Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Assumption 0 analysis: comparative phylogenetic studies in the age of complexity

Darwin's panoramic view of biology encompassed two metaphors: the phylogenetic tree, pointing to relatively linear (and divergent) complexity, and the tangled bank, pointing to reticulated (and convergent) complexity. The emergence of phylogenetic systematics half a century ago made it possible to investigate linear complexity in biology. Assumption 0, first proposed in 1986, is not needed for cases of simple evolutionary patterns, but must be invoked when there are complex evolutionary patterns whose hallmark is reticulated relationships. A corollary of Assumption 0, the duplication convention, was proposed in 1990, permitting standard phylogenetic systematic ontology to be used in discovering reticulated evolutionary histories. In 2004, a new algorithm, phylogenetic analysis for comparing trees (PACT), was developed specifically for use in analyses invoking Assumption 0. PACT can help discern complex evolutionary explanations for historical biogeographical, coevolutionary, phylogenetic, and tokogenetic processe

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Dynamic Discovery of Type Classes and Relations in Semantic Web Data

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method

Visual support for ontology learning: an experience report

Ontology learning methods aim to automate ontology construction. They are complex methods involving several elements such as documents, terms and concepts. During the development of an ontology learning method, as well as during its deployment, several situations occur where understanding the relations between these elements is crucial. Our hypothesis is that visual techniques can be used to aid this understanding. To support this claim, we present a set of such complex situations and describe the visual solutions that we developed to support them