# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 3 Inclusion Criteria and Comparison Framework  
In order to conduct our analysis we took inspiration from SLR (Systematic Literature Review) guidelines. A SLR is a method to identify, evaluate and interpret relevant scientific works with respect to a specific topic. We designed a protocol for conducting the SLR inspired to the guidelines and policies presented in [8], in order to ensure that the results are repeatable and the means of knowledge acquisition are scientific and transparent. The necessary steps to guarantee compliance with the guidelines include (i) the formulation of the research questions; (ii) the definition of a search string; (iii) the selection of the data sources on which the search is performed; (iv) the identification of inclusion and exclusion criteria; (v) the selection of studies; (vi) the method of extracting data from the studies; and (vii) the analysis of the data. Our analysis covers the different aspects of habit mining by separately analyzing the features requested to the sensor log, the modeling phase, and the runtime phase. The modeling phase is in charge of creating the models of human habits and environmental dynamics, whereas the runtime phase covers the aspect related to how these models are employed at runtime to recognize the context and to act on the environment. These two phases can even overlap in the case the system is able to refine models at runtime (either in collaboration or not with the user) or even to completely create them from scratch at runtime. In any case, the runtime phase covers only the way models are employed whereas the modeling phase cover any phase that is related to model production or update. On the basis of the above premises, the following research questions **RQ-x** have been defined:  
* Sensor measurements represent the traces of user behavior in the environment. This information is needed at runtime to understand the current context, but the specific available information must be known when the model is defined (through specification or learning). The current review will only take into account the sensors for which the method has been validated. Section 4 will present for each of the included papers the following information:
* which sensors are taken into account? * are the sensors only categorical or numerical? And in the latter case,
* which discretization strategy is employed? * Any proposed method has a way to represent models. Models can be represented using graphical or mathematical/logic formalisms. Some methods propose formalisms that are specifically designed for the particular approach. Other methods conversely employ standard formalisms from machine learning and data mining. Section 5 analyzes the following aspects about employed models:
* the type of adopted model;
* how does an instance of the model can be represented? Is it human readable? * which is the granularity at which the model works? * **RQ-B2:** Whichever model type is adopted by a specific method, each method introduces a different way to construct the model. Section 6 expands the following analysis:* **RQ-B2.1:** for methods involving an, optionally partial, automatic construction of models (i.e., through learning), a training set consisting of a sensor log must be fed as input. At first, learning methods in the field of ambient intelligence can be classified according to the effort devoted to label the training set;
* **RQ-B2.2:** does the system consider the possibility of having multiple users? How many? * **RQ-B2.3:** in the latter case, is some type of additional labeling needed? As terms in this specific research area are not yet standardized, we replaced the search string with a crawling-like procedure starting from a set of seed papers, recursively navigating papers through the \"Cited by\" feature of Google Scholar, and selecting influential works from the last 10 years. This kind of search was manually performed on Google Scholar, as it includes all the relevant sources of scientific papers available.