowledge into profit. Though some data mining algorithms are already being applied for BI, their knowledge discovery potential is still far from being fully utilized.
The sixth DM for BI conference, organized by the Department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, aims at bringing together researchers and practitioners in data mining, data science, machine learning, predictive analytics and related fields to discuss emerging trends and key issues in utilization of data mining methods for business intelligence.
The conference speakers include the most respected and knowledgeable data mining and business intelligence experts from Academia and Industry that will discuss the state-of-the-art and state-of-the-practice in data mining for BI, lessons learned, innovative ideas, and prospects for the future.
This year, the DMBI annual conference will be preceded by a Data Hackathon.
The conference is co-chaired by Prof. Bracha Shapira, Prof. Mark Last and Prof. Lior Rokach
Confirmed Speakers
Deep Trading ltd
Confirmed Talks
Keynote Talk: Context Aware Recommendations at Netflix, Dr. Linas Baltrunas (Abstract) (Bio) (Presentation)
How Data Mining is Turning the Art of Sales into a Learnable Science, Dr. Omri Allouche
(Abstract) (Bio) (Presentation)
Real-Time Deep Learning on Video Streams, Mr. Eran Avidan (Abstract) (Bio)
A Data Scientist’s Swiss Army Knife: A Cyber Defense System to Detect Low Footprint Campaigns in Multi-petabyte Network Data, Mr. Lee Blum (Abstract) (Bio) (Presentation)
Don't Believe Everything Your Network Tells You: Uncertainty in Deep Learning for Recommendation Systems, Dr. Gil Chamiel (Abstract) (Bio) (Presentation)
Machine Learning and Causal Inference for Healthcare Recommendation Applications, Dr. Yaara Goldschmidt (Abstract) (Bio)
Ensemble Learning Method for K-Means Clustering In Big Data Environment, Dr. Yossi Kuttner (Abstract) (Bio) (Presentation)
Introducing and Using Chatbots in the Financial Industry, Dr. Michaël Mariën (Abstract) (Bio)
Deep Meta Learning - Learning to Learn, Yam Peleg (Abstract) (Bio)
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware, Mr. Ishai Rosenberg (Abstract) (Bio) (Presentation)
Improving DevOps and QA Efficiency Using Machine Learning, Mr. Omer Sagi (Abstract) (Bio) (Presentation)
Accelerating Innovation through Analogy Mining, Dr. Dafna Shahaf (Abstract) (Bio)
Generic Black-Box End-to-End Attack against State of the Art API Call Based Malware Classifiers, Dr Asaf Shabtai (Abstract) (Bio) (Presentation)
ConStance: Modeling Annotation Contexts to Improve StanceClassification, Dr. Oren Tsur (Abstract) (Bio)
Accepted Posters
Anna Khalemsky - Dynamic Segmentation Rules for Online Classifier of Big Data via Small Data Buffers
Michael Khavkin - Preserving Differential Privacy and Utility of Non-Stationary Data Streams
Amit Livne - Deep Recommendation System Utilizing Sequential Latent Context
Avigail Perl, Sapir Natan - Health DSS for Vascular Disease
Adir Solomon - Predict Demographic Information Using Word2vec on Spatial Trajectories
Evgenia Wasserman Pritsker - Cold Start Recommendation by Social Media Information and Graph-based Approach
Abraham Itzhak Weinberg - Selecting a Representative Decision Tree from Multiple Decision-Tree Models
Yuval Zak - Dynamically Generating Visualizations for Unmanned Aerial Vehicle (UAV) Operators: Use of Machine Learning to Improve Mission Performance
The Schedule
09:00 - 09:30 – Registration and Refreshments
09:30 - 09:45 – Conference Opening - Greetings
Prof. Bracha Shapira, Department of Software and Information Systems Engineering
Session 1 - Machine Learning Applications – Session Chair – Prof. Lior Rokach
09:45 - 10:05 – Mr. Omer Sagi – Dell EMC and BGU - Improving DevOps and QA Efficiency Using Machine Learning
10:05 - 10:25 – Dr. Yaara Goldschmidt– IBM Research Haifa – Machine Learning and Causal Inference for Healthcare Recommendation Applications
Keynote: 10:25-11:05 –Dr. Linas Baltrunas – Netfilx - Context Aware Recommendations at Netflix
11:05-11:20 Coffee Break
Session 2 - ML for Cyber Security – Session Chair – Dr. Robert Moskovitch
11:20 - 11:40 – Mr. Ishai Rosenberg – DeepInstinct - End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
11:40 - 12:00 – Dr. Asaf Shabtai – BGU - Generic Black-Box End-to-End Attack against State of the Art API Call Based Malware Classifiers
12:00 - 12:20 – Mr. Lee Blum – Verint - A Data Scientist’s Swiss Army Knife: A Cyber Defense System to Detect Low Footprint Campaigns in Multi-petabyte Network Data
Session 3 - Machine Learning for NLP – Session Chair – Prof. Mark Last
12:20 - 12:40 – Dr. Oren Tsur –BGU - ConStance: Modeling Annotation Contexts to Improve Stance Classification
12:40 - 13:00 – Dr. Michaël Mariën - KBC - Introducing and Using Chatbots in the Financial Industry
Poster Teasers – 13:00 - 13:15
13:15 - 14:00 – Lunch and Poster Session
14:00 - 14:15- Hackathon Presentations – Prizes
Session 4 - Deep Learning - Session Chair – Dr. Gilad Katz
14:15 - 14:35 – Dr. Gil Chamiel – Taboola - Don't Believe Everything Your Network Tells You: Uncertainty in Deep Learning for Recommendation Systems
14:35 - 14:55 – Mr. Eran Avidan – Intel - Real-Time Deep Learning on Video Streams
14:55 - 15:15 – Mr. Yam Peleg - Deep Meta Learning - Learning to Learn
Session 5 - Machine Learning Algorithms – Session Chair- Dr. Rami Puzis
15:15 - 15:35 – Dr. Dafna Shahaf – Hebrew University - Accelerating Innovation through Analogy Mining
15:35 - 15:55 – Dr. Yossi Kuttner – Amdocs - Ensemble Learning Method for K-Means Clustering In Big Data Environment
15:55 - 16:15 – Dr. Omri Allouche – Gong.io - How Data Mining is Turning the Art of Sales into a Learnable Science
16:15-16:20- Conference Closing
Sponsors
Call for Student Posters
DMBI 2018 will provide an interaction opportunity for MSc and PhD students to present and demonstrate their new and innovative work, and to obtain feedback from their peers in an informal setting. A poster submission is an extended abstract of two pages and it should be submitted to dmbi@post.bgu.ac.il.
Submissions will be reviewed and evaluated by the Conference Chairs (Prof. Mark Last, Prof. Lior Rokach, and Prof. Bracha Shapira) based on relevance, significance, quality, and clarity. If accepted, the abstract will be published on the conference website, and one of the authors will be required to prepare a 70 x 100 cm poster and deliver a brief (2-min.) presentation in the poster session.
We encourage submission of posters derived from work-in-progress as well as from recently published papers and completed theses.
Topics of interest include, but are not limited to:
• Big Data
• Data Mining
• Machine Learning
• Predictive Analytics
• Business Intelligence
• Text Analytics
Important Dates
Poster submission deadline: April 8, 2018
Poster notification deadline: April 17, 2018
Conference: May 10, 2018