Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues, semantics) Robustness of the simulation is dependent upon the quality of the model. However, robustness of the computation itself, although non-trivial, is tractable. Visualization Would require very large amount of movement of data to enable visualization. Data Quality (syntax) Consistent due to generation from a model Data Types Primarily network data. Data Analytics Summary of various runs and replicates of a simulation Big Data Specific Challenges (Gaps) Computation of the simulation is both compute intensive and data intensive. Moreover, due to unstructured and irregular nature of graph processing the problem is not easily decomposable. Therefore it is also bandwidth intensive. Hence, a supercomputer is applicable than cloud type clusters. Big Data Specific Challenges in Mobility None Security & Privacy Requirements Several issues at the synthetic population-modeling phase (see social contagion model). Highlight issues for generalizing this use case (e.g. for ref. architecture) In general contagion diffusion of various kinds: information, diseases, social unrest can be modeled and computed. All of them are model that utilize the underlying interaction network to study the evolution of the desired phenomena. More Information (URLs) Note: <additional comments> Use Case Title Social Contagion Modeling Vertical (area) Social behavior (including national security, public health, viral marketing, city planning, disaster preparedness) Author/Company/Email Madhav Marathe or Chris Kuhlman /Virginia Bioinformatics Institute, Virginia Tech or ckuhlman@vbi.vt.edu /Actors/Stakeholders and their roles and responsibilities Goals Provide a computing infrastructure that models social contagion processes. The infrastructure enables different types of human-to-human interactions (e.g., face-to-face versus online media; mother-daughter relationships versus mother-coworker relationships) to be simulated. It takes not only human-to-human interactions into account, but also interactions among people, services (e.g., transportation), and infrastructure (e.g., internet, electric power). Use Case Description Social unrest. People take to the streets to voice unhappiness with government leadership. There are citizens that both support and oppose government. Quantify the degrees to which normal business and activities are disrupted owing to fear and anger. Quantify the possibility
However may require real time processing and analysis in case of the natural or industrial disaster. May require data streaming processing. Variety (multiple datasets, mashup) Variety and number of involved databases and observation data is currently limited by available tools; in principle, unlimited with the growing ability to process data for identifying ecological changes, factors/reasons, species evolution and trends. See below in additional information. Variability (rate of change) Structure of the datasets and models may change depending on the data processing stage and tasks Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) In normal monitoring mode are data are statistically processed to achieve robustness. Some biodiversity research are critical to data veracity (reliability/trustworthiness). In case of natural and technogenic disasters data veracity is critical. Visualization Requires advanced and rich visualization, high definition visualisation facilities, visualisation data 4D visualization Visualizing effects of parameter change in (computational) models Comparing model outcomes with actual observations (multi dimensional) Data Quality Depends on and ensued by initial observation data. Quality of analytical data depends on used mode and algorithms that are constantly improved. Repeating data analytics should be possible to initial observation data. Actionable data are human aided. Data Types Multi-type. Relational data, key-value, complex semantically rich data Data Analytics Parallel data streams and streaming analytics Big Data Specific Challenges (Gaps) Variety, multi-type data: SQL and no-SQL, distributed multi-source data. Visualisation, distributed sensor networks. Data storage and archiving, data exchange and integration; data linkage: from the initial observation data to processed data and reported/visualised data. Historical unique data Curated (authorized) reference data (i.e. species names lists), algorithms, software code, workflows Processed (secondary) data serving as input for other researchers Provenance (and persistent identification (PID)) control of data, algorithms, and workflows Big Data Specific Challenges in Mobility Require supporting mobile sensors (e.g. birds migration) and mobile researchers (both for information feed and catalogue search) Instrumented field vehicles, Ships, Planes, Submarines, floating buoys, sensor tagging on organisms Photos, video, sound recording evaluate