Visual Big Data Lifecycle Management

The age of big data offers not only promising opportunities but also many challenges. Whereas the analysis of big data allows for gaining valuable knowledge in many application fields and sectors, and for implementing innovative, data-driven business models, among many other benefits, these opportunities are not without a cost. Data volumes are not only becoming larger and larger, which leads to substantial increases in data-storage associated costs, but also increasingly complex to manage due to the variety of involved data types, formats and data sources. Also with regard to information security and privacy aspects, big data often requires to be appropriately handled. At this, the discipline of big data lifecycle management offers promising concepts to cope with the challenges of big data. Managing big data in a lifecycle-oriented manner usually involves handling data subject to its underlying value (importance), requirements of Personally Identifiable Information (PII), legislative and regulatory aspects, among many others. Although big data lifecycle management supports the management of big data-e.g., to control and optimize the consumption of storage resources and thus to reduce the associated costs, big data lifecycle management itself suffers from the increased complexity. In many cases, big data lifecycle management is faced with complex trade-off decision-making scenarios whereas multiple, conflictory objectives have to be weighed against each other.

Therefore, to reduce the inherent complexity of big data lifecycle management decision-making, this thesis aims at developing a conceptual framework that provides reasoning support, while harnessing the potential of interactive data visualization, and in the end assists information managers with mastering increasingly complex data landscapes.