Call for Papers

in 160 days

hayaData 2024

event date

24 Sep 2024

location

David InterContinental Hotel Tel Aviv, Israel

website

haya-data.com/


hayaData conference is a not-for-profit event built for the data community, data engineers & scientists, BI & data analysts, developers, researchers, and everyone interested in big data! 

This year will be the 4th year in a row that we're having the hayaData conference, and it is getting bigger and better every year.
hayaData is the place to share knowledge, hear insights from leading speakers, learn about the newest industry tools & best practices for working with big data, and mingle with data experts.

We <3 data, and we created this conference with the goal of bringing the data community together - Every fan of data is invited!

We invite the community to submit talks and to join us as speakers - everyone is welcome to submit (yes, even first-time speakers)! As our community grows, it would be great to see more and more people share their knowledge with one another. 

The talks are selected by the hayaData committee team, which includes experts from our community.

The selected talks will be recorded on the conference day and uploaded to the hayaData YouTube channel afterward. 

open, 32 days left
Call for Papers
Call opens at 10:00 AM

01 Apr 2024

Call closes at 11:59 PM

18 May 2024

Call closes in Jerusalem Daylight Time (UTC+03:00) timezone.
Closing time in your timezone () is .

Important details:

  • You can submit multiple talks
  • CFP will be open until May 18th
  • Talks can be either in Hebrew or English. CFP submissions must be in English


Available speaking slots:

  • 30-minute deep dive talks
  • 15-minute regular talks
  • 5-minute lightning talks


What are we looking for?

Below are suggested subjects (but topics are not limited to these suggestions)

Data Engineering

  • Data Architecture - High-level data architecture and/or deep dive into various related technologies (e.g. data mesh design, data lake implementations, data platform components, multi-site/cloud data strategy).
  • Data Governance - Technologies & concepts including data catalogs, data access control tools, data quality, and data privacy.
  • Real-Time Analytics Databases - Architectures, performance optimization, scalability solutions, and case studies demonstrating real-time data processing and analytics capabilities.
  • Advanced Data Storage Technologies - Innovations in data storage, including but not limited to, distributed databases, blockchain-based storage solutions, quantum data storage, and non-volatile memory technologies.
  • Cutting-Edge Data Retrieval Technologies - Techniques and algorithms for efficient data retrieval, including indexing strategies, machine learning-driven search optimizations, and graph databases.


Data Science

  • Deep Learning and Classical Machine Learning - Machine Learning Applications, Learning in knowledge-intensive systems, Learning Methods and analysis, Learning Problems, Deep Learning.
  • Pre-Processing Techniques, Visualization - Statistics for intelligent data analysis, Outlier analysis, data visualization, data drifting, and monitoring.
  • Data Types and Data Modeling - Time-series, Tabular data, Graphs, Text, Computer Vision, Web Mining, IOT, Voice and Multimedia.
  • Models optimization - Hyperparameters Tuning, Feature Selection, Imputation.
  • Data Science at scale - Large-scale data mining and analysis, Consistent Data Model.
  • Talking Business - Data Science for business applications.
  • MLOps and fastest path to production - ML experiment management - best practices and lessons learned, managing multiple models in production, moving from exploration to production, and API culture in DS teams.
  • Trends and future of Data Science
  • Retrieval-Augmented Generation (RAG) in Data Science - Insights on integrating RAG with machine learning and data analytics to enhance model performance and decision-making.

Data Analytics and BI

  • Business Intelligence (BI) Tools and Methodologies - Explore the latest tools, techniques, and best practices in BI, including data visualization and advanced analytics.
  • Data Warehouse (DWH) Architecture - Discuss the design, implementation, and optimization of DWH architectures, covering data modeling and integration strategies.
  • Data Modeling - Present innovative approaches to data modeling, including conceptual, logical, and physical models.
  • Data Visualization - Showcase cutting-edge visualization techniques and tools, including interactive visualizations and visual analytics.
  • Self-Service Analytics - Explore self-service analytics tools and methodologies, focusing on empowering users and data democratization.
  • Online vs. Offline Processing - Compare and contrast online and offline data processing techniques, covering real-time and batch processing.
  • Data Monitoring -Discuss strategies and tools for monitoring data quality, governance, and security, including real-time monitoring and anomaly detection.
  • Productizing BI - Explore the process of productizing BI solutions, including packaging for commercial use and best practices for product development.
  • Data Analytics - Discuss trends and developments in data analytics.
  • Data-Driven Culture - Examine strategies for fostering a data-driven culture within organizations, including promoting data literacy and building data-driven teams.
  • Semantic Layers for Data Analytics - Explore the use of semantic layers in data analytics.


Suggested general topics

  • Managing data teams for organizational scale
  • The boundaries and collaboration between different departments
  • Hiring, managerial, and data talent challenges
  • Model innovation vs/and service innovation
  • Working with data in real time - issues and solutions


And any other deep-dive topics related to Data Science, Big Data, Data Engineering, BI, and Data Analytics domains.



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