New York Conferences 2023 - International Conference on Data Science
September 1-3, 2023
New York City

Submission Guidelines

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.
Formatting Guidelines

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.
Topics of interest for submission include but are not limited to:

Data Science

Data Mining/Machine Learning Tasks

Regression/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 Algorithms

Artificial 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 Integration

Mining 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 Process

Data 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 Applications

Bioinformatics 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 Software

All 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 Data

Data 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 Fundamentals

Novel 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 Data

Cloud 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 Frameworks

Database 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 Search

Multimedia 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 Data

Cryptography 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 Data

Big Data as a Service Big Data Analytics in e-Government and Society Applications in Science, Engineering, Healthcare, ... Others