Track 1: Big Data Analytics

Huge information is information so vast that it doesn't fit in the fundamental memory of a solitary machine, and the need to prepare huge information by productive calculations emerges on Internet seeks, system activity checking, machine learning, experimental figuring, signal handling, and a few different territories. This course will cover numerically thorough models for growing such calculations and some provable confinements of calculations working in those models.

• Big data technology
• Gaining value from unstructured data
• Hadoop and Spark ecosystem
• Distributed and parallel computing
• Big data paradigm and V model

Track 2: Big Data in Bioinformatics, Multimedia, Smartphones

The volume of data is expanding fast in bioinformatics research. Big data sources are no longer limited to particle physics experiments or search-engine logs and indexes. Multimedia data makes up about 2/3rd of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and challenges.

• Mobile communications and networks
• Heterogeneous data sources
• Cognition
• Mobile data analytics
• Computing efficiency

Track 3: Big Data Analytics in Healthcare and Medicine

The big data is in extended use in the field of medicine and healthcare. In healthcare, large amounts of heterogeneous medical data have become applicable in various healthcare organizations.  The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. As the technology raises the cost of healthcare is also increasing more and more. Big data is a great helping hand on this issue. It is a great help for even physicians to keep track of all the patients’ history.

• Data mining and processing in bioinformatics, genomics and biometrics
• Bio-Surveillance
• Electronic Health Records
• Predictive Analytics
• Real-time Alerting

Track 4: Machine Learning and Statistical Methods for Data Mining

Machine learning is a part of data science which majorly focuses on writing algorithms in a way such that machines (Computers) are able to learn on their own and use the learning’s to tell about new dataset whenever it comes in. Machine learning uses the power of statistics and learns from the training dataset. It is the interesting data-driven disciplines that help organizations make better decisions and positively affect the growth of any business. Statistics also deal with designing surveys and experiments to get quality data which can further be used to make an estimation of the population.

• Supervised learning
• Empowering decision makers through data visualization
• Algorithms for data analysis in Statistics
• Designing surveys and experiments

Track 5: Computer science and Artificial Intelligence

Artificial Intelligence is a computer-controlled robot or software to think intelligently and focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes.

• Scientific computing
• Computer graphics
• Algorithmic trading
• Cybernetics
• Artificial Neural networks
• Adaptive Systems
• Ontologies and Knowledge sharing

Track 6: Networking Big Data Security

High-performance network capacity provides the backbone for high-end computing systems. These high-end computing systems play a vital role in Big Data. With the evolution of networks, threats or attacks with the intention of disrupting service or stealing confidential data are increasing tremendously. Networks have to be monitored constantly and protected against attacks.  

• Mining with data clouds
• Hive database in Hadoop Distributed File System
• Database management system
• IP networks

Track 7: Mobility Analytics from Spatial and Social Data

Recent developments in both social networks and spatial services have advanced significantly thanks to the prevalence of the online social platforms, smart devices, and geo-positioning components.  However, social and spatial processes interact dramatically. For instance, joint actions happen within space and social factors such as population migration or even just interacting with friends on a geo-enabled smartphone.

• Open Data
• Primary surveyed data
• Multimodal mobility
• Information retrieval and smart search
•  OLAP technologies

Track 8: Data Mining Meets Visual Analytics at Big Data Era

The Visual analytics technique enjoys the joint advantage of the human intelligence and the machine’s computational power. Various aspects of the data mining method need to be inspected, justified, organized and evaluated for a successful VA system. The challenges include but not limited to big data reduction method to enable large-scale visualization big data integration algorithms to fuse heterogeneous information source for efficient visualizations temporal data analysis techniques for the effective dynamic and streaming data visualization and the mechanism for data privacy and security to delivery trustworthy big data visualization for end users.

• Knowledge discovery with data mining and visual analytics technologies
• Novel methods on visualization-oriented data mining
• Visual representations and interaction techniques of data mining results

Track 9: Unlocking the Potential of Smart Data

The amount of data being created today is expected to increase ten-fold in less than a decade, it’s also anticipated that enterprises will produce around 60% of global data by 2025. However, while the amount of data may be growing exponentially, the intelligence gleaned from it is not.

• How to use big data to support a smarter city?
• Impact of Big Data Analytics
• Revolutionizing Business Models
• Big data and data science using intelligent approaches

Track 10: Challenges and opportunities of predictive analytics

Big Data is described by high dimensionality and large sample size. These two features that raise three unique challenges: (i) high dimensionality brings noise accumulation, spurious correlations and incidental homogeneity; (ii) high dimensionality combined with large sample size creates issues such as heavy computational cost and algorithmic instability; (iii) the massive samples in Big Data are typically aggregated from multiple sources at different time points using different technologies.  

• Need for synchronization across data sources
• Uncertainty of Data Management Landscape
• Big Data Talent Gap
• Getting important insights using Big data analytics

Relevant Conferences: Big Data Conferences | Data Management Meetings | Data Mining Congress | Cloud Computing Conference | Machine Learning Events | Data Science Congress | 

• Computer Science, Machine Learning and Big Data Analytics, August 30-31, 2018 Dubai, UAE
• 5^th Annual Smart Data Summit 17-18 APRIL 2018, DUBAI - UAE
• Big Data Show on 01-03 May 2018, Dubai World Trade Centre, Dubai, UAE
• 4^th International Conference on Data Mining and Applications, Feb 24, 2018 - Feb 25, 2018, Dubai, UAE
• Artificial Intelligence, April 16-17, 2018 Las Vegas, Nevada, USA
• Big Data Innovation, Data Mining and Analytics Summit, August 20-21, 2018, Singapore
• Big Data Analytics & Data Mining, September 26-27, 2018 Chicago, Illinois, USA
• 20^th International Conference on Data Mining, Big Data, Database and Data Technologies, November 26 - 27, 2018
• Big Data Analysis and Data Mining, 20-21 June 2018 Rome, Italy
• Big Data & Analytics Innovation Summit, March 15–16 Melbourne, 2018
• Big Data Analysis and Data Mining, September 07-08, 2017 Paris, France
• Big Data & Analytics Innovation Summit, 7–8 Singapore, 2018
• Big Data Innovation Summit, July17–18 Las Vegas, 2018
• Big Data Innovation Summit, March21–22 London, 2018
• Big Data & Analytics Innovation Summit, April 18–19 Hong Kong, 2018
• Big Data Innovation Summit, April 12–13 San Francisco, 2018
• Big Data & Analytics Innovation Summit, September 5–6 Shanghai, 2018
• Computer Science & Engineering, June 21-22, 2018 Oslo, Norway
• Global Artificial Intelligence Conference, January 17 to 19 2018, Santa Clara, USA.
• Marketing Analytics and Data Science, April 11 - 13, 2018 San Francisco, CA

Related Societies/Associations:

Advanced Analytics Institute, University of Technology, Sydney, National Center for Data Mining (NCDM) at the University of Illinois at Chicago, Germany, American Association of Engineering Societies, American Society for Engineering Education, National Society of Professional Engineers, Institution of Engineers, Bangladesh, Bangladesh Computer Society, Chinese Society for Electrical Engineering, Computer Society of India, Society of EMC Engineers (India), Japanese Union of Scientists and Engineers, Saudi Council of Engineers, European Federation of National Engineering Associations, Society of Professional Engineers UK, New Zealand Computer Society, Society of Professional Engineers UK, Germany, Big Data Europe Empowering Communities with Data Technologies, Europe, National Centre for Data Mining, Chicago, Web Analytics Association, Florida, SIAM society for industrial and applied mathematics, United States, IAENG Society of Data Mining, Hong Kong, IEEE Computer Society, United States, Data Mining Section of INFORMS, United States, International Institute for Analytics, Oregon, The International Machine Learning Society, Germany, International Institute for Business Analysis, Ontario, European Knowledge Discovery Network of Excellence, Germany

The global big data market size was valued at USD 25.67 billion in 2015 and it is expected to a significant growth over the forecast period. The elevating number of virtual online offices coupled with increasing popularity of social media producing enormous amount of data is a major factor driving growth. Increased internet penetration owing to the several advantages including unlimited communication, abundant information and resources, easy sharing, and online services generates huge chunks of data in everyday life, which is also anticipated to propel demand over the coming years.

The market is rapidly becoming a developing area of focus across numerous end-use industries. The technology adds up substantial value by providing useful information; enabling organizations to manage large chunks of data efficiently. Companies with the help of these solutions obtain both efficiency and quality in managing large volume of raw information, ultimately resulting in significant cost reduction.

2017 Market Research Report on Cloud Computing Services Industry was a professional and depth research report on Cloud Computing Services industry that you would know the world's major regional market conditions of Cloud Computing Services industry, the main region including North American, Europe and Asia etc., and the main country including United States, Germany, Japan and China etc.

In the recent decades, the development of information and communications technologies injects new vitality for enterprise marketing. For example, barcode technology and the emergence of online stores greatly enhance the efficiency of the enterprise because of which company managers are beginning to face the enormous data. However, the data and business profits are not directly proportional. Unfortunately, the human brain can’t handle so much data. In the meanwhile, data mining technology becomes very mature in theory. The technology-oriented applications for enterprise decision makers with a new perspective to look a t market. Those advanced technologies let enterprises obtain a lot of resources from different channels, and use

those effective tools to translate data into unlimited opportunities.

The industry has experienced the exponential growth in amount of both unstructured and structured data across several sectors. Gathering, storing and exploiting have become a vital task for companies. Need for tools to manage this substantial data amount is expected to fuel the market demand over the forecast period. Organizations collect and store data with a large view to extract information from data to gain better insights. This is done for analyzing and making precise decisions, which help in improving operational efficiencies, risk mitigation and cost reduction.

The Artificial Industry is separated by core technologies into Natural Language Processing(NLP), Machine Learning, Deep Learning, and Machine Vision archetype. Deep Learning technology segment is anticipated to dominate the AI market; both in terms of revenue and CAGR over the forecast period of 2017 to 2025. ‘Deep Learning’ technology is gaining prominence because of its complex data driven applications including voice and image recognition. It offers a huge investment opportunity as it can be leveraged over other technologies to overcome the challenges of high data volumes, high computing power, and improvement in data storage.

Rapid improvements in fast information storage capacity, high computing power, and parallelization have contributed to the swift uptake of the robotics and artificial intelligence technology in end-use industries such as automotive and healthcare. Further, the need for understanding and analyzing visual contents, for gaining meaningful insights, is expected to provide traction to the industry over the forecast period.

Artificial Intelligence - Direct & Enabled Revenue, 2014 - 2025 (USD Million)

Scope and importance

Big data is broadly influencing the IT industry, few technologies or trends. If analyzed effectively, massive information is helping the companies to improve in their decision-making and compete on another level. However, managing big data is a difficult endeavor, according to a recent report by Microsoft.

"Big data absolutely has the potential to change the way governments, organizations, and academic institutions conduct business and make discoveries, and its likely to change how everyone lives their day-to-day lives.

Big Data Discovery 2018

We gratefully thank all our wonderful Speakers, Conference Attendees, Students, Media Partners, Associations and Sponsors for making Data Mining 2017 Conference the best ever!

The 4^th International conference on Big Data Analysis and Data Mining, hosted by the Conferenceseries llc LTD was held during September 07-08, 2017 at Paris, France based on the theme “Future Technologies for Knowledge Discoveries in Data ". Benevolent response and active participation was received from the Organizing Committee Members along with Scientists, Researchers, Students and leaders from various fields of Big Data, Data Mining, who made this event a grand success.

Conferenceseries llc LTD expresses its gratitude to the conference Moderator, namely Dr. Fairouz Kamareddine and Dr. Nikolaos  Freris for taking up the responsibility to coordinate during the sessions. We are indebted to your support.

The conference was initiated with the Honourable presence of the Keynote forum. The list includes:

• Michael Valivullah, NASS, US Department of Agriculture, USA
• En-Bing Lin, Central Michigan University, USA
• Petra Perne, Institute of Computer Vision and applied Computer Sciences, Germany
• Fionn Murtagh, University of Huddersfield, UK
• Mikhail Moshkov, King Abdullah University of Science and Technology, Saudi Arabia
• Fuad Aleskerov, National Research University Higher School of Economics, Russia
• Omar M Knio, King Abdullah University of Science and Technology, Saudi Arabia

The meeting reflected various sessions, in which discussions were held on the following major scientific tracks:

• Data Mining Methods and Algorithms
• Data Mining Tasks and Processes
• Data Mining Applications in Science, Engineering, Healthcare and Medicine
• Big Data Applications
• Data Mining Tools and Software
• Data Warehousing
• Artificial Intelligence
• Cloud computing
• Forecasting from Big Data
• Complexity and algorithms
• Open data
• OLAP technologies
• Big Data algorithm
• ETL (Extract, Transform and Load)
• New visualization techniques
• Optimization and Big Data
• Social network analysis
• Search and data mining
• Data Mining analysis
• kernel methods
• Frequent pattern mining
• Clustering
• Data privacy and ethics
• Big data technologies
• Business analytics

Conferenceseries llc LTD offers its heartfelt appreciation to organizations such as Allied Academies, Andrew John Publishing Inc., New York private Equity Forum, Crowd Reviews, Enterprise Service Outlook, CIO Applications, Haptic, Bonbon Tech and other eminent personalities who supported the conference by promoting in various modes online and offline which helped the conference reach every nook and corner of the globe. Conferenceseries llc LTD also took privilege to felicitate the Keynote Speakers, Organizing Committee Members, Chairs and sponsors who supported this event

With the grand success of Data Mining 2017, Conferenceseries llc LTD is proud to announce the "6th International Conference on Big Data, Knowledge Discovery and Data mining" to be held during August 06-07, 2018 at Abu Dhabi.

For More details visit:  https://bigdata.conferenceseries.com/