Beyond Big Data -- Rethinking Programming Languages for Non-persistent Data

In "Big Data" research, the data acquired from many sources are fused and analyzed to obtain valuable and sometimes unexpected information. Even though the volumes of data are unprecedented, the data are usually stored for post-experiment analysis and for sharing among scientists. Typical scenarios implicitly assume that the data are stored and can be re-analyzed later. The reality is, unfortunately, not so ideal because the data may be "non-persistent" and allow only one-time use. We propose to reformulate how big data programs are developed, and introduce the notion of data-carrying programs that are, in a sense, self-validating. By writing these programs in a specially-defined language, and transforming them to store sample data, programs can save enough data to provide high-confidence validation of their results.