However may require real time processing and analysis in case of the natural or industrial disaster. May require data streaming processing. Variety Variety and number of involved databases and (multiple datasets, observation data is currently limited by available mashup) 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 Structure of the datasets and models may change change) depending on the data processing stage and tasks Big Data Science Veracity (Robustness In normal monitoring mode are data are statistically (collection, Issues) processed to achieve robustness. curation, Some biodiversity research are critical to data analysis, veracity (reliability/trustworthiness). action) 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 evaluate 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 Variety, multi-type data: SQL and no-SQL, distributed multi-source data. Challenges (Gaps) 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 Require supporting mobile sensors (e.g. birds migration) and mobile Challenges in Mobility researchers (both for information feed and catalogue search) Instrumented field vehicles, Ships, Planes, Submarines, floating buoys, sensor tagging on organisms Photos, video, sound recording
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