3rd International Conference on Big Data Analysis and Data Mining September 26-27,2016 London,UK

SIAM society for industrial and applied mathematics

SIAM society for industrial and applied mathematics

6^th International Conference on Bioinformatics ,March 29-30, 2016 Spain; 5^th World Congress on Materials Science & Engineering June 13-15, 2016 Spain; 2^nd World Congress on Automation and Robotics June 16-18, 2016 USA; International Conference on Mining and Metallurgy June 27-29, 2016 South Africa; 3^rd International Conference On Biomedical Engineering and Expo September 22-23, 2016 Austria; International Conference on Chemical Engineering September 12-14, 2016 USA; 4^thInternational Conference on Mechanical & Aerospace Engineering October 03-05, 2016, 2015 USA; 4^thInternational Conference on Bioprocess and Bio systems Engineering, October 20-21, 2016 USA; 12^th International Conference on Food Processing & Technology October 24-26, 2016 Turkey; 14^th International Conference On Food Engineering November 10-12, 2016 Australia; 2^nd International Conference on Innovations in Communications and Network Systems and Computer Science, January 10-11, 2016 UAE; Taiwan International Conference on Operation Research and Data Mining January 13-15, 2016 Taiwan; Taiwan International Conference on Innovation and Management January 13-15, 2016 Taiwan; 6^th International Conference on Applied Physics and Mathematics January 13-14, 2016 Singapore; 5^th International Conference on Information Computer Application January 18-19, 2016 Australia; The 7^th International Conference on Computer Modelling and Simulation,January 18-19, 2016 Australia; 4^th International Conference on Innovation and Information Management January 18-19, 2016 Australia; International Conference on Engineering Technologies and Big Data Analytics ,January 21-22, 2016 Thailand; 4^th International Conference on Data Mining & Knowledge Management Process January 22-23, 2016, UAE; International Conference on Computer Science Networks and Information Technology January 23-24, 2016 Thailand; International Conference on Computing and Communication, January 28-29, 2016 India; International Conference on System Modeling and Optimization February 18-19, 2016 France; 1^st Journal Conference on Knowledge Engineering February 22-23, 2016 China; 3^rd International Conference on Advances in Electronics Engineering February 22-23, 2016 Hong kong; 8^th International Conference on Machine Learning and Computing February 22-23, 2016 Hong kong; 4^th International Conference on Information and Computer Networks, February 22-23, 2016 Hong Kong; International Conference on Computer Science, Business, Economic and Management February 26-27, 2016 Singapore; The 8^th International Conference on Computer Research and Development February 26-28, 2016 Vietnam; 4^th International Conference on Intelligent and Automation Systems February 26-28, 2016 Vietnam; International Conference On Sustainable Development Technology & Sciences Research Conference, February 17-20, 2016 U.K; 4^th International Conference on Intelligent and Automation Systems February 26-28, 2016 Vietnam; 2^nd International Conference on Information Technology March 03-04, 2016 Australia; 4^th International Conference on Communication and Electronics Information March 03-04, 2016 Australia; International Conference on Robotics, Mechanics and Mechatronics March 12-14, 2016 Hong Kong; International Conference on Green Electronics March 12-14, 2016 China; International Multi Conference of Engineers and Computer Scientists March 16-18, 2016 Hong Kong; 2^ndInternational Conference on Knowledge March 17-18, 2016 France; 5^th International Conference on Educational and Information Technology March 17-18, 2016 France; 2^nd International Conference On Big Data Conference & Analytics for Business, March 17-18, 2016 India; 5^th International Conference on Engineering Mathematics and Physics, April 7-9, 2016 UAE; 3^rd International Conference On Big Data, July 21-23, 2016 Turkey; International Conference on Modern Trends in Science and Technology July 25-27, 2016 Spain.

Track 2: Big data technologies

Big data brings not only opportunities but also challenges. Traditional data process-sing has been unable to meet the massive real-time demand of big data; we need the new generation of information technology to deal with the outbreak of big data.

Related Societies:

Global Summit and Expo on Fluid Dynamics & Aerodynamics, March 07-09, 2016 Spain; Annual Conference on Biomaterials ,March 14-16, 2016 London; International Conference on Mining and Metallurgy June 27-29, 2016 South Africa; 3^rd Biomedical Engineering Conference and Expo September 22-23, 2016 Austria; 5^th World Congress on Materials Science & Engineering June 13-15,2016 Spain; 5^thInternational Conference and Exhibition on Biometrics & Biostatistics September 29-October 01, 2016 USA; 4^th International Conference on Mechanical & Aerospace Engineering October 03-05, 2016, 2015 USA; International Conference on Chemical Engineering, September 12-14, 2016 USA; 4^th International Conference on Bioprocess and Bio systems Engineering, October 20-21, 2016 USA; Global Summit on Electronics and Electrical Engineering, November 03-04, 2015 Spain; 12^th International Conference and Exhibition on Food Processing & Technology October 24-26, 2016 Turkey; 7^thInternational Conference on Bioinformatics, October 27-28, 2016 USA; 4th International Conference on Information and Education Technology January 4^th to 6^th, 2016 USA; 2^nd International Conference on Knowledge Engineering January 04-06, 2016 USA; International Conference on Electronics, Computer and Information Technology, January 05-06, 2016 Malaysia 3^rd International Conference on Mechanical, Electronics and Computer Engineering, January 07-09, 2016 USA; 2^nd International Conference on Networks and Information ,January 07-09, 2016 USA; International Conference On Emerging Trends In Computer Science, January 9-11, 2016 India; International Conference on Advances in Engineering & Technology Research, January 10-11, 2016 UAE; 2^nd International Conference on Innovations in Communications, Network Systems and Computer Science January 10-11, 2016 UAE; Taiwan International Conference on Operation Research and Data Mining January 13-15, 2016 Taiwan; Taiwan International Conference on Innovation and Management January 13-15, 2016 Taiwan; 6^th International Conference on Applied Physics and Mathematics January 13-14, 2016 Singapore; 5^th International Conference on Information Computer Application January 18-19, 2016 Australia; The 7^th International Conference on Computer Modeling and Simulation January 18-19, 2016 Australia; 4^th International Conference on Information Management and Innovation, January 18-19, 2016 Australia; International Conference on Engineering Technologies and Big Data Analytics January 21-22, 2016 Thailand; 4^th International Conference on Data Mining & Knowledge Management Process January 22-23, 2016, UAE; International Conference on Computer Science Networks and Information Technology January 23-24, 2016 Thailand; International Conference on Computing and Communication January 28-29, 2016 India; 4^th International Conference on Intelligent Mechatronics and Automation February 01-03, 2016 South Africa; 5^th International Conference on Information and Industrial Electronics February 01-03, 2016 South Africa; International Conference on Automation Sciences February 02-03, 2016 Malaysia; International Conference on Soft Engineering and Information Management February 02-03, 2016 Malaysia; European Sustainable Development on Technology & Sciences Research Conference in London February 17-20, 2016 U.K; 2^ndInternational Conference on Mechatronics and Robotics Engineering February 18-19, 2016 France; International Conference on System Modelling and Optimization February 18-19, 2016 France; 1^st Journal Conference on Knowledge Engineering February 22-23, 2016 China; 3^rd International Conference on Advances in Electronics Engineering February 22-23, 2016 Hong Kong; 8^th International Conference on Machine Learning and Computing February 22-23, 2016 Hong kong; 4^th International Conference on Information and Computer Networks February 22-23, 2016 Hong Kong; International Conference on Computer Science, Business, Economic and Management February 26-27, 2016 Singapore; The 8^th International Conference on Computer Research and Development February 26-28, 2016 Vietnam; 4^th International Conference on Intelligent and Automation Systems February 26-28, 2016 Vietnam; International Conference on Machine learning and Computing, February 22 – 23, 2016 Hong Kong; International Conference on Frontiers of Sensors Technologies 12-14 March, 2016 Hong Kong; SIAM International Conference on Data Mining 5-7 May, 2016 USA; 2^ndInternational Conference on Advances in Engineering Materials June 29-July 2, 2016 Romania; 3^rd International Conference on Big Data, July 21-23, 2016 Turkey.

Track 3: Business Analytics

Related Societies:

SIAM society for industrial and applied mathematics

Track 4: OLAP technologies

Online Analytical Processing (OLAP) is a technology that is used to create decision support software. OLAP enables application users to quickly analyse information that has been summarized into multidimensional views and hierarchies. By summarizing predicted queries into multidimensional views prior to run time, OLAP tools provide the benefit of increased performance over traditional database access tools. Most of the resource-intensive calculation that is required to summarize the data is done before a query is submitted.

Related Societies:

SIAM society for industrial and applied mathematics

SIAM society for industrial and applied mathematics

Track 5: Big Data algorithm

Big data is data so large that it does not fit in the main memory of a single machine, and the need to process big data by efficient algorithms arises in Internet search, network traffic monitoring, machine learning, scientific computing, signal processing, and several other areas. This course will cover mathematically rigorous models for developing such algorithms, as well as some provable limitations of algorithms operating in those models.

Related Societies:

Track 6: ETL (Extract, Transform and Load)

The process of extracting data from source systems and bringing it into the data warehouse is commonly called ETL, which stands for extraction, transformation, and loading. Note that ETL refers to a broad process, and not three well-defined steps. The acronym ETL is perhaps too simplistic, because it omits the transportation phase and implies that each of the other phases of the process is distinct. Nevertheless, the entire process is known as ETL.

Related Societies:

Track 7: New visualization techniques

Data visualization or data visualization is viewed by many disciplines as a current equivalent of visual communication. It is not owned by any one field, but rather finds interpretation across many It encompasses the formation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including characteristics or variables for the units of information".

Related Societies:

SIAM society for industrial and applied mathematics

Track 8: Data Mining Methods and Algorithms

Data mining systems and calculations an interdisciplinary subfield of software engineering is the computational procedure of finding examples in vast information sets including techniques like Big Data Search and Mining, Novel Theoretical Models for Big Data, High execution information mining calculations, Methodologies on expansive scale information mining, Methodologies on extensive scale information mining, Big Data Analysis, Data Mining Analytics, Big Data and Analytics.

Related Societies:

SIAM society for industrial and applied mathematics

SIAM society for industrial and applied mathematics

Track 9: Optimization and Big Data

The age of Big Data is here: data of huge sizes is becoming ubiquitous. With this comes the need to solve optimization problems of unprecedented sizes. Machine learning, compressed sensing, social network science and computational biology are some of several prominent application domains where it is easy to formulate optimization problems with millions or billions of variables.

Related Societies:

Track 10: Social network analysis

Social network analysis (SNA) is the development of examining social structures through the use of network and graph theories. It characterizes networked structures in terms of bulges (individual actors, people, or things within the network) and the ties or edges (relationships or interactions) that connect them.

Related Societies:

SIAM society for industrial and applied mathematics

Relate Data Mining Conferences of Social network analysis

Track 11: Data Mining Tasks and Processes

Data mining undertaking can be indicated as an information mining inquiry. An information mining inquiry is characterized as far as information mining assignment primitives. This track incorporates Competitive investigation of mining calculations, Semantic-based Data Mining , and Data Pre-preparing, Mining on information streams, Graph, and sub-diagram mining, Scalable information pre-handling and cleaning strategies, Statistical Methods in Data Mining, Data Mining Predictive Analytics.

Related Societies:

Track 12: Data Mining Applications

Data Mining Applications in Engineering and Medicine focuses on offering information excavators who wish to apply distinctive information some assistance with mining systems. These applications incorporate Data mining frameworks in money related business sector investigation, Application of information mining in training, Data mining and Web Application, Medical Data Mining, Data Mining in Healthcare, Engineering information mining, Data Mining in security, Social Data Mining, Neural Networks and Data Mining, these are a portion of the utilizations of information Mining.

Related Societies:

SIAM society for industrial and applied mathematics

Track 13: Cloud computing

Cloud computing is a kind of Internet-based computing that provides shared processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in third-party data centres’. It relies on sharing of resources to achieve coherence and economy of scale, similar to a utility over a network.

Related Societies:

SIAM society for industrial and applied mathematics

SIAM society for industrial and applied mathematics

Track 14: Big Data Applications

Huge information is an expansive term for information sets so substantial or complex that conventional information preparing applications are lacking. Uses of enormous information incorporate Big Data Analytics in Enterprises, Big Data Trends in Retail and Travel Industry, Current and future situation of Big Data Market, Financial parts of Big Data Industry, Big information in clinical and social insurance, Big information in Regulated Industries, Big information in Biomedicine, Multimedia and Personal Data Mining.

Related Societies:

Relate Data Mining Conferences of Big Data Applications

Track 15: Forecasting from Big Data

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data.

Related Societies:

Track 16: Data Mining Tools and Softwares

Data Mining devices and programming projects incorporate Big Data Security and Privacy, Data Mining and Predictive Analytics in Machine Learning, Interface to Database Systems and Software Systems.

Related Societies:

SIAM society for industrial and applied mathematics

Track 17: Data Warehousing

In figuring, an information distribution center, otherwise called an endeavor information stockroom (EDW), is a framework utilized for reporting and information investigation. Information Warehousing is focal archives of coordinated information from one or more dissimilar sources. This information warehousing incorporates Data Warehouse Architectures, Case ponders Data Warehousing Systems, Data warehousing in Business Intelligence, Role of Hadoop in Business Intelligence and Data Warehousing, Commercial utilizations of Data Warehousing, Computational EDA (Exploratory Data Analysis) Techniques, Machine Learning and Data Mining.

Related Societies:

SIAM society for industrial and applied mathematics

Track 18: Search and data mining

Over the past two decades, there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies.

Related Societies:

SIAM society for industrial and applied mathematics

Track 19: Complexity and algorithms

The complexity of an algorithm signifies the total time required by the program to run to completion. The complexity of algorithms is most commonly expressed using the big O notation. Complexity is most commonly estimated by counting the number of elementary functions performed by the algorithm. And since the algorithm's performance may vary with different types of input data, hence for an algorithm we usually use the worst-case complexity of an algorithm because that is the maximum time taken for any input size.

Related Societies:

SIAM society for industrial and applied mathematics

SIAM society for industrial and applied mathematics

Track 20: Artificial Intelligence

Artificial Intelligence is the knowledge showed by machines or software.AI exploration is exceptionally specialized and concentrated and is profoundly partitioned into subfields that frequently neglect to speak with one another. It incorporates Cybernetics, Artificial inventiveness, Artificial Neural systems, Adaptive Systems, Ontologies and Knowledge sharing.

Related Societies:

Track 21: Data Mining analysis

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyse patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics.

Related Societies:

SIAM society for industrial and applied mathematics

Track 22: kernel methods

In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets.

Related Societies:

SIAM society for industrial and applied mathematics

Track 23: Open data

Open data is the impression that some data should be freely available to everyone to use and republish as they wish, without restrictions from right, patents or other mechanisms of control. The goals of the open data movement are similar to those of other "open" movements such as open basis, open hardware, open satisfied, and open access.

Related Societies:

SIAM society for industrial and applied mathematics

Track 24: Frequent pattern mining

A Frequent pattern is a pattern that occurs frequently in a data set. First proposed by [AIS93] in the context of frequent item sets and association rule mining for market basket analysis. Extended too many different problems like graph mining, sequential pattern mining, times series pattern mining, text mining.

Related Societies:

SIAM society for industrial and applied mathematics

Track 25: Clustering

Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of un labelled data. A loose definition of clustering could be the process of organizing objects into groups whose members are similar in some way.

Related Societies:

SIAM society for industrial and applied mathematics