Monterey Workshop Call For Papers

  Theoretical Underpinnings for Big Data

• - Theoretical models for Big Data
• - Computational models for Big Data
• - Machine learning algorithms for Big Data Analytics
• - Time series analysis algorithms
• - Inconsistency theory for data/information/knowledge quality
• - Data uncertainty and inconsistency handling
• - Challenges due to volume, variety, velocity, veracity, distribution
• - Challenges in storage, robustness, curation, search, retrieval, visualization
• - Challenges in privacy, security, provenance

  Software Intensive Systems for Big Data Analytics

• - Cloud/grid/stream computing for Big Data
• - High performance/parallel computing platforms for Big Data
• - Autonomic computing
• - Energy-efficient computing for Big Data
• - Programming models and environments for Cluster, Cloud, and Grid Computing to Support Big Data
• - Software techniques and architectures for Big Data Computing
• - Open Platforms for Big Data computing
• - Models for Big Data beyond Hadoop/MapReduce, STORM
• - Software systems to support the life cycle of Big Data Analytics

  Big Data Management

• - Search and mining of data from a variety of sources and problem domains
• - Managing granularity of knowledge content in Big Data
• - Algorithms and systems for Big Data search
• - Distributed, and peer-to-peer Search
• - Software architecture for Big Data search, scalability and efficiency
• - Data acquisition, integration, pre-processing
• - Link and graph mining
• - Semantic-based data mining
• - Mobile, location-centric and unstructured Big Data
• - Multimedia and multi-structured data
• - Organizing and reorganizing Big Data

  Big Data Enabled Knowledge Engineering -Innovative, concurrent, and scalable big data intelligence analysis

• - Big Data enabled knowledge extraction, discovery, analysis, and mining
• - Big Data enabled knowledge integration, merging, migration, and transformation
• - Big Data enabled decision making
• - Big Data enabled perpetual learning
• - Big Data enabled knowledge systems development and tools
• - Context-driven user querying

  Big Data Simulation

• - Big data based modeling
• - Big Data in simulation systems
• - Big Data and simulation model management
• - Big Data enabled intelligent simulation
• - Big Data based embedded simulation
• - Simulation for visual analytics

  Big Data Visualization

• - Visual analytics of high-dimensional and spatio-temporal data
• - Scientific and information visualization
• - Machine learning and data mining for Big Data visual analytics
• - Visual representation and interaction
• - Perception and cognition of visual information
• - Analytical reasoning and sense-making on Big Data
• - Semantics-driven visual analytics
• - Visual analytics algorithms and foundations
• - High performance graphics for visualization
• - Advanced technologies and applications for visual analytics
• - Visual analytics processes, workflows and evaluation methods

  Big Data Applications

• - Complex Big Data applications in sciences, engineering, medicine, healthcare, finance, business, education, transportation, retail and services, law, telecommunication
• - Big Data Analytics in small business enterprises/li>
• - Big Data Analytics in government, public sector and society in general
• - Large-scale recommendation systems and social media systems
• - Intrusion detection based on Big Data Analytics
• - Anomaly Detection in very large scale systems
• - Threat detection using Big Data Analytics
• - Real-life case studies of value creation through Big Data Analytics
• - Big Data Analytics as a service
• - Big Data industry standards
• - Experiences with Big Data project deployments