Life Cycle and Management of Data Using Technologies and Terminologies of Big Data

During each stage of the data life cycle, the management of Big Data is the most demanding issue. This problem was first raised in the initiatives of UK e-Science a decade ago. In this case, data were geographically distributed, managed, and owned by multiple entities. The new approach to data management and handling required in e-Science is reflected in the scientific data life cycle management (SDLM) model. In this model, existing practices are analyzed in different scientific communities. The generic life cycle of scientific data is composed of sequential stages, including experiment planning (research project), data collection and processing, discussion, feedback, and archiving.

The following section presents a general data life cycle that uses the technology and terminology of Big Data. The proposed data life cycle consists of the following stages: collection, filtering & classification, data analysis, storing, sharing & publishing, and data retrieval & discovery. The following sections briefly describe each stage as exhibited in Figure 6.

Figure 6 Proposed data life cycle using the technologies and terminologies of Big Data.