YarcData's Urika Shows Big Data is More than Hadoop and Data Warehouses (Carl Claunch, Sept 11, 2012)
- Why data discovery is hard
- Analyst validation
- Discover unknown linkages
- Purpose built
- The power of graph analytics
- What fits into 512 TBs?
- Easy to deploy
Automatically discover unknown linkagesData relationships, or linkages, are the essence of big data discovery. Without these, the researcher cannot infer an opinion nor can the scientist posit a fact. When it comes to data discovery, bringing linkages to the surface is the machine’s job. It’s the only practicable way a human analyst can deduce, conclude, or reckon using big data.
How can a machine automatically bring linkages to the surface? Make it so the analyst can see the data relationships using visualization tools?
- It starts by having a way to uniquely describe each linkage. Called “edges” in a graph analytic database, our Urika solution adheres to the W3C standard Resource Definition Framework, or RDF, to create unique descriptions for each type of data relationship discovered within your structured, semi-structured, and unstructured data sources.
- Researchers and analysts then use the W3C standard SPARQL query language within Urika to run pattern searches against the data to find unknown or hidden relationships.
- With the linkages now brought to the surface, the researcher or analyst can view the results real-time or feed them to a visualization tool specific to their domain of expertise.
The critical next step is then left to the humans – making inferences, positing facts, or creating theories to validate. But without a purpose-built appliance first surfacing these linkages in real time, “Eureka!” would be slow to come, if at all…