A Step-By-Step Guide and AutoML Tool

Castelli, M., Pinto, D. C., Shuqair, S., Montali, D., & Vanneschi, L. (2022). The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool. Emerging Science Journal, 6(6), 1237-1254. https://doi.org/10.28991ESJ-2022-06-06-02. Funding:This study was supported by grant DSAIPA/DS/0113/2019 from FCT (Fundação para a Ciência e a Tecnologia), Portugal. This work was also supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data.publishersversionepub_ahead_of_prin

The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool

The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data. Doi: 10.28991/ESJ-2022-06-06-02 Full Text: PD

Spectrum of mutations in Italian patients with familial hypercholesterolemia: New results from the LIPIGEN study

Background Familial hypercholesterolemia (FH) is an autosomal dominant disease characterized by elevated plasma levels of LDL-cholesterol that confers an increased risk of premature atherosclerotic cardiovascular disease. Early identification and treatment of FH patients can improve prognosis and reduce the burden of cardiovascular mortality. Aim of this study was to perform the mutational analysis of FH patients identified through a collaboration of 20 Lipid Clinics in Italy (LIPIGEN Study). Methods We recruited 1592 individuals with a clinical diagnosis of definite or probable FH according to the Dutch Lipid Clinic Network criteria. We performed a parallel sequencing of the major candidate genes for monogenic hypercholesterolemia (LDLR, APOB, PCSK9, APOE, LDLRAP1, STAP1). Results A total of 213 variants were detected in 1076 subjects. About 90% of them had a pathogenic or likely pathogenic variants. More than 94% of patients carried pathogenic variants in LDLR gene, 27 of which were novel. Pathogenic variants in APOB and PCSK9 were exceedingly rare. We found 4 true homozygotes and 5 putative compound heterozygotes for pathogenic variants in LDLR gene, as well as 5 double heterozygotes for LDLR/APOB pathogenic variants. Two patients were homozygous for pathogenic variants in LDLRAP1 gene resulting in autosomal recessive hypercholesterolemia. One patient was found to be heterozygous for the ApoE variant p.(Leu167del), known to confer an FH phenotype. Conclusions This study shows the molecular characteristics of the FH patients identified in Italy over the last two years. Full phenotypic characterization of these patients and cascade screening of family members is now in progress

Familial hypercholesterolemia: The Italian Atherosclerosis Society Network (LIPIGEN)

BACKGROUND AND AIMS: Primary dyslipidemias are a heterogeneous group of disorders characterized by abnormal levels of circulating lipoproteins. Among them, familial hypercholesterolemia is the most common lipid disorder that predisposes for premature cardiovascular disease. We set up an Italian nationwide network aimed at facilitating the clinical and genetic diagnosis of genetic dyslipidemias named LIPIGEN (LIpid TransPort Disorders Italian GEnetic Network). METHODS: Observational, multicenter, retrospective and prospective study involving about 40 Italian clinical centers. Genetic testing of the appropriate candidate genes at one of six molecular diagnostic laboratories serving as nationwide DNA diagnostic centers. RESULTS AND CONCLUSIONS: From 2012 to October 2016, available biochemical and clinical information of 3480 subjects with familial hypercholesterolemia identified according to the Dutch Lipid Clinic Network (DLCN) score were included in the database and genetic analysis was performed in 97.8% of subjects, with a mutation detection rate of 92.0% in patients with DLCN score 656. The establishment of the LIPIGEN network will have important effects on clinical management and it will improve the overall identification and treatment of primary dyslipidemias in Italy

Direct mappings under the lens of information capacity

With the rising popularity of graph-based approaches to data management, exposing the content of traditional, often relational, sources as (knowledge) graphs becomes more and more relevant. In such scenarios, Direct Mapping approaches are often used to automatically transformsuch sources into graph-like formats. A "fundamental" property of these transformations is to be information preserving, that is, it should be always possible to (algorithmically) reconstruct the content of the original database. Information preservation, along with other "fundamental" or "desirable" properties proposed in the Semantic Web literature, has never been put into correspondence with over 40 years of extended literature coming from the traditional database perspective. In particular, to the best of our knowledge, it is unknown how classical results on information capacity, dominance, and equivalence, tailored towards specific tasks such as query answering or data update, relate to the results and definitions from the Semantic Web world.Extended abstract.</p

Direct mappings under the lens of information capacity

With the rising popularity of graph-based approaches to data management, exposing the content of traditional, often relational, sources as (knowledge) graphs becomes more and more relevant. In such scenarios, Direct Mapping approaches are often used to automatically transformsuch sources into graph-like formats. A "fundamental" property of these transformations is to be information preserving, that is, it should be always possible to (algorithmically) reconstruct the content of the original database. Information preservation, along with other "fundamental" or "desirable" properties proposed in the Semantic Web literature, has never been put into correspondence with over 40 years of extended literature coming from the traditional database perspective. In particular, to the best of our knowledge, it is unknown how classical results on information capacity, dominance, and equivalence, tailored towards specific tasks such as query answering or data update, relate to the results and definitions from the Semantic Web world.Extended abstract.</p

Conceptually-grounded Mapping Patterns for Virtual Knowledge Graphs

Knowledge Graphs (KGs) have been gaining momentum recently in both academia and industry, due to the flexibility of their data model, allowing one to access and integrate collections of data of different forms. Virtual Knowledge Graphs (VKGs), a variant of KGs originating from the field of Ontology-based Data Access (OBDA), are a promising paradigm for integrating and accessing legacy data sources. The main idea of VKGs is that the KG remains virtual: the end-user interacts with a KG, but queries are reformulated on-the-fly as queries over the data source(s). To enable the paradigm, one needs to define declarative mappings specifying the link between the data sources and the elements in the VKG. In this work, we try to investigate common patterns that arise when specifying such mappings, building on well-established methodologies from the area of conceptual modeling and database design