## 3 Results and Discussion
### End-User Interface for Automated IDIVS Generation  
To efficiently support the creation of new IDIVS and, thereby, new services, we explore interactive mechanisms that can aid the user throughout the process of creating new IDIVS structures, such as the one introduced in Figure 1. To this end, we assume an existing set of physical sensors and a predefined set of existing virtual sensors. The goal is to enable compositional reasoning based on the set of existing sensors in a way that more complex sensing tasks can be automatically performed. The frontend of the proposed approach consists of an interactive way for the user to specify the high-level sensing problem. We resort to a conversational interface, which is an intuitive and user-friendly way in which the user can express the sensing problem and interact with the system, i.e., natural language queries. The backend deploys a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation, i.e., a sequence of computational steps required to implement the desired sensing task. In [PERSON] et al. [32], we demonstrate the approach for a smart city environment, where various types of virtual sensors are scattered and a broad spectrum of sensing tasks can be constructed. First, we provide an automatic template-based procedure to generate a questions and answers dataset large enough to generalize the association between natural language queries (NLQs) and our proposed virtual sensor functional representation. The functional representation essentially specifies the sequence of functions to be applied to the incoming sensor data. Functions can either implement basic operations, such as arithmetic and logical operators, or correspond to the specific role played by an existing virtual sensor, e.g., car recognition on image data. A detailed discussion on the template-based dataset generation procedure is presented in [PERSON] et al. [32], where we construct a dataset consisting of 48,620 questions with their corresponding functional representation based on a limited number of templates and parameters. Second, we identify a suitable neural network-based machine learning approach using attention-based gated recurrent units (GRUs), which is able to learn the correspondence between NLQs and virtual sensor representations in a generalized manner. In other words, the model predicts the sequence of computational steps required to implement the desired sensing task expressed in the query. Essentially, this solves the problem introduced in Section 2 of selecting the right set of sensors and virtual sensors, as well as binding them through simple arithmetic and logical operators in order to perform a sensing task that can be expressed by the user through natural language. As an example, consider the following basic query: Is it colder at Malmo University compared to Malmo Arena? In order to provide an answer, the system constructs an IDIVS that starts by selecting the appropriate type of sensors corresponding to the specified location in the query and returns the result of an arithmetic operation which takes as input the two respective temperature values. This IDIVS generation mechanism can be understood as a configuration phase, where the human user taking the role of the system configurator presents the system with a high-level task specified in natural language, which prompts the system to propose a new IDIVS, constructed based on the pre-existing sensors and virtual sensors in the system. Importantly, the user can inspect the IDIVS structure to determine the validity of the solution (if such a solution exists). Currently, the mechanism can support four different categories of questions and, since the constructed IDIVS has to adhere to the previously introduced definition of IDIVS, it is mandatory that the question specifies the physical location being sensed.