We accept files in .docx/.doc/.pdf/Latex Pdf format - as per the call for papers schedule.
Papers should be thoroughly checked and proofread before submission. After you have submitted your article you are unable to make any changes to it during the refereeing process—although if accepted, you will have a chance to make minor revisions after refereeing and before the final submission of your paper.
Paper content must be original and relevant to one of the many conference topics.
Authors are required to ensure accuracy of quotations, citations, diagrams, maps, and tables.
Figures and tables need to be placed where they are to appear in the text and must be clear and easy to view.
Papers must follow format according to the template which will be sent to accepted papers.
Data Mining/Machine Learning TasksRegression/Classification Time series forecasting Segmentation/Clustering/Association Deviation and outlier detection> Explorative and visual data mining Web mining Mining text and semi-structured data Temporal and spatial data mining Multimedia mining (audio/video) Mining „Big Data“ Others
Data Mining AlgorithmsArtificial neural networks / Deep Learning Fuzzy logic and rough sets Decision trees/rule learners Support vector machines Evolutionary computation/meta heuristics Statistical methods Collaborative filtering Case based reasoning Link and sequence analysis Ensembles/committee approaches Others
Data Mining IntegrationMining large scale data/big data Data and knowledge representation Data warehousing and OLAP integration Integration of prior domain knowledge Metadata and ontologies Agent technolog ies for data mining Legal and social aspects of data mining Others
Data Mining ProcessData cleaning and preparation Feature selection and transformation Attribute discretisation and encoding Sampling and rebalancing Missing value imputation Model selection/assessment and comparison Induction principles Model interpretation Others
Data Mining ApplicationsBioinformatics Medicine Data Mining Business / Corporate / Industrial Data Mining Credit Scoring Direct Marketing Database Marketing Engineering Mining Military Data Mining Security Data Mining Social Science Mining Data Mining in Logistics Others
Data Mining SoftwareAll aspects, modules, frameworks Alternative and additional examples of possible topics include: Data Mining for Business Intelligence Emerging technologies in data mining Computational performance issues in data mining Data mining in usability Advanced prediction modelling using data mining Data mining and national security Data mining tools Data analysis Data preparation techniques (selection, transformation, and preprocessing) Information extraction methodologies > Clustering algorithms used in data mining Genetic algorithms and categorization techniques used in data mining Data and information integration Microarray design and analysis Privacy-preserving data mining Active data mining Statistical methods used in data mining Multidimensional data Case studies and prototypes Automatic data cleaning Data visualization Theory and practice – knowledge representation and discovery Knowledge Discovery in Databases (KDD) Uncertainty management Data reduction methods Data engineering Content mining Indexing schemes Information retrieval Metadata use and management Multidimensional query languages and query optimization Multimedia information systems Search engine query processing Pattern mining Applications
Algorithms for Big DataData and Information Fusion Algorithms (including Scalable methods) Natural Language Processing Signal Processing Simulation and Modeling Data-Intensive Computing Parallel Algorithms Testing Methods Multidimensional Big Data Multilinear Subspace Learning Sampling Methodologies Streaming Others
Big Data FundamentalsNovel Computational Methodologies Algorithms for Enhancing Data Quality Models and Frameworks for Big Data Graph Algorithms and Big Data Computational Science Computational Intelligence Others
Infrastructures for Big DataCloud Based Infrastructures (applications, storage & computing resources) Grid and Stream Computing for Big Data High Performance Computing, Including Parallel & Distributed Processing Autonomic Computing Cyber-infrastructures and System Architectures Programming Models and Environments to Support Big Data Software and Tools for Big Data Big Data Open Platforms Emerging Architectural Frameworks for Big Data Paradigms and Models for Big Data beyond Hadoop/MapReduce, … Others
Big Data Management and FrameworksDatabase and Web Applications Federated Database Systems Distributed Database Systems Distributed File Systems Distributed Storage Systems Knowledge Management and Engineering Massively Parallel Processing (MPP) Databases Novel Data Models Data Preservation and Provenance Data Protection Methods Data Integrity and Privacy Standards and Policies Data Science Novel Data Management Methods Crowdsourcing Stream Data Management Scientific Data Management Others
Big Data SearchMultimedia and Big Data Social Networks Data Science Web Search and Information Extraction Scalable Search Architectures Cleaning Big Data (noise reduction), Acquisition & Integration Visualization Methods for Search Time Series Analysis Recommendation Systems Graph Based Search and Similar Technologies Others
Privacy in the Era of Big DataCryptography Threat Detection Using Big Data Analytics Privacy Threats of Big Data Privacy Preserving Big Data Collection Intrusion Detection Socio-economical Aspect of Big Data in the Context of Privacy and Security Others
Applications of Big DataBig Data as a Service Big Data Analytics in e-Government and Society Applications in Science, Engineering, Healthcare, ... Others