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This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform.

The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a real-world dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data.

In the last few years, manufacturing systems are getting gradually transformed into smart factories. In this context, an increasing number of information and communication technologies is incorporated towards facilitating management, production, and control processes. The introduction of advanced embedded systems with enhanced connectivity produces a vast amount of data, posing a challenge in terms of data analytics. However, the in-time collection and analysis of acquired data can create insight into the manufacturing process as well as its assets. One aspect of major importance for every production system is preserving its equipment in operational condition, and within those limits that could minimize unplanned breakdowns and production stoppages. This paper details the predictive analytics methodology integrated into the SERENA platform able to: (i) streamline the prognostics of the industrial components, (ii) characterize the health status of the monitored equipment, (iii) generate an early warning related to the condition of the equipment, and (iv) forecast the future evolution of the monitored equipment’s degradation. To demonstrate the effectiveness of the proposed methodology, different use cases are discussed with results obtained on real-data collected in real-time from the industrial environments.

Cet article présente le projet E-MIMIC, une application qui vise à éliminer les préjugés et la non-inclusion dans les textes administratifs rédigés dans les pays européens, à commencer par ceux qui sont rédigés dans les langues romanes. Il présente une méthodologie conçue à partir de critères discursifs inspirés de l’analyse du discours française et utilisés pour étiqueter un corpus de documents institutionnels, qui sont utilisés pour l’apprentissage profond des réseaux neuronaux. Des architectures de modélisation profonde du langage sont exploitées pour identifier automatiquement les extraits de texte non inclusifs, suggérer des formes alternatives et produire des reformulations inclusives. Une évaluation préliminaire menée sur un ensemble de données de référence pour la langue italienne montre des résultats prometteurs, qui poussent à finaliser l’application et à la réaliser également pour d’autres langues, tel le français.

Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and extreme close up. The cinematographic shot classification is a complex task, so we combined state-of-the-art techniques to deal with it. Specifically, we finetuned three separated VGG-16 models and combined their predictions in order to obtain better performances by exploiting the stacking learning technique. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy. Our method was able to achieve 77% accuracy without relying on data augmentation techniques. We also evaluated our approach in terms of f1 score, precision, and recall and we showed confusion matrices to show that most of our misclassified samples belonged to a neighboring class.

In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the necessity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision-making process. This article discusses the latest software technologies needed to collect, manage, and elaborate all data generated through innovative Internet-of-Things (IoT) architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step toward the rich landscape of the literature for readers approaching this field and as a global yet detailed overview of the current state of the art in the Industry 4.0 domain for experts. As a case study, we discuss, in detail, the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs.

A Predictive Maintenance strategy for a complex machine requires a sophisticated and non-trivial analytical stage to provide accurate and trusted predictions. It must be planned and carried out carefully to maximise the information extracted from available data. The SERENA project provided an excellent methodological framework and a solid technical and software foundation to deliver a robust and applicable Predictive Maintenance solution for the White Goods industry. The proposed data-driven methodology was applied on a real use case, that is, estimating the degradation trend of industrial foaming machines and predicting their remaining useful life. The models were built based on historical data and are applied in real-time adjourning their predictions every time new data are collected. The results are promising and highlight how the proposed methodology can be used to achieve a fairly accurate estimate of machinery degradation and plan maintenance interventions accordingly, with significant savings in terms of costs and time.

The increasing capability to collect data gives us the possibility to collect a massive amount of heterogeneous data. Among the heterogeneous data available, time-series represents a mother lode of information yet to be fully explored. Current data mining techniques have several shortcomings while analyzing time-series, especially when more than one time-series, i.e., multi-dimensional timeseries, should be analyzed together to extract knowledge from the data. In this context, we present K-MDTSC (K-Multi-Dimensional Time-Series Clustering), a novel clustering algorithm specifically designed to deal with multi-dimensional time-series. Firstly, we demonstrate K-MDTSC capability to group multi-dimensional time-series using synthetic datasets. We compare K-MDTSC results with k-Shape, a state-of-art time-series clustering algorithm based on K-means. Our results show both K-MDTSC and k-Shape create good clustering results. However, K-MDTSC outperforms k-Shape when complicating the synthetic dataset. Secondly, we apply K-MDTSC in a real case scenario where we are asked to replace a scheduled maintenance with a predictive approach. To this end, we create a generalized pipeline to process data from a real industrial plant welding process. We apply K-MDTSC to create clusters of weldings based on their welding shape. Our results show that K-MDTSC identifies different welding profiles, but that the aging of the electrode does not negatively impact the welding process.

Nowadays, cars are instrumented with thousands of sensors continuously collecting data about its components. Thanks to the concept of connected cars, this data can be now transferred to the cloud for advanced analytics functionalities, such as prognostic or predictive maintenance. In this paper, we dissect a data-driven prognostic pipeline and apply it in the automotive scenario. Our pipeline is composed of three main steps: (i) selection of most important signals and features describing the scenario for the target problem, (ii) creation of machine learning models based on different classification algorithms, and (iii) selection of the model that works better for a deployment scenario. For the development of the pipeline, we exploit an extensive experimental campaign where an actual engine runs in a controlled test bench under different working conditions. We aim to predict failures of the High-Pressure Fuel System, a key part of the diesel engine responsible for delivering high-pressure fuel to the cylinders for combustion. Our results show the advantage of data-driven solutions to automatically discover the most important signals to predict failures of the High-Pressure Fuel System. We also highlight how an accurate model selection step is fundamental to identify a robust model suitable for deployment.

Predictive maintenance aims at proactively assessing the current condition of assets and performing maintenance activities if and when needed to preserve them in the optimal operational condition. This in turn may lead to a reduction of unexpected breakdowns and production stoppages as well as maintenance costs, ultimately resulting in reduced production costs. Empowered by recent advances in the fields of information and communication technologies and artificial intelligence, this chapter attempts to define the main operational blocks for predictive maintenance, building upon existing standards discusses and key data-driven methodologies for predictive maintenance. In addition, technical information related to potential data models for storing and communicating key information are provided, finally closing the chapter with different deployment strategies for predictive analytics as well as identifying open issues.

In recent years various maintenance strategies have been adopted to maintain industrial equipment in an operational condition. Adopted techniques include approaches based on statistics generated by equipment manufacturers, human knowledge, and intuition based on experience among others. However, techniques like those mentioned above often address only a limited set of the potential root causes, leading to unexpected breakdown or failure. As a consequence, maintenance costs were considered a financial burden that each company had to sustain. Nevertheless, as technology advances, user experience and intuition are enhanced by artificial intelligence approaches, transforming maintenance costs into a company’s strategic asset. In particular, for manufacturing industries, a large volume of data is generated on a shop floor as digitisation advances. Combining information and communication technologies (ICT) with artificial intelligence techniques may create insight over production processes, complement or support human knowledge, revealing undetected anomalies and patterns that can help predict maintenance actions. Consequently, the company yields a reduction of unexpected breakdowns, production stoppages, and production costs. The outcomes are significant but selecting an appropriate data-driven method that can generate helpful and trustworthy results is challenging. It is mainly affected by the quality of the available data and the capability to understand the process under analysis correctly. This chapter reviews architectures for data management and data-driven methodologies for enabling predictive maintenance policies. Then follows the presentation of integrated solutions for predictive analytics to conclude with the main challenges identified and future outlook.

The role of maintenance in the industry has been shown to improve companies’ productivity and profitability. Industry 4.0 revolutionised this field by exploiting emergent cloud technologies and IoT to enable predictive maintenance. Significant benefits can be obtained by taking advantage of historical data and Industrial IoT streams, combined with high and distributed computing power. Many approaches have been proposed for predictive maintenance solutions in the industry. Typically, the processing and storage of enormous amounts of data can be effectively performed cloud-side (e.g., training complex predictive models), minimising infrastructure costs and maintenance. On the other hand, raw data collected on the shop floor can be successfully processed locally at the edge, without necessarily being transferred to the cloud. In this way, peripheral computational resources are exploited, and network loads are reduced. This work aims to investigate these approaches and integrate the advantages of each solution into a novel flexible ecosystem. As a result, a new unified solution, named SERENA Cloud Platform. The result addresses many challenges of the current state-of-the-art architectures for predictive maintenance, from hybrid cloud-to-edge solutions to intermodal collaboration, heterogeneous data management, services orchestration, and security.

The ever increasing demand for shorter production times and reduced production costs require manufacturing firms to bring down their production costs while preserving a smooth and flexible production process. To this aim, manufacturers could exploit data-driven techniques to monitor and assess equipmen’s operational state and anticipate some future failure. Sensor data acquisition, analysis, and correlation can create the equipment’s digital footprint and create awareness on it through the entire life cycle allowing the shift from time-based preventive maintenance to predictive maintenance, reducing both maintenance and production costs. In this work, a novel data analytics workflow is proposed combining the evaluation of an asset’s degradation over time with a self-assessment loop. The proposed workflow can support real-time analytics at edge devices, thus, addressing the needs of modern cyber-physical production systems for decision-making support at the edge with short response times. A prototype implementation has been evaluated in use cases related to the steel industry.

In the last few years, cities have become engines of wealth creation thanks to the advent of the new information and communication [...]

The advent of Industry4.0 in automation and data exchange leads to a constant evolution towards intelligent environments, including an intensive adoption of Cyber-Physical System features. Thus, full integration of manufacturing IT and control systems with physical objects embedded with electronics, software, and sensors are being deployed. This new industrial model leads to a pervasive integration of information and communication technology into production assets, generating massive amounts of data. Several software applications use such information for addressing versatile production requirements, such as predictive analytics, visualisation, and scheduling. Such services aim to facilitate daily maintenance activities and provide insight upon production assets and processes in a human-understandable way. In this chapter, the main services of the SERENA system exposed through its platform and towards supporting maintenance activities on the shopfloor are presented.

In this paper, we present CAROL (unConventionAl suRvey methOdoLogy) to collect users’ opinions, to analyze them effectively, and to graphically provide interesting summaries in near-real-time. CAROL has been designed to collect citizens’ opinions, needs, and a perception that can be very useful to redesign cities and drive the innovation process. To this aim, particular importance is given to the architecture implemented to collect the data, which involves sending voice messages instead of text, allowing users greater freedom of expression. Moreover, to make the survey more friendly, an initial analysis is done in real-time thanks to an intuitive dashboard, which shows the first results in the form of graphs and charts, updated after each message. Finally, a more in-depth analysis can be carried out at the end of the survey, thanks to text-mining techniques adapted to the context. As use case, the proposed methodology has been tested in a public event organized by Politecnico di Torino and addressed to female students, with the aim to collect their ideas about new services/opportunities that can be offered to support women in STEM.

During the last years an increasing number of university-level and post-graduation courses on Data Science have been offered. Practices and assessments need specific learning environments where learners could play with data samples and run machine learning and data mining algorithms. To foster learner engagement many closed-and open-source platforms support the design of data science competitions. However, they show limitations on the ability to handle private data, customize the analytics and evaluation processes, and visualize learners' activities and outcomes. This paper presents Data Science Lab Environment (DSLE, in short), a new open-source platform to design and monitor data science competitions. DSLE offers a easily configurable interface to share training and test data, design group works or individual sessions, evaluate the competition runs according to customizable metrics, manage public and private leaderboards, monitor participants' activities and their progress over time. The paper describes also a real experience of usage of DSLE in the context of a 1st-year M.Sc. course, which has involved around 160 students.

International audience

Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.

Recent trends in intelligent manufacturing are transforming shop floor environments into digital factories, thanks to a pervasive integration of information and communication technologies in production lines. Industrial processes become the source of high-volume heterogeneous data, paving the way to create manufacturing intelligence by means of machine learning and data-driven methodologies. In such settings, predictive diagnostics play a crucial role, as they promise to predict future critical conditions in the production process. Unfortunately, the diffusion of data-driven predictive maintenance methodologies is limited by (i) the absence of timely ground-truth knowledge (i.e., class labels), required in the learning phase of data-driven supervised approaches, and (ii) the limited availability of data-mining expertise among application-domain experts, required to harness the power of machine learning techniques. Innovative data-driven services are needed to support domain experts in (i) applying powerful self-learning intelligent techniques with limited technical expertise and (ii) easily understanding results and choices operated by such intelligent techniques, to increase trust by means of transparency. To this aim, this paper presents UDaMP, an integrated platform to support manufacturing intelligence by providing a transparent, self-tuning, unsupervised discovery and assisted data labelling service for predictive maintenance, specifically targeted at cyclic industrial processes. UDaMP includes (i) production-cycle-aware feature engineering, (ii) unsupervised discovery of production-cycle categories, (iii) self-tuning of the optimal number of categories, (iv) human-readable characterisation of production-cycle categories, and (v) assisted data labelling for domain experts. Scalable clustering algorithms automatically discover groups of production cycles sharing common time-independent properties. A self-tuning strategy is integrated to automatically configure the specific input parameter and select the best approach for the data under analysis. Each cluster is then locally characterised through the data distribution of the top 10 most relevant features to support domain experts in uncovering its meaning. Experimental evaluation of UDaMP has been performed on real-world data collected in two different industrial settings.

In today’s world, social networks and online communities continuously generate tons of data that reflect users’ habits, personal interests, opinions and emotions. However, little profit can be gained from such huge raw data collections unless we are able to translate them into useful knowledge. Microblogs like Twitter have recently attracted a great body of research works to mine useful insights about users interests and preferences in different geographical areas and time periods. Indeed, the rather heterogeneous dimensions characterizing Twitter data, such as space, time and text content, impose innovative methods in the data mining discovery process. This paper presents TCharM , a data analytics methodology based on cluster analysis and association rule discovery to gain interesting knowledge from large collections of Twitter data. TCharM explores tweet collections along the three dimensions characterizing tweets (i.e., text content, posting time and place) to support context-aware topic trend analysis. To discover groups of tweets with a good cohesion on the three tweet features, TCharM exploits a novel distance measure ( TASTE ) which allows driving the clustering task by considering in one step the three tweet features. Association rule analysis is then exploited to concisely describe the cluster content with a set of understandable and significant patterns which reveal underlying correlations among frequent topics, tweeting times and places. TCharM can provide useful information to understand the evolution of people’s involvement in different topics, across geographical areas and over time. TCharM find applications in various domains by providing a valuable support in decision making to domain experts. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in discovering cohesive clusters and actionable knowledge from Twitter data.

In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle issues and optimized maintenance, reducing the resulting costs. In this paper, we focus on analyzing data collected from heavy trucks during their use, a relevant task for companies due to the high commercial value of the monitored vehicle. The proposed methodology, named TETRAPAC, offers a generalizable approach to estimate vehicle health conditions based on monitored features enriched by innovative key performance indicators. We discussed performance of TETRAPAC in two real-life settings related to trucks. The obtained results in both tasks are promising and able to support the company’s decision-making process in the planning of maintenance interventions.

Intraday traders buy and sell financial instruments in the short term, typically within the same trading day. Stocks are notable examples of financial instruments. However, since hundreds of stocks are listed on the stock exchange selecting on each trading day the most tradeable stocks is a challenging task, which is commonly addressed through manual inspection of historical stock prices and technical indicators. This paper aims at discovering tradeable stocks on a given trading day by analyzing the historical prices assumed by the same stocks or by other ones on the preceding days by means of regression and weighted sequence mining techniques. The use of regression and weighted sequence mining techniques allows traders to automatically consider a potentially large number of candidate stocks and to effectively analyze their price variations across consecutive days. The experimental results, which were achieved on data acquired from different markets and under different market conditions, show that sequence mining algorithms yield profits higher than both regression techniques and naive strategies.

Itemset mining is a well-known exploratory data mining technique used to discover interesting correlations hidden in a data collection. Since it supports different targeted analyses, it is profitably exploited in a wide range of different domains, ranging from network traffic data to medical records. With the increasing amount of generated data, different scalable algorithms have been developed, exploiting the advantages of distributed computing frameworks, such as Apache Hadoop and Spark. This paper reviews Hadoop- and Spark-based scalable algorithms addressing the frequent itemset mining problem in the Big Data domain through both theoretical and experimental comparative analyses. Since the itemset mining task is computationally expensive, its distribution and parallelization strategies heavily affect memory usage, load balancing, and communication costs. A detailed discussion of the algorithmic choices of the distributed methods for frequent itemset mining is followed by an experimental analysis comparing the performance of state-of-the-art distributed implementations on both synthetic and real datasets. The strengths and weaknesses of the algorithms are thoroughly discussed with respect to the dataset features (e.g., data distribution, average transaction length, number of records), and specific parameter settings. Finally, based on theoretical and experimental analyses, open research directions for the parallelization of the itemset mining problem are presented.

This paper presents a new data mining engine, named EXTREMA (EXploitation of Turin high Resolution Energy MAps), to automatically visualise high-resolution energy maps exploring interesting and human-readable knowledge items from large collections of EPCs. EXTREMA, developed in Python, generates geo-located maps to summarise the main relationships among variables affecting the energy efficiency of buildings at different spatial granularity levels. The visualised knowledge is discovered through a two-level data analytics methodology based on exploratory and unsupervised algorithms. First an unsupervised algorithm divides EPCs into homogeneous groups of buildings with similar thermo-physical characteristics. Each group is then locally characterised through interesting patterns to concisely represent each group. The experimental evaluation, performed on a real dataset collected in a major Italian city in North-West Italy, demonstrates the effectiveness of EXTREMA in extracting and graphically display on geo-located multivariate energy maps a manageable set of human-readable knowledge items.

Automating predictive machine learning entails the capability of properly triggering the update of the trained models. To this aim, the degradation of predictive models has to be continuously evaluated over time to detect data distribution drifts between the original training set and the new data. Traditionally, prediction performance is used as a degradation metric. However, prediction quality indices require ground-truth class labels to be known for the newly classified data, making them unsuitable for real-time applications, as ground-truth labels might be totally absent or be available only later.In this paper, we propose a novel unsupervised methodology to automatically detect prediction-quality degradation of machine learning models. Thanks to the unsupervised approach and a novel scalable estimation technique, we provide an effective and efficient solution to the above-mentioned problem with soft real-time constraints. Specifically, our approach is able to detect class-based concept drift, i.e., when new data contain samples that do not fit the set of class labels known by the currently-trained predictive model. Experiments on synthetic and real-world public datasets show the effectiveness of the proposed methodology in automatically detecting and describing concept drift caused by changes in the class-label data distributions. Thanks to its scalability performance, the proposed approach is suitable for soft real-time applications such as predictive maintenance, Industry 4.0, and text mining.

In recent years, the number of industry-4.0-enabled manufacturing sites has been continuously growing, and both the quantity and variety of signals and data collected in plants are increasing at an unprecedented rate. At the same time, the demand of Big Data processing platforms and analytical tools tailored to manufacturing environments has become more and more prominent. Manufacturing companies are collecting huge amounts of information during the production process through a plethora of sensors and networks. To extract value and actionable knowledge from such precious repositories, suitable data-driven approaches are required. They are expected to improve the production processes by reducing maintenance costs, reliably predicting equipment failures, and avoiding quality degradation. To this aim, Machine Learning techniques tailored for predictive maintenance analysis have been adopted in PREMISES (PREdictive Maintenance service for Industrial procesSES), an innovative framework providing a scalable Big Data service able to predict alarming conditions in slowly-degrading processes characterized by cyclic procedures. PREMISES has been experimentally tested and validated on a real industrial use case, resulting efficient and effective in predicting alarms. The framework has been designed to address the main Big Data and industrial requirements, by being developed on a solid and scalable processing framework, Apache Spark, and supporting the deployment on modularized containers, specifically upon the Docker technology stack.

Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

The continuous evolution of internet of things technologies is constantly evolving the concept of smart cities as well as the surrounding environments. From pervasive sensors through computational nodes at the edge of the network to the cloud and final user applications, the data flow chain makes available to the user a very large and heterogeneous amount of data. IoT platforms are at the core of this chain, providing seamless access to data independently from the hardware devices and making possible the interoperability with other data sources (e.g., GIS, SIM, etc.). Despite the availability of IoT platform solutions either commercial and open-source, research is still very active to design and implement flexible, easy-to-use, and efficient web-service-oriented software infrastructures. This chapter will review the current IoT platform infrastructures making also reference to state-of-the-art solutions in literature and proposed in recent research projects. The chapter will outline the main challenges and directions about future platforms, putting them in the context of realistic case studies.

Nowadays, an exponential growth in biological data has been recorded, including both structured and unstructured data. One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured textual corpora to effectively support the decision making process. Since the emergence of topic modelling, new and interesting approaches to compactly represent the content of a document collection have been proposed. However, the effective exploitation of the proposed strategies requires a lot of expertise.

The use of big data in transportation research is increasing and this leads to new approaches in modeling the traffic flow, especially for what concerns metropolitan areas. An open and interesting research issue is city-wide traffic representation, correlating both spatial and time patterns and using them to predict the traffic flow through the whole urban network. In this paper we present a machine learning based methodology to model traffic flow in metropolitan areas with the final aim to address short-term traffic forecasting at various time horizons. Specifically, we introduce an ad-hoc neural network model (GBNN, Graph Based Neural Network) that mirrors the topology of the urban graph: neurons corresponds to intersections, connections to roads, and signals to traffic flow. Furthermore, we enrich each neuron with a memory buffer and a recurrent self loop, to model congestion and allow each neuron to base its prediction on previous local data. We created a GBNN model for a major Italian city and fed it one year worth of fine-grained real data. Experimental results demonstrate the effectiveness of the proposed methodology in performing accurate lookahead predictions, obtaining 3% and 16% MAAPE error for 5 min and 1 h forecasting respectively.

Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making a shift into cyber-physical system of systems, connected devices provide massive data, that are usually streamed to a central node for further processing. In particular and related to the manufacturing domain, Data processing can provide insight in the operational condition of the organization or process monitored. However, there are near real time constraints for such insights to be generated and data-driven decision making to be enabled. In the context of internet of things for smart manufacturing and empowered by the aforementioned, this study discusses a fog computing paradigm for enabling maintenance related predictive analytic in a manufacturing environment through a two step approach: (1) Model training on the cloud, (2) Model execution on the edge. The proposed approach has been applied to a use case coming from the robotic industry.

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings&rsquo, energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process.

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.

This paper addresses the methodology for determining suitable groups of residential consumers, based on time series of their hourly energy consumption and contractual data. Salient aspects are the discussion on the importance of the data representation in terms of data normalisation, choice of the appropriate features to be used as inputs in clustering procedures, and computation of clustering validity indicators. The analysis is carried out on real hourly-metered electricity consumption data of 10,000 residential consumers. We discuss the main insights obtained with the application of conventional approaches based on time series data handled with different distance metrics (e.g., Euclidean distance and dynamic time warping) and alternative approaches exploring data transformations, among which the CONsumption DUration Curve Time Series (CONDUCTS) methodology proposed by the authors.

This paper presents an innovative and scalable methodology named CONDUCTS (CONsumption DUration Curve Time Series) to discover residential electricity consumption behaviours over time. CONDUCTS exploits data stream processing in time windows jointly with unsupervised machine learning on time-independent data. Specifically, time series consumption data for every consumer is split into N time windows. For a particular time window, a duration curve is calculated providing significant shape-based information disregarding temporal aspects. Each duration curve is sampled according to statistical characteristics and its relevant shape is captured. Therefore, every individual is represented by the evolution of N simplified duration curves. A cluster analysis, based on the K-means algorithm and the Euclidean distance, provides the different consumer profiles in a given time window. CONDUCT's current implementation runs on Apache Spark, a state-of-the-art distributed computing framework. As a case study, CONDUCTS has been experimentally assessed on the real hourly metered data collected in the time frame of one year for a large number of Spanish residential consumers. The experiments highlighted CONDUCT's ability to identify time-variable well-cohesive and well-separated groups of individual electricity consumption patterns with similar characteristics.

Computer Aided Diagnosis (CAD) systems are designed to aid the radiologist in interpreting medical images. They are usually based on lesion detection and segmentation algorithms whose performance depends on a large number of parameters. While time consuming and sub-optimal, parameter values are most often selected through manual search strategies. Genetic or evolutionary algorithms (GA) are effective optimization methods that mimic biological evolution. Genetic algorithms have been shown to efficiently manage complex search spaces, and can be applied to all kinds of objective functions, including discontinuous, nondifferentiable, or highly nonlinear ones. In this study, we have adopted an evolutionary approach to the problem of parameter optimization. We show that the genetic algorithm is able to effectively converge to a better solution than manual optimization on a case study for digital breast tomosynthesis CAD. Parameter optimization was framed as a constrained optimization problem, where the function to be maximized was defined as the weighted sum of sensitivity, false positive rate and segmentation accuracy. A modified Dice coefficient was defined to assess the segmentation quality of individual lesions. Finally, all viable solutions evaluated by the GA were studied by means of exploratory data analysis techniques, such as association rules, to gain useful insights on the strength of the influence of each parameter on overall algorithm performance. We showed that this combination was able to identify multiple ranges of viable solutions with good segmentation accuracy.

The recent expansion of IoT-enabled (Internet of Things) devices in manufacturing contexts and their subsequent data-driven exploitation paved the way to the advent of the Industry 4.0, promoting a full integration of IT services, smart devices, and control systems with physical objects, their electronics and sensors. The real-time transmission and analysis of collected data from factories has the potential to create manufacturing intelligence, of which predictive maintenance is an expression. Hence the need to design new approaches able to manage not only the data volume, but also the variety and velocity, extracting actual value from the humongous amounts of collected data. To this aim, we present iSTEP, an integrated Self-Tuning Engine for Predictive maintenance, based on Big Data technologies and designed for Industry 4.0 applications. The proposed approach targets some of the most common needs of manufacturing enterprises: compatibility with both the on-premises and the in-the-cloud environments, exploitation of reliable and largely supported Big Data platforms, easy deployment through containerized software modules, virtually unlimited horizontal scalability, fault-tolerant self-reconfiguration, flexible yet friendly streaming-KPI computations, and above all, the integrated provisioning of self-tuning machine learning techniques for predictive maintenance. The current implementation of iSTEP exploits a distributed architecture based on Apache Kafka, Spark Streaming, MLlib, and Cassandra; iSTEP provides (i) a specific feature engineering block aimed at automatically extracting metrics from the production monitoring time series, which improves the predictive performance by 77% on average, and (ii) a self-tuning approach that dynamically selects the best prediction algorithm, which improves the predictive performance up to 60%. The iSTEP engine provides transparent predictive models, able to provide end users with insights into the knowledge learned, and it has been experimentally evaluated on a public unbalanced failure dataset, whose extensive results are discussed in the paper.

Predictive maintenance is an ever-growing area of interest, spanning different fields and approaches. In the automotive industry faulty behaviors of the oxygen sensor are a key challenge to address. This paper presents OxyClog, a data-driven framework that, given a large number of time series collected from a vehicle's ECU (engine control unit), builds a model to predict if the oxygen sensor is currently unclogged, almost clogged (since the clogging of the sensor happens gradually), or clogged. OxyClog is characterized by a tailored preprocessing, which includes a custom and interpretable feature selection algorithm, along with a summarization strategy to transform a time-dependent problem into a time-independent one. Furthermore, a semi-supervised labeling methodology has been devised to use different data sources with different characteristics to define meaningful clogging labels. OxyClog integrates state-of-the-art classification algorithms – both interpretable and non-interpretable – to process real ECU data with good prediction performance.

Wireless Sensor Network (WSN) monitoring takes a primary role in many industrial and research processes. Huge amounts of WSN sensor readings are nowadays available and can be analyzed to discover fruitful knowledge. This paper focuses on analyzing historical WSN sensor readings to identify the combinations of sensors whose readings show an unexpected trend. Although significant variations of single sensor readings may be easily detected, discovering correlations between multiple sensor readings is challenging without using advanced data analytics tools. To tackle this issue, we present an itemset-based data mining approach to analyzing WSN data. It identifies the combinations of sensors (of arbitrary size) whose readings are unexpectedly low in a given time period. Since the readings acquired by multiple sensors may decrease in an alternate fashion, the discovered patterns provide new information compared to single sensor analysis. To make the mined patterns manageable by domain experts for manual inspection, the mining algorithm is driven by spatial constraints defined on the WSN topology. The experimental results, achieved on real WSN data, demonstrate the effectiveness of the proposed approach in detecting heating system malfunctioning.

Physicians and health care organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, analyzing these datasets to figure out interesting and hidden knowledge is a challenging task. This article proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. Specifically, correlations among prescribed examinations, drugs, and patient profiles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules (i.e., nonredundant generalized rules) is considered. Furthermore, rules are classified according to the involved data features (medical treatments or patient profiles) and then explored in a top-down fashion: from the small subset of high-level rules, a drill-down is performed to target more specific rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels.

In this paper we propose a new self-learning engine to streamline the analytics process, as it enables analysts to mine massive data repositories with minimal user intervention. In the context of cluster analysis on a collection of documents this new system, named SELF-DATA (SELF-learning DAta TrAnsformation), suggests to the analyst how to configure the whole mining process for a given dataset. SELF-DATA relies on an engine exploring different data weighting schemas (e.g., normalized term frequencies) and data transformation methods (e.g., PCA) before applying the cluster analysis, evaluating and comparing solutions through different quality indices (e.g., weighted Silhouette), and presenting the k-top solutions to the analyst. SELF-DATA will also include a knowledge base storing results of experiments on previously processed datasets, and a classification algorithm trained on the knowledge base content to forecast the best configuration for the whole mining process for an unexplored dataset. The first development of SELF-DATA running on Apache Spark has been validated on 5 collections of documents. Experimental results highlight that TF-IDF and logarithmic entropy are effective to measure item relevance with sparse datasets, and the LSI method outperforms PCA with a large dictionary.

Energy Certificates of Buildings (ECB) provide interesting information on the standard energy performance, thermo-physical and geometrical related properties of existing buildings. The analysis of such data collection is challenging due to data volume and heterogeneity of attributes. This paper presents EPICA a data mining framework to automatically explore a collection of ECB to extract interesting knowledge items. To this aim, EPICA first reduces the data dimensionality through the Principal Component Analysis, then a clustering algorithm is exploited to discover groups of ECB with similar features. Each group is then locally characterized by a set of relevant generalized association rules able to summarize interesting relations among variables influencing energy performance of buildings at different coarse granularities. Experimental results, obtained on real data collected from an energy certification dataset related to Piedmont Region, in North Western of Italy, shows the effectiveness of EPICA in extracting a manageable set of human-readable knowledge items characterizing the groups of buildings with different energy performance levels.