Exploring Data Hierarchies to Discover Knowledge in Different Domains

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An explainable data-driven approach to web directory taxonomy mapping

5noThe spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.partially_openopenElena Daraio, Luca Cagliero, Silvia Anna Chiusano, Paolo Garza, Giuseppe RicuperoDaraio, Elena; Cagliero, Luca; Chiusano, SILVIA ANNA; Garza, Paolo; Ricupero, Giusepp

Heritage Machine

Heritage Machine is an architectural project based on the conceptual approach of building ephemeral placemaking that dissolves back into nature as ruins over time. The ideas of physical memory, and an original approach to sustainability and building life cycle are the ground themes of the project. Physically, the gradual transition from the exterior to the interior world through a series of thresholds, and the grand roof canopy of the common hub large enough to form its own microclimate, are low-tech architectural gestures that respond to local environmental constraints of the desert between Dubai and Abu Dhabi

Discovering air quality patterns in urban environments

Monitoring air quality is currently a critical issue in smart cities. Air pollution-related data are commonly acquired through sensors deployed throughout the city area. To analyze these data collections, data analytics algorithms should be combined with reporting tools for discovering critical conditions and informing citizens and municipality actors. This paper proposes a new data mining engine to discover air quality patterns from air pollution-related data. This class of patterns includes many established patterns proposed in the data mining literature. In this study, we focused on a specific type of patterns, namely the frequent weighted itemsets, to identify combinations of pollutants that are, on average, in a critical condition. To show the usefulness of the proposed approach, the proposed engine was tested on real data acquired in a major Italian cit

Modeling Correlations among Air Pollution-Related Data through Generalized Association Rules

Today’s citizens and city administrations have an increasing interest in monitoring the air quality in urban areas. Studying the causes of air pollution entails analyzing the correlations between heterogeneous data, among which pollutant concentrations, traffic flow measurements, and meteorological data. To this end, innovative data analytics solutions able to acquire, integrate, and analyze very large amounts of data are needed. This paper presents a new data mining system, named GEneralized Correlation analyzer of pOllution data (GECKO), to discover interesting and multiple-level correlations among a large variety of open air pollution-related data. Specifically, correlations among pollutant levels and traffic and climate conditions are discovered and analyzed at different abstraction levels. The knowledge extraction process is driven by a taxonomy to generalize low-level measurement values as the corresponding categories. To ease the manual inspection of the result, the extracted correlations are classified into few classes based on the semantics of underlying data. The experiments, performed on real data acquired in a major Italian Smart City, demonstrate the effectiveness of the proposed analytics engine in discovering correlations among pollutant data that are potentially useful for supporting city administrators in decision-making

Characterizing Situations of Dock Overload in Bicycle Sharing Stations

Bicycle sharing systems are becoming increasingly popular in cities around the world as they are an inexpensive and sustainable means of transportation. Promoting the use of these systems substantially improves the quality of life in cities by reducing pollutant emissions and traffic congestion. In these systems, bikes are made available for shared use to individuals on a short-term basis. They allow people to borrow a bike in one dock and return it to any other station with free docks belonging to the same system. The occupancy level of the stations can be constantly monitored. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty when the user needs to access the station. The aim of this paper is to analyze occupancy level data acquired from real systems to determine situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type called Occupancy Monitoring Pattern is proposed here to detect situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, occupancy patterns can be filtered and evaluated by taking into consideration both the spatial and temporal correlation of the acquired measurements. The results achieved on real data highlight the potential of the proposed methodology in supporting domain experts in their maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving user experience by suggesting alternative stations in the nearby area

Le competenze per l’innovazione

Negli ultimi anni abbiamo assistito ad un progressivo evolversi delle modalità con cui le Pubbliche Amministrazioni centrali e locali si rivolgono al cittadino. La chiave di tale cambiamento sta nell’impiego delle tecnologie ICT, che permettono di realizzare reti di servizi integrati aumentando la trasparenza e l’efficienza nelle relazioni tra PA e cittadino-utente. L’implementazione di servizi innovativi, però, richiede sempre di più un insieme articolato di competenze. È la combinazione di queste ultime che permette, in un primo momento, di individuare i fabbisogni e di progettare le soluzioni; successivamente, reingegnerizzando i processi, di realizzare e gestire le soluzioni; infine di offrire all’utenza il supporto nel cambiamento