  Data provenance collects comprehensive information about the events and
operations in a computer system at both application and system levels. It
provides a detailed and accurate history of transactions that help delineate
the data flow scenario across the whole system. Data provenance helps achieve
system resilience by uncovering several malicious attack traces after a system
compromise that are leveraged by the analyzer to understand the attack behavior
and discover the level of damage. Existing literature demonstrates a number of
research efforts on information capture, management, and analysis of data
provenance. In recent years, provenance in IoT devices attracts several
research efforts because of the proliferation of commodity IoT devices. In this
survey paper, we present a comparative study of the state-of-the-art approaches
to provenance by classifying them based on frameworks, deployed techniques, and
subjects of interest. We also discuss the emergence and scope of data
provenance in IoT networks. Finally, we present the urgency in several
directions that data provenance needs to pursue, including data management and
analysis.
