Imagine Industrial IoT as the nervous system of a company: it is a network of sensors that collects valuable information from all corners of a production plant and stores it in a repository for data analysis and exploitation. This network is necessary to measure and obtain data in order to make informed decisions. We always talk about making good decisions based on reliable information, but although it may sound obvious, it is not always that easy to achieve that goal. In this article, we will go a bit beyond IoT and will focus on the data and how to leverage it with AIoT and data analytics. We'll be discussing specifically the analysis phase, the process that turns data first into information and then into knowledge (sometimes also referred to as business logic). In the end, however, we won't stray far from the core subject of IoT, because for us IoT without Big Data is meaningless.

One drawback of Hierarchical Task Network (HTN) planning is the difficulty of providing complete domain knowledge, i.e., a complete and correct set of HTN methods for every task. To provide a principled way to overcome this difficulty, we define a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism. In our formalism, the methods specify ways to achieve goals (rather than tasks as in conventional HTN planning), and goals may be achieved even when no methods are available. Our planning algorithm, GoDeL (Goal Decomposition with Landmarks), is sound and complete irrespective of whether the domain knowledge (i.e., the set of methods given to the planner) is complete. By comparing GoDeL's performance with varying amounts of domain knowledge across three benchmark planning domains, we show experimentally that (1) GoDeL works correctly with partial planning knowledge, (2) GoDeL's performance improves as more planning knowledge is given, and (3) when given full domain knowledge, GoDeL matches the performance of a state-of-the-art hierarchical planner.