Advances in real-time indexing models and techniques for the web of things

Real-time indexing is a core component in retrieval systems and search services of any type in complex domains ranging from analytics to the Web of Things. As greatest challenges, the efficiency, scalability, and performance are central factors to consider at any level when designing or building an index. The multidimensional characteristics of data exposed by smart things interacting with the World concerning volume, velocity, variety, and volatility (4V) leverage advances and research in models and techniques for the indexing process. We present a state-of-the-art regarding them focusing on the Web of Things and related research areas such as Big data. Furthermore, we propose a methodology for comparing them and some guidelines for designing real-time indexes for the Web of Things. Our novel approach brings together the recent advances in traditional domains and adjusts models and techniques for the emerging Web of Things.