Call for Abstract
2nd International Conference on Big Data Analysis and Data Mining, will be organized around the theme “Emerging Future Technologies of Big Data and Data Mining”
Data Mining 2015 is comprised of 7 tracks and 48 sessions designed to offer comprehensive sessions that address current issues in Data Mining 2015.
Submit your abstract to any of the mentioned tracks. All related abstracts are accepted.
Register now for the conference by choosing an appropriate package suitable to you.
Data mining methods and algorithms an interdisciplinary subfield of computer science is the computational process of discovering patterns in large data sets involving methods like Big Data Search and Mining, Novel Theoretical Models for Big Data, New Computational Models for Big Data, High performance data mining algorithms, Methodologies on large-scale data mining, Methodologies on large-scale data mining, Big Data Analysis, Data Mining Analytics, Big Data and Analytics. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.Aside from the raw analysis step, it involves database and data management aspects, data pre- processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
- Track 1-1Big Data Search and Mining Track
- Track 1-2Novel Theoretical Models for Big Data
- Track 1-3New Computational Models for Big Data
- Track 1-4High Performance Data Mining Algorithms
- Track 1-5Methodologies on large-scale Data Mining
- Track 1-6Empirical study of Data Mining Algorithms
Data mining task can be specified in the form of a data mining query. A data mining query is defined in terms of data mining task primitives. This track includes Competitive analysis of mining algorithms, Computational Modeling and Data Integration, Semantic-based Data Mining and Data Pre-processing, Mining on data streams, Graph and sub-graph mining, Scalable data preprocessing and cleaning techniques, Statistical Methods in Data Mining, Data Mining Predictive Analytics.
- Track 2-1Competitive Analysis of Mining Algorithms
- Track 2-2Computational Modeling and Data Integration
- Track 2-3Semantic-based Data Mining and Data Pre-processing
- Track 2-4Mining on Data Streams
- Track 2-5Graph and sub-graph Mining
- Track 2-6Scalable Data Preprocessing and Cleaning Techniques
- Track 2-7Statistical Methods in Data Mining
Data Mining Applications in Engineering and Medicine targets to help data miners who wish to apply different data mining techniques. These applications include Data mining systems in financial market analysis, Application of data mining in education, Data mining and processing in bioinformatics, genomics and biometrics, Advanced Database and Web Application, Medical Data Mining, Data Mining in Healthcare, Engineering data mining, Data Mining in security, Social Data Mining, Neural Networks and Data Mining, these are some of the applications of datamining.
- Track 3-1Data Mining systems in Financial Market Analysis
- Track 3-2Application of Data Mining in Education
- Track 3-3Data Mining and processing in Bioinformatics, Genomics and Biometrics
- Track 3-4Advanced Database and Web Application
- Track 3-5Medical Data Mining
- Track 3-6Data Mining in Healthcare Data
- Track 3-7Engineering Data Mining
- Track 3-8Data Mining in Security
- Track 3-9Sports Analytics
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Applications of bigdata include Big Data Analytics in Enterprises, Big Data Trends in Retail, Big Data in Travel Industry, Current and future scenario of Big Data Market, Financial aspects of Big Data Industry, Big data in clinical and healthcare, Big data in Regulated Industries, Big data in Biomedicine, Multimedia and Personal Data Mining.
- Track 4-1Big Data Analytics in Enterprises
- Track 4-2Big Data Trends in Retail
- Track 4-3Big Data in Travel Industry
- Track 4-4Current and Future Scenario of Big Data Market
- Track 4-5Financial aspects of Big Data Industry
- Track 4-6Big data in Clinical and Healthcare
- Track 4-7Big data in Regulated Industries
- Track 4-8Big data in Biomedicine
- Track 4-9Big Data in Sports
Data Mining tools and softwares include Big Data Security and Privacy, E-commerce 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.
- Track 5-1Big Data Security and Privacy
- Track 5-2E-commerce and Web services
- Track 5-3Medical informatics
- Track 5-4Visualization Analytics for Big Data
- Track 5-5Predictive Analytics in Machine Learning and Data Mining
- Track 5-6Interface to Database Systems and Software Systems
In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. Data Warehousing are central repositories of integrated data from one or more disparate sources. This data warehousing includes 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, Machine Learning and Data Mining.
- Track 6-1Data Warehouse Architectures
- Track 6-2Case studies: Data Warehousing Systems
- Track 6-3Data warehousing in Business Intelligence
- Track 6-4Role of Hadoop in Business Intelligence and Data Warehousing
- Track 6-5Commercial applications of Data Warehousing
- Track 6-6Computational EDA (Exploratory Data Analysis) Techniques
Artificial intelligence (AI) is the intelligence exhibited by machines or software.AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.It includes Cybernetics, Artificial creativity, Artificial Neural networks, Adaptive Systems, Ontologies and Knowledge sharing.
- Track 7-1Cybernetics
- Track 7-2Artificial creativity
- Track 7-3Artificial Neural networks
- Track 7-4Adaptive Systems
- Track 7-5Ontologies and Knowledge sharing