Join us for an immersive conference exploring the processes, techniques and technologies to efficiently extract valuable insights from massive datasets. This conference will delve into the fundamental concepts and techniques of Mining Massive Datasets (MMD), exploring how to effectively harness the power of big data.

What to Expect:

Fundamental concepts and techniques of data mining and machine learning for massive datasets.

Explore the underlying technologies, algorithms, and tools to extract valuable insights from large-scale data.

Gain hands-on experience in processing, analyzing, and modeling massive datasets.

Networking Opportunities: Connect with like-minded professionals and build valuable relationships with industry leaders.

Key Topics:

Distributed Systems and Frameworks:

Deep dive into HDFS and Cloud Storage for efficient data storage.
Comparative analysis of Apache Spark and Apache Flink for parallel data processing.
Understanding batch and stream processing paradigms.

Data Mining Techniques:

Similarity Search: Mastering minhashing and locality-sensitive hashing for recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering association rules and market-basket analysis for retail and healthcare.
Clustering: Grouping data points with k-means, hierarchical clustering, and DBSCAN for customer segmentation and image clustering.

Advanced Topics:

Graph Analytics: Unraveling complex relationships with social network analysis and graph mining for fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying data with SVD and LSI for text mining and feature engineering.
Machine Learning for Massive Datasets: Scaling machine learning algorithms and leveraging cloud-based platforms.

Why Attend?

Master the Fundamentals: Gain a solid understanding of the core concepts and techniques of data mining and machine learning.
Learn from Experts: Hear from practicing data scientists who are shaping the future of data science.
Gain Expertise: Learn from top experts in the field of machine learning and data engineering.
Stay Ahead of the Curve: Discover the latest trends and advancements.
Network with Industry Leaders: Connect with like-minded professionals and build valuable relationships.
Advance Your Career: Enhance your skills and knowledge to advance your career in data science.

Primary Audience for Conference Attendees

AI and Data Science Professionals:

Data Scientists
AI/Machine Learning Engineers
Data Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers

Business and Leadership Audience:

C-Suite Executives (CEOs, CTOs, CIOs): Leaders who need to understand the strategic implications of big data and data-driven decision-making.
Business Analysts: Professionals who use data to analyze business performance and identify opportunities.
Product Managers: Individuals responsible for developing data-driven products and services.

Investors, Traders and Venture Capitalists:

Investors: Benefit from optimizing investment decision-making processes.
Traders: Benefit from processing, analyzing, and modeling massive datasets.
Venture Capitalists: Professionals who fund early-stage technology companies.

Government and Policymakers:

Government Officials: Policymakers who need to understand the impact of big data on society and regulations.
Data Privacy Officers: Professionals responsible for ensuring data privacy and security.
Regulatory Agencies: Organizations that oversee data usage and compliance.

Other Potential Attendees:

Academics and Researchers: Individuals from universities and research institutions.
Students and Aspiring Data Professionals: Students and early-career professionals interested in learning about big data.
Consultants: Professionals who advise organizations on data strategy and implementation.
Data Journalists: Individuals who use data to tell stories and inform the public.

Main Conference Subject Areas

Scaling Data Processing: Distributed Systems and Frameworks

Distributed File Systems: The Foundation of Big Data

Deep dive into HDFS, Cloud Storage, and their role in storing massive datasets.
Explore the advantages and trade-offs of different storage solutions.

Distributed Processing Frameworks: Unleashing the Power of Parallelism

Comparative analysis of Apache Spark and Apache Flink.
Hands-on demonstrations of real-world data processing pipelines.

Data Processing Paradigms: Batch vs. Stream

Discuss the strengths and weaknesses of batch and stream processing.
Explore hybrid approaches and real-time analytics use cases.

Uncovering Hidden Patterns: Data Mining Techniques

Similarity Search: Finding Your Matches in the Haystack

In-depth exploration of minhashing and locality-sensitive hashing.
Practical applications in recommendation systems and anomaly detection.

Frequent Itemset Mining: Discovering the Rules of the Game

Dive into association rule mining and market-basket analysis.
Learn how to apply these techniques to retail, healthcare, and other domains.

Clustering: Grouping Together What Belongs Together

Explore various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN.
Hands-on exercises in customer segmentation and image clustering.

Advanced Topics in Data Mining and Machine Learning

Graph Analytics: Unraveling Complex Relationships

Social network analysis and graph mining techniques.
Real-world applications in fraud detection and knowledge graph construction.

Dimensionality Reduction: Simplifying the Complex

In-depth discussion of SVD and LSI.
Practical applications in text mining and feature engineering.

Machine Learning for Massive Datasets: Scalable Algorithms and Techniques

Explore scalable machine learning algorithms, including gradient descent and distributed deep learning.
Hands-on experience with cloud-based machine learning platforms.

Experiencing Paris

Paris, the heart of France, is a city of contrasts. Its grand boulevards lined with elegant cafes and historic buildings coexist with bustling markets and modern art galleries. The Seine River, winding its way through the city, reflects the iconic Eiffel Tower, a symbol of Paris's romantic charm.

The city's history is evident in its architecture, from the Gothic grandeur of Notre-Dame Cathedral to the classical beauty of the Louvre Museum. The Latin Quarter, with its cobblestone streets and student-filled cafes, offers a glimpse into Paris's intellectual heritage.

Beyond its famous landmarks, Paris is a city of neighborhoods, each with its own unique character. The Marais, with its historic houses and trendy boutiques, is a popular destination for both locals and tourists. Montmartre, perched on a hill overlooking the city, is known for its artists' studios and bohemian atmosphere.

Paris is a city of sensory experiences. The aroma of freshly baked croissants fills the air, while the sound of accordion music echoes through the streets. The city's vibrant food scene offers everything from Michelin-starred restaurants to casual bistros.

Whether you're strolling along the Champs-Élysées, exploring the Louvre, or simply people-watching at a sidewalk cafe, Paris is a city that captivates the senses and leaves a lasting impression.

Here are some cool adventures:

Seine River Cruise: Embark on a relaxing cruise along the Seine River, admiring the city's skyline and iconic landmarks from a unique perspective.
Louvre Museum: Immerse yourself in art history at the world-renowned Louvre Museum, home to masterpieces like the Mona Lisa and Venus de Milo. Get lost in its vast galleries and discover treasures from ancient civilizations to Renaissance masterpieces.
Picnic in the Luxembourg Gardens: Pack a picnic basket and enjoy a leisurely afternoon in these beautiful gardens, complete with fountains, statues, and a charming pond.
Montmartre Exploration: Wander through the charming streets of Montmartre, home to artists' studios, the iconic Sacré-Cœur Basilica, and stunning views of the city.
Bike Tour Along the Seine River: Rent a bike and explore Paris's scenic riverbanks, passing iconic landmarks like the Eiffel Tower, Notre-Dame Cathedral, and the Louvre Museum.

Paris to Versailles Private Day Trip (Optional)

Immerse yourself in the grandeur of the Sun King's palace on this exclusive private day trip to Versailles. Journey from the bustling streets of Paris to the opulent halls and meticulously manicured gardens of this UNESCO World Heritage site. With a dedicated private guide and private transport, you'll explore the palace at your own pace, uncovering hidden stories and admiring breathtaking masterpieces.

Call for Presentations & Papers

We're excited to announce our Call for Presentations & Papers for a premier conference dedicated to the cutting-edge of scalable data processing and data mining. This event will bring together leading data professionals and innovators to share their insights, discoveries, and practical applications in this rapidly evolving field. We invite you to contribute your expertise and join us in exploring the future of big data.​

We're looking for compelling presentations, papers, workshops, and lightning talks that offer original research, innovative solutions, real-world case studies, and insightful analyses.

Main Conference Subject Areas

Scaling Data Processing: Distributed Systems and Frameworks

This track focuses on the foundational and advanced aspects of processing large-scale datasets efficiently. We encourage submissions on:

• Distributed File Systems: The Foundation of Big Data
  • Deep dives into HDFS, Cloud Storage, and their crucial role in storing massive datasets.
  • Explorations of the advantages and trade-offs of different storage solutions in various contexts.
• Distributed Processing Frameworks: Unleashing the Power of Parallelism
  • Comparative analyses of Apache Spark and Apache Flink, including performance benchmarks and use cases.
  • Hands-on demonstrations of real-world data processing pipelines built with these frameworks.
• Data Processing Paradigms: Batch vs. Stream
  • Discussions on the strengths and weaknesses of batch and stream processing.
  • Explorations of hybrid approaches and real-time analytics use cases across different industries.

Uncovering Hidden Patterns: Data Mining Techniques

This track delves into methodologies for extracting valuable insights and patterns from large datasets. We're particularly interested in:

• Similarity Search: Finding Your Matches in the Haystack
  • In-depth explorations of minhashing and locality-sensitive hashing.
  • Practical applications in recommendation systems and anomaly detection.
• Frequent Itemset Mining: Discovering the Rules of the Game
  • Deep dives into association rule mining and market-basket analysis.
  • How to apply these techniques to various domains like retail, healthcare, and cybersecurity.
• Clustering: Grouping Together What Belongs Together
  • Explorations of various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN.
  • Hands-on exercises in practical applications such as customer segmentation and image clustering.

Advanced Topics in Data Mining and Machine Learning

This track covers cutting-edge techniques and scalable approaches for complex data challenges. We welcome submissions on:

• Graph Analytics: Unraveling Complex Relationships
  • Social network analysis and advanced graph mining techniques.
  • Real-world applications in fraud detection and knowledge graph construction.
• Dimensionality Reduction: Simplifying the Complex
  • In-depth discussions of SVD and LSI.
  • Practical applications in text mining and feature engineering.
• Machine Learning for Massive Datasets: Scalable Algorithms and Techniques
  • Explorations of scalable machine learning algorithms, including gradient descent and distributed deep learning.
  • Hands-on experiences and best practices with cloud-based machine learning platforms.

Submission Types

We invite various types of submissions to ensure a rich and diverse program:

• Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices in a focused presentation.

• Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that will undergo a rigorous peer-review process. Accepted papers will be published in the conference proceedings.

• Experience & Insight Papers (4-6 pages, formatted for readability): This category is for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives on the challenges and successes encountered in scalable data processing and data mining. These papers should focus on practical applications, best practices, and actionable insights. Submissions will be peer-reviewed for clarity, relevance, and practical value.

• Poster Presentations: Visually showcase preliminary results, ongoing research, or innovative concepts. There will be a dedicated poster session for interactive discussions.

• Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic within scalable data processing and data mining.

• Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to data processing, data mining, or machine learning for massive datasets.

Submission Guidelines

• Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
• Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
• Keywords: Provide 3-5 relevant keywords that best describe your submission.
• Originality: Submissions must represent original work that has not been previously published or is not currently under review elsewhere.
• Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
• Formatting: Specific formatting guidelines for full papers will be provided upon the submission portal opening.

Review Process

All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in data science, distributed systems, machine learning, and related domains. Submissions will be evaluated based on:

• Relevance to conference themes
• Originality and novelty of contributions
• Technical merit and soundness (for technical papers)
• Clarity, organization, and presentation quality
• Potential impact and practical applicability

We look forward to receiving your valuable contributions and seeing you at the conference!