We are very glad to welcome you to participate at our “international conference on datamining and bigdata analytics” on november 18-19, 2019 at barcelona, spain with the theme “current trends and technologies in bigdata and datamining”.
Our aim is to bring engineers/employees, global scientists/ researchers and business professionals to the one platform. Datamining-2019 includes the series of b2b & b2c meetings, oral talks, poster presentations, and workshops & exhibitions by industries, academicians, professors, and young forum researchers & students.
Why choose us?
• To acquire the broad set of perspectives and insights on some of the critical issues in the field of datamining and bigdata.
• To transmit current issues and advances of your latest research.
• For thought provoking speeches by scientists, professors, ph.d.'s and young researchers.
• For exhibit your devices, tools, applications, and services.
• For network development with academic and business professionals.
• For developing highly effective techniques for identification by sharing knowledge.
Who can attend?
• Academicians, directors/ceo.
• Researchers from universities
• Data scientists from research institutes
• Data/ cloud engineers and developers
• Scholars/ laureates, associations and societies
• Business entrepreneurs
• Training institutes
• Manufacturing companies
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.
• Data mining systems in financial market analysis
• High performance data mining algorithms
• Data mining in security
• Engineering data mining
• Data Mining in Healthcare data
• Medical Data Mining
• Advanced Database and Web Application
• Data mining and processing in bioinformatics, genomics and biometrics
• Application of data mining in education
• Methodologies on large scale data mining
Data mining and machine learning: Data mining is the subset of business analytics; it is similar to experimental research. The origins of data mining are databases, statistics. Whereas machine learning involves the algorithm that improves automatically through experience based on data.
• Machine learning and statistics
• Machine learning tools and techniques
• Bayesian networks
• Fielded applications
• Generalization as search
Big data analytics: Big data analytics is the often complex process of examining large and varied data sets or big data to uncover information including hidden patterns, unknown correlations, market trends and customer preferences that can help organizations make informed business decisions.
• 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
Optimization and big data: The main objective of this big data optimization is to supply the mandatory background to figure with huge knowledge by introducing some novel improvement algorithms and codes capable of operating within the big knowledge setting furthermore as introducing some applications in huge knowledge improvement for each teachers and practitioners interested, and to learn society, industry, academia, and government. Presenting applications in a very sort of industries, this book are helpful for the researchers getting to analyses massive scale knowledge. Several improvement algorithms for large knowledge together with confluent parallel algorithms, restricted memory bundle formula, diagonal bundle methodology, confluent parallel algorithms, network analytics, and lots of a lot of are explored.
• Computational problems in magnetic resonance imaging
• Optimization of big data in mobile networks
Big data technologies, algorithm and applications: Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Big data Algorithm is defined as sets of process rules for a computer to follow, to analyze massive amounts of data. Big data used in so many applications they are banking, agriculture, chemistry, data mining, cloud computing, finance, marketing, stocks, healthcare etc.
• Big data storage architecture
• GEOSS clearinghouse
• Distributed and parallel computing
• Data Stream Algorithms
• Randomized Algorithms for Matrices and Data
• Algorithmic Techniques for Big Data Analysis
• Models of Computation for Massive Data
• The Modern Algorithmic Toolbox
• Ecommerce and customer service
• Finances and Frauds services
• Regulated Industries
• Clinical and healthcare
• Financial aspects of Big Data Industry
• Current and future scenario of Big Data Market
• Travel Industry
• Retail / Consumer
• Big Data Analytics in Enterprises
• Public administration
• Security and privacy
• Web and digital media
Artificial intelligence: The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
• Artificial creativity
• Artificial Neural networks
• Adaptive Systems
• Ontologies and Knowledge sharing
Data privacy, ethics and warehousing: Data privacy, also called information privacy, is the aspect of information technology (IT) that deals with the ability an organization or individual has to determine what data in a computer system can be shared with third parties. Data ethics is about responsible and sustainable use of data. It is about doing the right thing for people and society and warehousing is a large store of data accumulated from a wide range of sources within a company and used to guide management decisions.
• Data encryption
• Data Hiding
• Public key cryptography
• Quantum Cryptography
• 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
Data mining tasks, process, tools, analysis and software: There are a number of data mining tasks such as classification, prediction, time-series analysis, association, clustering, summarization etc. Data Mining is the set of methodologies used in analyzing data from various dimensions and perspectives, finding previously unknown hidden patterns, classifying and grouping the data and summarizing the identified relationships. large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is the analysis step of the "knowledge discovery in databases.
• Big Data Security and Privacy
• Ecommerce and Web services
• Medical informatics
• Visualization Analytics for Big Data
• Predictive Analytics in Machine Learning and Data Mining
• Interface to Database Systems and Software Systems
• Competitive analysis of mining algorithms
• Computational Modelling and Data Integration
• Semantic based Data Mining and Data Pre-processing
• Mining on data streams
• Graph and sub-graph mining
• Scalable data pre-processing and cleaning techniques
• Statistical Methods in Data Mining
• Numeric attributes
• Categorical attributes
• Graph data
Social network analysis: Social network analysis (SNA) is a process of quantitative and qualitative analysis of a social network. SNA measures and maps the flow of relationships and relationship changes between knowledge-possessing entities. The SNA structure is made up of node entities, such as humans, and ties, such as relationships.
• Networks and relations
• Development of social network analysis
• Analyzing relational data
• Dimensions and displays
• Positions, sets and clusters
Complexity and algorithms: Algorithm complexity is a measure which evaluates the order of the count of operations, performed by a given or algorithm as a function of the size of the input data. To put this simpler, complexity is a rough approximation of the number of steps necessary to execute an algorithm.
• Mathematical Preliminaries
• Recursive Algorithms
• The Network Flow Problem
• Algorithms in the Theory of Numbers
Business analytics and Open data: open data for business intelligence. Apart from big data, businesses are making optimum use of open data - data that is inexpensive, easily accessible, and a profitable resource like goldmine.
• Emerging phenomena
• Technology drives and business analytics
• Capitalizing on a growing marketing opportunity
• Open Data, Government and Governance
• Open Development and Sustainability
• Open Science and Research
• Technology, Tools and Business
New visualization techniques: Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science.
• Analysis data for visualization
• Scalar visualization techniques
• Frame work for flow visualization
• System aspects of visualization applications
• Future trends in scientific visualization
Search and data mining: Data mining is defined as a process used to extract usable data from a larger set of any raw data. For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms. Data mining is also known as Knowledge Discovery in Data
• Multifaceted and task driven search
• Personalized search and ranking
• Data, entity, event, and relationship extraction
• Data integration and data cleaning
• Opinion mining and sentiment analysis
Frequent pattern mining and Clustering: A Frequent pattern is a pattern (a set of items, subsequences, subgraphs, etc.) that occurs frequently in a data set. Finding inherent regularities in data. Forms the foundation for many essential data mining tasks, association, correlation, and causality analysis.
• Frequent item sets and association
• Item Set Mining Algorithms
• Graph Pattern Mining
• Pattern and Role Assessment
• Hierarchical clustering
• Density Based Clustering
• Spectral and Graph Clustering
• Clustering Validation
OLAP technologies: OLAP (online analytical processing) systems typically fall into one of three types: Multidimensional OLAP (MOLAP) is OLAP that indexes directly into a multidimensional database. Relational OLAP (ROLAP) is OLAP that performs dynamic multidimensional analysis of data stored in a relational database.
• Data Storage and Access
• OLAP Operations
• OLAP Architechture
• OLAP tools and internet
• Functional requirements of OLAP systems
• Limitation of spread sheets and SQL
ETL(extract, transform, load) : ETL is short for extract, transform, load, three database functions that are combined into one tool to pull data out of one database and place it into another database. Extract is the process of reading data from a database. In this stage, the data is collected, often from multiple and different types of sources.
• ETL Basics in Data Warehousing
• ETL Tools for Data Warehouses
• Logical Extraction Methods
• ETL data structures
• Cleaning and conforming
• Delivering dimension tables
Kernel methods: In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine. The general task of pattern analysis is to find and study general types of relations in datasets.
• Kernel operations in feature space
• Kernel for complex objectives
• High dimensional data
• Density of the multivariate normal
• Dimensionality reduction
• Kernel principal component analysis
MACHINE LEARNING: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
• Predictive maintenance or condition monitoring
• Warranty reserve estimation
• Propensity to buy
• Demand forecasting
• Process optimization
• Predictive inventory planning
• Recommendation engines
• Upsell and crosschannel marketing
• Market segmentation and targeting
• Customer ROI and lifetime value
Healthcare and Life Sciences
• Alerts and diagnostics from realtime patient data
• Disease identification and risk stratification
• Patient triangle optimization
• Proactive health management
• Healthcare provider sentiment analysis
Travel and Hospitality
• Aircraft scheduling
• Dynamic pricing
• Social mediaconsumer feedback and interaction analysis
• Customer complaint resolution
• Traffic patterns and congestion management
• Risk analytics and regulation
• Customer Segmentation
• Cross selling and up selling
• Sales and marketing campaign management
• Credit worthiness evaluation
Energy, Feedstock and Utilities
• Power Usage analytics
• Seismic data processing
• Carbon emission and trading
• Customer specific pricing
• Smart grid management
• Energy demand and supply optimization
Advantages of Machine Learning: Machine learning helps use to handle multi-dimensional data which have various varieties of data types in a dynamic environment. Machine learning can be used in various sectors such as Banking systems, Health care, financial sectors, etc.
• Useful where large scale data is available
• Large scale deployments of Machine Learning beneficial in terms of improved speed and accuracy
• Understands nonlinearity in the data and generates a function mapping input to output (Supervised Learning)
• Recommended for solving classification and regression problems
• Ensures better profiling of customers to understand their needs
• Helps serve customers better and reduce attrition
Deep learning: Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
• Unsupervised Pretrained Networks (UPNs)
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks
• Recursive Neural Networks
• Microsoft Cognitive Toolkit
Artificial neural networks (ANN)& Chainer : Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not necessarily identical to, the biological neural networks that constitute animal brains. The connections between artificial neurons are called edges. Chainer is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia.
• Feed forward and neural network
• Radial basis function (RBF) network
• Kohonen self-organizing network
• Learning vector quantization
• Recurrent neural network
• Modular neural networks
• Physical neural networks
• Other types of networks
Computer vision and image processing: Image processing is a subset of computer vision. A computer vision system uses the image processing algorithms to try and perform emulation of vision at human scale. For example, if the goal is to enhance the image for later use, then this may be called image processing.
• Controlling processes
• Automatic inspection
• Organizing information
• Modelling objects or environments
• Detecting events
• Recognize objects
• Locate objects in space
• Recognize actions
• objects in motion
Pattern recognition: Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases, and is often used interchangeably with these terms.
• Decision Tree
• Networks (of any kind)
• Reinforced learning
Predictive analysis: 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.
Processes included in Predictive Analytics are
• Define Project
• Data Collection
• Data Analysis
• Model Monitoring
There are many applications of Predictive Analytics. Few of them are
• Customer Relationship Management (CRM)
• Collection Analytics
• Fraud Detection
• Cross Sell
• Direct Marketing
• Risk Management
• Health Care
Predictive Analytics plays a very strong role in Industry Applications like
• Predictive Analytics Software
• Predictive Analytics Software API
• Predictive Analytics Programs
• Predictive Lead Scoring Platforms
• Predictive Pricing Solutions
• Customer Churn, Renew, Upsell, Cross Sell Software Tools
Dimensionality reduction: Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
• Principal component analysis (PCA)
• Kernel PCA
• Graph based kernel PCA
• Linear Discriminant Analysis (LDA)
• Generalized discriminant analysis (GDA)
Landmarks in the development of robotics: Although the origin of Robot is rooted in ancient world the modern concept of Robotics started with the advent of Industrial Revolution. In the modern era the term ‘Robot’ has been introduced for the first time by Karel Capek in 1921. Today, one can envisage human-sized robots with the ability of near-human thoughts and works, which are being launched into space to complete the next stages of extra-terrestrial and extrasolar research. Commercial and engineering Robots are now in extensive use performing jobs more economically or with greater accuracy and reliability than humans. This session related to the History of Robotics is meant to drive through the entire journey and visit through the process of creating the human invented automation system.
• Ancient Era and the Concept of Robot
• Asimov and Laws of Robotics
• The First “Programmable” Mechanism, a PaintSprayer
• The Turtle Robots
• Landing in the Moon
• Deep Blue beat World Chess Champion
Robots saving our life: Robots are also used in factories to build things like cars, candy bars, and electronics. Robots are now used in medicine, for military tactics, for finding objects underwater and to explore other planets. Robotic technology has helped people who have lost arms or legs. Robots are a great tool to help mankind.
• Artificial Health Professionals
• Remote Surgery
• Robotic Surgery
• Bio Inspired Robotics
• Designing Treatment Practices
• Elderly Care
• Medical Research and Robotics
• Putting the “Care” Back in Healthcare
• AI for Disabled Person
Cloud Services, Security, Privacy, trust and Applications: A cloud service is any service made available to users on demand via the Internet from a cloud computing provider's servers as opposed to being provided from a company's own on-premises servers. Cloud security is the protection of data, applications, and infrastructures involved in cloud computing. Many aspects of security for cloud environments are the same as for any on-premise IT architecture. Privacy concerns are increasingly important in the online world. It is widely accepted that cloud computing has the potential to be privacy disabling. The secure processing of personal data in the cloud represents a huge challenge. The Cloud Trust Protocol (CTP) is a procedure for establishing digital trust between a cloud computing customer and a cloud service provider. The goal of CTP is allow customers to make customers make informed decisions when evaluating cloud service providers.
• XaaS (everything as a service including IaaS, PaaS, and SaaS)
• Cloud services models & frameworks
• Service deployment and orchestration in the Cloud
• Cloud service management
• Cloud workflow management
• Cloud services reference models & standardization
• Cloudpowered services design
• Cloud elasticity
• Machine learning and systems interactions
• Data management applications & services
• Service for computingintensive applications
• Mining and analytics
• Dataprovisioning services
• Cloud programming models, benchmarks, and tools
• Cloudbased services & protocols
• Faulttolerance & availability of cloud services and applications
• Application development and debugging tools
• Business models & economics of cloud services
• Accountability & audit
• Authentication & authorization
• Cloud integrity
• Blockchain Cloud services
• Cryptography in the Cloud
• Hypervisor security
• Identity management & security as a service
• Prevention of data loss or leakage
• Secure, interoperable identity management
• Trust & credential management
• Trust models for cloud services
• Usable security Risk management in cloud computing environments
• Privacypreserving data mining for clouds
• Information sharing and data protection in the cloud
• Cryptographic protocols against internal attacks in clouds
• Privacy protection in cloud platforms
• Energy/cost/efficiency of security in clouds
Everyone can learn, without exposure to new points of view, we can miss new ideas and trends that can impact future results. DMBA 2019 conference can expose you to new ways of conducting your research and help you discover how to be more innovative
Networking with Peers:
Academic and Industrial conferences provide a great opportunity to network. Often researchers and scientists from other regions of the country can become valuable resources for referrals and best-practices. Avoiding peers for fear of others discovering your competitive advantage can actually limit your own success. Collaboration is the way to approach networking. While there are those whose intentions can be suspect, most people can help each other uncover ideas and spark inspiration when they get to know each other on a personal level.
Position yourself as an Expert:
DMBA 2019 helps you to position you as an expert and you can develop a reputation as an expert to your peers. As you are engaged over the long term are often asked to speak at the events and to write articles for their academic and industrial publications. Like it or not, others like to associate with the experts in any industry. We feel good about meeting experts with those that are celebrated by their peers.
Encounter New Exhibitors and Sponsor’s:
A chance to meet some of the best people for you to get to know if you want to learn more about the current business climate. Discovering innovative products and services for your research and business is necessary to stay competitive in today’s fast-paced world. Plus, these exhibitors and sponsors who sell to your industry fully grasp what is happening inside your competition. Invest time with the sponsors at the event and turn them into your friends and allies.