Call for Abstract

6th International Conference on Big Data Analysis and Data Mining , will be organized around the theme “Future Technologies for Knowledge Discoveries in Data”

data mining 2019 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in data mining 2019

Submit your abstract to any of the mentioned tracks.

Register now for the conference by choosing an appropriate package suitable to you.

\r\n 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.

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  • Track 1-1Bayesian networks
  • Track 1-2Methodologies on large-scale data mining
  • Track 1-3High performance data mining algorithms
  • Track 1-4Data mining in security
  • Track 1-5Engineering data mining
  • Track 1-6Data Mining in Healthcare data
  • Track 1-7Medical Data Mining
  • Track 1-8Advanced Database and Web Application
  • Track 1-9Data mining and processing in bioinformatics, genomics and biometrics
  • Track 1-10Application of data mining in education
  • Track 1-11Case Studies and Implementation
  • Track 1-12Data mining systems in financial market analysis

\r\n 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 2-1Big data in nursing inquiry
  • Track 2-2Methods, tools and processes used with big data with relevance to nursing
  • Track 2-3 Big Data and Nursing Practice

\r\n 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 3-1Big data storage architecture
  • Track 3-2GEOSS clearinghouse
  • Track 3-3Distributed and parallel computing

\r\n 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 4-1Big Data Analytics Adoption
  • Track 4-2Benefits of Big Data Analytics
  • Track 4-3Barriers to Big Data Analytics
  • Track 4-4Volume Growth of Analytic Big Data
  • Track 4-5Managing Analytic Big Data
  • Track 4-6Data Types for Big Data

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

\r\n The term oBig 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 the 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 6-1Optimization of big data in mobile networks
  • Track 6-2Computational problems in magnetic resonance imaging

\r\n 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 7-1Challenges for Forecasting with Big Data
  • Track 7-2Applications of Statistical and Data Mining Techniques for Big Data Forecasting
  • Track 7-3Forecasting the Michigan Confidence Index
  • Track 7-4Forecasting targets and characteristics

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.

  • Track 8-1Ecommerce and customer service
  • Track 8-2Biomedicine
  • Track 8-3Finances and Frauds services
  • Track 8-4Web and Digital Media
  • Track 8-5Data Integration, Aggregation, and Representation
  • Track 8-6Query Processing, Data Modeling, and Analysis
  • Track 8-7Heterogeneity and Incompleteness
  • Track 8-8Scale, Timeliness and Privacy
  • Track 8-9System Architecture and Human Collaboration
  • Track 8-10New innovations and business opportunities
  • Track 8-11Regulated Industries
  • Track 8-12Clinical and Healthcare
  • Track 8-13Financial aspects of Big Data Industry
  • Track 8-14Security and privacy
  • Track 8-15Manufacturing
  • Track 8-16Telecommunication
  • Track 8-17E-Government
  • Track 8-18Public administration
  • Track 8-19Big Data Analytics in Enterprises
  • Track 8-20Retail / Consumer
  • Track 8-21Travel Industry
  • Track 8-22Current and future scenario of Big Data Market
  • Track 8-23Business Proliferation

\r\n 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 9-1Machine learning and statistics
  • Track 9-2Machine learning tools and techniques
  • Track 9-3Fielded applications
  • Track 9-4Generalization as search
  • Track 9-5Bayesian networks

\r\n 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 10-1Novel Theoretical Models for Big Data
  • Track 10-2New Computational Models for Big Data
  • Track 10-3Empirical study of data mining algorithms

\r\n 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 11-1Big Data Security and Privacy
  • Track 11-2E-commerce and Web services
  • Track 11-3Medical informatics
  • Track 11-4Visualization Analytics for Big Data
  • Track 11-5Predictive Analytics in Machine Learning and Data Mining
  • Track 11-6Interface to Database Systems and Software Systems

\r\n 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 12-1Competitive analysis of mining algorithms
  • Track 12-2Categorical attributes
  • Track 12-3Numeric attributes
  • Track 12-4Statistical Methods in Data Mining
  • Track 12-5Scalable data pre-processing and cleaning techniques
  • Track 12-6Graph and sub-graph mining
  • Track 12-7Mining on data streams
  • Track 12-8Semantic-based Data Mining and Data Pre-processing
  • Track 12-9Computational Modelling and Data Integration
  • Track 12-10Graph data

\r\n 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 13-1Data encryption
  • Track 13-2Data Hiding
  • Track 13-3Public key cryptography
  • Track 13-4Quantum Cryptography
  • Track 13-5Convolution
  • Track 13-6Hashing

\r\n 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.

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  • Track 14-1Data Warehouse Architectures
  • Track 14-2Case studies: Data Warehousing Systems
  • Track 14-3Data warehousing in Business Intelligence
  • Track 14-4Role of Hadoop in Business Intelligence and Data Warehousing
  • Track 14-5Commercial applications of Data Warehousing
  • Track 14-6Computational EDA (Exploratory Data Analysis) Techniques

\r\n 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 15-1Cybernetics
  • Track 15-2Artificial creativity
  • Track 15-3Artificial Neural networks
  • Track 15-4Adaptive Systems
  • Track 15-5Ontologies and Knowledge sharing

\r\n 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 16-1Cloud Computing Applications
  • Track 16-2Emerging Cloud Computing Technology
  • Track 16-3Cloud Automation and Optimization
  • Track 16-4High Performance Computing (HPC)
  • Track 16-5Mobile Cloud Computing

\r\n 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 17-1Networks and relations
  • Track 17-2Development of social network analysis
  • Track 17-3Analyzing relational data
  • Track 17-4Dimensions and displays
  • Track 17-5Positions, sets and clusters

\r\n 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 18-1Emerging phenomena
  • Track 18-2Technology drives and business analytics
  • Track 18-3Capitalizing on a growing marketing opportunity

\r\n 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 19-1Medical and Healthcare
  • Track 19-2Transportation
  • Track 19-3Environmental monitoring
  • Track 19-4Infrastructure Management
  • Track 19-5Consumer application

\r\n 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 20-1Open Data, Government and Governance
  • Track 20-2Open Development and Sustainability
  • Track 20-3Open Science and Research
  • Track 20-4Technology, Tools and Business

\r\n 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 21-1Analysis data for visualization
  • Track 21-2Scalar visualization techniques
  • Track 21-3Frame work for flow visualization
  • Track 21-4System aspects of visualization applications
  • Track 21-5Future trends in scientific visualization

\r\n 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 22-1Multifaceted and task-driven search
  • Track 22-2Personalized search and ranking
  • Track 22-3Data, entity, event, and relationship extraction
  • Track 22-4Data integration and data cleaning
  • Track 22-5Opinion mining and sentiment analysis
  • Track 22-6Frequent pattern mining

\r\n 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 on 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 23-1Frequent item sets and association
  • Track 23-2Item Set Mining Algorithms
  • Track 23-3Graph Pattern Mining
  • Track 23-4Pattern and Role Assessment

\r\n 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 24-1Hierarchical clustering
  • Track 24-2Density Based Clustering
  • Track 24-3Spectral and Graph Clustering
  • Track 24-4Clustering Validation

\r\n 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 25-1Mathematical Preliminaries
  • Track 25-2Recursive Algorithms
  • Track 25-3The Network Flow Problem
  • Track 25-4Algorithms in the Theory of Numbers
  • Track 25-5NP-completeness

\r\n 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 26-1Consortium for Coordinating Nanomaterials Research Data
  • Track 26-2Nanomaterials Development Using Nanoinformatics
  • Track 26-3Meta data for Cross-discipline, Cross-sector Information Exchange
  • Track 26-4Meta-crawler for Mining Nanotechnology Repositories and Open Access Sources
  • Track 26-5Nano SAR Education and Dissemination
  • Track 26-6Simulation Resources and Challenge

\r\n 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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  • Track 27-1Big Data and Analytics in weather forecasting
  • Track 27-2Advanced cloud imaging technology
  • Track 27-3Big data infrastructure layers
  • Track 27-4Data sources, Ingestion, Processing and Storage Layer
  • Track 27-5Predicting Weather Conditions Based on Historical Data
  • Track 27-6Streamlining Operation and Maintenance Processes
  • Track 27-7Case Studies and Implementation
  • Track 27-8Economic Growth in Renewable Energy Industry