Abstract Estimating redshift is a central task in astrophysics, but its measurement is costly and time-consuming. In addition, current image-based methods are often validated on homogeneous datasets. The development and comparison of networks able generalize across different morphologies, ranging from galaxies to gravitationally-lensed transients, and observational conditions, remain an open challenge. This work proposes DeepRed, a deep learning pipeline that demonstrates how modern computer vision architectures , including ResNet, EfficientNet, Swin Transformer, and MLP-Mixer, can estimate redshifts from images of galaxies, gravitational lenses, and gravitationally-lensed supernovae. We compare these architectures and their ensemble to both neural networks ( A1 , A3, NetZ, and PhotoZ ) and a feature-based method (HOG+SVR) on simulated (DeepGraviLens) and real (KiDS, SDSS) datasets. Our approach achieves state-of-the-art results on all datasets. On DeepGraviLens , DeepRed achieves a significant improvement in the Normalized Mean Absolute Deviation (NMAD) compared to the best baseline (PhotoZ): 55% on DES-deep (using EfficientNet), 51% on DES-wide (Ensemble), 52% on DESI-DOT (Ensemble), and 46% on LSST-wide (Ensemble). On real observations from the KiDS survey, the pipeline outperforms the best baseline (NetZ), improving NMAD by 16% on a general test set without high-probability lenses (Ensemble) and 27% on high-probability lenses (Ensemble). For non-lensed galaxies in the SDSS dataset, the MLP-Mixer architecture achieves a 5 % improvement over the best baselines (A3 and NetZ). In addition, SHapley Additive exPlanations (SHAP) shows that the models correctly focus on the objects of interest with over 95% localization accuracy on high-quality images, validating the reliability of the predictions. These findings suggest that deep learning architectures are a scalable, robust, and interpretable solution for redshift estimation in future large-scale surveys comprising heterogeneous astronomical objects.

Abstract In both industrial and residential contexts, compressor-based machines, such as refrigerators, heat, ventilation, and air conditioning systems, heat pumps, and chillers, are essential to fulfil production and consumers’ needs. The diffusion of sensors and internet of things connectivity support the development of monitoring systems that can detect and predict faults, identify behavioural shifts, and forecast the operational status of machines and their components. The focus of this paper is to survey the recent research on such tasks as fault detection (FD), fault prediction (FP), forecasting, and change point detection (CPD) applied to multivariate time series characterizing the operations of compressor-based machines. These tasks play a critical role in improving the efficiency and longevity of machines by minimizing downtime and maintenance costs and improving energy efficiency. Specifically, FD detects and diagnoses faults, FP predicts such occurrences, forecasting anticipates the future value of characteristic variables of machines, and CPD identifies significant variations in the behaviour of the appliances, such as a change in the working regime. We identify and classify the approaches to the tasks mentioned above, compare the algorithms employed, highlight the gaps in the current state of the art, and discuss the most promising future research directions in the field.

Abstract Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic results and studying faint astrophysical objects that would not be visible otherwise. In recent years, machine learning methods have been applied to support the analysis of the gravitational lensing phenomena by detecting lensing effects in datasets consisting of images associated with brightness variation time series. However, the state-of-the-art approaches either consider only images and neglect time-series data or achieve relatively low accuracy on the most difficult datasets. This paper introduces DeepGraviLens, a novel multi-modal network that classifies spatio-temporal data belonging to one non-lensed system type and three lensed system types. It surpasses the current state-of-the-art accuracy results by $$\approx 3\%$$ ≈ 3 % to $$\approx 11\%$$ ≈ 11 % , depending on the considered data set. Such an improvement will enable the acceleration of the analysis of lensed objects in upcoming astrophysical surveys, which will exploit the petabytes of data collected, e.g., from the Vera C. Rubin Observatory.

The concurrent development of applications requires reconciling conflicting code updates by different developers. Recent research on the nature of merge conflicts in open source projects shows that a significant fraction of merge conflicts have limited size (one or two lines of code) and are resolved with simple strategies that use code present in the merged versions. Thus the opportunity arises of supporting the resolution of merge conflicts automatically by learning the way in which developers fix them. In this paper we propose a framework for automating the resolution of merge conflicts which learns from the resolutions made by developers and encodes such knowledge into conflict resolution rules applicable to conflicts not seen before. The proposed approach is text-based, does not depend on the programming languages of the merged files and exploits a well-known and general language (search and replacement regular expressions) to encode the conflict resolution rules. Evaluation results on 14,872 conflicts from 25 projects show that the system can synthesize a resolution for$\approx$49% of the conflicts occurred during the merge process ($\approx$89% if one considers conflicts that have at least one similar conflict in the data set) and can reproduce exactly the same solution that human developers have applied in$\approx$55% of the cases ($\approx$62% for single line conflicts).

Remote sensing image scene classification consists of classifying images of the Earth surface into scene categories that represent different semantic ones based on the ground objects and their spatial arrangement. Finding the objects within a scene is not trivial, because they can appear in different sizes and mutual positions. An open issue in scene classification with CNNs is understating if the network prediction relies on the clues that human Earth Observation experts consider. A suitable approach for investigating the inference process of neural models relies on Class Activation Maps, which emphasize the areas of an image contributing the most to the classification. This work evaluates CAMs for different CNNs methods, in terms of their capacity to identify the objects that determine the classification of scenes for the illegal landfill detection. Quantitative and qualitative analyses show that ECA-Net has consistent performance across all metrics, resulting the most promising approach to obtain CNNs that focus on the most relevant points with the higher IoU. The illustrated analysis is a step towards the computer-aided study of the variations of scene elements positioning and spatial relations that constitute hints of the presence of illegal waste dumps and opens the way to the application of weakly supervised techniques for training detectors of illegal landfills in large scale remote sensing image repositories.

Gamification is defined as the injection of game elements in applications with non-gaming purposes.This technique has shown outstanding results in promoting the engagement and activity on communities of users, in both business and non-for-profits fields.Often, gamification features are added late in the application life-cycle and must be weaved into the existing functions.In this paper, we present a model-driven approach to the design of gamified applications, which accelerates the introduction of gamification elements in pre-existing or new applications.The approach relies on a data model of gamification features and on design patterns for the front-end, which encode the essential elements of gamification in a platform independent way.

Model Driven Development (MDD) requires proper tools to derive the implementation code from the application models. However, the integration of handwritten and generated code is a long-standing issue that affects the adoption of MDD in the industry. This article presents a model and code co-evolution approach that addresses such a problem a posteriori , using the standard collision detection capabilities of Version Control Systems to support the semi-automatic merge of the two types of code. We assess the proposed approach by contrasting it with the more traditional template-based, forward-engineering process, adopted by most MDD tools.

Open Source Geographical Information Systems, such as OpenStreetMap (OSM), offer a valuable alternative to proprietary solutions for the development of voluntary environment monitoring systems. However, the quantity and quality of information stored in such systems must be carefully evaluated and the contributions of volunteers must be boosted by means of effective engagement methods. This paper reports the results of the assessment of the quality and quantity of OpenStreetMap mountain information: different types of information and world regions have different gaps and improvement requirements. To address this issue, we propose a hybrid approach, in which an open Digital Elevation Model data set is processed with a heuristic algorithm to find candidate mountain information and uncertainty in the automatically extracted candidates is reduced by means of voluntary expert crowdsourcing. The improvement of landform information (not only about mountains, but also about orography and hydrography in general) can support the development of environment monitoring applications.

Mobile Augmented Reality (AR) applications offer a new way to promote the collection of geo-referenced information, by engaging citizens in a useful experience and encouraging them to gather environment data, such as images of plant species or of mountain snow coverage. The distinctive characteristic of mobile AR applications is the overlay of information directly on top of what the user sees, based on the user's context estimated from the device sensors. The application analyzes the sensor readings (GPS position, phone orientation and motion, and possibly also the camera frame content), to understand what the user is watching and enriches the view with contextual information. Developing mobile AR applications poses several challenges related to the acquisition, selection, transmission and display of information, which gets more demanding in mountain applications where usage without Internet connectivity is a strong requirement. This paper discusses the experience of a real world mobile AR application for mountain exploration, which can be used to crowdsource the collection of mountain images for environmental purposes, such as the analysis of snow coverage for water availability prediction and the monitoring of plant diseases.

The wide range of different platforms for web and mobile applications development requires fast prototyping and the evaluation of multiple releases. Model Driven Development (MDD) represents the application at high level with textual or visual languages and can be used to automatically generate the final product by means of model-to-model and model-to-code transformations. We describe IFMLEdit.org, an open-source, online MDD tool for the specification and automatic generation of fast prototypes of Web and mobile application from IFML (Interaction Flow Modeling Language) specifications. The tool also supports the semantic mapping from IFML to PCN, a variant of Petri Nets, for model simulation and checking.

Both social media and sensing infrastructures are producing an unprecedented mass of data characterized by their uncertain nature, due to either the noise inherent in sensors or the imprecision of human contributions. Therefore query processing over uncertain data has become an active research field. In the well-known class of applications commonly referred to as “top-K queries”, the objective is to find the best K objects matching the user's information need, formulated as a scoring function over the objects' attribute values. If both the data and the scoring function are deterministic, the best K objects can be univocally determined and totally ordered so as to produce a single ranked result set (as long as ties are broken by some deterministic rule). However, in application scenarios involving uncertain data and fuzzy information needs, this does not hold: when either the attribute values or the scoring function are nondeterministic, there may be no consensus on a single ordering, but rather a space of possible orderings. To determine the correct ordering, one needs to acquire additional information so as to reduce the amount of uncertainty associated with the queried data and consequently the number of orderings in such a space. An emerging trend in data processing is crowdsourcing, defined as the systematic engagement of humans in the resolution of tasks through online distributed work. Our approach combines human and automatic computation in order to solve complex problems: when data ambiguity can be resolved by human judgment, crowdsourcing becomes a viable tool for converging towards a unique or at least less uncertain query result. The goal of this paper is to define and compare task selection policies for uncertainty reduction via crowdsourcing, with emphasis on the case of top-K queries.

This paper merges multimedia and environmental research to verify the utility of public web images for improving water management in periods of water scarcity, an increasingly critical event due to climate change. A multimedia processing pipeline fetches mountain images from multiple sources and extracts virtual snow indexes correlated to the amount of water accumulated in the snow pack. Such indexes are used to predict water availability and design the operating policy of Lake Como, Italy. The performance of this informed policy is contrasted, via simulation, with the current operation, which depends only on lake water level and day of the year, and with a policy that exploits official Snow Water Equivalent (SWE) estimated from ground stations data and satellite imagery. Virtual snow indexes allow improving the system performance by 11.6% w.r.t. the baseline operation, and yield further improvement when coupled with official SWE information, showing that the two data sources are complementary. The proposed approach exemplifies the opportunities and challenges of applying multimedia content analysis methods to complex environmental problems.

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top-K queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set.

In this paper, we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user-generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite, and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to such sources, addressing the specific challenges posed by each of them, e.g., identifying the mountain peaks, filtering out images taken in bad weather conditions, and handling varying illumination conditions. The final outcome is summarized in a snow cover index, which indicates for a specific mountain and day of the year the fraction of visible area covered by snow, possibly at different elevations. We created a manually labeled dataset to assess the accuracy of the image snow covered area estimation, achieving 90.0% precision at 91.1% recall. In addition, we show that seasonal trends related to air temperature are captured by the snow cover index.

Despite the success of crowdsourcing marketplaces, fully harnessing their massive workforce remains challenging. In this work we study the effect on crowdsourcing campaigns of different feedback and payment strategies. Our results reveal the joint effect of feedback and payment on the quality and quantity of the outcome.

We propose a method for the environmental monitoring through the publicly available media User Generated Content (UGC). In particular we address the problem of the snow cover and level estimation by analyzing the social media data such as geotagged photographs and public webcams installed in mountain regions. The entire pipeline of the process is presented to the audience: from the data crawling and automatic relevance classification (does or does not the photograph contain a significant mountain profile) to the image content analysis and environmental models (identification of the snow covered area on the photograph). Each presented component is self-contained and can be inspected individually, the connections between the components however are strongly highlighted allowing the viewer to understand intuitively the entire pipeline structure.

This paper describes the design and development of histoGraph, an interactive tool for explorative visualization and collaborative investigation of historical social networks from multimedia collections. Developed in an interdisciplinary collaboration of computer scientists, historians, HCI researchers and interface designers, the tool aims at supporting historians in the discovery and historical analysis of relationships between people, places and events. A special focus is on the identification and interactive visualization of social relations from historical photo collections through a combination of automatic analysis and expert-based crowd sourcing. The tool design bridges the gap between established network analysis and visualization techniques and traditional hermeneutic research methods in historical research. It integrates visual exploration with hybrid social graph construction, hypothesis formulation and the consultation of digitized primary sources. A formative evaluation of the current prototype, developed as a domain-specific application for historians in the field of European integration points to opportunities and critical factors in applying this approach to support and further current research practices in digital humanities.

The possibility of assigning labels to localized regions in an image enables flexible image retrieval paradigms. However, the process of automatically segmenting and tagging images is notoriously hard, due to the presence of occlusions, noise, challenging illumination conditions, background clutter, etc. For this reason, human computation has recently emerged as a viable alternative when computer vision algorithms fail to provide a satisfactory answer. For example, Games with a purpose (GWAP) represent a powerful crowdsourcing mechanism to collect implicit annotations from human players. In this paper we consider the problem of aggregating the gaming tracks collected by a GWAP we developed to solve challenging instances of image segmentation problems. In particular we consider the existence of malicious players, who might try to fool the rules of the game to achieve higher rewards. The proposed solution can automatically estimate the reliability of human players, thus identifying cheaters. This information is exploited to aggregate the gaming tracks, thus significantly improving the image segmentation result and the quality of local image annotations.

Model-driven engineering relies on collections of models, which are the primary artifacts for software development. To enable knowledge sharing and reuse, models need to be managed within repositories, where they can be retrieved upon users’ queries. This article examines two different techniques for indexing and searching model repositories, with a focus on Web development projects encoded in a domain-specific language. Keyword-based and content-based search (also known as query-by-example) are contrasted with respect to the architecture of the system, the processing of models and queries, and the way in which metamodel knowledge can be exploited to improve search. A thorough experimental evaluation is conducted to examine what parameter configurations lead to better accuracy and to offer an insight in what queries are addressed best by each system.

Top-k diversity queries over objects embedded in a low-dimensional vector space aim to retrieve the best k objects that are both relevant to given user's criteria and well distributed over a designated region. An interesting case is provided by spatial Web objects, which are produced in great quantity by location-based services that let users attach content to places and are found also in domains like trip planning, news analysis, and real estate. In this article we present a technique for addressing such queries that, unlike existing methods for diversified top- k queries, does not require accessing and scanning all relevant objects in order to find the best k results. Our Space Partitioning and Probing (SPP) algorithm works by progressively exploring the vector space, while keeping track of the already seen objects and of their relevance and position. The goal is to provide a good quality result set in terms of both relevance and diversity. We assess quality by using as a baseline the result set computed by MMR, one of the most popular diversification algorithms, while minimizing the number of accessed objects. In order to do so, SPP exploits score-based and distance-based access methods, which are available, for instance, in most geo-referenced Web data sources. Experiments with both synthetic and real data show that SPP produces results that are relevant and spatially well distributed, while significantly reducing the number of accessed objects and incurring a very low computational overhead.

This paper investigates diversity queries over objects embedded in a low-dimensional vector space. An interesting case is provided by spatial Web objects, which are produced in great quantity by location-based services that let users attach content to places, and arise also in trip planning, news analysis, and real estate scenarios. The targeted queries aim at retrieving the best set of objects relevant to given user criteria and well distributed over a region of interest. Such queries are a particular case of diversified top-k queries, for which existing methods are too costly, as they evaluate diversity by accessing and scanning all relevant objects, even if only a small subset is needed. We therefore introduce Space Partitioning and Probing (SPP), an algorithm that minimizes the number of accessed objects while finding exactly the same result as MMR, the most popular diversification algorithm. SPP belongs to a family of algorithms that rely only on score-based and distance-based access methods, which are available in most geo-referenced Web data sources, and do not require retrieving all the relevant objects. Experiments show that SPP significantly reduces the number of accessed objects while incurring a very low computational overhead.

Multi-domain search answers to queries spanning multiple entities, like "Find an affordable house in a city with low criminality index, good schools and medical services", by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user's preferences. Due to the combinatorial nature of results, good entity instances (e.g., inexpensive houses) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance (i.e., high values of the ranking function) with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.

We demonstrate the Search Computing framework for multi-domain queries upon ranked data collected from Web sources. Search Computing answers to queries like "Find a good Jazz concert close to a specified location, a good restaurant and a hotel at walking distance" and fills the gap between generic and domain-specific search engines, by proposing new methods, techniques, interfaces, and tools for building search-based applications spanning multiple data services. The main enabling technology is an execution engine supporting methods for rank-join execution upon ranked data sources, abstracted and wrapped by means of a unifying service model. The demo walks through the interface for formulating multi-domain queries and follows the steps of the query engine that builds the result, with the help of run-time monitors that clearly explain the system's behavior. Once results are extracted, the demonstration shows several approaches for visualizing results and exploring the information space.

In this paper we propose the Liquid Query paradigm, to support users in finding responses to multi-domain queries through exploratory information seeking across structured information sources (Web documents, deep Web data, and personal data repositories), wrapped by means of a uniform notion of search service. Liquid Query aims at filling the gap between general-purpose search engines, which are unable to find information spanning multiple topics, and domain-specific search systems, which cannot go beyond their domain limits. The Liquid Query interface consists of interaction primitives that let users pose questions and explore results spanning over multiple sources incrementally, thus getting closer and closer to the sought information. We demonstrate our approach with a prototype built upon the YQL (Yahoo! Query Language) framework.

Rich Internet Applications (RIAs) have introduced powerful novel functionalities into the Web architecture, borrowed from client-server and desktop applications. The resulting platforms allow designers to improve the user's experience, by exploiting client-side data and computation, bidirectional client-server communication, synchronous and asynchronous events, and rich interface widgets. However, the rapid evolution of RIA technologies challenges the Model-Driven Development methodologies that have been successfully applied in the past decade to traditional Web solutions. This paper illustrates an evolutionary approach for incorporating a wealth of RIA features into an existing Web engineering methodology and notation. The experience demonstrates that it is possible to model RIA application requirements at a high-level using a platform-independent notation, and generate the client-side and server-side code automatically. The resulting approach is evaluated in terms of expressive power, ease of use, and implementability.

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This paper addresses the problem of defining a Web design process suitable for the multi-cultural audience of a Web application in the globalization era. Traditional usability guidelines implicitly depend on the background of the audience, and may overlook specific preferences dictated by the cultural context. To overcome this limit, the notion of culturability has been proposed, which extends traditional usability to take into account usage preferences stemming from multiple cultural conventions. The paper builds upon a case study (the comparison of Chinese and Western e-commerce Web sites) and proposes a working definition of culturability, based on cultural markers; moreover, an effective methodology for identifying cultural markers and turning them into design guidelines is established and validated by letting groups of users with different cultures evaluate exemplary Web applications incorporating different versions of the candidate cultural markers.

This paper proposes an innovative use of a mix of networking standards and software implementation technologies for the design of industrial human machine interface (HMI) systems. We describe how well known technologies and practices can be transferred from internet-based architectures to embedded systems. Several advanced features can be achieved thanks to this architecture, such as application adaptivity, interface personalization, control remotization, and multi-channel notification.

This paper addresses conceptual modeling and automaticcode generation for Rich Internet Applications, a variant ofWeb-based systems bridging desktop and thin-client Webinterfaces. We show how classical Web modeling conceptsare not enough to capture the specificity of RIAs, extend anexisting Web modeling language, and provide an implementationof a CASE tool for visual modeling and code generationfrom RIA-aware specifications. Experimentation of theproposed approach in real-world scenarios is also reported.

This work addresses conceptual modeling and automatic code generation for Rich Internet Applications, a variant of Web-based systems bridging the gap between desktop and Web interfaces. The approach we propose is a first step towards a full integration of RIA paradigms into the Web development process, enabling the specification of complex Web solutions mixing HTTP+HTML and Rich Internet Applications, using a single modeling language and tool.

While Web applications evolve towards ubiquitous, enterprise-wide or multienterprise information systems, they face new requirements, such as the capability of managing complex processes spanning multiple users and organizations, by interconnecting software provided by different organizations. Significant efforts are currently being invested in application integration, to support the composition of business processes of different companies, so as to create complex, multiparty business scenarios. In this setting, Web applications, which were originally conceived to allow the user-to-system dialogue, are extended with Web services, which enable system-to-system interaction, and with process control primitives, which permit the implementation of the required business constraints. This article presents new Web engineering methods for the high-level specification of applications featuring business processes and remote services invocation. Process- and service-enabled Web applications benefit from the high-level modeling and automatic code generation techniques that have been fruitfully applied to conventional Web applications, broadening the class of Web applications that take advantage of these powerful software engineering techniques. All the concepts presented in this article are fully implemented within a CASE tool.

Significant efforts are currently invested in application integration to enable the interaction and composition of business processes of different companies, yielding complex; multi-party processes. Web service standards, based on WSDL, have been adopted as a process-to-process communication paradigm. This paper presents an industrial experience in integrating data-intensive and process-intensive Web applications through Web services. Design of sites and of Web services interaction exploits modern Web engineering methods, including conceptual modeling, model verification, visual data marshalling and automatic code generation. In particular, the applied method is based on a declarative model for specifying data-intensive Web applications that enact complex interactions, driven by the user, with remote processes implemented as services. We describe the internal architecture of the CASE tool that has been used, and give an overview of three industrial applications developed with the described approach.

Significant effort is currently invested in application integration, enabling business processes of different companies to interact and form complex multiparty processes. Web service standards, based on WSDL (Web Service Definition Language), have been adopted as process-to-process communication paradigms. However, the conceptual modeling of applications using Web services has not yet been addressed. Interaction with Web services is often specified at the level of the source code; thus, Web service interfaces are buried within a programmatic specification.In this article, we argue that Web services should be considered first-class citizens in the specification of Web applications. Thus, service-enabled Web applications should benefit from the high-level modeling and automatic code generation techniques that have long been advocated for Web application design and implementation. To this end, we extend a declarative model for specifying data-intensive Web applications in two directions: (i) high-level modeling of Web services and their interactions with the Web applications which use them, and (ii) modeling and specification of Web applications implementing new, complex Web services.Our approach is fully implemented within a CASE tool allowing the high-level modeling and automatic deployment of service-enabled Web applications.

This demo presents an extension of a declarative language for specifying data-intensive Web applications. We demonstrate a scenario extracted from a real-life application, the Web portal of a computer manufacturer, including interactions with third-party service providers and enabling distributors to participate in well-defined business processes. The crucial advantage of our framework is the high-level modeling of a complex Web application, extended with Web service and workflow capabilities. The application is automatically verified for correctness and the code is automatically generated and deployed.

Conceptual modelling has recently gained attention as a means for formalizing the development process of Web applications, opening the way to the benefits of software engineering also in the Web context. New applications demand the extension of hypertextual front-ends with workflow functionality and with the ability of interacting with remote Web services. This scenario poses new challenges to Web conceptual models, which must be able to capture new requirements.

Abstract This paper introduces a model‐driven approach to the design of collaborative Web‐based applications, i.e. applications in which several users play different roles, in a collaborative way, to pursue a specific goal. The paper illustrates a conference management application (CMA), whose main requirements include: (i) the management of users profiles and access rights based on the role played by users during the conference life cycle; (ii) the delivery of information and services to individual users; (iii) the management of the sequence of activities that lead to the achievement of a common goal. The presented approach is based on WebML, a conceptual modelling language for the Web. The paper also highlights some general properties—as understood by the practical experience of CMA development—that a Web modelling language should feature in order to fully support the development of collaborative applications. Copyright © 2003 John Wiley & Sons, Ltd.

The rapid evolution of XML from a mere data exchange format to a universal syntax for encoding domain-specific information raises the need for new query languages specifically conceived to address the characteristics of XML. Such languages should be able not only to extract information from XML documents, but also to apply powerful transformation and restructuring operators, based on a well-defined semantics. Moreover, XML queries should be natural to write and understand, as nontechnical persons also are expected to access the large XML information bases supporting their businesses. This article describes XML-GL, a graphical query language for XML data. XML-GL's uniqueness is in the definition of a graph-based syntax to express a wide variety of XML queries, ranging from simple selections to expressive data transformations involving grouping, aggregation, and arithmetic calculations. XML-GL has an operational semantics based on the notion of graph matching, which serves as a guideline both for the implementation of native processors, and for the adoption of XML-GL as a front-end to any of the XML query languages that are presently under discussion as the standard paradigm for querying XML data.

This paper describes a methodology for the development of WWW applications and a tool environment specifically tailored for the methodology. The methodology and the development environment are based upon models and techniques already used in the hypermedia, information systems, and software engineering fields, adapted and blended in an original mix. The foundation of the proposal is the conceptual design of WWW applications, using HDM-lite, a notation for the specification of structure, navigation, and presentation semantics. The conceptual schema is then translated into a “traditional” database schema, which describes both the organization of the content and the desired navigation and presentation features. The WWW pages can therefore be dynamically generated from the database content, following the navigation requests of the user. A CASE environment, called Autoweb System, offers a set of software tools, which assist the design and the execution of a WWW application, in all its different aspects, Real-life experiences of the use of the methodology and of the AutoWeb System in both the industrial and academic context are reported.

With the spreading of the World Wide Web as a uniform and ubiquitous interface to computer applications and information, novel opportunities are offered for introducing significant changes in all organizations and their processes. This demo presents the IDEA Web Laboratory (Web Lab), a Web-based software design environment available on the Internet, which demonstrates a novel approach to the software production process on the Web.

Summary form only given. The authors demonstrate the main results of the Esprit Project P6333 IDEA, which lasted from June 1992 to March 1997, whose main contributors are five industrial partners from five European countries: Bull, the main contractor (France), TXT-Informatica (Italy), Sema (Spain), Dutch PTT (Holland), and Bim (Belgium), and four research institutes: ECRC in Munich, INRIA in Paris, the University of Bonn, and Politecnico di Milano. The main objective of the IDEA Project is to investigate the use and promote the spread of object-oriented and rule-based technologies in next-generation information systems. One of the major results of the IDEA Project is the IDEA Methodology and its supporting environment. The IDEA Methodology addresses the analysis, design, prototyping, and implementation of modern database systems applications, taking advantage of modern approaches developed in the context of database design, but also in the broader area of object-oriented software engineering. The distinguishing feature of the IDEA Methodology is the emphasis on both deductive rules and active rules, which significantly enrich the semantics supported within database applications.

Presents a technique for analyzing the run-time behavior of integrity constraint repair actions, i.e. active database rules that are specifically designed to correct violations of database integrity. When constraints become violated due to an incorrect user transaction, rule computation is started to restore the database to a correct state. Since repair actions may be numerous and may conflict with each other, automated support for the analysis of their run-time behavior is necessary. The proposed technique helps the rule base administrator define a repair rule selection strategy such that the computation terminates for every input transaction, the final database state satisfies all the constraints, and the user's preferences among different ways to restore integrity are taken into account. In addition, it can be used by the rule designer to spot "dangerous" rules that may be subject to redesign. This problem is formulated as an optimization problem on directed hypergraphs, which we demonstrate to be NP-hard and which we solve by means of a heuristic algorithm.

Active DBMSs couple database technology with rule-based programming to achieve the capability of reaction to database (and possibly external) stimuli, called events . The reactive capabilities of active databases are useful for a wide spectrum of applications, including security, view materialization, integrity checking and enforcement, or heterogeneous database integration, which makes this technology very promising for the near future. An active database system consists of a (passive) database and a set of active rules ; the most popular form of active rule is the so-called event-condition-action (ECA) rule, which specifies an action to be executed upon the occurrence of one or more events, provided that a condition holds. Several active database systems and prototypes have been designed and partially or completely implemented. Unfortunately, they have been designed in a totally independent way, without the support of a common theory dictating the semantics of ECA rules, and thus often show different behaviors for rules with a similar form. In this article we consider a number of different possible options in the behavior of an active DBMS, based on a broad analysis of some of the best known implemented systems and prototypes. We encode these options in a user-readable form, called Extended ECA . A rule from any existing system can be rewritten in this formalism making all the semantic choices apparent. Then an EECA rule can be automatically translated into an internal (less readable) format, based on a logical style, which is called core format: the execution semantics of core rules is specified as the fixpoint of a simple transformation involving core rules. As an important premise to this research, a semantics for database updates and transactions has also been established, with respect to a notion of state that comprises both data and events. The article also presents an extensive bibliography on the subject of active databases.

In this article we present an approach to integrity maintenance, consisting of automatically generating production rules for integrity enforcement. Constraints are expressed as particular formulas of Domain Relational Calculus; they are automatically translated into a set of repair actions, encoded as production rules of an active database system. Production rules may be redundant (they enforce the same constraint in different ways) and conflicting (because repairing one constraint may cause the violation of another constraint). Thus, it is necessary to develop techniques for analyzing the properties of the set of active rules and for ensuring that any computation of production rules after any incorrect transaction terminates and produces a consistent database state. Along these guidelines, we describe a specific architecture for constraint definition and enforcement. The components of the architecture include a Rule Generator , for producing all possible repair actions, and a Rule Analyzer and Selector , for producing a collection of production rules such that their execution after an incorrect transaction always terminates in a consistent state (possibly by rolling back the transaction); moreover, the needs of applications are modeled, so that integrity-enforcing rules reach the final state that better represents the original intentions of the transaction's supplier. Specific input from the designer can also drive the process and integrate or modify the rules generated automatically by the method. Experimental results of a prototype implementation of the proposed architecture are also described.

Logres is a new-generation database system integrating\nfeatures from deductive and object-oriented\ndatabases [1, 2, 3, 4, 5]. The data model of Logres supports\nstructural and semantic complexity through a rich\ncollection of concepts from object-oriented models. The\nrule language allows for the manipulation of complex objects,\nthe generation of new objects, and the definition of\npassive and active constraints. The application of set of\nrules to database states is controlled by means of qualifiers,\nwhich dictate the side effects of rules; qualifiers\nare the unique procedural feature of Logres, otherwise\na fully declarative language.

The system described is a knowledge-based expert system for short-term scheduling in environments characterized by a large number of production constraints and conflicting production goals. It can be viewed either as a high-level environment for developing and testing applications in the field of manufacturing production scheduling or a tool for building effective schedules in complex real world cases.