# SAES: An Introduction to Self-Adapting Exploratory Structures
## 1 Introduction  
Research prior to 2000 on information systems mainly concentrated on efficient ways of querying large structured databases, and, in later years, large text information bases. Data or text queries were assumed to be precise, e.g., the user was expected to know exactly what she wanted. As an example, consider a user who wants to buy a Sony A7 RIII camera: the query \"Sony A7 RIII\" will precisely describe her information needs. However, it has been increasingly recognized that such a type of search (called the locator phase by [PERSON] [1]) is the exception rather than the norm in practice, and that users very often do not exactly know what they want, or they are unable to precisely frame their information needs. Consider the user above, but this time let her information need be to find the \"best\" camera for her needs. This information need is practically impossible to satisfy within the boundaries of traditional query systems. What does \"best\" mean? It depends on the user's priorities, which can be extremely diverse not only among different users, but even for the same user at different times. It also depends on the features cameras have, such as price, resolution, etc. What is required here is that users be able to explore the information base and freely select the features they are more interested in, until they find a camera that satisfies their needs:  
1. The user is shown a summary view of the information base, with available features;
2. From these features, the user selects the most important one;
3. The system adjusts the summary view, as to show only the features present in the items with the selected feature;
4. The user iterates between 2 and 3, until the number of items is sufficiently small for manual inspection. Additional selected features are composed in \"AND\" with previous ones. It can be contended that this is the natural interaction in e-commerce [2, 3], and not surprisingly it is now the de-facto standard in e-commerce. Implementations of this strategy are based on a model first proposed by [PERSON] in 1987 [4] (and in an extended version in 2000 [1]), called dynamic taxonomies. Later proposals of this model (such as [5], though informal and usually not as powerful as the originalmodel) made the alternative term \"faceted search\" catch, even though it is a misnomer. In fact, a faceted taxonomy design, after [PERSON] (2016), is just a design guideline, and the term faceted search fails to capture the essential characteristics of the model. Exploratory search based on dynamic taxonomies has an extremely wide application range: from e-commerce to encyclopedias, to medical diagnosis, to e-government, to multimedia databases, etc. The reader is referred to [PERSON] (2016), which is the most complete monograph to date and which describes several case studies and applications. Are dynamic taxonomies the only way to support exploratory search? [PERSON] (2016) proposed an abstraction of the way dynamic taxonomies work (self-adapting exploratory structures or SAES for short), and indicated a series of features that exploratory structures must exhibit to be useful for exploratory search. In Section 2, this abstraction is reviewed and extended. In Section 3, the most important SAES, dynamic taxonomies, are reviewed. Section 4 discusses other SAES, including extended tag clouds, continuous sliders, geographical maps, and clustering strategies, when used in isolation, i.e., as the only way of accessing the information base. Finally, Section 5 discusses the integration of these structures into a single framework provided by dynamic taxonomies. The focus of this paper is an introduction to and a comparison of structures that afford exploration, and only the features of these structures which are relevant for the discussion are considered here.