This paper explores whether older adults can remotely participate in museum visits with the help of virtual environments. We design and build a system that supports shared museum co-visits between onsite visitors inside the museum and older adults from a care home. We make the experience more engaging by providing a meaningful story, connecting the objects in the museum. The aim of the study is to understand whether older adults are able to use such technology and to study the mediated sense of spatial presence, the experienced social closeness, and the level of participants' engagement and enjoyment in the visit. We discuss the relationship between these aspects and factors leading to a better remote experience for older adults. The results show that older adults enjoy and engage in remote visits, and that there is a positive correlation between enjoyment, engagement and social closeness.

Bots, i.e., algorithmically driven entities that behave like humans in on-line communications, are increasingly infiltrating social conversations on the Web. If not properly prevented, this presence of bots may cause harm to the humans they interact with. This article aims to understand which types of abuse may lead to harm and whether these can be considered intentional or not. We manually review a dataset of 60 Twitter bot code repositories on GitHub, derive a set of potentially abusive actions, characterize them using a taxonomy of abstract code patterns, and assess the potential abusiveness of the patterns. The article then describes the design and implementation of a code pattern recognizer and uses the pattern recognizer to automatically analyze a dataset of 786 Python bot code repositories. The study does not only reveal the existence of 28 communication-specific code patterns - which could be used to assess the harmfulness of bot code - but also their consistent presence throughout all studied repositories.

Social networks are huge continuous sources of information that can be used to analyze people's behavior and thoughts. Our goal is to extract such information and predict political inclinations of users. In particular, this paper investigates the importance of syntactic features of texts written by users in the process.Our hypothesis is that people belonging to the same political party write in similar ways, thus they can be classified properly on the basis of the words that they use. We analyze tweets because Twitter is commonly used in Italy for discussing about politics; moreover, it provides an official API that can be easily exploited for data extraction.Many classifiers were applied to different kinds of features and NLP vectorization methods in order to obtain the best method capable of confirming our hypothesis. To evaluate their accuracy, a set of current Italian deputies with consistent activity in Twitter has been selected as ground truth, and we have then predicted their political party. Using the results of our analysis, we also got interesting insights into current Italian politics.

Task execution timeliness, i.e., the completion of a task within a given time frame, is a known open issue in crowdsourcing. While running tasks on crowdsourcing platforms a requester experiences long tails in execution caused by abandoned assignments (those left by workers unfinished), which become available for other workers only after some expiration time (e.g., 30 minutes in CrowdFlower). These abandoned assignments result in significant delays and a poor predictability of the overall task execution time. In this paper, we propose an approach and an implementation called ReLauncher to identify such abandoned assignments and relaunch them for other workers. We evaluate our implementation with an experiment on CrowdFlower that provides substantive evidence for a significant execution speed improvement with an average extra cost of about 10%.

Pattern mining, that is, the automated discovery of patterns from data, is a mathematically complex and computationally demanding problem that is generally not manageable by humans. In this article, we focus on small datasets and study whether it is possible to mine patterns with the help of the crowd by means of a set of controlled experiments on a common crowdsourcing platform. We specifically concentrate on mining model patterns from a dataset of real mashup models taken from Yahoo! Pipes and cover the entire pattern mining process, including pattern identification and quality assessment. The results of our experiments show that a sensible design of crowdsourcing tasks indeed may enable the crowd to identify patterns from small datasets (40 models). The results, however, also show that the design of tasks for the assessment of the quality of patterns to decide which patterns to retain for further processing and use is much harder (our experiments fail to elicit assessments from the crowd that are similar to those by an expert). The problem is relevant in general to model-driven development (e.g., UML, business processes, scientific workflows), in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices, organizational conventions, or technical choices, that modelers can benefit from when designing their own models.

There are two key issues which are critical for a person crowdsourcing work: quality control and timeliness. While the first topic is extensively covered in the literature, the timeliness is still not very well analyzed. In our past experiments on CrowdFlower we noticed that 90% of task instances were completed in only 10 minutes, the rest 10% could take hours to be completed. There were also extra peaks in performance on the 30th and 60th minutes since the task publication. From the observation of these patterns we wanted to understand better how the crowd workers execute tasks, to try to describe this behavior mathematically and to identify ways to speed up the tasks execution.

Crowdsourcing (CS) is the outsourcing of a unit of work to a crowd of people via an open call for contributions. Thanks to the availability of online CS platforms, such as Amazon Mechanical Turk or CrowdFlower, the practice has experienced a tremendous growth over the past few years and demonstrated its viability in a variety of fields, such as data collection and analysis or human computation. Yet it is also increasingly struggling with the inherent limitations of these platforms: each platform has its own logic of how to crowdsource work (e.g., marketplace or contest), there is only very little support for structured work (work that requires the coordination of multiple tasks), and it is hard to integrate crowdsourced tasks into state-of-the-art business process management (BPM) or information systems. We attack these three shortcomings by (1) developing a flexible CS platform (we call it Crowd Computer , or CC) that allows one to program custom CS logics for individual and structured tasks, (2) devising a BPMN--based modeling language that allows one to program CC intuitively, (3) equipping the language with a dedicated visual editor, and (4) implementing CC on top of standard BPM technology that can easily be integrated into existing software and processes. We demonstrate the effectiveness of the approach with a case study on the crowd-based mining of mashup model patterns.

Crowdsourcing platforms are changing the way people can work and earn money. The population of workers on crowdsourcing platforms already counts millions and keeps growing. Workers on these platforms face several usability challenges, which we identify in this work by running two surveys on the CrowdFlower platform. Our surveys show that the majority of workers spend more than 25% of their time on searching tasks to work on. Limitations in the current user interface of the task listing page prevent workers from focusing more on the execution. In this work we present an attempt to design and implement a specific user interface for task listing aimed to help workers spend less time searching for tasks and thus navigate among them more easily.

With this article, we give an answer to one of the open problems of mashup development that users may face when operating a model-driven mashup tool, namely the lack of modeling expertise . Although commonly considered simple applications, mashups can also be complex software artifacts depending on the number and types of Web resources (the components) they integrate. Mashup tools have undoubtedly simplified mashup development, yet the problem is still generally nontrivial and requires intimate knowledge of the components provided by the mashup tool, its underlying mashup paradigm, and of how to apply such to the integration of the components. This knowledge is generally neither intuitive nor standardized across different mashup tools and the consequent lack of modeling expertise affects both skilled programmers and end-user programmers alike. In this article, we show how to effectively assist the users of mashup tools with contextual, interactive recommendations of composition knowledge in the form of reusable mashup model patterns. We design and study three different recommendation algorithms and describe a pattern weaving approach for the one-click reuse of composition knowledge. We report on the implementation of three pattern recommender plugins for different mashup tools and demonstrate via user studies that recommending and weaving contextual mashup model patterns significantly reduces development times in all three cases.

Despite the common claim by mashup platforms that they enable end-users to develop their own software, in practice end-users still don't develop their own mashups, as the highly technical or inexistent user bases of today's mashup platforms testify. The key shortcoming of current platforms is their general-purpose nature, that privileges expressive power over intuitiveness. In our prior work, we have demonstrated that a domain-specific mashup approach, which privileges intuitiveness over expressive power, has much more potential to enable end-user development (EUD). The problem is that developing mashup platforms—domain-specific or not—is complex and time consuming . In addition, domain-specific mashup platforms by their very nature target only a small user basis, that is, the experts of the target domain, which makes their development not sustainable if it is not adequately supported and automated. With this article, we aim to make the development of custom, domain-specific mashup platforms cost-effective. We describe a mashup tool development kit (MDK) that is able to automatically generate a mashup platform (comprising custom mashup and component description languages and design-time and runtime environments) from a conceptual design and to provision it as a service . We equip the kit with a dedicated development methodology and demonstrate the applicability and viability of the approach with the help of two case studies.

The industrial adoption of wireless sensor networks (WSNs) is hampered by two main factors. First, there is a lack of integration of WSNs with business process modeling languages and back-ends. Second, programming WSNs is still challenging as it is mainly performed at the operating system level. To this end, we provide makeSense: a unified programming framework and a compilation chain that, from high-level business process specifications, generates code ready for deployment on WSN nodes.

Business process management, service-oriented architectures and software back-engineering heavily rely on the fundamental processes of mining of processes and web service business protocols from log files. Model extraction and mining aim at the (re)discovery of the behavior of a running model implementation using solely its interaction and activity traces, and no a priori information on the target model. This paper presents an approach for correlating messages and extracting the business protocol of a web service in the realistic scenario in which correlation information is entirely absent from interaction and activity logs. Correlation is achieved through deterministic computations that result in an extremely efficient method whose extensive experiments have shown its solid reliability, robustness when dealing with complex structures, and very high performance and scalability. This approach and the underlying algorithms extend what is actually possible to achieve in the web service business protocol mining domain using incomplete and noisy data logs, and opens new horizons in back-engineering of web services. The theoretical and experimental results clearly show the leap forward achieved herein.

The concept of application composition at the presentation layer, i.e., the development of web applications with user interfaces (UI) starting from stand-alone, reusable components, is a relatively new research area. The recent advent of web mashups and component-based web applications has produced promising results, but we argue that there is still a lot of space for improvement. By looking at three advanced approaches in this area, we investigate the current solution space and consequently unveil challenges and problems still to be solved in order to turn presentation composition into common practice.

Governing business compliance with regulations, laws, best practices, contracts, and the like is not an easy task, and so far there are only limited software products available that help a company to express compliance rules and to analyze its compliance state. We argue that todaypsilas SOA-based way of implementing and conducting business (e.g., using Web services and business process engines) lends itself very well to the development of a comprehensive compliance government solution that effectively aids companies in being compliant. In this paper, we contextualize the compliance problem in SOA-based businesses, we highlight which are the most salient research challenges that need to be addressed, and we describe our approach to compliance governance, spanning design, execution, and evaluation concerns.

While business process definition is becoming more and more popular as an instrument for describing human activities, there is a growing need for software tools supporting business process abstractions to help users organize and monitor their desktop work. Tools are most effective when they embed some knowledge about the process, e.g., in terms of the typical activities required by the process, so that users can execute the activities without having to define them. Tools must be lightweight and flexible, so as to enable users to create or change the process as soon as there is a new need. In this article, we first describe an application-independent approach to flexible process support by discussing the abstractions required for modeling, creating, enacting, and modifying flexible processes. Then, we show our approach at work in the context of project-centered learning. In this application, learners are challenged to perform concrete tasks in order to master specific subjects; in doing so, they have to conduct significant projects and cope with realistic (or even real-life) working conditions and scenarios. Often, students are geographically dispersed or under severe timing constraints, because these activities intertwine with their normal university activity. As a result, they need communication technology in order to interact and workflow technology in order to organize their work. The developed platform provides a comprehensible, e-learning-specific set of activities and process templates, which can be combined through a simple Web interface into project-centered collaboration processes. We discuss how the general paradigm of flexible processes was adapted to the learning concept, implemented, and experienced by students.

Event monitoring and active behaviors are important aspects in many software systems and application domains, not only in database management systems. In this article, we propose an event-condition-action (ECA) approach that spans from application data to application components and behaviors. Starting from an exception manager we previously developed in the context of a workflow management system, we derived an autonomous active component capable of handling a variety of events and enacting actions in response to detected events. The ECA server runs as an autonomous engine and can be seamlessly integrated with existing systems, thus enhancing the systems’ functionalities and maintainability by separating active and nonactive design concerns.

Where can I attend an interesting database workshop close to a sunny beach? Who are the strongest experts on service computing based upon their recent publication record and accepted European projects? Can I spend an April weekend in a city served by a low-cost direct flight from Milano offering a Mahler's symphony? We regard the above queries as multi-domain queries, i.e., queries that can be answered by combining knowledge from two or more domains (such as: seaside locations, flights, publications, accepted projects, conference offerings, and so on). This information is available on the Web, but no general-purpose software system can accept the above queries nor compute the answer. At the most, dedicated systems support specific multi-domain compositions (e.g., Google-local locates information such as restaurants and hotels upon geographic maps). This paper presents an overall framework for multi-domain queries on the Web. We address the following problems: (a) expressing multi-domain queries with an abstract formalism, (b) separating the treatment of "search" services within the model, by highlighting their differences from "exact" Web services, (c) explaining how the same query can be mapped to multiple "query plans", i.e., a well-defined scheduling of service invocations, possibly in parallel, which complies with their access limitations and preserves the ranking order in which search services return results; (d) introducing cross-domain joins as first-class operation within plans; (e) evaluating the query plans against several cost metrics so as to choose the most promising one for execution. This framework adapts to a variety of application contexts, ranging from end-user-oriented mash-up scenarios up to complex application integration scenarios.

The development of user interfaces (UIs) is one of the most time-consuming aspects in software development. In this context, the lack of proper reuse mechanisms for UIs is increasingly becoming manifest, especially as software development is more and more moving toward composite applications. In this paper we propose a framework for the integration of stand-alone modules or applications, where integration occurs at the presentation layer. Hence, the final goal is to reduce the effort required for UI development by maximizing reus.

Context-aware, multi-channel Web applications are more and more gaining consensus among both content providers and consumers, but very few proposals exist for their conceptual modeling. This article illustrates a conceptual framework that provides modeling facilities for context-aware, multichannel Web applications; it also shows how high-level modeling constructs can drive the application development process through automatic code generation. Our work stresses the importance of user-independent, context-triggered adaptation actions, in which the context plays the role of a “first class” actor , operating independently of users on the same hypertext the users navigate. Modeling concepts are based on WebML (Web Modeling Language), an already established conceptual model for data-intensive Web applications, which is also accompanied by a development method and a CASE tool. However, given their general validity, the concepts of this article shape up a complete framework that can be adopted independently of the chosen model, method, and tool.

As the amount of information on the World Wide Web continues to grow, efficient hypertext navigation mechanisms are becoming crucial. Among them, effective history mechanisms play an important role. We therefore decided to provide a new method to access users' navigation histories, called xMem (Extended Memory Navigation), building on semantic-based and associative accesses, so as to imitate some of the features of the human memory. Such a memory may give users better understanding of the context of their searches, intermixing semantic aspects with the temporal dimension.The paper presents the experimental study conducted on the xMem approach to revisit the Web interaction history. Two controlled experiments have been performed with the aim to evaluate the effectiveness of the xMem history mechanism with respect to traditional Web browser histories. The results from the first experiment show a clear advantage, in terms of the time needed to complete a retrieving task, for the subjects that used the xMem prototype. Accordingly, users found retrieving previously visited pages with xMem more satisfying than using Web interaction histories sorted by the only time dimension. The results from the second experiment show the relevance in the process of information retrieval of clusters and keywords semantically related to the context of the search.

memorie semantiche per la navigazione web centrata sull'utente