EuroSciCon is organizing meeting 8th Edition of International Conference on Big Data & Data Science 2019 is scheduled from March 04-05 at Barcelona, Spain. EuroSciCon is the UK based independent life science Events Company with predominantly business and academic client base.
The 2019 meeting promises to be a dynamic and informative event and going to explore the issues on work related factors, innovations and integrated approaches towards data mining & analysis, the speakers are a multidisciplinary gathering of globally perceived specialists that speak on the Data Mining with a theme on Exploring Future Technologies for Data Mining & Analysis!!!
This is 2-day Meeting and you can participate in a number of educational formats including General Sessions, Poster Presentations, and Workshops/Symposium, Meet-the-Professor Sessions, Oral Presentations and other interactive and informal exchanges.
Topics will cover the latest advances in Big Data Analytics, Big data technologies, Big Data algorithm, Big Data Applications, Artificial Intelligence and many more.
We trust you will discover the Meeting beneficial, enlightening and agreeable. We want to thank all EuroSciCon Members and participants whose commitments and cooperation have been basic to the accomplishment of the association!!
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.
• Big data analytics in the cloud
• Big data analytics technologies and tools
• Big data analytics uses and challenges
• New tools to visualize and interrogate data
• Big Data Analytics in Healthcare and Medicine
• 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.
• Data Stream Algorithms
• Randomized Algorithms for Matrices and Data
• Algorithmic Techniques for Big Data Analysis
• Models of Computation for Massive Data
• 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.
• Machine Learning & role in Biology
• Discovery of molecular subtypes of cancers
• Supervised Mining of “Big Data” Without Programming
• Challenges in Bioinformatics
• Mobile communications and networks
• Heterogeneous data sources
• Mobile data analytics
• Computing efficiency
• DNA sequencing
• Genome annotation
• Human bioinformatics
• Structural informatics
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.
• System Architecture
• Big Data Characteristics
• Prospects & Challenges in Online Data Mining
• Data Mining Challenges with Big Data
• Research Initiatives and Projects
• 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.
• Data Analysis in visualization
• Frame work for flow visualization
• System aspects in visualization
• Techniques for data visualization
• Conventional Data Visualization Methods
• Challenges of Big Data Visualization
• 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.
• Artificial creativity
• Artificial Neural networks
• Adaptive Systems
• Ontologies and Knowledge sharing
• Image recognition
• Visual art processing
• Drug discovery & toxicology
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.
• Modern Computer Architectures
• Programming and Tuning Software
• Single-processor Computing
• Parallel computing
• Computer Arithmetic
• 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.
• Machine learning algorithm
• Texture image classification
• Supervised learning
• Unsupervised learning
• Dashboards and BI
• Linear Regression
• Logistic Regression
• Bayesian network and naïve Bayes
• 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.
• CODATA(committee on data for science and technology)
• Technology and tools in open science
• Open development and sustainability
• Extensible Framework for Analysis of Big Data
• Preserving Privacy of Big Data
• 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.
• Apache hadoop
• HDFS(Hadoop Distributed File System)
• Hadoop YARN
• Hadoop MR
• Apache Pig
• Data lakes
• 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.
• Polynomial Regression
• Stepwise Regression
• Ridge Regression
• Lasso Regression
• 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.
• Banking and Securities
• Communications, Media and Entertainment
• International development
• Manufacturing and Natural Resources
• Retail and Whole sale trade
• Energy and Utilities
• Web and digital media
• Travel industry
• Healthcare Providers
The worldwide income for big data and business analytics will raise from $130.1 billion in 2016 to more than $203 billion in 2020, at a compound annual growth rate (CAGR) of 11.7%. In addition to this the production will be the major share in big data and business analytics results (nearly $17 billion in 2016), banking will see the fastest spending growth.
The statistic shows a revenue forecast for the global big data industry from 2011 to 2026. In 2017, the source of project has the global big data market size to grow to just under 34 billion U.S. dollars in revenue.
The market is rapidly becoming and developing in area of focus across numerous end-use industries. This technology used in substantial values by providing useful information; enable organizations to manage large block of data effiectively. With the help of these solutions companies obtain both efficiency and quality will manage large volume of raw information, ultimately result in significant cost reduction.
In the recent decades, the development in information and communications technologies introduces new life for enterprise marketing. For example, barcode technology and the emergence of online stores greatly enhance the efficiency of the enterprise because of which company managers are beginning to face the enormous data. However, the data and business profits are not directly proportional. Unfortunately, the human brain can’t handle so much data. In the meanwhile, data mining technology becomes very mature in theory. The technology-oriented applications for enterprise decision makers with a new perspective to look a t market. Those advanced technologies let enterprises obtain a lot of resources from different channels, and use those effective tools to translate data into unlimited opportunities. This is done by analyzing and making correct decisions, which help in improving operational efficiencies, risk mitigation and cost reduction.