Data Mining webinar Conferences | Big Data Analysis Conference | Big Data Conferences

Theme: Knowledge discovery in databases: Step towards recovering economy after the pandemic: Covid-19

Data Mining 2021

Scientific Program

Renowned Speakers

Hongfei Li,
IBM Analytics
USA

Michael Valivullah,
Chief Technology Officer National Agricultural Statistics Service USDA
USA

En-Bing Lin,
Central Michigan University
USA

Morgan C. Wang,
University of Central Florida
USA

Thomas Sterling,
Indiana University
USA

Iftikhar U Sikder,
Cleveland State University
USA

Cosimo Spera,
Bee Bell Inc
USA

Sumit Pal,
Independent Consultant (Architect, Developer) Big Data & Data Science
USA

Boris Mirkin,
Birkbeck University of London
UK

Fionn Murtagh,
University of Derby
UK

Petra Perne,
Institute of Computer Vision and applied Computer Sciences
Germany

Pierre Hansencan,
GERAD and HEC MontrÃ©al
Canada

Jacopo Biancat,
Systems & Advanced Technologies Engineering
Italy

Burcin Ozturk Demirhanoz,
WesTrac CAT
Australia

Hongqin Fan,
The Hong Kong Polytechnic University
Hong Kong

Carlo A Trugenberger,
InfoCodex AG-Semantic Technologies
Switzerland

Sophia Tsoka,
Kings College London
UK

Alla Sapronova,
Uni Research
Norway

Robert S Laramee,
Swansea University
UK

Jiping Liu,
Chinese Academy of Surveying and Mapping
China

Fuad Aleskerov,
National Research University Higher School of Economics
Russia

Peter Lowe,
Leibniz Information Centre for Science and Technology
Germany

Wangjun He,
Chinese Academy of Surveying and Mapping
China

Aliyu Usman Ahmad,
University of Aberdeen
UK

Pavel kisilev,
Huawei Research / Toga networks
Israel

Ruben Jerves cobo,
Ghent University
Belgium

Daniele Menezes Nascimento,
McGill University
Canada

Han-joon Kim,
University of Seoul
South Korea

Fred Jacquet,
Evangelist
France

Abdelhamid Bouchachia,
Bournemouth University
UK

Shawki A. Al-Dubaee,
Taiz University
UK

Fionn Murtagh,
University of Derby
UK

Marija Milojevic Jevric,
Imperial College London
UK

Pilar Rey del Castillo,
Instituto de Estudios Fiscales
Spain

Abdulmohsen Algarni,
King Khalid University
Saudi Arabia

LuÃs Sousa,
University of Porto
Portugal

Ogunjobi Olivia Abiola,
Dangote Group
Nigeria

Mikhail Moshkov,
King Abdullah University of Science and Technology
Saudi Arabia

Ahmed N. AL-Masri,
American University in the Emirates
UAE

Dominik Slezak,
University of Warsaw
Poland

E. Cabral Balreira,
Trinity University
USA

David Sung,
KyungHee University School of Management Graduate School
South Korea

SookYoung Son,
Hyundai Heavy Industries
South Korea

Fairouz Kamareddine,
Heriot-Watt University
UK

Bennett B. Borden,
Drinker Biddle & Reath
USA

Giovanni Rossi,
University of Bologna
Italy

Jenny Lundberg,
Linnaeus University
Sweden

Tommi KÃ¤rkkÃ¤inen,
University of JyvÃ¤skylÃ¤
Finland

Witold Dzwinel,
AGH University of Science and Technology
Poland

S.N.Mohanty,
KIIT University
India

Kazumi Nakamatsu,
University of Hyogo
Japan

Nicolas Wesner,
Mazars Actuariat
France

Eleni Rozaki,
Institute of Technology Tallaght
Ireland

Karmen Kern Pipan,
Ministry of Public Administration
Slovenia

Bertrand Hassani,
UniversitÃ© Paris
France

Derrick K. Rollins,
Iowa State University
USA

Nikolaos Freris,
New York University Abu Dhabi
USA

Parameshwar P. Iyer,
Indian Institute of Science
India

Ergi Sener,
Bonbon Tech
Turkey

Abdul Basit,
State Bank of Pakistan
Pakistan

Data Mining 2021

Conference series LTD cordially invites all participants across the globe to attend the 8^th International Conference on Webinar on Big Data Analysis and Data Mining (Data Mining 2021) which is going to be held during August 09 -10 2021 to share the knowledge. The main theme of the conference is “Knowledge discovery in databases: Step towards recovering economy after the pandemic: Covid-19". This conference aimed to expand its coverage in the areas of Big Data and Data Mining where expert talks, young researcher’s presentations will be placed in every session of the meeting will be inspired and keep up your enthusiasm. We feel our expert Organizing Committee is our major asset. However, your presence over the venue will add one more feather to the crown of Data Mining 2021.

Data Mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data Mining 2021 is comprised of the following sessions with 27 tracks and 160 sub-sessions designed to offer comprehensive sessions that address current issues of Big Data and Data Mining. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time-consuming to resolve.

The target audience for the conference:

Business Delegates
Industrial Experts
Data Scientists
Data Base engineers
Scientists/Researchers
Professors
President/Vice president
Chairs/Directors
Students
Experts and Delegates

Young Researchers Forum - Young Scientist Awards Data Mining 2021

Young Research’s Awards at Data Mining 2021 for the Nomination: Young Researcher Forum - Outstanding Masters/Ph.D./Post Doctorate thesis work Presentation, only 25 presentations acceptable at the Data Mining 2020 young research forum.

Benefits:

Young Scientist Award recognition certificate and memento to the winners
Our conferences provide best Platform for your research through oral presentations.
Learn about career improvement with all the latest technologies by networking.
Young Scientists will get appropriate and timely information by this Forum.
Platform for collaboration among young researchers for better development.
Provide an opportunity for research interaction and established senior investigators across the globe in the field
Share the ideas with both eminent researchers and mentors.
It’s a great privilege for young researchers to learn about the research areas for expanding their research knowledge.

Deadline for Registrations:

Platform for collaboration among young researchers for better development

Till December 30 – $399
Till January 30 – $499
Till February 28 – $599

To register, please follow the link: https://www.conferenceseries.com/onlinepayment/

Note: Please choose the Conference short name as Data Mining 2021, August 09-10, 2021 – Webinar

Conference series LTD cordially invites all the participants from all over the world to attend the Webinatr on 8^th International Conference on Big Data Analysis and Data Mining during August 09-10, 2021. This includes prompt keynote presentations, Oral talks, Poster presentations and Exhibitions.

Track 1: Data Mining Applications in Science, Engineering, Healthcare and Medicine

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.

Related Societies:

Big Data and Society, United Kingdom | Big Data Europe Empowering Communities with Data Technologies, Europe | Technische Hochschule Ingolstadt, Germany | American Statistical Association, United States | Advanced Analytics Institute, Australia

Related Big Data Conferences of Data Mining Applications in Science, Engineering, Healthcare and Medicine

6^th World Machine Learning and Deep Learning Conference, October 24-25, 2020 Helsinki, Finland; 4th European Conference on Design and Production Engineering, October 28-29, 2020 Rome, Italy; 3rd International Conference on Advanced Robotics, Mechatronics and Artificial Intelligence, November 11-12, 2020 Tokyo, Japan; 4th International Conference on Battery and Fuel Cell Technology, November 04-05, 2020 London UK; International Conference on Satellite and Space Communications, November 18-19, 2020 Holiday Inn Rome Aurelia, Rome, Italy; 4th International Conference on Robotics and Artificial Intelligence, November 11-12, 2020 Tokyo, Japan; Animation and Multimedia Conference , December 2-3, 2020 Manila, Philippines; 11th Euro Biosensors and Bioelectronics Conferences, October 23-24, 2020 Rome, Italy;

SIAM International Conference on Data Mining, May 7-9, 2021, Ohio, U.S; 14^th International Conference on Data Mining, July 30-31, 2021 in Istanbul, Turkey; International Conference on Data Mining and Knowledge Discovery, Sydney, Australia February 17-19, 2021; Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore, May 11 - May 14, 2021; ACM International Conference on Web Search and Data Mining Houston, USA; 16^th International Conference on Machine Learning and Data Mining MLDM 2021, July 18-23, 2021, New York, USA; Data Mining Conferences and Engineering in Finance, 20-21 Feb 2021, London, UK; 5th International Conference on Cloud Computing and Big Data Analytics April 10, 2021 - Apr 13, 2021, Chengdu, China; 6th Int’l Conference on Data Science and Machine Learning Applications, May 3- 5, 2021, Riyadh, Saudi Arabia; 9^th International Conference on Data Mining, Jul 11, 2021 - Jul 12, 2021, Toronto, Canada; 6^th International Conference on Data Mining and Applications, January 25-26, 2021, Zurich, Switzerland;

Track 2: 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.

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Track 3: 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.

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Track 4: Big Data Analytics

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.

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Track 5: Big Data Algorithm

Big data is data of wide range that it does not fit in the main memory of a single machine, and the need to process big data by organised algorithms arises in machine learning, scientific computing, signal processing, Internet search, network traffic monitoring and some other areas. Data must be processed with advanced tools (analytics and algorithms) to make meaningful information.

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Track 6: Big Data Optimization

The term Big Data is here: information of immense sizes is getting to be universal. With this, there is a need to take care of advancement issues of exceptional sizes. Machine learning, compacted detecting, informal organization science and computational science are some of meager clear application areas where it is anything but difficult to plan improvement issues with millions or billions of variables. The long-established advance calculations are not intended to scale to occasions of this size, new methodologies are required.

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Track 7: Forecasting from Big Data

Big Data is a revolutionary phenomenon has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. Big data play a critical role in reshaping the key aspects of forecasting by identifying and reviewing the problems, potential, better predictions, challenges and most importantly the related applications.

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Track 8: 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.

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Track 9: Data Mining and Machine Learning

Both data mining 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.

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Track 10: Data Mining Methods and Algorithms

Data mining structures and calculations an interdisciplinary subfield of programming building is the computational arrangement of finding case in information sets including techniques like Big Data Search and Mining, Data Mining Analytics, High execution information mining figuring's, Methodologies on sweeping scale information mining, Methodologies on expansive scale information mining, Big Data and Analytics, Novel Theoretical Models for Big Data.

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Track 11: Data Mining Tools and Software

Information Mining gadgets and programming ventures join Big Data Security and Privacy, Data Mining and Predictive Analytics in Machine Learning, Software Systems and Boundary to Database Systems.

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Track 12: Data Mining Tasks, Processes and Analysis

Information mining undertaking can be shown as a data mining request. A data mining request is portrayed similarly as the data mining task first. This track joins complete examination of mining figuring’s, Semantic-based Data Mining and Data Pre-planning, Mining on data streams, Graph and sub-outline mining, Statistical Methods in Data Mining, Data Mining Predictive Analytics. The basic calculations in information mining and investigation shape the theory for the developing field of information science, which incorporates robotized techniques to examine examples and models for a wide range of information, with applications widening from logical revelation to business insight and examination.

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Track 13: Data Privacy and Ethics

In our e-world, information protection and cyber security have gotten to be respective terms. In this business, we have a commitment to secure our customer's information, which has been acquired as per their permission exclusively for their utilization. That is an all-important point if not promptly obvious. There's been a ton of speak of late about Google's new protection approaches, and the discussion rapidly spreads to other Internet beasts like Facebook and how they likewise handle and treat our own data.

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Track 14: Data Warehousing

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 a central repository of integrated data from one or more disparate sources.

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Track 15: 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.

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Track 16: 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.

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Track 17: Social Network Analysis

Social network analysis (SNA) is the advancement process of looking at social structures through the use of networks and graph theory. It characterizes networked structures in terms of lumps (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.

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Track 18: Business Analytics

Business analytics refers to the skills, technologies, practices for continuous rerun exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies enact data-driven decision-making.

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Track 19: Internet of Things (IOT)

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.

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Track 20: Open Data

Open data is the thought that some data should be freely available to everyone to use and republish as they wish, without restrictions from patents, copyright or other mechanisms of control. Open data can also be linked data; when it is linked open data, is a method of publishing structured data so that it can be interlinked and become more useful through semantic queries.

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Track 21: New Visualization Techniques

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".

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Track 22: Search and Data Mining

In the course of recent times, there has been an immense increase in the measure of information being put away in databases and the number of database applications in business and the investigative space. This blast in the measure of electronically put away information was accelerated by the achievement of the social model for putting away information and the improvement and developing of information recovery and control innovations.

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Track 23: Frequent Pattern Mining

Frequent pattern mining (or) Pattern mining consists of using/developing data mining algorithms to discover interesting, unpredicted and useful patterns in databases. Pattern mining algorithms can be applied to different types of data such as sequence databases, transaction databases, streams, strings, spatial data, and graphs. Pattern mining algorithms can be designed to discover various types of patterns such as subgraphs, associations, sequential rules, lattices, sequential patterns, indirect associations, trends, periodic patterns and high-utility patterns.

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Track 24: Clustering

Cluster analysis or clustering is the task of organizing a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

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Track 25: 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.

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Track 26: Nanoinformatics

Nanoinformatics is the science and practice of determining which information is relevant to the nanoscale science and engineering community, and then developing and implementing effective mechanisms for collecting, storing, validating, modelling, applying, analysing and sharing that information. Nano informatics also involves the utilization of networked communication tools to launch and support efficient communities of practice. Nanoinformatics is necessary for intelligent development and comparative characterization of nanomaterials, for design and use of optimized Nanodevices and Nanosystems, and for development of advanced instrumentation and manufacturing processes.

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Track 27: Renewable Energy Forecasting with Big Data

Hybrid Renewable Energy Forecasting (HyRef), which uses big data analytics to predict the appropriable of renewable energy. With the use of this system, help bring more renewable energy to the power grid by predicting the availability of such energy. It uses data gathered from monitoring devices and analytical technology to generate accurate weather forecast within renewable energy system devices.

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Data Mining Journals:

Data Mining Universities:

Data Mining Target Audience:

Data Base engineers
Scientists/Researchers
Professors
President/Vice president
Chairs/Directors
Data Scientists
Students
Experts and Delegates

Market Analysis

A new update to the Worldwide Semiannual Big Data and Analytics Spending Guide from International Data Corporation (IDC) forecasts worldwide revenues for big data and business analytics (BDA) will reach $150.8 billion in 2017, an increase of 12.4% over 2016. Commercial purchases of BDA-related hardware, software, and services are expected to maintain a compound annual growth rate (CAGR) of 11.9% through 2020 when revenues will be more than $210 billion. Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, reaching a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B.

FRAMINGHAM, Mass., April 4, 2019 – Worldwide revenues for big data and business analytics (BDA) solutions are forecast to reach $189.1 billion this year, an increase of 12.0% over 2018. A new update to the Worldwide Semi-annual Big Data and Analytics Spending Guide from International Data Corporation (IDC) also shows that BDA revenues will maintain this pace of growth throughout the 2018-2022 forecast with a five-year compound annual growth rate (CAGR) of 13.2%. By 2022, IDC expects worldwide BDA revenue will be $274.3 billion.

Digital transformation is a key driver of BDA spending with executive-level initiatives resulting in deep assessments of current business practices and demands for better, faster, and more comprehensive access to data and related analytics and insights," said Dan Vesset, group vice president, Analytics and Information Management at IDC. "Enterprises are rearchitecting to meet these demands and investing in modern technology that will enable them to innovate and remain competitive. BDA solutions are at the heart of many of these investments."

IT services will be the largest category of the BDA market in 2019 ($77.5 billion), followed by hardware purchases ($23.7 billion), and business services ($20.7 billion). Together, IT and business services will account for more than half of all BDA revenues throughout the forecast and will be among the categories with the fastest growth. BDA-related software revenues will be $67.2 billion in 2019, with end-user query, reporting, and analysis tools ($13.6 billion) and relational data warehouse management tools ($12.1 billion) being the two largest software categories. The BDA technology categories that will see the fastest revenue growth will be non-relational analytic data stores (34.0% CAGR) and cognitive/AI software platforms (31.4% CAGR).

In terms of deployment, more than 70% of BDA software revenues in 2019 will go toward on-premises solutions. However, revenue for BDA software delivered via the public cloud will experience very strong growth over the five-year forecast (32.3% CAGR) and will represent more than 44% of the total BDA software opportunity in 2022.

"Big Data technologies can be difficult to deploy and manage in a traditional, on premise environment. Add to that the exponential growth of data and the complexity and cost of scaling these solutions, and one can envision the organizational challenges and headaches. However, cloud can help mitigate some of these hurdles. Cloud's promise of agility, scale, and flexibility combined with the incredible insights powered by BDA delivers a one-two punch of business benefits, which are helping to accelerate BDA adoption," said Jessica Goepfert, program vice president, Customer Insights & Analysis at IDC. "When we look at the opportunity trends for BDA in the cloud, the top three industries for adoption are professional services, personal and consumer services, and media. All three industries are rife with disruption and have high levels of digitization potential. Additionally, we often find many smaller, innovative firms in this space; firms that appreciate the access to technologies that may have historically been out of reach to them either due to cost or IT complexity."

The industries currently making the largest investments in big data and business analytics solutions are banking, discrete manufacturing, professional services, process manufacturing, and federal/central government. Combined, these five industries will account for nearly half ($91.4 billion) of worldwide BDA revenues this year. The industries that will deliver the fastest BDA growth are securities and investment services (15.3% CAGR) and retail (15.2% CAGR). Retail's strong growth will enable it to move ahead of federal/central government as the fifth largest industry in 2022.

On a geographic basis, the United States will be the largest country market by a wide margin with nearly $100 billion in BDA revenues this year. Japan and the UK will generate revenues of $9.6 billion and $9.2 billion respectively this year, followed by China ($8.6 billion) and Germany ($7.9 billion). The fastest growth in the BDA market will be in Argentina and Vietnam with five-year CAGRs of 23.1% and 19.4%, respectively. China will have the third fastest growth rate with a 19.2% CAGR, which will enable it to become the second largest country for BDA revenues in 2022.

From a company size perspective, very large businesses (those with more than 1,000 employees) will be responsible for nearly two thirds of all BDA revenues throughout the forecast. Small and medium businesses (SMBs) will also be a significant contributor to BDA revenues with nearly a quarter of the worldwide revenues coming from companies with fewer than 500 employees.

The Worldwide Semi-annual Big Data and Analytics Spending Guide is designed to address the needs of organizations assessing the big data and business analytics opportunity by geography, industry, and company size. The Spending Guide provides subscribers with revenue projections for 19 technology and service categories across 19 industries, five company size bands, and 53 countries. Unlike any other research in the industry, the comprehensive Spending Guide was designed to help IT decision makers to clearly understand the industry-specific scope and direction of big data and business analytics opportunity today and over the next five years.

Scope and Importance

Data mining derives its name from the similarities between searching for valuable business information in a large database for example, finding linked products in gigabytes of store scanner data and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

Automated prediction of trends and behaviors.

The method of discovering predictive information in large databases is automated through data mining. Questions historically requiring extensive hands-on research can now be answered quickly directly from the results. Targeted marketing is a typical example of a predictive problem. Data mining uses data on past promotional mailings to determine the goals that will most likely optimize investment returns in the future mailings. Other predictive concerns include predicting bankruptcy and other types of default, and recognizing segments of a population likely to respond to events in a similar manner.

• Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.

Decision trees: Tree-shaped structures that represent sets of decisions. Such decisions create guidelines for a dataset's classification. Different decision tree approaches include Automatic Interaction Detection (CHAID) and Classification and Regression and Trees (CART) and Chi Square.

• Genetic algorithms:

Techniques of optimization use processes such as genetic variation, mutation, and natural selection in a design based on evolution principles.

• Rule induction:

Extracting useful if-then rules from statistical significance-based data. For more than a decade, most of these techniques have been used in advanced analytical methods that operate with fairly limited quantities of data. These capabilities are now evolving to integrate directly with industry-standard data warehouse and OLAP platforms

New and increasingly creative applications for it are emerging as data mining matures. Although it is possible to describe a wide variety of data mining scenarios. For the purpose of this paper the applications of data mining are divided in thefollowing categories:• Healthcare• Finance • Retail industry • Telecommunication • Text Mining & Web Mining • Higher Education

UBIQUITOUS DATA MINING (UDM)

The invention of smartphones, palmtops, cell phones, and portable devices allows omnipresent access to large amounts of information. The next natural step in the field of omnipresent computing is advanced data analysis to derive useful knowledge. Accessing and analyzing data from a ubiquitous computing device offer many challenges example, Due to communication, computing, security, and other factors, UDM adds additional costs. So one of UDM's goals is to mine data while minimizing the omnipresence value. Human-computer interaction is another challenging aspect of UDM. Visualizing patterns like classifiers, clusters, associations and others, in portable devices are usually difficult. The small display areas of serious challenges to interactive data mining environments Data management is also a difficult problem in a mobile world. In fact, the sociological and psychological implications of combining data mining technology with our lifestyle are still to be discussed. Key issues to address include UDM concepts, advanced mobile and distributed algorithms, data management problems, markup languages and other data representation techniques; compatibility with mobile database systems, technical issues (architecture, control, security and communication issues), specialized mobile devices for UDM, computer agents and UDM (agent-based approaches in UDM, interaction agents — cooperation, collaboration, negotiation, organizational behaviour), UDM (Business, Research, Engineering, Medicine and Other Disciplines Applications), UDM Location Management Issues and UDM Web-based Software Technology.

Conference Highlights

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Conference Date	August 09-10, 2021
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