2nd International Conference on
Big Data Analytics and Data Science

 Theme  :  Building the Data-Driven Future of Big Data

  September 24-25, 2020

 Millennium Hotel Paris Charles De Gaulle, Paris, France

 Conference Brochure  Abstract Submission  Organizing Committee  Conference Program

Data Science Conference 2020

Coalesce Research Group cordially invite you to participate in the ‘2nd International Conference on Big Data Analytics and Data Science’ scheduled on September 24-25, 2019 in Paris, France.

Data Science 2020 aims to bring together the professionals, Industrialists, researchers, and practitioners to present their latest achievements and innovations in the area of Big Data Analytics and Data Science. The International Conference on Big Data Analytics and Data Science provide an international forum for the presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of Big Data, Data Science and Data Mining including algorithms, software and systems, and applications. Data Science 2020 draws researchers and application developers from a wide range of data science-related areas such as data mining, machine learning, statistics, data visualization, pattern recognition, databases and data warehousing, knowledge-based systems, and high-performance computing. Besides the scientific program, the conference features workshops, poster presentations, panels. At Data Science 2020, you’ll hear the innovative approach of the world’s leading companies that are solving today’s key challenges in data management. 

Artificial Intelligence:
Automated thinking is the data performed by machines or software demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. AI examination is amazingly particular and focus, and is essentially isolated into subfields that a great part of the time hatred to chat with each other. It solidifies Artificial Creative Ability, Artificial Neural structures, Adaptive Systems, Cybernetics, Ontologies and Knowledge sharing.
•    Cybernetics
•    Artificial creativity
•    Artificial Neural networks
•    Adaptive Systems
•    Ontologies and Knowledge sharing

Big Data Analytics and Algorithms:
Big data analytics probe and analyse huge amounts of data to i.e., big data - to uncover hidden patterns, unknown co-relations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Operate and carry by specialized analytics systems and software, big data analytics can lay the way to various business benefits, including new revenue opportunities, more effective marketing, improved operational efficiency, competitive advantages and better customer service.
•    Big Data Analytics Adoption
•    Benefits of Big Data Analytics
•    Barriers to Big Data Analytics
•    Volume Growth of Analytic Big Data
•    Managing Analytic Big Data
•    Data Types for Big Data

Big Data in Nursing Research:
With advances in technologies, nurse scientists are increasingly generating and using large and complex datasets, sometimes called “Big Data,” to promote and improve Health Conditions. New strategies for collecting and detailed examination large datasets will allow us to better understand the biological, genetic, and behavioural underpinnings of health, and to improve the way we prevent and manage illness.
•    Big data in nursing inquiry
•    Methods, tools and processes used with big data with relevance to nursing
•    Big Data and Nursing Practice

Big Data Technologies:
Big Data is the name given to huge amounts of data. As the data comes in from a variety of sources, it could be too diverse and too massive for conventional technologies to handle. This makes it very important to have the skills and infrastructure to handle it intelligently. There are many of the big data solutions that are particularly popular right now fit for the use
•    Big data storage architecture
•    GEOSS clearinghouse
•    Distributed and parallel computing

Big Data Applications, Challenges and Opportunities:
Big data has increased the demand of information management so much that most of the world’s big software companies are investing in software firms specializing in data management and analytics. According to one rough calculation, one-third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications. Since most of the data is directly generated in digital format, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. There are different phases in the Big Data analysis process and some common challenges that underlie many, and sometimes all, of these phases.

•    Ecommerce and customer service
•    Security and privacy
•    Manufacturing
•    Telecommunication
•    E-Government
•    Public administration
•    Big Data Analytics in Enterprises
•    Retail / Consumer
•    Travel Industry
•    Current and future scenario of Big Data Market
•    Financial aspects of Big Data Industry
•    Clinical and Healthcare
•    Regulated Industries
•    Biomedicine
•    Finances and Frauds services
•    Web and Digital Media
•    Data Integration, Aggregation, and Representation
•    Query Processing, Data Modeling, and Analysis
•    Heterogeneity and Incompleteness
•    Scale, Timeliness and Privacy
•    System Architecture and Human Collaboration
•    New innovations and business opportunities
•    Business Proliferation    

Business Intelligence:

The term Business Intelligence (BI) represents the tools and systems that play a key role at intervals the strategic designing methodology of the corporation. These systems allow a corporation to gather, store, access and analyze company info to assist in decision-making. Generally these systems will illustrate business intelligence at intervals the areas of consumer identification, client support, research, market segmentation, product profit, math’s analysis, and inventory and distribution analysis to decision several. Most corporations collect AN outsized amount of data from their business operations. to remain track of that info, a business and would need to use an honest vary of package programs, like surpass, Access and utterly totally different information applications for various departments throughout their organization. exploitation multiple package programs makes it difficult to retrieve information in a {very} very timely manner and to perform analysis of the data.

Cloud Computing:

Cloud computing is the delivery of computing services—servers, storage, databases, networking, software, analytics, and more—over the Internet (“the cloud”).  Cloud computing relies on sharing of resources to achieve coordination and economies of scale, similar to a public utility. Companies offering these computing services are called cloud providers and typically charge for cloud computing services based on usage.

•    Cloud Computing Applications
•    Emerging Cloud Computing Technology
•    Cloud Automation and Optimization
•    High Performance Computing (HPC)
•    Mobile Cloud Computing

Complexity and Algorithms:

The uncertainty of a calculation indicates the aggregate time required by the system to rush to finish. The many-sided quality of calculations is most generally communicated using the enormous O documentation. Many-sided quality is most usually assessed by tallying the number of basic capacities performed by the calculation. What's more, since the calculation's execution may change with various sorts of info information, subsequently for a calculation we normally use the most pessimistic scenario multifaceted nature of a calculation since that is the extended time taken for any information size.

•    Mathematical Preliminaries
•    Recursive Algorithms
•    The Network Flow Problem
•    Algorithms in the Theory of Numbers
•    NP-completeness

Data Engineering and Architecture:

The data architect and data engineer work in tandem – conceptualizing, visualizing, and then building an Enterprise Data Management Framework. The data architect visualizes the complete framework and creates the blueprint, which the data engineer uses to build the “digital framework.”

The data engineering role has recently evolved from the traditional software-engineering field. Recent Enterprise Data Management experiments have proven beyond doubt that these data-focused software engineers are needed to work along with the data architects to build a strong Data Architecture. Between 2013 and 2015, the growth of data engineers was around 122 percent in response to a massive data industry need.

Data Mining Applications in Science, Engineering, Healthcare:

Data mining is the process of discovering patterns to extract information with an intelligent method from a data set and transform the information into a comprehensible structure for further use. Data mining is the detailed examination step of the "knowledge discovery in databases" process. These applications relate Data mining structures in genuine cash related business territory examination, Application of data mining in positioning, Data mining and Web Application, Engineering data mining, Data Mining in security, Social Data Mining, Neural Networks and Data Mining, Medical Data Mining, Data Mining in Healthcare.

•    Bayesian networks
•    Case Studies and Implementation
•    Application of data mining in education
•    Data mining and processing in bioinformatics, genomics and biometrics
•    Advanced Database and Web Application
•    Medical Data Mining
•    Data Mining in Healthcare data
•    Engineering data mining
•    Data mining in security
•    High performance data mining algorithms
•    Methodologies on large-scale data mining
•    Data mining systems in financial market analysis

Data Science and Machine Learning:

Both data science and machine learning are rooted in data science and generally fall under that category. They often intersect or are confused with each other, but there are a few key contrasts between the two. The major difference between machine learning and data mining is how they are used and applied in our everyday lives. Data mining can be used for a variety of purposes, including financial research, Investing, sales trends and marketing. Machine learning visible form of the principles of data mining, but can also make automatic correlations and learn from them to apply to new algorithms.

•    Machine learning and statistics
•    Machine learning tools and techniques
•    Fielded applications
•    Generalization as search
•    Bayesian networks

Data Visualization:

Information representation is seen by numerous orders as a present likeness visual correspondence. It is not held by any one field, yet rather discovers translation crosswise over numerous. It covers the arrangement and investigation of the visual representation of information, indicating "data that has been dreamy in some schematic structure, including attributes or variables for the units of data".

•    Analysis data for visualization
•    Scalar visualization techniques
•    Frame work for flow visualization
•    System aspects of visualization applications
•    Future trends in scientific visualization

Data Warehousing and Security:

In computing, a Data Warehouse (DW or DWH), also known as an Enterprise Data Warehouse (EDW), is a system used for reporting and data analysis and is considered a central component of business intelligence. Data Warehouse or Enterprise Data Warehouse is central repositories of integrated data from one or more disparate sources.

•    Data Warehouse Architectures
•    Case studies: Data Warehousing Systems
•    Data warehousing in Business Intelligence
•    Role of Hadoop in Business Intelligence and Data Warehousing
•    Commercial applications of Data Warehousing
•    Computational EDA (Exploratory Data Analysis) Techniques    

Optimization and Big Data:

With pervasive sensors continuously collecting and storing massive amounts of information, there is no doubt this is an era of data deluge. Learning from these large volumes of data is expected to bring significant science and engineering advances along with improvements in quality of life. However, with such a big blessing come big challenges. Running analytics on voluminous data sets by central processors and storage units seems infeasible, and with the advent of streaming data sources, learning must often be performed in real time, typically without a chance to revisit past entries. “Workhorse” signal processing (SP) and statistical learning tools have to be re-examined in today’s high-dimensional data regimes.

Natural Language Processing:

Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
IoT and edge computing applications    :
The internet of things, or IoT, is the network of physical devices interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers(UIDs) and the ability to connect, collect and exchange data or transfer data over a network without requiring human-to-human or human-to-computer interaction.
•    Medical and Healthcare
•    Transportation
•    Environmental monitoring
•    Infrastructure Management
•    Consumer application

Predictive Analytics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Data-driven Analytics and Business Management:

In business, you constantly have to make decisions — from how much raw material to order to how to optimize retail traffic for changing weather. In days gone by, you might have consulted the person who had been around the longest for their best guess; for a more scientific approach, you might have also looked at sales records. Today, companies are finding that the best answers to these questions come from another source entirely: large amounts of data and computer-driven analysis that you rigorously leverage to make predictions. This is called data-driven decision making (DDDM).      

For Speakers: 
•    Keep the number of slides in your Presentation to a minimum and follow the assigned slots.
•    Please stop when signaled to do so by the Chair.
•    Personal laptops should not be used unless in any unavoidable conditions.
•    The Videos will not be recorded.
•    Question Sessions, thanks and acknowledgement of the speakers will take place during the session or after completion of the session, so please stay until the end of the session.

For Poster: 
•    Each poster should be approximately 1x1 M in Size The title, contents, text and the author’s information should be clearly visible even from 1-2 feet.
•    Present numerical data in the form of graphs, rather than tables.
•    If data must be presented in table-form, keep it Simple to be easily understandable.
•    Visuals should be simple, clear and bold. Avoid acronyms and mathematical notations as much as possible.
•    Posters with 800-1000 words or less are perfect.
•    Avoid submitting compactly packed, highly worded- count posters.
•    Categorize your poster into subdivisions, e.g., Introduction, Methods, Results; Discussion, Conclusions, and Literature Cited.
•    Use bright colors to enhance the better visibility Besides your project, you can also include future research plans or questions.

Opportunities for Conference Attendees: 

For Researchers & Faculty: 
•    Speaker Presentations
•    Poster Display
•    Symposium hosting
•    Workshop organizing

For Universities, Associations & Societies: 
•    Association Partnering
•    Collaboration proposals
•    Academic Partnering
•    Group Participation

For Students & Research Scholars: 
•    Poster Presentation Competition (Winner will get Best Poster Award)
•    Young Researcher Forum (Award to the best presenter)
•    Student Attendee
•    Group Registrations

For Business Speakers: 
•    Speaker Presentations
•    Symposium hosting
•    Book Launch event
•    Networking opportunities
•    Audience participation

For Companies: 
•    Exhibitor and Vendor Booths
•    Sponsorships opportunities
•    Product launch
•    Workshop organizing
•    Scientific Partnering
•    Marketing and Networking with clients

Abstract Peer-review Process/Guidelines:
•    The Reviewing Committee of Data Science Conferences ensures high-quality peer review process for all abstracts submitted to the conference.
•    The decision of abstract acceptance will be judged by a panel of experts emphasizing whether the findings and / or conclusions are novel and make useful contributions to the field.
•    The committee operates a single / double-blind peer review process for all the abstracts submitted, where both the reviewer and the author remain anonymous.

The following are the steps that each abstract of Data Science Conferences undergoes during the process of peer review:
•    All submitted abstracts are reviewed by internal editorial team to ensure adherence to the conference scope and abstracts which have passed this initial screening are then assigned to the session chair / review committee for evaluation.
•    Once the reviews have been received, the review committee decides to accept or reject a manuscript, or to request revisions from the author in response to the reviewers’ comments. If the decision tends to be minor revision or major revision, authors will be given 14 days to resubmit the revised abstract.

Criteria to be considered for Scoring:
The abstract should be reviewed according to the following criteria:
•    Originality of concept/approach and level of innovativeness
•    Significance/impact/relevance to conference theme
•    Quality of research design/theoretical argument
•    Conclusions and interpretations of results
•    Presentation style: Coherence and clarity of structure

Presenting Your Organization’s Work on a Global Stage: 
As a speaker you will be presenting to a room full of senior representatives from all over the world, each providing a different perspective from the sector. Your organization’s expertise and knowledge will be showcased to key players in the field of Data Science, Big Data and will be a unique platform to increase your reputation within the sector 

New Places; New People: 
Each time will be held at a different place, new and different people will attend. This can enlarge building collaborations and help you in developing new relationships. 

Learn From Other Speakers:
As a speaker you will be provided with free access to two days of the conference and associated workshops and will be given the opportunity to hear from other senior representatives from the sector and consider problems and solutions in the field of Big Data, our numerous Q&A sessions and panel discussions. 

Discuss And Overcome Issues In The Field: 
This conference offers unrivalled opportunities to work with other key leading experts from the Universities and Companies to discuss the main challenges in the sector and to come together to produce strategies to find solutions to these problems Competitive Advantage: You’ll stand out if you’re a sponsor and your major competitors aren’t. If your competitors have already decided to be sponsors, your sponsorship becomes even more important, to assert your comparative market strength and your commitment to healthy products. 

Leading a Workshop: 
By leading one of the renowned Workshops, you will be presented with a perfect forum for an in depth discussion and debate into a key issue. These sessions can vary in format from case-study-led debate with interactive breakout sessions to a presentation based discussion group on a topic that may need a particular in-depth focus. 

The Opportunity to Collaborate and Sponsor: 
While we determine our conference theme and flow, we invite our key sponsors to suggest potential speakers, Delegate and topics that might also enhance the program. That’s why it’s important to commit early to sponsorship, before the program is final.

To increase your presence at the event, why not chair the event, a day, or a specific session to present yourself and your organization as one the leading players in a specific topic area? As a chair, you will work closely with us and our line-up of senior level speakers to ensure an event’s success.


Organizing Committee

Peter Z. Revesz

Peter Z. Revesz

University of Nebraska


T. Scott Clendaniel

T. Scott Clendaniel

Chief Data Scientist


Ali Hurson

Ali Hurson

Missouri University of Science and Technology


Tao Liu

Tao Liu

Brown University
Rhode Island


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Know Your Registration

Student Registration

  • Access to all Conference Sessions
  • Opportunity to give an Oral/ Poster Presentation
  • Opportunity to publish your Abstract in any of our esteemed Journals & in the Conference Proceedings Book
  • Certificate Accredited by our Organizing Committee Member
  • Handbook & Conference Kit
  • Tea/Coffee & Snack
  • Lunch during the Conference

Delegate Registration (No Presentation)

  • Access to all Conference Sessions
  • Can meet the Experts of your Area of expertise arriving from 22+ different Countries
  • Participation Certificate Accredited by our Organizing Committee Member
  • Delegates are not allowed to present their papers in Oral or Poster sessions
  • Handbook & Conference Kit
  • Tea/Coffee & Snack
  • Lunch during the Conference

Speaker Registration

  • Access to all Conference Sessions
  • Opportunity to give a Keynote/ Plenary/ Poster Presentations/ Workshop
  • Opportunity to publish your Abstract in any of our esteemed Journals & in the Conference Proceedings Book
  • Certificate Accredited by our Organizing Committee Member
  • Handbook & Conference Kit
  • Tea/Coffee & Snack
  • Lunch during the Conference