CfP (Late)/Topics

 

CALL FOR LATE PAPERS – LIST OF TOPICS

ICDATA’20

The 2020 International Conference on Data Science

https://icdata.org

Date and Location: July 27 – 30, 2020, Las Vegas, USA
Luxor (MGM property; renovated)

Extended Paper Submission Deadline: June 8, 2020

Co-Located: July 27-30, 2020, Luxor (MGM), Las Vegas, USA

In case of hesitation by some authors/speakers to travel during Year 2020 (due to Coronavirus), the CSCE Steering Committee has developed a policy for Non-Attendance. In summary, for Year 2020, the non-attendance by any registered author would not negatively impact the publication of his/her
paper (i.e., for Year 2020, formal presentation is optional.) Having said the above, based on a survey conducted recently, we anticipate that the majority of speakers/authors to physically attend the CSCE 2020 Congress in late July.

Non-Attendance Policy due to Coronavirus/COVID-19: please see infos here.

Proceedings Publisher:  Springer Nature – Book Series: Transactions on Computational Science & Computational Intelligence, https://www.springer.com/series/11769
Indexation: Subject to Springer science indexation which includes: online Springer Link (link.springer.com/), Scopus (www.info.scopus.com), SCI Compendex, EI Compendex (www.ei.org), EMBASE, Web of Science, Inspec, ACM digital library, Google Scholar, EBSCO, and others.

All accepted papers will be published by Springer Nature in research book series: Transactions on Computational Science & Computational Intelligence. The books will be widely disseminated (to over 1,000 libraries) and will be accessible by tens of thousands of individuals). It should be noted that publication of papers in research books would permit each paper to be quite comprehensive (if need be) and so the publication value would be comparable to journal publications and thus would be listed as such in the list of research contributions by most individuals. Our set of previous published books by Springer and others received the top 25% downloads in their respective fields with many papers receiving over 100 citations (some received over 700 citations).


INTRODUCTION:

In order to leverage synergy between various CS & CE fields, the program committees of a number of premier conferences have their 2020 events held at one venue (same location and dates). Thus, this year, The Congress is composed of a number of tracks (joint-conferences, tutorials, sessions, workshops, poster and panel discussions); all will be held simultaneously, same location and dates: July 27-30, 2020. For the complete list of joint conferences, see below (more detailed information
appears at: https://www.americancse.org/events/csce2020 )

We anticipate having between 1,000 and 2,000 participants in the Congress. The congress includes 20 major tracks, composed of: 122 technical, research, and panel sessions as well as a number of keynote lectures and tutorials; all will be held simultaneously, same location and dates: July 27-30, 2020. Last year, the Congress had attracted speakers/authors and participants affiliated with over
158 different universities (including many from the top 50 ranked institutions), major IT corporations (including: Microsoft, Google, Apple, SAP, Facebook, Oracle, Amazon, Yahoo, Samsung, IBM, Ebay, GE, Siemens, Philips, Ericsson, BAE Systems, Hitachi, NTT, Twitter, Uber Technologies, …), major corporations (including: Exxon Mobil, Johnson & Johnson, JPMorgan Chase, PetroChina, GlaxoSmithKline, HSBC, Airbus, Boeing, Hyundai, Goldman Sachs, Deutsche Bank, …), government research agencies (NSF, NIH, DoE, US Air Force, NSA National Security Agency, Central Intelligence Agency, …), US national laboratories (including, NASA National Aeronautics and Space Administration, ANL Argonne National Lab, Sandia National Lab, ORNL Oak Ridge National Lab, Lawrence Berkeley National Lab, Lawrence Livermore National Lab, Los Alamos National Lab, Pacific Northwest National Lab, …), and a number of Venture Capitalists as well as distinguished speakers discussing Intellectual Property issues. Last year, 54% of attendees were from academia, 25% from industry; 20% from government and funding agencies; and 1% unknown. About half of the attendees were from outside USA; from 69 nations.

ICDATA is part of the Congress.

SCOPE: Submitted papers should be related to Data Science, Data Mining, Machine Learning and similar topics.

Topics of interest include, but are not limited to, the following:

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

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We particularly encourage submissions of industrial applications and case studies from practitioners. These will not be evaluated using solely theoretical research criteria, but will take general interest and presentation into consideration.

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 (examples: data mining in education, marketing, finance and financial services, business applications, medicine, bioinformatics, biological sciences, science and technology, industry and government, …)

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, Visualization, Business, Education, Security, Humanities, Bioinformatics, Health Informatics, Medicine, Finance, Law, Transportation, Retailing, Telecommunication, all Search-based applications, …
  • Others
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For special sessions, please have a look here.