Fields for efficient analysis of big data

This paper introduces the concept of fields for the purpose of increasing the efficiency of the analysis of big data. We focus specifically on time series data. Data are treated as points in a space. A field is a named subspace within that space. A field may restrict the position of a point. The subspace of a field may change according to the points included in the field. It may also be nested. After formally defining the concept of a field, we describe an approach to processing big data that incorporates this notion. By assigning a field to a meaningful portion, we can treat only the portions that we are interested in. As this reduces the amount of data processed, it results in the efficient processing of big data.