Search

Any search engine can match a query string to data stored in the database – but only if it's exact. What happens when there are variations, inconsistencies, or differences in either? Even more challenging and common in the real world: what if there are different errors in both?

TIBCO Patterns – Search finds the right data – even when incomplete or partial similarities are present. This is highly beneficial because finding the right data can be tough when the differences and errors aren't known.

How it works: using graph theory – a branch of theoretical mathematics – the engine identifies similarity much like people do. Measuring similarity between a query and target data with human-like perception, algorithms can identify regions of similarity wherever they're located in fields or however data is aligned in respect to each other.

Accuracy is never compromised. Able to connect the dots between any type of structured data entity in any language or format, TIBCO Patterns – Search can quickly handle many of the issues that plague real-world data – from transpositions and typos to information that’s entered in the wrong field.

Not only is this approach extremely efficient, it’s also highly scalable; large datasets can be analyzed with sub-second response times and using a fraction of the computational power of conventional methods – where performance tends to exponentially degrade as dataset size increases.

Leveraging increased accuracy, less manual work needs to done by staff to find problematic data – allowing domain experts to gain time and focus on efforts that add business value. In one use case, for example, the amount of manual effort was reduced sevenfold, thereby collapsing a business process from five days to four hours.

Further, since TIBCO Patterns – Search integrates directly to the database and functions as an independent index, existing applications are barely affected.