We present in this study an enhancement to our previous work, in which an automated method, based on Convolutional Neural Networks (CNNs), has been developed for identifying co-seismic ground deformation patterns within the Differential SAR Interferometry (DInSAR) maps generated through the EPOSAR service of the European Plate Observing System (EPOS) Research Infrastructure. The implemented improvements have been achieved through two main lines of actions. Firstly, the generation of the synthetic dataset, used to train the developed CNN, has been enhanced; this concerns the improvement of the simulation of possible atmospheric disturbance within the DInSAR interferograms, and the introduction of a simulator of phase unwrapping errors. Secondly, the original CNN-based deformation pattern detector layout, performing a binary classification, has been modified to account for a multiclass deformation pattern classification. This allows us extending the capability of the system which, in addition to detect co-seismic deformation patterns, may also provide information on the earthquake source characteristics. The presented solution will be included in the EPOSAR DInSAR processing chain.

Nowadays, the popularity of chatbots, such as ChatGPT, has brought research attention to question answering systems, capable to generate natural language answers to user’s natural language queries. However, also in other kinds of systems, flexibility of querying, including but also going beyond the use of natural language, is an important feature. With this consideration in mind the paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs). Flexible query answering is a multidisciplinary research field that is not limited to question answering in natural language, but comprises other query forms and interaction modalities, which aim to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information. It adopts methods at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, knowledge and data engineering, Natural Language Processing (NLP) and the semantic web may be mentioned. The analysis principles are inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources And complementing these articles with other relevant articles from seed sources Identified by a bottom-up process. to mine the retrieved publication data a network analysis is performed Which allows to present in a synthetic way intrinsic topics of the selected publications. issues dealt with are related to query answering methods Both model-based and data-driven (the latter based on either machine learning or deep learning) And to their needs for explainability and fairness to deal with big data Notably by taking into account data veracity. conclusions point out trends and challenges to help better shaping the future of the FQAS field.

Over the past 30 years it has been widely demonstrated the effectiveness of the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to retrieve surface deformation information relevant to tectonically active areas. However, this technique exhibits some limitations due the presence of possible decorrelation effects, phase unwrapping errors, and artefacts due to the temporal/spatial variability of the atmospheric conditions between the SAR acquisition pairs exploited to generate the interferograms and the corresponding deformation maps or time series. A challenging situation may arise when an earthquake event occurs and a co-seismic DInSAR deformation map is generated to quickly support risk management operations. In this scenario, having an automatic process reducing the uncertainties of the retrieved, DInSAR-based, surface deformation information could highly improve the quality of the products made available to the scientific community and of the service provided to the national disaster recovery authorities. We present in this work a solution, based on standard CNN architectures embedded in the DInSAR processing chain of the EPOSAR service, developed within the European Plate Observing System (EPOS) Research Infrastructure, to automatically identify co-seismic ground deformation patterns.

The present short paper describes the functionalities of a desktop tool enabling non-expert users to develop downstream services for the periodic download and processing of data from Sentinel sources.

About 54% of world population nowadays lives in urban areas and this percentage is expected to increase up to 66% by 2050. Therefore, it is crucial to manage this social and cultural change by collecting, integrating and sharing reliable and open spatial information concerning the urban environments where we leave in. The present availability of huge archives of synthetic aperture radar (SAR) data collected since 1992 by ESA, in conjunction with the available Earth-Observation (EO) optical data, represents a unique possibility to derive valuable information for the understanding of the ongoing urban processes. In this framework, the three-year project financed by the Italian Ministry of Instruction, Research and University, entitled “URBAN GEOmatics for bulk information generation, data assessment and technology awareness” may play a role for the assessment and the development of new replicable methodologies for the study of soil consumption and mobility in urban zones. The paper aims to present some preliminary results achieved during the project, clarifying how the new emerging technologies for managing big EO data are proficient for the investigation of urban processes.

The need for cross-analyzing JSON objects representing heterogeneous geo-referenced information coming from multiple sources, such as open data published on the Web by public administrations and crowd-sourced posts and images from social networks, is becoming common for studying, predicting and planning social dynamics. Nevertheless, although NoSQL databases have emerged as a de facto standard means to store JSON objects, a query language that can be easily used by not-programmers to manipulate and correlate such data is still missing. Furthermore, when the information is geo-referenced, we also need both spatial analysis and mapping facilities.

Nowadays a great deal of attention is devoted to the issue of supporting big data analytics over big mobile social data. These data are generated by modern emerging social systems like Twitter, Facebook, Instagram, and so forth. Mining big mobile social data has been of great interest, as analyzing such data is critical for a wide spectrum of big data applications (e.g., smart cities). Among several proposals, clustering is a well-known solution for extracting interesting and actionable knowledge from massive amounts of big mobile (geo-located) social data. Inspired by this main thesis, this paper proposes an effective and efficient similarity-matrix-based algorithm for clustering big mobile social data, called TourMiner, which is specifically targeted to clustering trips extracted from tweets, in order to mine most popular tours. The main characteristic of TourMiner consists in applying clustering over a well-suited similarity matrix computed on top of trips. A comprehensive experimental assessment and analysis over Twitter data finally comfirms the benefits coming from our proposal.

Nowadays Spatial Data Infrastructures are the best practice to publish huge amount of spatial data on the Web in an interoperable and distributed way. Nevertheless, this operation requires a significant effort, expertise and motivation to data providers. In this paper, we propose an original approach to support geo-data providers by automating the workflows for publishing geo-data and relative web services for a given application. The prototypal solution has been tested on a real case study to support the regional or national agricultural sector in Italy.

The paper addresses the problem of e-memes (electronic memes) representation and identification in the blogosphere, and it aims at defining the basis of a new conceptual model. First, an ememe is formally defined as a conceptual information granule by means of an OWL schema; then a process is proposed to retrieve from the blogosphere the posts dealing with the ememe. The proposed process extracts from the formal definition of the ememe some keyword-based queries that are submitted to a search engine to retrieve posts that are potentially related to the ememe. The post contents are then filtered by evaluating their fidelity to the ememe conceptual definition by applying a lexicographic analysis and part of speech tagging. Ordered Weighted Averaging (OWA) operators are used to this purpose. Measures of mutation, spread, and longevity of an ememe are also proposed.

A straightforward way to map burned areas from remotely sensed imagery is to integrate partial evidence of burn provided by multiple spectral indices (SIs). Our approach relies on fuzzy set theory to generate integrated layers of overall positive evidence (PE) and negative evidence (NE) scores. In order to reduce commission errors, we propose the use of NE for revising the overall PE. Revised layers are input for a region growing algorithm to produce a map of burned areas. Thematic Mapper (TM) images, acquired over the Mediterranean area, were used to derive the SIs and to define the soft constraints (membership functions). The performance of the revision process is tested for a TM image acquired over Portugal: The revision decreases the commission error from 59.5% to 1.3% and increases the overall accuracy from 42.6% up to 91.3%.

Abstract The authors describe a flexible model and a system for content‐based image retrieval of objects' shapes. Flexibility is intended as the possibility of customizing the system behavior to the user's needs and perceptions. This is achieved by allowing users to modify the retrieval function. The system implementing this model uses multiple representations to characterize some macroscopic characteristics of the objects shapes. Specifically, the shape indexes describe the global features of the object's contour (represented by the Fourier coefficients), the contour's irregularities (represented by the multifractal spectrum), and the presence of concavities and convexities (represented by the contour scale space distribution). During a query formulation, the user can specify both the preference for the macroscopic shape aspects that he or she considers meaningful for the retrieval, and the desired level of accuracy of the matching, which means that the visual query shape must be considered with a given tolerance in representing the desired shapes. The evaluation experiments showed that this system can be suited to different retrieval behaviors, and that, generally, the combination of the multiple shape representations increases both recall and precision with respect to the application of any single representation.

In this paper an original soft hierarchical Fuzzy Clustering algorithm is proposed, named Hierarchical Hyper-spherical Divisive Fuzzy C-Means (H2D-FCM), with the following characteristics: it generates a “soft” hierarchy in which a document can belong to several child clusters of a node, and the clusters in the same hierarchical level are more specific (general) than the clusters in the upper (lower) level. The proposed algorithm is a divisive algorithm based on a modified bisective K-Means, applying a modified probabilistic Fuzzy C Means algorithm to divide each node into child-nodes. The algorithm determines the proper number of cluster to generate at the first level based on an entropy measure and decides if a node can be further split based on a “density” measure. The paper presents the algorithm and its evaluations on two standard collections.

In this paper a model of soft aggregation of preferences in flexible database querying is proposed, based on the vector p-norm operator. The model allows aggregating conditions with distinct importance by modeling both veto and favour semantics of the conditions. We outline how the semantics of the compound query varies for increasing values of the parameter p, when the selection conditions are ANDed and ORed.

We propose a novel conception language for exploring the results retrieved by several internet search services (like search engines) that cluster retrieved documents. The goal is to offer users a tool to discover relevant hidden relationships between clustered documents.

This paper describes the architecture and the functionalities of a clustering search engine prototypal system, named Matrioshka, whose peculiarity is the flexibility of the interaction framework on which it is based.

The monitoring of the environment's status at continental scale involves the integration of information derived by the analysis of multiple, complex, multidisciplinary, and large‐scale phenomena. Thus, there is a need to define synthetic Environmental Indicators (EIs) that concisely represent these phenomena in a manner suitable for decision‐making. This research proposes a flexible system to define EIs based on a soft fusion of contributing environmental factors derived from multi‐source spatial data (mainly Earth Observation data). The flexibility is twofold: the EI can be customized based on the available data, and the system is able to cope with a lack of expert knowledge. The proposal allows a soft quantifier‐guided fusion strategy to be defined, as specified by the user through a linguistic quantifier such as ‘most of’. The linguistic quantifiers are implemented as Ordered Weighted Averaging operators. The proposed approach is applied in a case study to demonstrate the periodical computation of anomaly indicators of the environmental status of Africa, based on a 7‐year time series of dekadal Earth Observation datasets. Different experiments have been carried out on the same data to demonstrate the flexibility and robustness of the proposed method.

In this paper, we model the evaluation of soft conditional preferences in flexibly querying fuzzy databases. We assume that soft conditional preferences are expressed in the form “If C then Q1 is better than Q2,” where C is the primary condition, and Q1, Q2 are the conditions with preferences. They are fuzzy predicates, which represent soft constraints admitting satisfaction degrees. The satisfaction degree of C tunes the preference of Q1 over Q2, so that as it increases also the preference of Q1 increases with respect to Q2. When C is not satisfied at all there is no preference between Q1 and Q2. The basic idea of the proposed model is to compute the preference degree of Q1 with respect to Q2 depending on the degree of satisfaction of C, and to use this value to modify the evaluation function of the soft conditions Q1 and Q2. This way we fuse in a single step the evaluations of both the selection conditions and their preference, which are the two subsequent phases necessary for evaluating queries with preferences in fuzzy databases. Specifically, the preference degree is used to relax the soft constraint imposed by the evaluation function of the most preferred condition Q1 as well as to restrict the evaluation function of the less preferred condition Q2. The more a soft condition is preferred, the more its evaluation function becomes tolerant to undersatisfaction; similarly, the less a condition is preferred the more its evaluation function is restricted so as to make more difficult its satisfaction. © 2008 Wiley Periodicals, Inc.

Multisource spatial data fusion strategies have generally to cope with distinct kinds of uncertainty, deriving by both the trust of the source, the imperfection of the spatial data, and the vagueness of the fusion strategy itself. In this paper we propose a consensual fusion that allows to flexibly model several fusion strategies ranging from a risk-taking to a risk-adverse attitude, and capable to cope with both data imprecision and uncertainty and source reliability. Uncertainty and imprecision in spatial data is represented by associating a fuzzy value with each spatial unit. The fusion function has a quantifier-guided nature reflecting the concept of a fuzzy majority and works on imprecise values to compute an imprecise result. It is formalized by a generalized OWA operator defined in the paper for aggregating imprecise values with distinct importance. The consensual fusion works so that the greater the trust score of the source and its agreement with the other sources, the more influent (important) is the data from the source in determining the consensual values. Further, when the data are affected by uncertainty one can require to fuse them so as to compute a result affected by at most a given maximum uncertainty level.

This poster overviews the main characteristics of a flexible retrieval systems of shapes present in binary images and discusses some evaluation results. The system applies multiple indexing criteria of the shapes synthesizing distinct characteristics such as global features of the objects contour (Fourier Coefficients), boundary irregularities (Multifractal Spectrum), presence of concavities and convexities on the boundary (Contour Scale Space distribution). The system is flexible since it allows customizing the retrieval function to fit an application need. The query is a binary image containing the desired shape and a set of parameters specifying the distinct importance of the shape characteristics that must be taken into account to evaluate the relevance of the retrieved shapes. The retrieval function is then defined as a Flexible Multicriteria fusion Function producing ranked results. The evaluation experiments showed that this system can be suited to different retrieval purposes, and that generally the combination of the distinct shape indexing criteria increases both Recall and Precision with respect to the application of any single indexing criterion alone.

In this paper we present a novel filtering system, based on a new model which reshapes the aims of content-based filtering. The filtering system has been developed within the EC project PENG, aimed at providing news professionals, such as journalists, with a system supporting both filtering and retrieval capabilities. In particular, we suggest that in tackling the problem of information overload, it is necessary for filtering systems to take into account multiple aspects of incoming documents in order to estimate their relevance to a user's profile, and in order to help users better understand documents, as distinct from solely attempting to either select relevant material from a stream, or block inappropriate material. Aiming to so this, a filtering model based on multiple criteria has been defined, based on the ideas gleamed in the project requirements stage. The filtering model is briefly described in this paper.

A flexible model for evaluating soft query with unequal preferences in fuzzy databases is proposed. We assume that conditions with unequal preferences have an exclusive meaning like in the request “find a holiday accommodation such that big apartments are preferred to high rating hotels.” In this case it is assumed that the aggregator of the soft conditions is an implicit OR. Conversely, conditions with unequal importance have an inclusive meaning, like in the query “find a house to rent that is cheap (most important), big (important), new (fairly important).” In this case the implicit aggregator is an AND. What we propose in this article is to model preferences as modifiers of the semantics of the evaluation function of the conditions. Because the soft conditions are aggregated by an OR, the more a soft condition is preferred, the more its evaluation function tolerates a greater undersatisfaction of the soft condition. The proposed approach is formalized by considering two alternative semantics of the evaluation function: the first semantics defines the evaluation function by means of a generalized fuzzy inclusion measure, and the second one as a generalized similarity measure. These functions are parameterized so that their modification is simply achieved by tuning the functions' parameters. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 665–689, 2007.

In this contribution a fuzzy object-based data model for imperfect spatial information is proposed within the formal framework of fuzzy set theory and possibility theory. The proposal integrates the two opposite views of geographic entities seen as either exact objects or continuous fields in current Geographic Information Systems. By assuming a Fuzzy Object-Oriented Data modelling approach new data types for representing geographic entities characterized by either unsharp boundaries or indeterminate boundaries and relationships are defined. Linguistic qualifiers are adopted both to express the extent by which an element of the spatial domain satisfies a gradual property, and to identify regions with indeterminate boundaries.

In this paper the definition of a meta search engine model, named SOFIA (soft fusion of information accesses), is proposed that applies a flexible and soft fusion of the ranked lists of documents retrieved by distinct search engines available over the Internet. The decision function that specifies the fusion criterion is expressed by a linguistic quantifier modeled by an induced ordered average (IOWA) operator. Fuzzy quantifiers make soft fusion criteria possible. Further flexibility is obtained by computing the distinct fitness scores of the search engines based on a relevance feedback mechanism and using these scores to define the IOWA reorder vector. In this way the search engines with highest fitness influence more heavily the ranking of the documents in the fused list.

In this contribution a representation of imperfect spatial information is proposed based on linguistic qualifiers within fuzzy set theory and possibility theory. By adopting a fuzzy object oriented database approach new data types for representing geographic entities characterized by either unsharp boundaries or indeterminate boundaries and relationships are defined. Finally, the evaluation mechanism of vague topological constraints between fuzzy and indeterminate spatial objects is described.

In this paper we present an indexing model of multimedia documents having a hierarchical structure; this model allows users to generate personal document representations. The documents are organised into logical (thematic) sections where the index terms play a distinct role as descriptors of the documents' information contents. The indexed archive can be heterogeneous, i.e. it may contain documents with distinct logical structures. The proposed indexing model is adaptive to the user, who can indicate in the retrieval phase the preferred sections of documents, i.e. those which they estimate to bear the most interesting information, and can linguistically quantify the number of sections which determine the global potential interest of the documents. The adaptation is carried out by a flexible query language for expressing the aforementioned soft selection conditions on the structured documents.

In this paper, the definition of graph-based operations to select and browse a fuzzy object oriented database which manages both crisp and fuzzy information is proposed. The underlying data model is a fuzzy graph-based model (FOOD), previously defined by Bordogna et al., within fuzzy set theory and possibility theory. The evaluation mechanism of the graph-based operations is formalized in terms of graph transformations and fuzzy pattern matching. © 2001 John Wiley & Sons, Inc.

In this paper a flexible query language for expressing soft selection conditions on structured documents is presented and formalized within fuzzy set theory. Documents are represented as entities structured into logical sections in which the index terms play a distinct role. Users can indicate the preferred sections of documents, i.e., those which they estimate bearing the most interesting information, as well as quantify the number of sections which determine the global potential interest of the documents. A linguistic quantifier that specifies the approximate number of the sections in which the query terms should appear in the relevant documents expresses this last information. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this contribution an interpretation of linguistic qualifiers of uncertainty is proposed in the context of fuzzy databases; these qualifiers are defined in the framework of possibility theory to express incomplete knowledge on the represented world. The uncertain and vague information represented in a fuzzy database is formalized based on the notion of prioritized constraints with safeguard. © 2000 John Wiley & Sons, Inc.

In this paper, a fuzzy Object-Oriented Data model (FOOD) is defined based on the extension of a Graph-based Object model (D. Lucarella and A. Zanzi “A graph-oriented data model,” in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer-Verlag, Berlin, 1996, pp. 197–206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation. ©1999 John Wiley & Sons, Inc.

In this paper, a model for group decision-making is proposed and defined in a linguistic context. A multiperson multicriteria decision problem is considered, in which a group of experts is involved in the evaluation of the performances of a set of alternatives with respect to a predefined set of criteria. The objective is to evaluate a consensual judgement and a consensus degree on each alternative. Both the experts' evaluations of the alternatives and the degree of consensus are expressed linguistically. A "soft" consensus degree referred to a fuzzy majority of the experts is proposed based on the concept of linguistic quantifier. The entire process is defined in a linguistic domain and modeled within fuzzy set theory by ordered weighted average (OWA) operators.

A “softening” of the hard Boolean scheme for information retrieval is presented. In this approach, information retrieval is seen as a multicriteria decision-making activity in which the criteria to be satisfied by the potential solutions, i.e., the archived documents, are the requirements expressed in the query. the retrieval function is then an overall decision function evaluating the degree to which each potential solution satisfies a query consisting of information requirements aggregated by operators. Linguistic quantifiers and a connector dealing with primary and optional criteria are defined and introduced in the query language in order to specify the aggregation criteria of the single query requirements. These criteria make it possible for users to express queries in a simple and self-explanatory manner. In particular, linguistic quantifiers are defined which capture the intrinsic vagueness of information needs. © 1995 John Wiley & Sons, Inc.

The generalization of Boolean Information Retrieval Systems (IRS) is still an open research field; in fact, though such systems are diffused on the market, they present some limitations; one of the main features lacking in these systems is the ability to deal with the “imprecision” and “subjectivity” characterizing retrieval activity. However, the replacement of such systems would be much more costly than their evolution through the incorporation of new features to enhance their efficiency and effectiveness. Previous efforts in this area have led to the introduction of numeric weights to improve both document representation and query language. By attaching a numeric weight to a term in a query, a user can provide a quantitative description of the “importance” of that term in the documents he or she is looking for. However, the use of weights requires a clear knowledge of their semantics for translating a fuzzy concept into a precise numeric value. Our acquaintance with these problems led us to define, starting from an existing weighted Boolean retrieval model, a linguistic extension, formalized within fuzzy set theory, in which numeric query weights are replaced by linguistic descriptors which specify the degree of importance of the terms. This fuzzy linguistic model is defined and an evaluation is made of its implementation on a Boolean IRS. © 1993 John Wiley & Sons, Inc.