The increasing adoption of digital communication technologies has led to the accumulation of vast amounts of unstructured data, providing new opportunities to analyze organizational dynamics. In this study, we present a comprehensive framework for capturing, processing, and analyzing large-scale digital communication data to uncover hidden informal communication patterns within a real-world corporate environment. Utilizing a unique dataset of 40.425.247 emails within a banking company, spanning a two-year period, we construct a social network highlighting the email interactions. The graph is compared with organizational data, including business unit affiliations, enabling a detailed analysis of the relationship between formal organizational structures and informal social networks. By applying state-of-the-art community detection algorithms, we extract informal communities and compare them to the formal structure using various external validation metrics. Our results reveal that while informal communities may overlap with the formal structure to some extent, they often diverge in ways that may reveal organizational dynamics, communication flow, and inter-departmental collaboration that are not apparent from the formal structure alone. This study aims to pave the way for future research by exploring how human resource management can leverage insights derived from digital communication interactions to foster positive organizational behavior and enhance management practices.

Abstract In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and longevity of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policy-makers with useful and actionable insights stemming from those processes. In this paper, we propose a research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policy-makers engaged in the policy-making process. Thanks to our methodology, policy-makers can design challenges for citizens to partake, cooperate and provide their input. We also propose a web-based tool that allow citizens to participate and produce content to support the policy-making processes through a gamified interface that focuses on emotional and vision-oriented content.

In this paper, we propose two approaches to the problem of finding similar users to a set of champions representing domains of interest on social media. The first approach is based on the content shared by the users, while the second one relies on the social network connections (following, followers, and mentions). Given a small set of champion accounts, we construct a centroid and we rank candidates by computing their distance from the centroid. Experiments show that social network features provide better performance, but they are computationally much more intensive. This approach can be used for providing highly reliable recommendations of the top-k instances which are most similar to a given target, specified through examples rather than through specific properties.

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.

We propose a method and a system that aim to gauge local support for the two major US political parties in the 68 most competitive House of Representative districts during the mid-term elections. We analyze tweets explicitly posted from locations within each district. To distinguish between Republican and Democratic tweets, we adopt a RNN-LSTM binary classifier which reached validation accuracy of 85% over individual tweets, despite the highly implicit and short content shared on the social network. The method was able to predict the correct winner on 60% of the highly competitive (and thus extremely hard to predict also with traditional methods) districts. The lower result at district level is also an indicator of the population bias of the Twitter platform with respect to the actual voters. The classifier architecture, along with the other methods and tools we propose, is domain- and language- independent and may be applied to any highly polarizing topic with enough social media activity.

Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

Massive data integration technologies have been recently used to produce very large ontologies. However, knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail. Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities. Thus, we propose a method for discovering emerging entities by extracting them from social content.

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.

Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks.

An essential aspect for building effective crowdsourcing com- putations is the ability of "controlling the crowd", i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers. Most crowdsourcing systems only provide limited and predefined controls; in contrast, we present an approach to crowdsourcing which provides fine-level, powerful and flexible controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal flexibility with limited effort.

As a wealth of data services is becoming available on the Web, building and querying Web applications that effectively integrate their content is increasingly important. However, schema integration and ontology matching with the aim of registering data services often requires a knowledge-intensive, tedious, and error-prone manual process. We tackle this issue by presenting a bottom-up, semi-automatic service registration process that refers to an external knowledge base and uses simple text processing techniques in order to minimize and possibly avoid the contribution of domain experts in the annotation of data services. The first by-product of this process is a representation of the domain of data services as an entity-relationship diagram, whose entities are named after concepts of the external knowledge base matching service terminology rather than being manually created to accommodate an application-specific ontology. Second, a three-layer annotation of service semantics (service interfaces, access patterns, service marts) describing how services “play” with such domain elements is also automatically constructed at registration time. When evaluated against heterogeneous existing data services and with a synthetic service dataset constructed using Google Fusion Tables, the approach yields good results in terms of data representation accuracy. We subsequently demonstrate that natural language processing methods can be used to decompose and match simple queries to the data services represented in three layers according to the preceding methodology with satisfactory results. We show how semantic annotations are used at query time to convert the user's request into an executable logical query. Globally, our findings show that the proposed registration method is effective in creating a uniform semantic representation of data services, suitable for building Web applications and answering search queries.

Retrieval and management of Web data is becoming a more and more complex problem, due to the amount of information to be dealt with, to the diversity of the information sources and of the data formats, and to the evolving expectations of users. In particular, some tasks such as quality assessment, opinion making, and sense extraction cannot be completely delegated to automatic procedures. More and more users are increasingly relying on social interaction to complete and validate the results of their online activities. For instance, scouting "interesting" results, or suggesting new, unexpected search directions in information seeking processes occurs in most times aside of the search systems and processes, possibly instrumented and mediated by a social network. In this paper we propose paradigm that embodies crowds and social network communities as first-class sources for the information management and extraction on the Web. Our approach aims at filling the gap between traditional Web systems (CMS, search engines and others), which operate upon world-wide information, with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, and emotions by leveraging crowd sourcing practices and making them viable upon a social network. This enormously enriches the data manipulation experience for the user can be enormously enriched.

Web users are increasingly relying on social interaction to complete and validate the results of their search activities. While search systems are superior machines to get world-wide information, the opinions collected within friends and expert/local communities can ultimately determine our decisions: human curiosity and creativity is often capable of going much beyond the capabilities of search systems in scouting "interesting" results, or suggesting new, unexpected search directions. Such personalized interaction occurs in most times aside of the search systems and processes, possibly instrumented and mediated by a social network; when such interaction is completed and users resort to the use of search systems, they do it through new queries, loosely related to the previous search or to the social interaction. In this paper we propose CrowdSearcher, a novel search paradigm that embodies crowds as first-class sources for the information seeking process. CrowdSearcher aims at filling the gap between generalized search systems, which operate upon world-wide information - including facts and recommendations as crawled and indexed by computerized systems - with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, emotions. The technical contribution of this paper is the discussion of a model and architecture for integrating computerized search with human interaction, by showing how search systems can drive and encapsulate social systems. In particular we show how social platforms, such as Facebook, LinkedIn and Twitter, can be used for crowdsourcing search-related tasks; we demonstrate our approach with several prototypes and we report on our experiment upon real user communities.

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.

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Modern Web applications are characterized by a high level of complexity and deal with huge amounts of data. When the application grows in complexity, manual code development is not suitable, because it lacks in efficiency, reuse, reliability, maintainability, and group work facilities. On the other hand, several Web engineering approaches are too far away from the average developer and designer way of working to be widely adopted. In this demonstration paper we propose a light-weight design notation (with its companion editing tool) that leads to the development of MVC applications. We present an on-line visual editing tool called MVC-Webflow for the specification of simple conceptual models for MVC applications and we provide partial automatic code generation, that can be performed on the flight directly on the deployed application. The advantages of the approach are the closeness to the well known MVC paradigm, the foundation on solid web engineering models.

This paper presents a methodology and a mix of conceptual models for addressing design and development of Web applications supported by rich interfaces. For specifying the high level design of the user tasks, we exploit business process models. In particular, we describe how to model the business process, transform it into data and navigation model of a Web application, and apply a presentation model for obtaining a Rich Internet Application (RIA). A standard business modeling language (BPMN) is used for describing workflows, which are then translated to a WebML specification of a Web application implemented according to the Single Page Paradigm, typical of RIAs. Finally, by integrating the RUX-Method features, refined Rich Interface design can be achieved.

Personalization of contents accessed on the Web can be improved by proper exploitation of user preferences and navigation behavior. Such profile information may be provided by metadata and should be managed through state-of-the-art Web engineering methodologies and notations. In our proposal, we exploit existing solutions in the field of knowledge representation and Web applications conceptual specification, and we provide: (i) a set of ontologies for representing both user profile and preferences, together with a good metadata design for describing them; (ii) a set of techniques for registering preferences explicitly declared by the user, as well as user navigation behavior automatically recorded; and (iii) some basic methods for delivering personalized content to the user based on metadata management. We base our method on the well known Web modeling language called WebML, extended with a set of new primitives for exploiting personalization metadata.

Available methodologies for developing Sematic Web applications do not fully exploit the whole potential deriving from interaction with ontological data sources. Here we introduce an extension of the WebML modeling framework to fulfill most of the design requirements emerging for the new area of Semantic Web. We generalize the development process to support Semantic Web applications and we introduce a set of new primitives for ontology importing and querying.

Enriching Web applications with personalized data is of major interest for facilitating the user access to the published contents, and therefore, for guaranteeing successful user navigation. We propose a conceptual model for extracting personalized recommendations based on user profiling, ontological domain models, and semantic reasoning. The approach offers a high-level representation of the designed application based on a domain-specific metamodel for Web applications called WebML.

This short paper aims at demonstrating the integration of workflow specifications with conceptual models for Web application design. In particular, we propose a visual editor of business process diagrams (according to the BPMN notation), which seamlessly integrates into the commercial CASE tool called WebRatio, which allows the designer to model data-intensive and service-intensive Web applications based on WebML (Web Modeling Language) models. A set of model transformations from BPMN workflow diagrams to WebML hypertext diagrams allows fast generation of site skeletons implementing the specified business process. Such skeletons can be modified by the designer that is relieved from the time-consuming and error-prone task of specifying all the details of the workflow constraints.

Since the Web is becoming a platform for implementing complex B2C and B2B applications instead of simple content publishing sites, the need arises of imposing constraints on the navigation and on the managed information. Web conceptual modeling languages allow to a small extent to specify constraints on the application, by means of some extensions to their basic primitives; however these approaches do not provide a comprehensive framework for efficient and effective constraint management. The goal of this paper is to present a general framework to facilitate the integration of efficient integrity checking methods in Web applications. Our main contribution is the study of a set of parameters that can be inferred from high level Web application modeling, for allowing the tuning of the constraint optimization techniques for each specific Web application. From these parameters we derive the optimal enforcement techniques to be implemented.

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.

As the Web becomes a platform for implementing B2B applications, the need arises of Web conceptual models for describing Web oriented workflow applications implementing business processes. In this context, new problems about process correctness arise, due to the loose control of Web applications upon the behavior of their Web clients. Indeed, incoherent user's behavior can lead to inconsistent processes.This paper presents a high level approach to the management of exceptions that occur during the execution of processes on the Web. We present a classification of exceptions that can be raised inside workflow-driven Web applications, and recovery policies to retrieve coherent status and data after an exception. We devise these concepts at high level and then we exploit them using a Web modeling language (WebML) that in turn provides development facilities like automatic code generation, validation of hypertext models, and so on. An industrial implementation experience is briefly presented too.

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.