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In recent years, studying and predicting alternative mobility (e.g., sharing services) patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services. This need is aggravated during the current pandemic crisis, which pushes policymakers and private citizens to seek social-distancing compliant urban mobility services, such as electric bikes and scooter sharing offerings. However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel deep learning network with a multi-attention (spatial and temporal) mechanism that effectively captures and exploits complex spatial and temporal patterns in mobility data. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task., Comment: submitted to Information Sciences

In this paper we consider the case of the ICT adoption and use in an agriculture cluster in Lombardy, a northern region of Italy. At the state of the art, relationships among key factors of adoption and use of ICT in agriculture area received little attention by the academic literature. Thus, in this paper we aim to identify a research model in order to provide evidence of four different research questions concerning the determining factors for ICT adoption. The proposed case study reports and discusses the results obtained by analysing data from a survey of about 600 agricultural farms. Finally, Belief Bayesian Networks (BBNs) are used to analyse the complex influence relationships detected between research variables

In large organizations the database architecture is typically built through a series of projects and realizations that result in a number of heterogeneous and overlapping data sources. This trend is worsened by merger and acquisition activities that add new data sources from external organizations to the existing data architecture. Data fragmentation significantly reduces the possibility for an organization to exploit its information assets. A technology that partially alleviates these problems is data integration middleware that allows users to read-only access data stored in heterogeneous data sources through the presentation of a unified view of these data. In this paper we introduce a new concept to measure the improvement in information capacity enabled by data integration solutions, and propose an original framework which can support the evolution of the organization data architecture by identifying the optimal solution that maximizes such improvement within a given cost threshold.

In order to achieve higher data quality targets, organizations need to identify the data quality dimensions that are affected by poor quality, assess them, and evaluate which improvement techniques are suitable to apply. Data quality literature provides methodologies that support complete data quality management by providing guidelines that organizations should contextualize and apply to their scenario. Only a few methodologies use the cost-benefit analysis as a tool to evaluate the feasibility of a data quality improvement project. In this paper, we present an ontological description of the cost-benefit analysis including the most important contributes already proposed in literature. The use of ontologies allows the knowledge improvement by means of the identification of the interdependencies between costs and benefits and enables different complex evaluations. The feasibility and usefulness of the proposed ontology-based tool has been tested by means of a real case study. © (2009) by the AIS/ICIS Administrative Office All rights reserved.

The wide diffusion of ICTs has unchained subsequent waves of expectations about how the new ways of communicating and ex- changing information and knowledge would affect inter-firm relation- ships, hopefully improving their performance. However, facts often down- sized expectations. Even the so called Web revolution did not solve above mentioned issues. In fact the adoption of Internet-based tools by many SMEs have still limited to the Internet access, e-mail and static company Web sites only. Today the new frontier is the adoption of peer-to-peer approach and semantic-based technologies such as semantic Web ser- vices and ontologies. However, a crucial node for the future development of peer-to-peer semantic networks is understanding if, and eventually how, software architectures based on those concepts can create economic value for all network members. This paper presents relevant issues in the provisioning of electronic services in peer-to-peer environments and proposes preliminary solutions under developing in the context of the NeP4B (Network Peers for Business) project

Multichannel Adaptive Information Systems (ISs) are becoming the new paradigm for building complex interorganization ISs based on the use of different devices, Web services, and XML specifications. Modeling Multichannel Adaptive ISs requires complex interactions between the users and the system front-end due to the different capabilities of existing devices. Unfortunately, the expressive power and effectiveness of visual languages have not been fully exploited in this new area. In this paper we address two related issues. First we propose a visual language that allows for adapting the presentation layer to different physical devices; then we describe a technique to evaluate possible tradeoffs between usability and other qualities of service in Web services design. (c) 2007 Elsevier Ltd. All rights reserved.

Web services are imposing as the technology to integrate highly heterogeneous systems. BPEL, the standard technology to compose services, assumes a single ”orchestrator” that controls the execution flow and coordinates the interactions with selected services. This centralized approach simplifies the coordination among components, but it is also a too heavy constraint. To this end, the paper introduces the idea of distributed orchestrations and presents a proposal to couple BPEL and distributed execution in mobile settings. The approach —exemplified on a simple case study— transforms a centralized BPEL process into a set of coordinated processes. An explicit meta-model and graph transformation supply the formal grounding to obtain a set of related processes, and to add the communication infrastructure among the newly created processes. The paper also presents a communication infrastructure based on tuple spaces to make the different orchestrators interact in mobile contexts. Keywords: WS-BPEL, Graph, Electronic Communications of the EASST, Volume 3: Software Evolution through Transformations 2006

Data quality (DQ) is emerging as a new relevant area for the improvement of the effectiveness of organizations. Despites the consequences of poor quality of data are often experienced in everyday life of enterprises, very few organizations adopt specific methodologies for assessing and monitoring quality of their data. In this paper we present the first results of an Italian project whose goal is to produce an enhanced version of well known approaches to Basel II operational risk evaluation, with a significant relevance to information and data quality, and its effects on operational risk. In particular in this paper we focus on the definition of an assessment methodology and a supporting tool for DQ.

Esiste un numero significativo di documenti, report e pagine Web – un'analisi riporta 233 milioni di tabelle relazionali nel repository Common Crawl contenente un totale 2,85 miliardi di documenti – che fanno uso di tabelle per fornire informazioni che non possono essere facilmente elaborate dagli umani o capite dai computer. Per risolvere questo problema proponiamo un nuovo approccio che permetterà ai computer di interpretare la semantica di una tabella, e fornirà agli umani una rappresentazione più accessibile dei dati contenuti in essa. Per conseguire questo obiettivo, il problema principale è stato suddiviso in tre sotto-problemi: (i) la definizione di un metodo per fornire un'interpretazione semantica dei dati di una tabella; (ii) la definizione di un modello descrittivo che permetta ai computer di capire e condividere dati di una tabella; e (iii) la definizione di processi, tecniche e algoritmi per generare rappresentazioni dei dati in linguaggio naturale. Per quanto riguarda il sotto-problema (i), la rappresentazione semantica dei dati è stata ottenuta attraverso l'applicazione di tecniche di interpretazione di tabelle (table interpretation), che aiuta gli utenti ad identificare, in una maniera semi-automatica, il significato dei dati di una tabella e le relazioni tra di essi. Queste tecniche considerano in input una tabella e un Knowledge Graph, e restituiscono una rappresentazione RDF – un set di tuple – del contenuto della tabella, facendo riferimento ai concetti e alle proprietà del KG. Questa dissertazione presenta un nuovo approccio che, a partire dai lavori presenti in letteratura, ha portato allo sviluppo di un nuovo strumento, chiamato MantisTable, che effettua automaticamente un'interpretazione semantica completa della tabella. Gli esperimenti condotti hanno mostrato buoni risultati, rispetto alle tecniche e ai tool simili. Il sotto-problema (ii) è stato affrontato con la definizione di nuovi modi di rappresentazione dei dati: è stato definito un nuovo tipo di descrizione che combina la specifica OpenAPI con il linguaggio JSON-LD. I risultati delle tecniche di interpretazione semantica delle tabelle vengono così sfruttati per migliorare un formato già popolare, permettendo il recupero e il processamento dei dati tabellari. Il sotto-problema (iii) è stato affrontato definendo una tecnica di generazione del linguaggio naturale che utilizza una rete neurale per trasformare dati RDF, ottenuti dall'interpretazione delle tabelle, in frasi. Grazie a queste frasi, è possibile creare una rappresentazione testuale del contenuto delle tabelle. Questa è poi estendibile con informazioni aggiuntive provenienti da fonti che possono essere selezionate automaticamente utilizzando l'annotazione semantica. A significant number of documents, reports and Web pages –an analysis reports 233M relational tables within the Common Crawl repository of 1.81 billion documents– makes use of tables to convey information that cannot be easily processed by humans, and understood by computers. To address this issue, we propose a new approach that allows computers to interpret the semantics of a table, and provides humans with a more accessible representation of the data contained in a table. To achieve the objective, the general problem has been broken down into three sub-problems: (i) define a method to provide a semantic interpretation of table data; (ii) define a descriptive model that allows computers to understand and share table data; and (iii) define processes, techniques and algorithms to generate natural language representation of the table data. Regarding sub-problem (i), the semantic representation of a data has been obtained through the application of table interpretation techniques, which supports users to identify in a semi-automatic way the meaning of the data in the table and the relationships between them. Such techniques take a table and a Knowledge Graph (KG) as input, and deliver as output an RDF representation –a set of tuples –. The output contains the input table annotated with the KG concepts and properties. This thesis presents a new approach, rooted in the existing literature, to laid the foundations for the development of a new tool -called MantisTable- which automatically performs a complete semantic interpretation of a table. The conducted experiments have shown good results compared to similar techniques. Sub-problem (ii) has been tackled by defining new ways of representing data. A new kind of description has been defined that combines the OpenAPI specification with the JSON-LD. The results of semantic table interpretation techniques are exploited to enhance a popular description format and allow automatic retrieval and processing of table data. Sub-problem (iii) has been addressed by defining a natural language generation technique that uses a neural network to translate RDF data obtained from table interpretation into sentences. Thanks to these sentences, it is possible to create a textual representation of the content of the table, and possibly extend it with additional information from data sources that can be selected automatically using semantic annotations.

Lo scopo di questa tesi è di studiare due principali categorie di algoritmi per la detection di oggetti e il loro uso in particolari applicazioni. La prima categoria esaminata riguarda approcci basati su Keypoint. Diversi esperimenti comparativi vengono eseguiti all'interno della pipeline standard del modello di test MPEG CDVS e viene proposta una pipeline estesa che fa uso di informazione colore. La seconda categoria di object detectors oggetto di indagine si basa su Reti neurali convoluzionali. In particolare, vengono affrontate due applicazioni di reti neurali convoluzionali per il riconoscimento di oggetti. Il primo riguarda il riconoscimento di loghi commerciali. Due pipeline di classificazione sono progettate e testate su un set di immagini raccolte da Flickr. La prima architettura utilizza una rete pre-addestrata come feature extractor e raggiunge risultati comparabili a quelli di algoritmi basati Keypoint. La seconda architettura si avvale di una rete neurale che supera le performances di metodi stato dell'arte basati su Keypoint. L'altra applicazione esaminata è la categorizzazione di dipinti che consiste nell'associare l'autore, nell'assegnare un dipinto alla scuola o al movimento artistico a cui appartiene, e classificare il genere del dipinto, ad es. paesaggio, ritratto, illustrazione ecc. Per affrontare questo problema, viene proposta una struttura di rete neurale multibranch e multitask che beneficia dell'uso congiunto di approcci basati su keypoint e di features neurali. In entrambe le applicazioni viene anche esaminato l'uso di tecniche di data augmentation per ampliare il training set. In particolare per i dipinti, un algoritmo di trasferimento di stile pittorico basato su reti neurali viene sfruttato per generare quadri sintetici da utilizzare in fase di training. The aim of this thesis is to study two main categories of algorithms for object detection and their use in particular applications. The first category that is investigated concerns Keypoint-based approaches. Several comparative experiments are performed within the standard testing pipeline of the MPEG CDVS Test Model and an extended pipeline which make use of color information is proposed. The second category of object detectors that is investigated is based on Convolutional Neural Networks. Two applications of Convolutional Neural Networks for object recognition are in particular addressed. The first concerns logo recognition. Two classification pipelines are designed and tested on a real-world dataset of images collected from Flickr. The first architecture makes use of a pre-trained network as feature extractor and it achieves comparable results keypoint based approaches. The second architecture makes use of a tiny end-to-end trained Neural Network that outperformed state-of-the-art keypoint based methods. The other application addressed is Painting Categorization. It consists in associating the author, assigning a painting to the school or art movement it belongs to, and categorizing the genre of the painting, e.g. landscape, portrait, illustration etc. To tackle this problem, a novel multibranch and multitask Neural Network structure is proposed which benefit from joint use of keypoint-based approaches and neural features. In both applications the use of data augmentation techniques to enlarge the training set is also investigated. In particular for paintings, a neural style transfer algorithm is exploited for generating synthetic paintings to be used in training.

Nonostante l'elevato numero di dati pubblicati come LD, il loro utilizzo non ha ancora mostrato il loro potenziale per l’assenza di comprensione dei metadati. I consumatori di dati hanno bisogno di ottenere informazioni dai dataset in modo veloce e concentrato per poter decidere se sono utili per il loro problema oppure no. Le tecniche di profilazione dei dati offrono una soluzione efficace a questo problema in quanto sono utilizzati per generare metadati e statistiche che descrivono il contenuto dei dataset. Questa tesi presenta una ricerca, che affronta i problemi legati alla profilazione Linked Data. Nonostante il termine profilazione dei dati è usato in modo generico per diverse informazioni che descrivono i dataset, in questa tesi noi andiamo a ricoprire tre aspetti della profilazione; topic-based, schema-based e linkage-based. Il profilo proposto in questa tesi è fondamentale per il processo decisionale ed è la base dei requisiti che portano verso la comprensione dei dataset. In questa tesi presentiamo un approccio per classificare automaticamente insiemi di dati in una delle categorie utilizzate nel mondo dei LD. Inoltre, indaghiamo il problema della profilazione multi-topic. Per la profilazione schema-based proponiamo un approccio riassuntivo schema-based, che fornisce una panoramica sui rapporti nei dati. I nostri riassunti sono concisi e chiari sufficientemente per riassumere l'intero dataset. Inoltre, essi rivelano problemi di qualità e possono aiutare gli utenti nei compiti di formulazione dei query. Molti dataset nel LD cloud contengono informazioni simili per la stessa entità. Al fine di sfruttare appieno il suo potenziale LD bisogna far vedere questa informazione in modo esplicito. Profiling Linkage fornisce informazioni sul numero di entità equivalenti tra i dataset e rivela possibili errori.Le tecniche di profiling sviluppate durante questo lavoro sono automatiche e possono essere applicate a differenti insiemi di dati indipendentemente dal dominio. Recently, the increasing diffusion of Linked Data (LD) as a standard way to publish and structure data on the Web has received a growing attention from researchers and data publishers. LD adoption is reflected in different domains such as government, media, life science, etc., building a powerful Web available to anyone. Despite the high number of datasets published as LD, their usage is still not exploited as they lack comprehensive metadata. Data consumers need to obtain information about datasets content in a fast and summarized form to decide if they are useful for their use case at hand or not. Data profiling techniques offer an efficient solution to this problem as they are used to generate metadata and statistics that describe the content of the dataset. Existing profiling techniques do no cover a wide range of use cases. Many challenges due to the heterogeneity nature of Linked Data are still to overcome. This thesis presents the doctoral research which tackles the problems related to Profiling Linked Data. Even though the term of data profiling is the umbrella term for diverse descriptive information that describes a dataset, in this thesis we cover three aspects of profiling; topic-based, schema-based and linkage-based. The profile provided in this thesis is fundamental for the decision-making process and is the basic requirement towards the dataset understanding. In this thesis we present an approach to automatically classify datasets in one of the topical categories used in the LD cloud. Moreover, we investigate the problem of multi-topic profiling. For the schema-based profiling we propose a schema-based summarization approach, that provides an overview about the relations in the data. Our summaries are concise and informative enough to summarize the whole dataset. Moreover, they reveal quality issues and can help users in the query formulation tasks. Many datasets in the LD cloud contain similar information for the same entity. In order to fully exploit its potential LD should made this information explicit. Linkage profiling provides information about the number of equivalent entities between datasets and reveal possible errors. The techniques of profiling developed during this work are automatic and can be applied to different datasets independently of the domain.

Over the last few years, there has been an increasing di↵usion of Linked Data as a standard way to publish interlinked structured data on the Web, which allows users, and public and private organizations to fully exploit a large amount of data from several domains that were not available in the past. Although gathering and publishing such massive amount of structured data is certainly a step in the right direction, quality still poses a significant obstacle to the uptake of data consumption applications at large-scale. A crucial aspect of quality regards the dynamic nature of Linked Data where information can change rapidly and fail to reflect changes in the real world, thus becoming out-date. Quality is characterised by di↵erent dimensions that capture several aspects of quality such as accuracy, currency, consistency or completeness. In particular, the aspects of Linked Data dynamicity are captured by Time-Related Quality Dimen- sions such as data currency. The assessment of Time-Related Quality Dimensions, which is the task of measuring the quality, is based on temporal information whose collection poses several challenges regarding their availability, representation and diversity in Linked Data. The assessment of Time-Related Quality Dimensions supports data consumers in their decisions whether information are valid or not. The main goal of this thesis is to develop techniques for assessing Time-Related Quality Dimensions in Linked Data, which must overcome several challenges posed by Linked Data such as third-party applications, variety of data, high volume of data or velocity of data. The major contributions of this thesis can be summarized as follows: it presents a general settings of definitions for quality dimensions and measures adopted in Linked Data; it provides a large-scale analysis of approaches for representing temporal information in Linked Data; it provides a sharable and interoperable conceptual model which integrates vocabularies used to represent temporal information required for the assessment of Time-Related Quality Di- mensions; it proposes two domain-independent techniques to assess data currency that work with incomplete or inaccurate temporal information and finally it pro- vides an approach that enrich information with time intervals representing their temporal validity.

Genetics and Molecular Biology are keys for the understanding the mechanisms of many of the human diseases that have strong harmful effects. The empirical mission of Genetics is to translate these mechanisms into Clinical benefits, thus bridging in-silico findings to patient bed side: approaching this goal means achieving what is commonly referred as clinical genomics or personalized medicine. In this process, technologies are assuming an increasing role. With the introduction of new experimental platforms (microarrays, sequencing, etc), today's analyses are much more detailed and can cover a wide spectrum of applications, from gene expression to Copy Number Variants detection. The advantages of technological improvements are usually followed by data management drawbacks due to the explosion of data throughput that reflects on a real need for new systems of data rationalization and management, data access, query and extraction. Our genetic laboratories partners encountered all those issues: what they need is a tool that allows data-integration and supports biological data analysis exploiting computational infrastructures on distributed environment. From such needs, we defined two main goals: (1) Computer Science goal: to design and implement a framework that integrates and manages data and genetic analyses; (2) Genetics and Molecular Biology goal (application domains): to solve biological problems through the framework and develop new methods. Given these requirements and related specifications, we designed an extensible framework based on three inter-connected layers: (1) Experimental data layer, that provides data integration of data from high-throughput platforms (also called horizontal data integration); (2) Knowledge data layer, that provides data integration of knowledge data (also called vertical integration); (3) Computational layer, that provides access to distributed environments for data analysis, in our cases GRID and Cluster technologies. Above the three design blocks, single biological problems can be supported and custom user interfaces are implemented. From our partner laboratories, two main relevant biological problems have been addressed: (1) Linkage Analysis: given a large pedigree in which subjects were genotyped with chips of 1 million of SNPs, the linkage analysis problem presented real computational limits. We designed a heuristic method to overcome computational restrictions and implemented it within our framework, exploiting GRID and Cluster environments. Using our approach, we obtained genetic results, successfully validated by end-users. We also tested performances of the system, reporting compared results. (2) SNP selection and ranking: given the problem of ranking SNPs based on a-priori information, we developed a novel method for biological data mining on genes' annotations. The method has been implemented as a web tool, SNP Ranker, that is under deep validation by our partners laboratories. The framework here designed and implemented demonstrated that this approach is consistent and can have potential impacts on the scientific community.