Research in Big Data

Type of Grant

Projects

• acquisition,
• storage and preservation,
• use and
• re-use

III Topics of Interest

• Transformation of massive volumes of data from disparate sources into useful information and actionable knowledge;
• Persistent, long-term preservation of valuable data and knowledge assets that overcome transitions in technologies and culture;
• Re-using, re-purposing, and integrating disparate data, information, and knowledge in ways that preserve provenance and appropriate protections;
• Integrative, generalized approaches to data, knowledge, and information integration and processing with a variety of data types
• Individual and group-oriented information management, supporting personalization, contextualization, interaction, and collaboration;
• Data processing, management, and inference techniques using advancing computing and communication platforms;
• Exploration of the limits and applicability of approaches in information integration and informatics;
• Energy-, computing resource- and memory-conserving approaches to storing, querying, indexing, updating, and processing data;
• Support for interactivity collaboration, adaptability, and evolvability with process, workflow, provenance, lifecycle, or inconsistency management;
• Managing, querying, and analysis of social media for leveraging new forms of interaction (e.g., crowd-sourcing)
• Management of uncertainty, including expressive representation of and reasoning about preferences, uncertainty, noise, inconsistency, and changing context;
• Challenges presented by informatics-enabled applications of societal importance;
• New information architectures, e.g., new database designs, new data models, etc.

Using Semantics to Guide Big Data Analytics

• Integration of data, hypothesis, predictive modeling and knowledge-based inference, experimentation, and simulation to support decision making and discovery;
• Analytics for massive, distributed, dynamic, uncertain, heterogeneously structured and unstructured data, for long-term, real-time or predictive techniques;
• Usable semantics and ontologies to enrich data for new uses;
• Ontology construction, selective knowledge sharing, and inference with large distributed sources;

Recently Funded Projects in IIS (III, RI, BIGDATA)

• RI: Small: Model-Directed Hybridization: Principled Design of Hybrids of Model Building, Metaheuristics and More Traditional Optimization Techniques
• III: Small: Combinatorial Optimization Methods for Problems in Molecular Biology and Genetics
• III-COR-Small: Efficient Matching for Large Real-World Schemas and Ontologies
• RI: Small: Integrating Paradigms for Approximate Stochastic Planning
• III: Small: A Development Environment for Query Optimizer Engineering
• BIGDATA: Small: DCM: Collaborative Research: An efficient, versatile, scalable, and portable storage system for scientific data containers
• III: Small: Towards Spatial Database Management Systems for Flash Memory Storage
• BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing
• RI: Small: Temporal and Spatiotemporal Processing in Recurrent Neural Networks with Unsupervised Learning
• BIGDATA: Small: DCM: JetStream: A Flexible Distributed System for Online and In-Place Data Analysis
• III: Small: Optimization Techniques for Scalable Semantic Web Data Processing in the Cloud
• BIGDATA: Small: DCM: DA: Advancing real-time data processing and reduction in radio astronomical detectors
• III: Small: Collaborative Research: Conflicts to Harmony: Integrating Massive Data by Trustworthiness Estimation and Truth Discovery
• III: Small: A Theoretical Framework for Practical Entity Resolution in Network Data
• III: Small: TROn - Tractable Reasoning with Ontologies
• III: Small: Robust and Scalable Reputation Management and Recommender Systems Using Belief Propagation
• III: EAGER: Automatically Building Test Collections Using Implicit Relevance Signals from the Web
• III: Small: Parametric Statistical Models to Support Statistical Hypothesis Testing over Graphs
• BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature
• III: Small: Exploring Social and Behavioral Contexts for Information Retrieval
• BIGDATA: Small: DA: Choosing a Needle in a Big Data Haystack
• RI: Small: Collaborative Research: Statistical ranking theory without a canonical loss
• III: Small: Collaborative Research: Supporting Efficient Discrete Box Queries for Sequence Analysis on Large Scale Genome Databases
• ...