Big Data, Machine Learning and Artificial Intelligence

Author Guidelines

Submissions in the form of extended abstracts, short papers, and full manuscripts are welcome.

The Conference Proceedings will be published in an international journal.
The Conference Proceedings will be indexed by Google Scholar.
Each paper will be assigned a unique DOI number by Crossref.

Paper Topics
Big Data 2025 is now accepting papers on the following topics through its OpenConf system. If you have a paper on an additional topic, please write an email to bigdatasummit@eventcontact.org The current topics include but are not limited to:

• Big Data Analytics
• Big Data Analytics in Healthcare and Medicine
• Big Data in Bioinformatics, Multimedia, Smartphones
• Machine Learning and Statistical Methods for Data Mining
• Artificial Intelligence
• Networking Big Data Security
• Mobility Analytics from Spatial and Social Data
• Data Mining Meets Visual Analytics at Big Data Era
• Unlocking the Potential of Smart Data
• Challenges and opportunities of predictive analytics
• Applied statistics
• Big data
• Bioinformatics
• Computational statistics
• Data science
• Data integration
• Data mining
• Medical statistics
• Time-series Analysis
• Web data collection

Manuscripts are invited for the 2nd World summit on Big Data, Machine Learning, and Artificial Intelligence 2022: Theory and Applications on topics lying within the scope of the conference. All contributions must be original and should not have been published elsewhere.

Three types of manuscripts could be published in the conference proceedings:

1. Extended Abstracts
2. Short Papers
3. Full Papers

You may choose to submit any of the above based on the weight of the contributions in your manuscript.

• Extended abstracts are welcome for publication. These submissions should be around 500 words and must give a clear indication of the objectives, scope, and results (if available) of the research. No figures or tables are allowed.
• Short papers must give a clear indication of the objectives, scope, and results of the paper, and must (at least) contain an abstract, an introduction, a results/discussion section, and a reference section. Short papers are limited to 3 pages.
• Full Papers are limited to 10 pages and are more descriptive of the work that has been done. Also, more sections, images, graphs, and results could be presented.

Oral Presentations: 30-45 minutes of a recorded video presentation (mp4) or PowerPoint slides with recorded voice (ppt/pptx)

Poster Presentations: There are two options.
First, authors can prepare a recorded short video presentation which can be either 8-10 minutes of a recorded video (mp4) or PowerPoint slides with recorded voice (ppt/pptx).
Second option is to prepare a virtual poster (jpeg/pdf) which includes the following: brief abstract, background, problem statements, results, and discussions. In this case, recorded voice is not necessary.

All Oral/Poster Presentations will be done through a video or PowerPoint prepared by authors.
 

Big Data 2025

Big Data, Machine Learning and Artificial Intelligence 2025

November 27-28, 2025  | Dubai, UAE

Theme:- Novel Technologies: Learning, Modelling, and Interface with Data.

Conference Series LLC Ltd welcomes you to attend the Big Data, Machine Learning, and Artificial Intelligence Conference to be held in Dubai, UAE on November 27-28, 2025 .

Details of Big Data 2022 Conferences in London, United Kingdom

Big Data 2025 Conference

Conference Series is pleased to ask you to participate in the “3rd World summit on Big Data, Machine Learning and Artificial Intelligence 2025”. This conference bids an unparalleled opportunity to network with colleagues and learn from the distinguished leaders in Information Technology. It is an international platform that brings together the collection of investigators who are at the forefront in the field of Information Technology. The scientific program will include oral presentations of sub-disciplines, keynote sessions led by eminent scientists, and poster sessions presented interactively by junior scientists and graduate students. It is the last word forum for all the experts worldwide for brand spanking new interdisciplinary scientific collaborations and networking.

Sessions and Track

Track 1: Big Data Analytics
Huge information is information so vast that it doesn't fit in the fundamental memory of a solitary machine, and the need to prepare huge information by productive calculations emerges on Internet seeks, system activity checking, machine learning, experimental figuring, signal handling, and a few different territories. This course will cover numerically thorough models for growing such calculations and some provable confinements of calculations working in those models.

• Big data technology
• Gaining value from unstructured data
• Hadoop and Spark ecosystem
• Distributed and parallel computing
• Big data paradigm and V model

Track 2: Big Data in Bioinformatics, Multimedia, Smartphones
The volume of data is expanding fast in bioinformatics research. Big data sources are no longer limited to particle physics experiments or search-engine logs and indexes. Multimedia data makes up about 2/3rd of internet traffic, provides unprecedented opportunities for understanding and responding to real-world situations and challenges.

• Mobile communications and networks
• Heterogeneous data sources
• Cognition
• Mobile data analytics
• Computing efficiency

Track 3: Big Data Analytics in Healthcare and Medicine
Big data is in extended use in the field of medicine and healthcare. In healthcare, large amounts of heterogeneous medical data have become applicable in various healthcare organizations.  The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. As technology raises the cost of healthcare is also increasing more and more. Big data is a great helping hand on this issue. It is a great help for even physicians to keep track of all the patients’ history.

• Data mining and processing in bioinformatics, genomics, and biometrics
• Bio-Surveillance
• Electronic Health Records
• Predictive Analytics
• Real-time Alerting

Track 4: Machine Learning and Statistical Methods for Data Mining
Machine learning is a part of data science that majorly focuses on writing algorithms in a way such that machines (Computers) are able to learn on their own and use the learnings to tell about new datasets whenever it comes in. Machine learning uses the power of statistics and learns from the training dataset. It is the interesting data-driven disciplines that help organizations make better decisions and positively affect the growth of any business. Statistics also deal with designing surveys and experiments to get quality data which can further be used to make an estimation of the population.

• Supervised learning
• Empowering decision-makers through data visualization
• Algorithms for data analysis in Statistics
• Designing surveys and experiments

Track 5: Artificial Intelligence
Artificial Intelligence is a computer-controlled robot or software to thinks intelligently and focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes.

• Scientific computing
• Computer graphics
• Algorithmic trading
• Cybernetics
• Artificial Neural networks
• Adaptive Systems
• Ontologies and Knowledge sharing

Track 6: Networking Big Data Security
High-performance network capacity provides the backbone for high-end computing systems. These high-end computing systems play a vital role in Big Data. With the evolution of networks, threats or attacks with the intention of disrupting service or stealing confidential data are increasing tremendously. Networks have to be monitored constantly and protected against attacks.  

• Mining with data clouds
• Hive database in Hadoop Distributed File System
• Database management system
• IP networks

Track 7: Mobility Analytics from Spatial and Social Data
Recent developments in both social networks and spatial services have advanced significantly thanks to the prevalence of online social platforms, smart devices, and geo-positioning components.  However, social and spatial processes interact dramatically. For instance, joint actions happen within space and social factors such as population migration or even just interacting with friends on a geo-enabled smartphone.

• Open Data
• Primary surveyed data
• Multimodal mobility
• Information retrieval and smart search
•  OLAP technologies

Track 8: Data Mining Meets Visual Analytics at Big Data Era
The Visual analytics technique enjoys the joint advantage of human intelligence and the machine’s computational power. Various aspects of the data mining method need to be inspected, justified, organized, and evaluated for a successful VA system. The challenges include but are not limited to big data reduction method to enable large-scale visualization big data integration algorithms to fuse heterogeneous information sources for efficient visualizations temporal data analysis techniques for the effective dynamic and streaming data visualization and the mechanism for data privacy and security to deliver trustworthy big data visualization for end users.

• Knowledge discovery with data mining and visual analytics technologies
• Novel methods on visualization-oriented data mining
• Visual representations and interaction techniques of data mining results

Track 9: Unlocking the Potential of Smart Data
The amount of data being created today is expected to increase ten-fold in less than a decade, it’s also anticipated that enterprises will produce around 60% of global data by 2025. However, while the amount of data may be growing exponentially, the intelligence gleaned from it is not.

• How to use big data to support a smarter city?
• Impact of Big Data Analytics
• Revolutionizing Business Models
• Big data and data science using intelligent approaches

Track 10: Challenges and opportunities of predictive analytics
Big Data is described by high dimensionality and large sample size. These two features raise three unique challenges: (i) high dimensionality brings noise accumulation, spurious correlations and incidental homogeneity; (ii) high dimensionality combined with large sample size creates issues such as heavy computational cost and algorithmic instability; (iii) the massive samples in Big Data are typically aggregated from multiple sources at different time points using different technologies.  

• Need for synchronization across data sources
• Uncertainty of Data Management Landscape
• Big Data Talent Gap
• Getting important insights using Big data analytics

Relevant Conferences: Big Data Conferences | Data Management Meetings | Data Mining Congress | Cloud Computing Conference | Machine Learning Events | Data Science Congress | 

• Computer Science, Machine Learning and Big Data Analytics, August 30-31, 2018 Dubai, UAE
• 5^th Annual Smart Data Summit 17-18 APRIL 2018, DUBAI - UAE
• Big Data Show on 01-03 May 2018, Dubai World Trade Centre, Dubai, UAE
• 4^th International Conference on Data Mining and Applications, Feb 24, 2018 - Feb 25, 2018, Dubai, UAE
• Artificial Intelligence, April 16-17, 2018 Las Vegas, Nevada, USA
• Big Data Innovation, Data Mining and Analytics Summit, August 20-21, 2018, Singapore
• Big Data Analytics & Data Mining, September 26-27, 2018 Chicago, Illinois, USA
• 20^th International Conference on Data Mining, Big Data, Database and Data Technologies, November 26 - 27, 2018
• Big Data Analysis and Data Mining, 20-21 June 2018 Rome, Italy
• Big Data & Analytics Innovation Summit, March 15–16 Melbourne, 2018
• Big Data Analysis and Data Mining, September 07-08, 2017 Paris, France
• Big Data & Analytics Innovation Summit, 7–8 Singapore, 2018
• Big Data Innovation Summit, July 17–18 Las Vegas, 2018
• Big Data Innovation Summit, March 21–22 London, 2018
• Big Data & Analytics Innovation Summit, April 18–19 Hong Kong, 2018
• Big Data Innovation Summit, April 12–13 San Francisco, 2018
• Big Data & Analytics Innovation Summit, September 5–6 Shanghai, 2018
• Computer Science & Engineering, June 21-22, 2018 Oslo, Norway
• Global Artificial Intelligence Conference, January 17 to 19 2018, Santa Clara, USA.
• Marketing Analytics and Data Science, April 11 - 13, 2018 San Francisco, CA

Related Societies/Associations:

Advanced Analytics Institute, University of Technology, Sydney, National Center for Data Mining (NCDM) at the University of Illinois at Chicago, Germany, American Association of Engineering Societies, American Society for Engineering Education, National Society of Professional Engineers, Institution of Engineers, Bangladesh, Bangladesh Computer Society, Chinese Society for Electrical Engineering, Computer Society of India, Society of EMC Engineers (India), Japanese Union of Scientists and Engineers, Saudi Council of Engineers, European Federation of National Engineering Associations, Society of Professional Engineers UK, New Zealand Computer Society, Society of Professional Engineers UK, Germany, Big Data Europe Empowering Communities with Data Technologies, Europe, National Centre for Data Mining, Chicago, Web Analytics Association, Florida, SIAM society for industrial and applied mathematics, United States, IAENG Society of Data Mining, Hong Kong, IEEE Computer Society, United States, Data Mining Section of INFORMS, United States, International Institute for Analytics, Oregon, The International Machine Learning Society, Germany, International Institute for Business Analysis, Ontario, European Knowledge Discovery Network of Excellence, Germany

Market Analysis

The global big data market size was valued at USD 25.67 billion in 2015 and it is expected to significant growth over the forecast period. The elevating number of virtual online offices coupled with the increasing popularity of social media producing an enormous amount of data is a major factor driving growth. Increased internet penetration owing to several advantages including unlimited communication, abundant information and resources, easy sharing, and online services generates huge chunks of data in everyday life, which is also anticipated to propel demand over the coming years.