Call for Abstract

Scientific Program

2nd World Summit on Big Data, Machine Learning and Artificial Intelligence, will be organized around the theme “Novel Technologies: Learning, Modelling and Interface with Data”

Big Data 2022 is comprised of 10 tracks and 0 sessions designed to offer comprehensive sessions that address current issues in Big Data 2022.

Submit your abstract to any of the mentioned tracks. All related abstracts are accepted.

Register now for the conference by choosing an appropriate package suitable to you.

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.

Track 2:Big Data Analytics in Healthcare and Medicine

The 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 the 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.

Track 3: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, provide unprecedented opportunities for understanding and responding to real world situations and challenges.

Track 4:Machine Learning and Statistical Methods for Data Mining

Machine learning is a part of data science which majorly focuses on writing algorithms in a way such that machines (Computers) are able to learn on their own and use the learning’s to tell about new dataset 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.

Track 5:Artificial Intelligence

Artificial Intelligence is a computer-controlled robot or software to think 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.

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.

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 the 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.

Track 8:Data Mining Meets Visual Analytics at Big Data Era


The Visual analytics technique enjoys the joint advantage of the 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 not limited to big data reduction method to enable large-scale visualization big data integration algorithms to fuse heterogeneous information source for efficient visualizations temporal data analysis techniques for the effective dynamic and streaming data visualization and the mechanism for data privacy and security to delivery trustworthy big data visualization for end users.


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.

Track 10:Challenges and opportunities of predictive analytics

Big Data is described by high dimensionality and large sample size. These two features that 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.