CfP LBP/PP/APP

ICDATA’18


CALL FOR PAPERS (LBP/PP/APP) – LIST OF TOPICS

Late Breaking Papers (LBP), Positions Papers (PP), Abstract/Poster Papers (APP)

Extended Submission Deadline: May 31, 2018

ICDATA’18

The 2018 International Conference on Data Science
(former DMIN: Int. Conf. on Data Mining,
merged with ABDA: Int. Conf. on Advances in Big Data Analytics)

http://icdata.org

Date and Location: July 30-August 2, 2018, Las Vegas, USA
Luxor (MGM property; renovated)

Download CfP ICDATA’18 LBP/PP/APP


This announcement is ONLY for those who MISSED the opportunity to submit their papers in response to earlier „Call For Papers“. Therefore, authors who have ALREADY submitted papers in response to earlier „Call For Papers“ should IGNORE this announcement. (Those who have been notified that their papers have been accepted, should still follow the instructions that were emailed to them; including meeting the deadlines mentioned in the notifications that were sent to them. Those who have not yet received a notification, will soon receive one).

INVITATION:

You are invited to submit a paper (Late Breaking Paper, Position Paper, Abstract/Poster Paper) for consideration. All accepted papers will be published in printed conference books/proceedings (each with a unique international ISBN number) and will also be made available online. The proceedings will be indexed in science citation databases that track citation frequency/data. In addition, like prior years, extended versions of selected papers (about 40%) will appear in journals and edited research books; publishers include, Springer, Elsevier, BMC, and others).

PAPER CATEGORIES:

1. LATE BREAKING PAPERS: describe late-breaking/recent developments in the field. The maximum number of pages is 7. If accepted, The length of the final/Camera-
Ready paper will be limited to 7 (two-column IEEE style) pages andthe author will be given the opportunity to present the paper in a formal session.

2. POSITION PAPERS: enable discussions on emerging topics without the experimentation normally present in an academic paper. Commonly, such papers will substantiate the opinions or positions put forward with evidence from an extensive objective discussion of the topic. The maximum number of pages is 4. If accepted, The length of the final/Camera-Ready paper will be limited to 4 (two-column IEEE style) pages and the author will be given the opportunity to present the paper in a formal session.

3. ABSTRACT/POSTER PAPERS: describe research roadmaps (similar to PhD plan or PhD prospectus). The maximum number of pages is 2. If accepted, The length of the final/Camera-Ready paper will be limited to 2 (two-column IEEE style) pages and the author will be given the opportunity to present the paper in a discussion/poster session.

The categories can be found in our paper submission system. Please select the appropriate category in the submission process.


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 30-August 2, 2018. The complete list of CSCE joint conferences can be found here. 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

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

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