Context Accumulation – is the highest important and one of the keystones strategy for Big Data solutions if you want to archive the high-resolution during Entities Analytic. It means that It’s needed to take all pieces of data from any available sources like own and partner’s systems, media, social networks (depends on your business target) and when put it together. The more pieces have been found the better you will be able to understand and predict behavior for your system and business entities, will be able to discover hidden facts and relationships. There are three basic Context Accumulation concepts we implement and follow.
- High tolerance for uncertainty
While most Big Data solutions use variety low-quality data sources with user generated non-structured content especially such as new media and social networks – where is an absolutely needed to verify, score data and use high tolerance algorithms to generate statement. - Final statement generation quality assurance
While we have very volatility input data quality the second important key is method to measure the assurance level for each generated statements and their components. - Flexibility in processing speed
For most business the critical factor is the time to generate the final analytical result. Especially when there are high saturated input data streams. The answer is to use the right architecture consist from combination of streams processing, map-reduce tasks and in-memory calculations.
Out company offers end-to-end complete verified compatibilities and frameworks to provide our customers the best and the unique service in this technical area.