Track 1: Data Mining and its Applications

Data mining is the way toward finding examples to remove data with a shrewd technique from an informational collection and change the data into an intelligible structure for additional utilization. Information mining is the point-by-point assessment venture of the "information revelation in data sets" measure. These applications relate Data mining structures in authentic money-related business domain assessment, Application of information mining in situating, Data mining and Web Application, Engineering information mining, Data Mining in safety, Social Data Mining, Neural Networks and Data Mining, Medical Data Mining, Data Mining in Healthcare.

Track 2: Big Data in Nursing Research

With progress in innovations, nurture researchers are progressively creating and utilizing enormous and complex datasets, now and again called "Large Data," to advance and improve Health Conditions. New methodologies for gathering and itemized assessment huge datasets will permit us to all the more likely comprehend the organic, hereditary, and conduct underpinnings of wellbeing, and to improve the manner in which we forestall and oversee disease.

Track 3: Big data for Industry

Over recent years, big data has become a distinct advantage in practically all enterprises. As large information is spreading wide in our day by day exercises, individuals are concentrating generally on the estimation of huge information more than its noteworthiness. A large portion of the associations set up objectives for embracing huge information ventures, which incorporates objectives, for example, improving client experience, cost decrease, better advertising, successful dynamic and so on., Due to data penetrates, which means the arrival of secret or private data either deliberately or unexpectedly, have made security a significant objective in enormous information task to work safely. Huge information empowers enveloping the bigger image of all the gathered information which further makes it simple to distinguish designs and get the necessary data for critical thinking and dynamic. In an assembling industry, this huge information is joined with assembling programming for data mining.

Track 4: Big Data Technologies

Big Data is the name given to tremendous measures of information. As the data roll in from an assortment of sources, it could be excessively various and excessively enormous for customary advancements to deal with. This makes it critical to have what it takes and the foundation to deal with it shrewdly. There are a considerable lot of the big data arrangements that are especially mainstream right currently fit for the utilization.

Track 5: Big Data Analytics

Big data analytics test and dissect tremendous measures of data to i.e., big data - to reveal concealed examples, obscure co-relations, market patterns, client inclinations and other valuable data that can assist associations with settling on more-educated business choices. Work and convey by particular examination frameworks and programming, big data investigation can lay the best approach to different business benefits, including new income openings, more powerful promoting, improved operational proficiency, upper hands, and better client care.

Track 6: Big Data Algorithm

Big data is data of a wide range that does not fit in the main memory of a single machine, and the need to process big data by organized 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.

Track 7: Artificial Intelligence for IT Operations

Artificial Intelligence is demonstrating its ascent all finished and it didn't leave operational exercises in the business behind. Utilization of AI, large information examination, and other counterfeit advancements for computerization of distinguishing and settling the basic data innovation issues, is alluded to as Artificial Intelligence for IT Operations. Sort of insight framework intended for genuine applications at business scale is called Operational Artificial Intelligence. It is not quite the same as essential counterfeit examination and mechanical man-made consciousness applications which are not the same as the normal utilization of business.

Track 8: Personalization with Deep Learning

Making or creating something to meet the individual prerequisites of an individual is called Personalization. Meeting those individual inclinations and necessities in machines is conceivable through profound learning in AI. Personalization empowers a client to encounter the administrations which are explicitly customized according to his/her inclinations and significance. This is conceivable through informal learning through the data sources that are given by the clients and showing the outcomes according to the client's decision. Crude information given by the clients is taken by the machines through different handling layers where the yield of the past layer is taken as the contribution of the presentation layer, etc utilizing profound learning calculations. This personalization improves the general understanding of the client which helps in expanding deals and better client commitment in business. AI can upgrade personalization through the prescient examination of information. Understanding the information, Content advancement and significance are the principal parts of personalization with Deep Learning.

Track 9: Cloud Computing

Cloud Computing is the conveyance of processing administrations—workers, stockpiling, information bases, organizing, programming, investigation, and that's only the tip of the iceberg—over the Internet ("the cloud"). Distributed computing depends on sharing of assets to accomplish coordination and economies of scale, like an open utility. Organizations offering these registering administrations are called cloud suppliers and regularly charge for distributed computing administrations dependent on use.

Track 10: Business Analytics

Business analytics alludes to the abilities, advances, rehearses for ceaseless rerun investigation, and examination of past business execution to pick up understanding and drive business arranging. The business investigation is utilized by organizations sanction data-driven dynamic.

Track 11: Data Mining and Machine Learning

Data mining is a subset of business analytics and refers to exploring a present large dataset to unearth previously unknown patterns, relationships, and anomalies that are existing in the data. Machine learning is a subset of artificial intelligence. With machine learning, computer systems analyze large data units and then learn patterns that will assist in making predictions about new data sets. Apart from the preliminary programming and maybe some fine-tuning, the computer doesn’t need human interaction to learn from the data.

Track 12: Data Privacy and Ethics

Data privacy is the right of a citizen to have control over how personal information is collected and used. Data privacy is the ability of individuals to control their personal information. Data privacy, also called information privacy, is the aspect of information technology that deals with the ability an organization or individual have to determine what data in a computer system can be shared with third parties.

Track 13: Social Network Analysis

Social network analysis is also known as network science, is a field of data analytics that uses networks and graph theory to understand social structures. Social network analysis can provide insight into social influences within teams and identify cultural issues.

Track 14: Internet of Things (IOT)

The Internet of Things (IoT) refers to a system of interrelated internet-connected objects that communicate over wireless networks. The Internet of Things (IoT) describes the network of physical objects things that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.

Track 15: Big Data and Deep Learning

Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cybersecurity, fraud detection, marketing, and medical informatics. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabelled and uncategorized.

Track 16: Resource Management Approaches for Big Data Systems

The goal of this RMBDS is to explore new directions and approaches for reasoning about advanced resource management and task scheduling methods and algorithms for Big Data platforms and to encourage the submission of ongoing work with already important theoretical and practical results, as well as position papers and case studies of existing verification projects to highlight the art in this domain.

Track 17: Mobile Applications of Big Data

Big Data Analytics considers client conduct and market patterns. In this way, mobile application designers get an exhaustive perspective on what their clients need and what sort of applications are drifting in the market. Fundamentally, Big Data causes mobile app developers to redo mobile applications to fit client inclinations, in this manner adding to the rich client experience. Big Data tools can reveal fundamental certainties about their preferences, abhorrence’s, necessities, and desires by examining client conduct.

Track 18: Big Data Applications for Internet of Things

There are various ways to get benefits from IoT big data in some cases, it’s enough to get by with quick analysis, while some valuable outcomes are available only after deeper data processing. The Internet of Things and big data are the most talked-about technology topics in recent years. Big data is characterized with the aid of ‘4 Vs’: volume, variety, velocity, and veracity. The IoT will hugely expand the amount of data on hand for evaluation by all methods of organizations. However, there are large limitations that must be overcome earlier rather than later before expertise advantages are thoroughly realized. The IoT and big data are certainly intimately related: billions of internet-connected ‘things’ will, by definition, generate giant amounts of data.

Track 19: Big Data and High-Performance Computing

There is a recent trend towards performing big data processing on HPC systems to benefit from their high computation capabilities.  Big data computing and high-performance computing (HPC) has evolved over the years as separate paradigms. With the explosion of data and the demand for machine learning algorithms, these two paradigms increasingly embrace each other for data management and algorithms.

As the world is getting more computerized and associated, large information and business investigation are making additional opportunities for information assortment, stockpiling, and insight cycle and examination. With the colossal measure of the information age, stockpiling, and catch, huge information and business investigation have developed as a significant innovation to contemplate and understand information related troubles for organizations that have a gigantic measure of information put away and utilized inside the association.

The expanding premium and interest in computerized reasoning, thusly, is prompting the beginning of new apparatuses for gathering and examining information and new venture jobs and duties which incorporates large information and business examination arrangements.

The Global Big Data market is assessed at $23.56 billion every 2015 and is hoping to reach $118.52 billion by 2022 developing at a CAGR of 26.0% from 2015 to 2022. Rushed development in customer information, predominant data security, upgraded business efficiencies are a portion of the key components fuelling market development. The information mining devices market is required to develop from USD 591.2 Million out of 2018 to USD 1,039.1 Million by 2023, at a Compound Annual Growth Rate of 11.9% during the conjecture time frame, attributable to the striking increment in information volume and expanded mindfulness among ventures to help the advantages of accessible information resources.

Overall Big Data market incomes for programming and administrations are extended to increment from $42B in 2018 to $103B in 2027, arriving at a Compound Annual Growth Rate (CAGR) of 10.48%. As a feature of this figure, Wikibon gauges the overall Big Data market is developing at a 11.4% CAGR somewhere in the range of 2017 and 2027, developing from $35B to $103B.

The enterprises at present creation the biggest interests in large information and business investigation arrangements are banking, discrete assembling, proficient administrations, measure assembling, and bureaucratic/focal government. Consolidated, these five businesses will represent almost half ($91.4 billion) of overall BDA incomes this year. The ventures that will convey the quickest BDA development are protections and speculation administrations (15.3% CAGR) and retail (15.2% CAGR). Retail's solid development will empower it to push forward of administrative/focal government as the fifth biggest industry in 2022.

Why Attend??

Data Mining 2022 welcomes participants from around the globe concentrated on finding out about Data Mining, this would be one of the best chances to arrive at the biggest array of members from the Data Science people group. Lead exhibits, circulate data, meet with current and likely clients, make a sprinkle with another product offering, and get name acknowledgment at this 2-day occasion. Incredibly famous speakers, the latest strategies, strategies, and the most current updates in the Data Mining fields are signs of this meeting.