Track Categories

The track category is the heading under which your abstract will be reviewed and later published in the conference printed matters if accepted. During the submission process, you will be asked to select one track category for your abstract.

 

 

Data analytics has become much easier after getting integrated with cloud computing. It also helped in making business more effective and the combination of both cloud computing and data analytics has helped the businesses to store, interpret, and process their big data to better meet their clients' needs. Thus cloud computing has become very important part in Data Analytics.

  • Track 1-1 Big Data Analytics in the Cloud
  • Track 1-2 Big Data Analytics Technologies and Tools
  • Track 1-3 Big Data Analytics Uses and Challenges
  • Track 1-4 New Tools to Visualize and Interrogate Data
  • Track 1-5 Big Data Analytics in Healthcare and Medicine
  • Track 1-6 Market Basket Analysis and Pricing Optimization

Huge information is so vast that it doesn't fit in the fundamental memory of a Unique machine, and the need to prepare huge information by productive calculations emerges in Internet world, system activity checking, machine learning, experimental figuring, signal handling, and a few different boundaries.

  • Track 2-1 Data Stream Algorithms
  • Track 2-2 Randomized Algorithms for Matrices and Data
  • Track 2-3 Algorithmic Techniques for Big Data Analysis
  • Track 2-4 Models of Computation for Massive Data
  • Track 2-5 The Modern Algorithmic Toolbox

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 3-1 Mobile Communications and Networks
  • Track 3-2 Heterogeneous Data Sources
  • Track 3-3 Cognition
  • Track 3-4 Mobile Data Analytics
  • Track 3-5 Computing Efficiency
  • Track 3-6 DNA Sequencing
  • Track 3-7 Genome Annotation
  • Track 3-8 Human Bioinformatics
  • Track 3-9 Structural Informatics
  • Track 3-10 Machine Learning & Role in Biology
  • Track 3-11 Discovery of Molecular Sub Types of Cancers
  • Track 3-12 Supervised Mining of Big Data Without Programming
  • Track 3-13 Challenges in Bioinformatics

Data mining is a fast-expanding field with great strengths. This blast in the measure of electronically put away information was increased by the achievement of the social model for putting away information and the improvement and developing of information recovery and control innovations.

  • Track 4-1 System Architecture
  • Track 4-2 Big Data Characteristics
  • Track 4-3 Prospects & Challenges in Online Data Mining
  • Track 4-4 Data Mining Challenges with Big Data
  • Track 4-5 Research Initiatives and Projects
  • Track 4-6 Efficiency and Scalability

Data visualization enables decision-making to see analytics presented by visually so that they can grasp difficult concepts or identify new patterns. Data visualization software plays an important role in big data and advanced analytics projects.

  • Track 5-1 Data Analysis in Visualization
  • Track 5-2 Frame Work for Flow Visualization
  • Track 5-3 System Aspects in Visualization
  • Track 5-4 Techniques for Data Visualization
  • Track 5-5 Conventional Data Visualization Methods
  • Track 5-6 Challenges of Big Data Visualization
  • Track 5-7 Future Trends in Visualization

Deep-Learning using Artificial Neural Networks is one of the popular methods for extracting information from complex datasets. Deep-learning is capable of more creating complex models than traditional probabilistic machine learning techniques. Python and Redis are the core supporting tools of this guide.

  • Track 6-1 Cybernetics
  • Track 6-2 Artificial Creativity
  • Track 6-3 Artificial Neural Networks
  • Track 6-4 Adaptive Systems
  • Track 6-5 Ontologies and Knowledge Sharing
  • Track 6-6 Visual Art Processing
  • Track 6-7 Drug Discovery & Toxicology
  • Track 6-8 Neuroscience

High performance computing is the domain that deals with transforming those requests into a instruction set that can be parallelized by your out-of-order CPU in order to achieve high throughput. It depends on sharing of assets to accomplish rationality and economy of scale, like a utility over a system.

  • Track 7-1 Modern Computer Architectures
  • Track 7-2 Programming and Tuning Software
  • Track 7-3 Single-Processor Computing
  • Track 7-4 Parallel Computing
  • Track 7-5 Computer Arithmetic
  • Track 7-6 Applications in HPC for Big Data

Machine learning is a part of data science which mainly focuses on writing algorithms in a way such that machines are able to learn on their own and use it to tell about new dataset whenever it comes. Machine learning uses the power of statistics and learns from the training dataset. It is the interesting data-driven technique that helps organizations to make better decisions and positively affect the growth of any business.

  • Track 8-1 Machine Learning Algorithm
  • Track 8-2 Texture Image Classification
  • Track 8-3 Supervised Learning
  • Track 8-4 Unsupervised Learning
  • Track 8-5 Dashboards and BI
  • Track 8-6 Linear Regression
  • Track 8-7 Logistic Regression
  • Track 8-8 Bayesian network and naïve Bayes
  • Track 8-9 Neural Network

Open science philosophy make a modification in the culture of research in developmental neuroscience it will increase the range of discovery and create meaningful change for children, families and communities when and where they need it most.

  • Track 9-1 CODATA(committee on data for science and technology)
  • Track 9-2 Technology and Tools in Open Science
  • Track 9-3 Open Development and Sustainability
  • Track 9-4 Extensible Framework for Analysis of Big Data
  • Track 9-5 Preserving Privacy of Big Data
  • Track 9-6 Challenges in open science

Hadoop also provides Hive and Pig Latin, which are high-level languages that generate Map Reduce programs. Hadoop focuses on moving code to data instead of vice versa. The client (Name Node) sends only the Map Reduce programs to be executed, and these programs are usually small (often in kilobytes). These programs may process data stored in different file and database systems.

  • Track 10-1 Apache Hadoop
  • Track 10-2 HDFS (Hadoop Distributed File System)
  • Track 10-3 Hadoop YARN
  • Track 10-4 Hadoop MR
  • Track 10-5 Hbase
  • Track 10-6 Strom
  • Track 10-7 Apache Pig
  • Track 10-8 Hive
  • Track 10-9 Data lakes
  • Track 10-10 NoSQL Data Base

It indicates the significant relationships between dependent variable and independent variable. It indicates the strength of impact of multiple independent variables on a dependent variable. Regression analysis also allows us to compare the effects of variables measured on different scales, such as the effect of price changes and the number of promotional activities. 

  • Track 11-1 Polynomial Regression
  • Track 11-2 Stepwise Regression
  • Track 11-3 Ridge Regression
  • Track 11-4 Lasso Regression
  • Track 11-5 Elastic Net Regression

The craft of  huge data integrate Big Data Analytics in Enterprises, Big Data Trends in Retail and Travel Industry, Financial, clinical and social protection, Regulated Industries, Biomedicine, Multimedia and Personal Data Mining.

  • Track 12-1 Banking and Securities
  • Track 12-2 Communications, Media and Entertainment
  • Track 12-3 Education
  • Track 12-4 E-Government
  • Track 12-5 International Development
  • Track 12-6 Manufacturing and Natural Resources
  • Track 12-7 Retail and Whole Sale Trade
  • Track 12-8 Transportation
  • Track 12-9 Energy and Utilities
  • Track 12-10 Web and Digital Media
  • Track 12-11 Travel industry
  • Track 12-12 Healthcare Providers