Abstract The increasing availability of Big Data is changing the way data exploration for Business Intelligence is performed, due to the volume, velocity and uncontrolled variety of data on which exploration relies. In particular, data exploration is required in Data Lakes that have been proposed to host heterogeneous data sources, given their flexibility to cope with cumbersome properties of Big Data. However, as data grows, new methods and techniques are required for extracting value and knowledge from data stored within Data Lakes, aggregating data into indicators according to multiple analysis dimensions, to enable a large number of users with different roles and competencies to capitalise on available information. In this paper, we propose PERSEUS (PERSonalised Exploration by User Support), a computer-aided approach for data exploration on top of a Data Lake, structured over three phases: (1) the construction of a semantic metadata catalog on top of the Data Lake, leveraging tools and metrics to ease the annotation of the Data Lake metadata; (2) modelling of indicators and analysis dimensions, guided by an openly available Multi-Dimensional Ontology to enable conformance checking of indicators and let users explore Data Lake contents; (3) enrichment of the definition of indicators with personalisation aspects, based on users’ profiles and preferences, to make easier and more usable the exploration of data for a large number of users. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.

Abstract Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

The implementation of remote services in the factory of the future is gaining more and more attention. A distributed process can be considered, where an Original Equipment Manufacturer supplies services based on data collected from the machines of its clients. Examples of services are those implementing anomaly detection and predictive maintenance techniques. Data on which remote services are based, as well as their execution logic, must be transparently shared among participants, to enforce trust among OEM and its clients. The goal of this paper is to propose an approach where blockchain technology and Smart Contracts are used to ensure the required level of trust when implementing the aforementioned services. Events occurring on monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation of data on which remote services are based. Moreover, trust-demanding tasks in the execution logic of services are considered to design Smart Contracts, that guarantee the required level of trustworthiness among participants. The example featuring Remote Monitoring Services for anomaly detection is used to demonstrate the feasibility of the approach.

Digital transformation and the adoption of ICT technologies in the factory of the future are growing faster and faster. In particular, data exploration methods and techniques are enabling the development of data-intensive Remote Monitoring Services for anomaly detection and predictive maintenance purposes. Remote Monitoring Services involve different actors across organizations. The Original Equipment Manufacturer explores high volume of data collected by sensors on the monitored machines to provide anomaly detection and predictive maintenance services. Insurance agencies may provide support to sustain maintenance costs. Spare parts suppliers can schedule the delivery of mechanical parts required for maintenance interventions. In this scenario, trust among participants becomes a critical issue. On the one hand, providers of anomaly detection and predictive maintenance services as well as insurance agencies must trust the way machines have been used by collecting and analysing sensors data. On the other hand, owners of monitored machines must trust the use of collected data to implement services, based on which maintenance costs are calculated. The goal of this paper is to leverage blockchain and smart contracts to ensure the required level of trust when implementing data exploration for Remote Monitoring Services. Events occurring on the monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation. Moreover, trust-demanding services are implemented as smart contracts, to guarantee the required level of trustworthiness among participants. The approach is integrated with a tool for data exploration in the digital factory, and has been validated taking into account performances and cost requirements.

In the era of Internet of Things and dynamically interconnected systems, real time data becomes a new industrial asset, used to create new opportunities for operations improvement and to increase industrial value through the capitalisation of immaterial assets. In the smart factory, big data acquisition, analysis and visualisation pave the way to the manufacturing servitization, defined as the strategic innovation of organisations capabilities and processes to shift from product offering to an integrated "product plus service" offering. According to this vision, interconnected physical systems are associated with a cyber twin, where innovative services for big data management should be provided. In this paper, we propose an interactive data exploration framework, that poses a service-oriented perspective on the smart factory. Large amounts of data are incrementally collected from physical systems, organized and analysed on the cloud and new services are provided to enable data exploration. Such services implement novel data summarisation techniques, based on clustering, to manage data abundance, and data relevance evaluation techniques, aimed to focus the attention on relevant data that is being explored. Services are based on a multi-dimensional model, that is suited for supporting the iterative and multi-step exploration of Big Data.

Mashups are agile applications that aggregate RESTful services, developed by third parties, whose functions are exposed as Web Application Program Interfaces (APIs) within public repositories. From mashups developers’ viewpoint, Web API search may benefit from selection criteria that combine several dimensions used to describe the APIs, such as categories, tags, and technical features (e.g., protocols and data formats). Nevertheless, other dimensions might be fruitfully exploited to support Web API search. Among them, past API usage experiences by other developers may be used to suggest the right APIs for a target application. Past experiences might emerge from the co-occurrence of Web APIs in the same mashups. Ratings assigned by developers after using the Web APIs to create their own mashups or after using mashups developed by others can be considered as well. This article aims to advance the current state of the art for Web API search and ranking from mashups developers’ point of view, by addressing two key issues: multi-dimensional modeling and multi-dimensional framework for selection. The model for Web API characterization embraces multiple descriptive dimensions, by considering several public repositories, that focus on different and only partially overlapping dimensions. The proposed Web API selection framework, called WISeR (Web apI Search and Ranking), is based on functions devoted to developers to exploit the multi-dimensional descriptions, in order to enhance the identification of candidate Web APIs to be proposed, according to the given requirements. Furthermore, WISeR adapts to changes that occur during the Web API selection and mashup development, by revising the dimensional attributes in order to conform to developers’ preferences and constraints. We also present an experimental evaluation of the framework.

Selection of existing services for agile Web application design offers many advantages, namely, the availability of widespread solutions in the form of data and services shared over the Web, and reduced development costs. In this paper, we propose a framework to support service selection, where a Web application developer takes into account choices made by other developers to learn from them in selecting suitable services. To this aim, we weight as more important the choices made by developers who present a larger number of followers in a network of social relationships. Such a network is used to compute developers' rank to support service selection.

Novel web application development techniques rely on selection of the right data services, and this task assumes relevance to properly support creativity of developers. To this aim, developers should not merely leverage service descriptions, that are often very simple, in terms of categories and (semantic) tags. Data service selection criteria should include the experience of other developers who used the services in the past for designing their own web applications. According to this viewpoint, a data service might be relevant since: (i) it has been already used in similar contexts (i.e., in applications designed with similar data services); (ii) it has been used by other developers whose service selection experiences are considered as relevant by the developer who is designing the new application. The importance given to past experiences of other developers might be qualified by exploring the social network that relates the developers themselves and its topology. In this paper, we test the effectiveness of a multi-layered model meant for the selection of data services for web application design, taking into account such a developers' network.

Service identification is one of the main phases in the design of a service-oriented application. The way in which services are identified may influence the effectiveness of the SOA architecture. More specifically, the granularity of the services is very important in reaching flexibility and reusing them. Such properties are crucial in interorganizational interactions based on collaborative business processes. In fact, collaboration is facilitated by ensuring a homogeneous description of services at the right level of granularity. In this paper, we provide a detailed description of P2S (Process-to-Services), a computer-aided methodology to enable the identification of services that compose a collaborative business process. The methodology is based on metrics defined to setup service granularity, cohesion, coupling, and reuse. A prototype tool based on the methodology is also described with reference to a real case scenario.

Nowadays web designers are able to perform fast development of web applications by aggregating Web APIs available in huge public repositories. This approach is referred to as web mashup. Web APIs may be searched and aggregated in different scenarios, namely the development of a new mashup, or the completion of an existing mashup, or the substitution of one or more Web APIs within it. Collective knowledge of other designers who used the same or similar Web APIs in their own mashups is of paramount relevance to support Web API selection. In this paper, we provide a framework of techniques based on such a collective knowledge to support Web API selection according to the different search patterns, which correspond to distinct mashup development scenarios. We also present a preliminary evaluation of the framework.

The design of Web applications from third-party Web APIs can be shortened by providing effective tools that abstract from heterogeneity of Web API descriptions and support the designer for their proactive selection. In this paper, we identify Web API selection patterns to support interactive and proactive Web application development according to an exploratory perspective. Selection patterns rely on a semantic characterization of Web API descriptions that abstracts from implementation details and semantics-enabled metrics for evaluation of coupling and similarity degree. A prototype tool that implements selection patterns is also presented.

Mashup of components made available on the Web is gaining more and more interest as an opportunity to integrate contents and application logics from independent sources in new, short-living and situational applications. These applications are usually meant to fill immediate needs and should be built according to simplified composition paradigms. Although existing mashup frameworks, like IBM Lotus Mashup or Yahoo! Pipes, provide valid solutions that make it easy to compose new applications, designers often have to deal with large and heterogeneous component repositories. Components are manually selected and combined, still requiring a support for their efficient selection and integration. In this context, we propose to use semantic annotation of components apt to abstract from the heterogeneity of underlying APIs. Then, we describe techniques for building a mashup component repository where semantically annotated components are organized according to similarity and coupling links. Finally, we discuss interactive, computer-aided design of mashup applications.

In the recent years distributed architectures and P2P tech-nology have been adopted to better support effective col-laboration among networked organizations. According to the P2P paradigm, many autonomous peers need to coop-erate under highly dynamic conditions by sharing their re-sources (such as data and services), without sharing a com-mon semantic model or ontology and without having a-priori knowledge about each other. In such a scenario, for effective collaboration, new advanced tools are required to support se-mantic representation and discovery of distributed resources such as data and services. In this paper we focus on seman-tic collaboration and service discovery in P2P systems. We propose the construction of a service semantic overlay over the P2P network, that is, a dynamic conceptual map across peers that provide similar services and constitute synergic service centers.

Abstract Web service composition is emerging as an interesting approach to integrate business applications and create intra‐organizational business processes. Single Web services are combined to create a complex Web service that will realize the process business logic. Once the process is created, it is executed by an orchestration engine that invokes individual Web services in the correct order. However, Web services composing the workflow sometimes become unavailable during the run‐time phase, blocking process execution. This paper describes an architecture that allows the flexible orchestration of business processes. With this approach, Web services composing the process can be automatically substituted with other compatible Web services during process execution. A methodology is defined to evaluate Web service compatibility based on interface matching, in order to select substitutable Web services. Copyright © 2005 John Wiley & Sons, Ltd.

The problem of defining global views of heterogeneous data sources to support querying and cooperation activities is becoming more and more important due to the availability of multiple data sources within complex organizations and in global information systems. Global views are defined to provide a unified representation of the information in the different sources by analyzing conceptual schemas associated with them and resolving possible semantic heterogeneity. We propose an affinity based unification method for global view construction. In the method: (1) the concept of affinity is introduced to assess the level of semantic relationship between elements in different schemas by taking into account semantic heterogeneity; (2) schema elements are classified by affinity levels using clustering procedures so that their different representations can be analyzed for unification; (3) global views are constructed starting from selected clusters by unifying representations of their elements. Experiences of applying the proposed unification method and the associated tool environment ARTEMIS on databases of the Italian Public Administration information systems are described.

To support the development of uniform query interfaces over distributed and heterogeneous databases, tools for the analysis and reconciliation of database conceptual schemas are required. The paper presents the ARTEMIS tool environment developed to support the analyst in the process of analyzing and reconciling sets of heterogeneous data schemas. Schema analysis in ARTEMIS is performed according to the concept of affinity, while schema reconciliation is performed interactively on clusters of elements with affinity, based on unification rules. Issues on expected tool interaction and on experimentation and applicability of ARTEMIS are discussed.

The problem of analyzing and classifying conceptual schemas is becomig increasingly important due to the availability of a large number of schemas related to existing applications. The purposes of schema analysis and classification activities can be different: to extract information on intensional properties of legacy systems in order to restructure or migrate to new architectures; to build libraries of reference conceptual components to be used in building new applications in a given domain; and to identify information flows and possible replication of data in an organization. This article proposes a set of techniques for schema analysis and classification to be used separately or in combination. The techniques allow the analyst to derive significant properties from schemas, with human intervention limited as far as possible. In particular, techniques for associating descriptors with schemas, for abstracting reference conceptual schemas based on schema clustering, and for determining schema similarity are presented. A methodology for systematic schema analysis is illustrated, with the purpose of identifying and abstracting into reference components the similar and potentially reusable parts of a set of schemas. Experiences deriving from the application of the proposed techniques and methodology on a large set of Entity-Relationship conceptual schemas of information systems in the Italian Public Administration domain are described

Criteria and techniques to support the establishment of a semantic dictionary for database interoperability are described. The techniques allow the analysis of conceptual schemas of databases in a federation and the definition and maintenance of concept hierarchies. Similarity-based criteria are used to evaluate concept closeness and, consequently, to generate concept hierarchies. Experimentation of the techniques in the public administration domain is discussed.

System evolution and re-engineering are becoming increasingly important as the size and complexity of Information Systems continue to grow. The paper addresses the problems related to the development of abstraction mechanisms for building reference conceptual architectures of Information Systems, i.e. standardized descriptions of their most significant concepts, to be used for matching and validating changes to the system.

A methodological approach is presented to guide the definition of reusable components to be exploited for designing applications not from scratch, but by tailoring and adapting existing components. Reusability at the requirements specification level is considered, and tools to define reusable components are illustrated. Reusable components are defined as generic components with associated meta-components providing guidelines for reuse in a given application.>

To define office requirements, the authors propose a disciplined language in which a good portion of conventionality and known and concrete concepts are associated with a minimum formalism. The language can be used in analyzing an office for the purpose of designing a computer-based office support system. Given the language, the designer can organize information obtained by people working in the office and state the office requirements that will be the basis for developing a suitable system. The features of the language are illustrated and its morphology and syntax are explained.>