Search

BACKGROUND: Aging is a process that does not refer only to the accumulation of damage in a human being over time but rather to individual changes determined by genetic, lifestyle, social and environmental factors. Aging is one of the greatest known risk factors for most human diseases. An older person in good health has a good level of independence, weighs less on the national health system and plays a productive and active role in his/her community; thus, the concept of 'healthy aging', reflecting older adult-environment fit should be promoted. The interactions between lifestyle, including nutrition, and health play a fundamental role in the aging process; eating habits and eating behaviours are recognized as important modifiable factors potentially leading to a healthy 'phenotype'. OBJECTIVE AND METHODS: A multidisciplinary consortium with three Italian universities (the University of Milano-Bicocca, University of Pavia and University of Calabria) and Italian Small-Medium Enterprises proposed the Food Social Network (Food NET) project. Food NET overall outcome is to achieve target-specific guidelines and exact technologies for accessible functional foods, aimed at improving the quality of life and nutritional status of citizens (aged>65) of the Metropolitan City of Milan (Italy). CONCLUSIONS: This project is part of the 'Smart Living' and food-related strategies aimed at responding to the needs of this target population, developing new food products, appropriate to meet the specific requirements and ensuring and promoting sustainable diets for healthy aging by effective food policy approaches.

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 rappresenta, 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 proce

The problem of reliably predicting vehicle flows is paramount for traffic management, risk assessment, and public safety. It is a challenging problem as it is influenced by multiple factors, such as spatio-temporal dependencies with external factors (as events and weather conditions). In recent years, with the exponential data growth and technological advancement, deep learning has been adopted to approach urban mobility problems by addressing spatial dependency with convolutional neural networks and the temporal one with recurrent neural networks. We propose a spatio-temporal flow prediction framework, called 3D-CLoST, that exploits the synergy between 3D convolution and long short-term memory (LSTM) networks to jointly learn the characteristics of the space-time correlation from low to high levels. To the best of our knowledge, no method currently proposes such a structure for this problem. The results achieved on the two real-world datasets show that 3D-CLoST can learn behaviors from the data effectively.

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 do, 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 exploi

Semantics-Aware recommendation engines have emerged as a new family of systems able to exploit the semantics encoded in un- structured and structured information sources to provide better results in terms of accuracy, diversity and novelty as well as to foster the pro- visioning of new services such as explanation. In the rising of these new recommender systems, an important role has been played by Linked Data (LD). However, as Linked Data is often very rich and contains many in- formation that may result irrelevant and noisy, an initial step of feature selection may be required in order to select the most meaningful portion of the original dataset. Many approaches have been proposed in the literature for feature selection that exploit different statistical dimensions of the original data. In this paper we investigate the role of the semantics encoded in an ontological hierarchy via schema-summarization when exploited to select the most relevant properties for a recommendation task.

Recommender systems are emerging as an interesting application scenario for Linked Data (LD). In fact, by exploiting the knowledge encoded in LD datasets, a new generation of semantics-aware recommendation engines have been developed in the last years. As Linked Data is often very rich and contains many information that may result irrelevant and noisy for a recommendation task, an initial step of feature selection is always required in order to select the most meaningful portion of the original dataset. Many approaches have been proposed in the literature for feature selection that exploit different statistical dimensions of the original data. In this paper we investigate the role of the semantics encoded in an ontological hierarchy when exploited to select the most relevant properties for a recommendation task. In particular, we compare an approach based on schema summarization with a \classical" one, i.e., Information Gain. We evaluated the performance of the two methods in terms of accuracy and aggregate diversity by setting up an experimental testbed relying on the Movielens dataset.

An increasing number of research and industrial initiatives have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. The proposed model produces a summary that is correct and complete with respect to the assertions of the data set and whose size scales well with respect to the ontology and data size. Our framework is evaluated by showing that it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.

An increasing number of research and industrial initiatives have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. Our framework is evaluated by showing that it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.

We study the problem of group linkage: linking records that refer to multiple entities in the same group. Applications for group linkage include finding businesses in the same chain, finding social network users from the same organization, and so on. Group linkage faces new challenges compared to traditional entity resolution. First, although different members in the same group can share some similar global values of an attribute, they represent different entities so can also have distinct local values for the same or different attributes, requiring a high tolerance for value diversity. Second, we need to be able to distinguish local values from erroneous values. We present a robust two-stage algorithm: The first stage identifies pivots-maximal sets of records that are very likely to belong to the same group, while being robust to possible erroneous values; the second stage collects strong evidence from the pivots and leverages it for merging more records into the same group, while being tolerant to differences in local values of an attribute. Experimental results show the high effectiveness and efficiency of our algorithm on various real-world data sets.

An increasing amount of data is published and consumed on the Web according to the Linked Open Data (LOD) paradigm. In such scenario, capturing the age of data can provide insight about their validity under the hypothesis that more up-to-date data is, more likely is to be true. In this paper we present a model and a framework for assessing the currency of the data represented in one of the most important LOD datasets, DBpedia. Existing currency metrics are based on the notion of date of last modification, but often such information is not explicitly provided by data producers. The proposed framework extrapolates such temporal metadata from time-labeled revisions of Wikipedia pages (from which data has been extracted). Experimental results demonstrate the usefulness of the framework and the effectiveness of the currency evaluation model to provide a reliable indicator of the validity of facts represented in DBpedia.

Open government is emerging as a core issue for increasing, on the one hand, participation of citizens, and, on the other hand, accountability, transparency, and the capability of delivering digital services by Public Administrations, with a consequent interest into public and social value as final outcomes. However, most of the open government initiatives actually concern the provision of public data under an open license and in an open and accessible format. From a policy as well as from a research point of view, open government raises a set of questions about how to manage and evaluate their quality, also considering the compliance with enforced legal frameworks, if any available focused specifically on open government issues. Thus, the paper discusses a quality-based framework for open government data compliance assessment, made up of quality dimensions and a set of criteria contributing to the measurement of a compliance index. The framework is then applied for the evaluation of the open government data initiatives of a sample of local public administration in Italy.

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 represen

The standardization and adoption of Semantic Web technologies has resulted in an unprecedented volume of data being published as Linked Data (LD). However, the "publish first, refine later" philosophy leads to various quality problems arising in the underlying data such as incompleteness, inconsistency and semantic ambiguities. In this article, we describe the current state of Data Quality in the Web of Data along with details of the three papers accepted for the International Journal on Semantic Web and Information Systems' (IJSWIS) Special Issue on Web Data Quality. Additionally, we identify new challenges that are specific to the Web of Data and provide insights into the current progress and future directions for each of those challenges.

Open government is emerging as a core issue for increasing, on the one hand, participation of citizens, and, on the other hand, accountability, transparency, and the capability of delivering digital services by Public Administrations, with a consequent interest into public and social value as final outcomes. However, most of the open government initiatives actually concern the provision of public data under an open license and in an open and accessible format. From a policy as well as from a research point of view, open government raises a set of questions about how to manage and evaluate their quality, also considering the compliance with enforced legal frameworks, if any available focused specifically on open government issues. Thus, the paper discusses a quality-based framework for open government data compliance assessment, made up of quality dimensions and a set of criteria contributing to the measurement of a compliance index. The framework is then applied for the evaluation of the open government data initiatives of a sample of local public administration in Italy

In this paper we discuss the experience of the development of a real system for integrating data about turnover, price and selling volume of AOP UnoLombardia, the biggest association of fruit and vegetable growers in the Lombardia region (Italy), that includes primary Italian and European brands such as Bonduelle and Dimmidisi. The system represents an adaptation and transformation of traditional data warehouse repository oriented development to comply the requirements of a coopetitive environment, where multiple organizations are willing to cooperate over some topics but, at the same time, they compete in the market. Readers may found useful insights and lessons learned from the following contributions of the present work: (i) a methodology to design data warehouse applications in a coopetitive environment and (ii) an architecture based on the combination of virtual data integration and traditional ETL enforcing protection of sensible data.

In this paper we discuss the experience of the development of a real system for integrating data about turnover, price and selling volume of AOP UnoLombardia, the biggest association of fruit and vegetable growers in the Lombardia region (Italy), that includes primary Italian and European brands such as Bonduelle and Dimmidisi. The system represents an adaptation and transformation of traditional data warehouse repository oriented development to comply the requirements of a coopetitive environment, where multiple organizations are willing to cooperate over some topics but, at the same time, they compete in the market. Readers may found useful insights and lessons learned from the following contributions of the present work: (i) a methodology to design data warehouse applications in a coopetitive environment and (ii) an architecture based on the combination of virtual data integration and traditional ETL enforcing protection of sensible data. © 2013 Springer-Verlag.

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

An increasing amount of data is published and consumed on the Web according to the Linked Data paradigm. In consideration of both publishers and consumers, the temporal dimension of data is important. In this paper we investigate the characterisation and availability of temporal information in Linked Data at large scale. Based on an abstract definition of temporal information we conduct experiments to evaluate the availability of such information using the data from the 2011 Billion Triple Challenge (BTC) dataset. Focusing in particular on the representation of temporal meta-information, i.e., temporal information associated with RDF statements and graphs, we investigate the approaches proposed in the literature, performing both a quantitative and a qualitative analysis and proposing guidelines for data consumers and publishers. Our experiments show that the amount of temporal information available in the LOD cloud is still very small; several different models have been used on different datasets, with a prevalence of approaches based on the annotation of RDF documents. © 2012 Springer-Verlag Berlin Heidelberg.

An increasing amount of data are published and consumed on the Web according to the Linked Data paradigm. In such scenario, understanding if the data consumed are up-to-date is crucial. Outdated data are usually considered inappropriate for many crucial tasks, such as make the consumer confident that answers returned to a query are still valid at the time the query is formulated. In this paper we present a first dataset-independent framework for assessing currency of Linked Open Data (LOD) graphs. Starting from the analysis of the 8,713,282 triples containing temporal metadata in the billion triple challenge 2011, we investigate which vocabularies are used to represent versioning metadata, we defined Onto Currency, an ontology that integrates the most frequent properties used in this domain, and supports the collection of metadata from datasets that use different vocabularies. The proposed framework uses this ontology to assess the currency of an RDF graph/statement, by extrapolating it from the currency of the documents that describe the resources occurring in the graphs (statement). The approach has been implemented and evaluated in two different scenarios. © 2012 IEEE.

Many data sets contain temporal records over a long period of time; each record is associated with a time stamp and describes some aspects of a realworld entity at that particular time. From such data, users often wish to search for entities in a particular period and understand the history of one entity or all entities in the data set. A major challenge for enabling such search and exploration is to identify records that describe the same realworld entity over a long period of time; however, linking temporal records is hard given that the values that describe an entity can evolve over time (e.g., a person can move from one affiliation to another). We demonstrate the CHRONOS system which offers users the useful tool for finding real-world entities over time and understanding history of entities in the bibliography domain. The core of CHRONOS is a temporal record-linkage algorithm, which is tolerant to value evolution over time. Our algorithm can obtain an F-measure of over 0.9 in linking author records and fix errors made by DBLP. We show how CHRONOS allows users to explore the history of authors, and how it helps users understand our linkage results by comparing our results with those of existing systems, highlighting differences in the results, explaining our decisions to users, and answering "what-if" questions. © 2012 VLDB Endowment.

Many data sets contain temporal records which span a long period of time; each record is associated with a time stamp and describes some aspects of a real-world entity at a particular time (e. g., author information in DBLP). In such cases, we often wish to identify records that describe the same entity over time and so be able to perform interesting longitudinal data analysis. However, existing record linkage techniques ignore temporal information and fall short for temporal data. This article studies linking temporal records. First, we apply time decay to capture the effect of elapsed time on entity value evolution. Second, instead of comparing each pair of records locally, we propose clustering methods that consider the time order of the records and make global decisions. Experimental results show that our algorithms significantly outperform traditional linkage methods on various temporal data sets. © 2012 Higher Education Press and Springer-Verlag Berlin Heidelberg.

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 thre

Service contracts represent the agreement between the service provider and potential service consumers to use a specific service under given conditions; for each service multiple service contracts are available. In this paper we investigate a new approach to support the service contract selection by exploiting preferences both explicitly defined by a user and implicitly inferred from his/her context. The core of our approach is the use of multi-constraint queries expressed on punctual values and on textual descriptions. Both semantic-based and information retrieval (IR) techniques are applied. Experimental evaluations show the effectiveness of the proposed approach

The paper discusses two concepts that have been associated with various approaches to data and information, namely capacity and value, focusing on data base architectures, and on two types of technologies diffusely used in integration projects, namely data integration, in the area of Enterprise Information Integration, and publish & subscribe, in the area of Enterprise Application Integration. Furthermore, the paper proposes and discusses a unifying model for information capacity and value, that considers also quality constraints and run time costs of the data base architecture.

The eighth international workshop on Quality in Database was held in Singapore, on September 13th, 2010 and co-located with the 36th Conference on Very Large DataBase (VLDB). The main objective of the workshop was to address the challenge to detect data anomalies and assess, monitor, improve, and maintain the quality of information. The keynote speech, titled 'SOLOMON: Seeking the Truth Via Copying Detection' was delivered by Xin Luna Dong, AT&T. The author presented the SOLOMON tool that supports the discovery of copying relationships between structured data source to improve data integration features. The goal of 'Deriving Effectiveness Measures for Data Quality Rules' by Lei Jiang, Alex Borgida, Daniele Barone, John Mylopoulos is the evaluation of data quality rules. Peter Yeh, and Colin Puri show in 'Discovering Conditional Functional Dependencies to Detect Data Inconsistencies' an approach that exploits conditional functional dependencies for detecting inconsistencies in data and hence improves data quality.

Many data sets contain temporal records over a long period of time; each record is associated with a time stamp and describes some aspects of a realworld entity at that particular time (e.g., author information in DBLP). In such cases, we often wish to identify records that describe the same entity over time and so be able to enable interesting longitudinal data analysis. However, existing record linkage techniques ignore the temporal information and can fall short for temporal data. This paper studies linking temporal records. First, we apply time decay to capture the effect of elapsed time on entity value evolution. Second, instead of comparing each pair of records locally, we propose clustering methods that consider time order of the records and make global decisions. Experimental results show that our algorithms significantly outperform traditional linkage methods on various temporal data sets.

From a user perspective, data and services provide a complementary view of an information source: data provide detailed information about specific needs, while services execute processes involving data and returning an informative result as well. For this reason, users need to perform aggregated searches to identify not only relevant data, but also services able to operate on them. At the current state of the art such aggregated search can be only manually performed by expert users, who first identify relevant data, and then identify existing relevant services. In this paper we propose a semantic approach to perform aggregated search of data and services. In particular, we define a technique that, on the basis of an ontological representation of both data and services related to a domain, supports the translation of a data query into a service discovery process. In order to evaluate our approach, we developed a prototype that combines a data integration system with a novel information retrieval-based Web Service discovery engine (XIRE). The results produced by a wide set of experiments show the effectiveness of our approach with respect to IR approaches, especially when Web Service descriptions are expressed by means of a heterogeneous terminology. © 2010 Elsevier B.V. All rights reserved.

The vast amount of data available on the Internet introduces new challenging data quality problems, such as accessibility and usability. Low information quality is common in various Web applications, including Web 2.0 tools. Consequently, information quality on the Internet is one of the most crucial requirements for an effective use of data from the Web and pervasive deployment of Web-based applications.

In medium-big enterprise it is quite typical that the database architecture is defined through a sequence of projects and realizations that result a number of different and sometime overlapping data sources. This trend is worsened by merger and acquisition activities that add in existing data archi-tecture new data sources from external organizations. Data fragmentation reduces significantly the possibility to exploit organizational information assets and it needs the building of a data integration architecture. This architecture allows the organization to access to data stored by heterogeneous data sources and to manage updates through a unified view of this data. In this paper we present an original framework able to support the evolution of data architecture by identi-fying the optimal solution that maximize the quality of the overall architecture within a given cost threshold. In partic-ular, we focus on Publish and Subscribe as solution for data architecture. Copyright 2010 ACM.

While several techniques have been introduced for specifying and acquiring context and quality information associated with Web services, they consider such information representing the whole Web services. However, accessing to context and quality of data resources provided by Web services is crucial. This is particularly relevant for data-intensive Web services of which the context and quality of data resources will strongly impact on the service development and composition. In this paper we contribute an analysis of relationships among context, quality, and relevance, as well as their impact on the design and composition of Web services, in particular at the data resource level. Then, we propose several techniques to incorporate context and quality descriptions into REST APIs and RESTful services publishing. By implementing these features, RESTful Web services could allow the consumer to specify and query context and quality information associated with services and data resources, thus fostering the provision of high relevant data resources. © 2010 Springer-Verlag.

The increasing availability of massive information on the Web causes the need for information aggregation by filtering and ranking according to user's goals. In the last years both industrial and academic researchers have investigated the way in which quality of services can be described, matched, composed and monitored for service selection and composition. However, very few of them have considered the problem of evaluating and certifying the quality of the provided service information to reduce irrelevant information for service consumers, which is crucial to improve the efficiency and correctness of service composition and execution. This paper discusses several problems due to the lack of appropriate way to manage quality and context in service composition and execution, and proposes a research roadmap for reducing irrelevant service information based on context and quality aspects. We present a novel solution for dealing with irrelevant information about Web services by developing information quality metrics and by discussing experimental evaluations

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.

In this paper we propose a meta-model for non- functional property descriptions targeted to support the se- lection of Web Services. The approach is based on the ex- plicit distinction between NFP offered by providers and re- quested by users, on the concept of policy that aggregates NFP descriptions into single entities with an applicability condition, and finally on a set of constraint operators, which is particularly relevant for NFP requests. The semantic meta-model embracing the above perspective is defined by a BNF syntax whose semantics is formalized by an ontology. The ontology has been formalized in OWL-DL and WSML to provide for logical syntaxes. The logic upon which the meta-model supports NFP-based selection is discussed in the paper.

Web service evaluation is a phase of the Web service selection,in which discovered Web services are evaluated with,respect to user request, which means that the non functional,properties (NFPs) offered by Web services are compared,with the non functional properties requested by users. The,fact that users and providers can express their NFPs in very,flexible ways makes the management of NFPs a very complex,task. In this paper we propose a computing-oriented,description of NFPs and a novel approach to NFP-based,service evaluation based on Hierarchical Constraint Logic,Programming. This proposal extends our previous work on,Policy Centered Meta-model (PCM) [2]. © 2009 IEEE.