SCIENTIFIC SESSIONS


  • Big data:

    Every organization today has enormous data that keeps on increasing every minute. To manage such data you need advanced technology. Big Data Analytics is bringing in a new revolution in the field of big data concepts analysis. Big data analyses a large amount of data to get deeper knowledge about the data and find out its hidden patterns and correlations. It will help the business to understand the information in a more better manner. It will help the business to identify the data that is more important to the organization.


  • Data science:

    Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.

  • Data Analytics:

     

    In Big Data 2019 the discussions would be on Data analytics. Abstracts are invited on the topics like Big data, Big data analytics, Social media analytics, Accounting analytics, Financial analytics, Marketing analytics, Management analytics, Business analytics, Supply chain analytics, Operations management analytics and Descriptive, predictive and prescriptive analytics. 

  • Machine Learning and Artificial Intelligence:

    The Big Data 2019 conference would be discussing on the  various topics like Artificial Intelligence and Philosophy, Automated reasoning and inference, Case-based reasoning, Cognitive aspects of AI, Commonsense reasoning, Constraint processing, Heuristic search, High-level computer vision, Intelligent interfaces, Intelligent robotics, Knowledge representation, Machine learning, Multiagent systems, Natural language processing, Planning and theories of action, Reasoning under uncertainty or imprecision. 

     


  • Advances in Data Science:


    The Big Data 2019  is dedicated to the advancement of data science and its application in policies, practices and management as Open Data to ensure that data are used in the most effective and efficient way in promoting knowledge and learning. Abstracts are invited on the computational, natural and social science and the humanities. The scope of the Big Data Meeting 2019 includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for data.


  • Database Management:


    Big Data 2019 Conference would be discussing on the various topics like statistical and mathematical foundations for data science and analytics; understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences; creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence; data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data, behaviors and systems; active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture and infrastructure.


  • Big Data Algorithms:


    Big data meetings 2019 will deal with the subjects like computer science, theory, methods and interdisciplinary applications, data and information systems, software engineering, artificial intelligence, automation and control systems. Big Data meeting 2019 provides an advanced forum for studies related to algorithms and their applications. Abstracts are invited on the topics like  Algorithm engineering, Algorithmic game theory, Algorithms for databases and database design, Algorithms for language processing, Algorithms in biology, chemistry, physics, etc., Algorithms in relation with automata theory and formal languages, Algorithms on strings, Analysis of algorithms and Approximation algorithms.


  • Machine Learning in Data Science:


    Big data Conference 2019 is an international forum for research on computational approaches to learning. The conference would summarize the reporting substantive results on a wide range of learning methods applied to a variety of learning problems and abstracts are invited on the topics like Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.


  • Database management and Analysis  :


    Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.


  • Big Data Analytics:


    Big Data 2019 invites abstracts related to which welcomes cutting-edge concepts describing original basic and applied work involving biologically-inspired computational accounts of all aspects of big data science analytics. Spanning the life sciences, social sciences, engineering, physical and mathematical sciences, Big Data 2019 aims to provide a platform for the dissemination of research, current practices, and future trends in the emerging discipline of big data analytics.


  • Advances in Big Data:

     

    Big Data 2019 Conference will have key discussions on  big data analytics to data-intensive computing and all applications of big data research. The conferences will address the key challenges facing big data today and going forward including, but not limited to: data capture and storage,  search, sharing, and analytics,  big data technologies,  data visualization,  architectures for massively parallel processing,  data mining tools and techniques,  machine learning algorithms for big data,  cloud computing platforms,  distributed file systems and databases. 


  • Big Data Management:

    Big data is term refer to huge data sets, have high Velocity , high Volume and high Variety and complex structure with the difficulties of management , analyzing, storing and processing .Due to characteristic of big data it becomes very difficult to Management, analysis, Storage, Transport and processing the data using the existing traditional techniques. This paper introduces Big Data Management and Analysis. First introduction of big data, Big Data Impact on Storage Infrastructure, Big Data Analysis and Management include (Big Data over Cloud computing and Hadoop HDFS and MapReduce), Finally Conclusion and Future work.


  • Big data analytics and social media:

    The digitized world of today presents us with an issue we’ve never faced before. Each and every little device in our home is either now or soon to be connected to the Internet of Things (IoT) and that means it is able to collect data.The influx of collected data allows businesses to better understand behavioural and buying patterns of the customers, but big data goes even beyond. It is able to help scientists deal with global issues, while also providing marketers with information needed for proper decision-making.


  • Infrastructure and platform for smart computing:

    The technology industry has entered a new cycle of tech innovation and growth, which we are calling “Smart Computing.” Like prior cycles of mainframe computing, personal computing, and network computing, Smart Computing will power a seven- to eight-year period when business and government investment in technology grows at twice the rate of the overall economy. Smart Computing will be more complex than what came before — blending elements of hardware, software, and network technologies. Similar to earlier cycles, Smart Computing will grow rapidly because it will help business solve problems that it couldn’t address before; in this cycle, Smart Computing will help companies optimize process results and the returns from their balance sheets. Unlike the horizontal technologies of personal computing and network computing, Smart Computing will have a highly vertical industry focus. Vertical solutions will differ significantly from vertical offerings in the past thus the advent of verticals 3.0 as a result. Tech vendors will have great growth opportunities in this new cycle, but also big challenges in navigating the shift to Smart Computing.


  • Data mining, graph mining and data science:

    Data mining is the process of uncovering patterns and finding anomalies and relationships in large datasets that can be used to make predictions about future trends. The main purpose of data mining is extracting valuable information from available data.Data mining is considered an interdisciplinary field that joins the techniques of computer science and statistics. Note that the term “data mining” is a misnomer. It is primarily concerned with discovering patterns and anomalies within datasets, but it is not related to the extraction of the data itself.


  • Tools and systems for big data:

    Developers prefer to avoid vendor lock-in and tend to use free tools for the sake of versatility, as well as due to the possibility to contribute to the evolvement of their beloved platform. Open source products boast the same, if not better level of documentation depth, along with a much more dedicated support from the community, who are also the product developers and Big Data practitioners, who know what they need from a product. Thus said, this is the list of 8 hot Big Data tool to use in 2018, based on popularity, feature richness and usefulness.


  • Multimedia & Smartphones

    Multimedia is more than one concurrent presentation medium (for example, on CD-ROM or a Web site). Although still images are a different medium than text, multimedia is typically used to mean the combination of text, sound, and/or motion video. Some people might say that the addition of animated images produces multimedia .


  • Smart devices and hardware

    Smart appliances utilize modern computer and communications technology to make functions faster, cheaper and more energy-efficient. The appliances can take advantage of an energy "smart grid," being implemented by utility companies nationwide. When the smart grid technology is finally implemented, refrigerators, toasters, dishwashers and washing machines can tap into the smart grid power source.


  • Security and privacy for big data:

    The Big Data is an emerging area applied to manage datasets whose size is beyond the ability of commonly used software tools to capture, manage, and timely analyze that amount of data. The quantity of data to be analyzed is expected to double every two years (IDC, 2012). All these data are very often unstructured and from various sources such as social media, sensors, scientific applications, surveillance, video and image archives, Internet search indexing, medical records, business transactions and system logs. Big data is gaining more and more attention since the number of devices connected to the so-called “Internet of Things” (IoT) is still increasing to unforeseen levels, producing large amounts of data which needs to be transformed into valuable information. Additionally, it is very popular to buy on-demand additional computing power and storage from public cloud providers to perform intensive data-parallel processing. In this way, security and privacy issues can be potentially boosted by the volume, variety, and wide area deployment of the system infrastructure to support Big Data applications.


  • Cloud and grid computing for big data:

    With the current advances of today's technology in many sectors such as manufacturing, business, science and web application, a variety of data to be processed continues to witness an exponential rise. This data is referred to as big data. Efficient management and processing of this data poses an interesting but significant problem. To utilize the numerous benefits of grid computing, Big data processing and management techniques should be integrated in the current grid environment. In this paper, the definition, features and requirements of big data platform are explored. Incorporating Hadoop is suggested as it the most commonly used technique in handling Big Data as it offers reliability, ease of use, ease of maintenance and scalability.


  • Models and tools for smart computing:

    We are now in the era of intelligent and smart computing by utilizing the power of many advanced and hybrid computing techniques. The available range of techniques includes; artificial intelligence, soft computing, machine learning, simulation and modelling, pattern recognition, robotics, machine vision, signal and image processing, biomedical computing, bioinformatics, green computing, ubiquitous computing, cryptography with the advanced form of those techniques hybridized with ant colony, swarm optimization, genetic algorithm, evolutionary algorithm, nature inspired computing, neuro-fuzzy approach for solving the real life problems with the help of intelligence and smart science.The purpose of this special issue is to seek high quality research papers that contribute to the advancement of knowledge in advanced and hybridized intelligence science, intelligent analytics and smart technologies by using the power of computational intelligence and bridges theoretical research with applications. The major aim of this special issue will be to achieve a multi-disciplinary balance between research advancements in theories and methods associated with Artificial Intelligent and smart computing techniques. This special issue is devoted to address the research areas that deepen the understanding of logical, cognitive, and computational foundations of the future perspectives and exploring the most essential issues and innovations in Advanced computing, Intelligent modeling and smart applications.


  • Web search

    A website that maintains an index and short summaries of billions of pages on the Web, Google being the world's largest. China's Baidu, Microsoft's Bing and Yahoo! follow in size.Most search engine sites are free and paid for by ads. Yahoo! was the first to gain worldwide attention. Yahoo! was originally known as a "directory" rather than a search engine, because it indexed much of its content by human observation. However, as Web content grew exponentially, it became impossible to index everything manually.


  • Machine learning and AI for big data:

     Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.


  • Techniques, models and algorithms for big data:

    Computer systems pervade all parts of human activity and acquire, process, and exchange data at a rapidly increasing pace. As a consequence, we live in a Big Data world where information is accumulating at an exponential rate and often the real problem has shifted from collecting enough data to dealing with its impetuous growth and abundance. In fact, we often face poor scale-up behavior from algorithms that have been designed based on models of computation that are no longer realistic for big data.While it is getting more and more difficult to build faster processors, the hardware industry keeps on increasing the number of processors/cores per board or graphics card, and also invests into improved storage technologies. However, all these investments are in vain, if we lack algorithmic methods that are able to efficiently utilize additional processors or memory features.


  • Crowd computing:

     Crowdsourcing is a process through which a task, problem or project is solved and completed through a group of unofficial and geographically dispersed participants. Crowdsourcing is a joint process development or problem-solving technique that requires help from a network of people, or crowd. This network is usually connected via the Internet or through a specific website

  • Data science:

    Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.


  • Big data:

    Every organization today has enormous data that keeps on increasing every minute. To manage such data you need advanced technology. Big Data Analytics is bringing in a new revolution in the field of big data concepts analysis. Big data analyses a large amount of data to get deeper knowledge about the data and find out its hidden patterns and correlations. It will help the business to understand the information in a more better manner. It will help the business to identify the data that is more important to the organization.