Sequential task assignment is a crucial process in many contexts, where resource allocation over time is a key step to consider and often involves groups of people with diverse objectives, preferences, and constraints. Fairness in these scenarios is paramount, as it implies efficiency and satisfaction while also impacting performance. Although the definition of fairness depends on the context and domain, it generally ensures an equal distribution of tasks among participants, subject to certain constraints and guidelines. Moreover, it mitigates biases and disparities, promoting inclusivity and diversity within teams. In this paper, we highlight the different aspects of fairness in sequential task assignments and emphasize that the perspectives of various stakeholders must be considered. As motivating examples, we concentrate on two scenarios: (a) the timetable creation problem in the university domain, showing that the notion of fairness must be considered from both the students’ and professors’ points of view, and (b) the tourism traveling planning, where the perspectives of tour guides and tourists are taken into account during a planning process. We propose a generic formalization of the problem that an optimization algorithm can easily manage. The aim is to find and compare the fairness of different stakeholders and evaluate whether a fair solution for one of them can be fair for another with different constraints and preferences. We introduce the notion of local and global fairness to highlight that an optimal solution for one stakeholder does not necessarily mean it is optimal also for others, and some compromises need to be identified. Finally, we explore how global fairness can be achieved by integrating multiple solutions, each aligned with a local fairness perspective.

Understanding tourist behavior patterns is crucial for developing effective recommendation and decision support systems. The behaviors are often captured through the trajectories followed by tourists during their journeys or the sequences of visited Points of Interest (PoIs). Identifying common patterns and tracking their evolution over time can enhance the ability to understand, predict, and influence tourist choices, ultimately supporting goals like promoting specific destinations and fostering sustainable visitation patterns. Clustering algorithms like k-Means are commonly used to extract frequent patterns, requiring a tailored distance metric suited to the task. Since tourist trajectories combine spatial, temporal, and semantic features, defining a distance function that accurately captures these multifaceted aspects is essential. This paper examines various methods for encoding trajectory data and explores their effects on the clustering process. Finally, we compare and validate their suitability by using a real-world dataset of visits performed by tourists in Verona (Italy) from 2014 to 2022.

In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has been propagated through social media. Tracking the provenance of information is becoming increasingly important as social platforms acquire more relevance as source of news. In this scenario, Twitter is considered one of the most important social networks for information sharing and dissemination which can be accelerated through the use of retweets and quotes. However, the Twitter API does not provide a complete tracking of the retweet chains, since only the connection between a retweet and the original post is stored, while all the intermediate connections are lost. This can limit the ability to track the diffusion of information as well as the estimation of the importance of specific users, who can rapidly become influencers, in the news dissemination. This paper proposes an innovative approach for rebuilding the possible chains of retweets and also providing an estimation of the contributions given by each user in the information spread. For this purpose, we define the concept of Provenance Constraint Network and a modified version of the Path Consistency Algorithm. An application of the proposed technique to a real-world dataset is presented at the end of the paper.

During the life of a database, systematic and frequent violations of a given constraint may suggest that the represented reality is changing and thus the constraint should evolve with it.In this paper we propose a method and a tool to (i) find the functional dependencies that are violated by the current data, and (ii) support their evolution when it is necessary to update them.The method relies on the use of confidence, as a measure that is associated with each dependency and allows us to understand "how far" the dependency is from correctly describing the current data; and of goodness, as a measure of balance between the data satisfying the antecedent of the dependency and those satisfying its consequent.Our method compares favorably with literature that approaches the same problem in a different way, and performs effectively and efficiently as shown by our tests on both real and synthetic databases.

Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very relevant. In this paper we study ephemeral groups, i.e., groups where the members might be together for the first time. Recent approaches have tackled this issue introducing complex models to be learned offline, making them unable to deal with new items; on the contrary, we propose a group recommender able to manage new items too. In more detail, our technique determines the preference of a group for an item by combining the individual preferences of the group members on the basis of their contextual influence, where the contextual influence represents the ability of an individual, in a given situation, to direct the group's decision. We conducted an extensive experimental evaluation on a TV dataset containing a log of viewings performed by real groups, showing how our approach outperforms the comparable techniques from the literature.

The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. As a result, effective sense-making of semantically rich and big datasets has received a lot of attention, and new search approaches, such as Exploratory Computing (EC), have seen the light. In this paper we present IQ4EC, a system for data exploration inspired by EC, that supports users in the inspection of huge amounts of relational data through a step-by-step process, providing feedback based on approximate, intensional information expressed in terms of association rules. At each step of the process, the users can choose a portion of data to examine, and the system guides them to the next step by providing synthetic information and visualization of the resulting dataset.

The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. Many approaches have been developed, but for the most part, they attack one specific side of the problem: e.g. efficient querying, analysis techniques that summarize data or reduce its dimensionality, data visualization, etc. The approach proposed in this paper aims instead at taking a comprehensive view: first of all, it takes into account that human exploration is an iterative and multi-step process and therefore allows building upon a previous query on to the next, in a sort of “dialogue” between the user and the system. Second, it aims at supporting a variety of user experiences, like investigation, inspiration seeking, monitoring, comparison, decision-making, research, etc. Third, and probably most important, it adds to the notion of “big” the notion of “rich”: Exploratory Computing (EC) aims at dealing with datasets of semantically complex items, whose inspection may reach beyond the user's previous knowledge or expectations: an exploratory experience basically consists in creating, refining, modifying, comparing various datasets in order to “make sense” of these meanings.

This demo presents a framework for personalizing data access on the basis of the users' context and of the preferences they show while in that context. The system is composed of (i) a server application, which “tailors” a view over the available data on the basis of the user's contextual preferences, previously inferred from log data, and (ii) a client application running on the user's mobile device, which allows to query the data view and collects the activity log for later mining. At each change of context detected by the system the corresponding tailored view is loaded on the client device: accordingly, the most relevant data is available to the user even when the connection is unstable or lacking. The demo features a movie database, where users can browse data in different contexts and appreciate the personalization of the data views according to the inferred contextual preferences.

Extracting information from semistructured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the Internet grows. Indeed, documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this paper, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intensional information on both the structure and the contents of Extensible Markup Language (XML) documents, and can be stored in XML format as well. This mined knowledge is later used to provide: 1) a concise idea-the gist-of both the structure and the content of the XML document and 2) quick, approximate answers to queries. In this paper, we focus on the second feature. A prototype system and experimental results demonstrate the effectiveness of the approach.

The widespread use of mobile appliances, with limitations in terms of storage, power, and connectivity capability, requires to minimize the amount of data to be loaded on user's devices, in order to quickly select only the information that is really relevant for the users in their current contexts: in such a scenario, specific methodologies and techniques focused on data reduction must be applied. We propose an extension to the data tailoring approach of Context-ADDICT, whose aim is to dynamically hook and integrate heterogeneous data to be stored on small, possibly mobile devices. The main goal of our extension is to personalize the context-dependent data obtained by means of the Context-ADDICT methodology, by allowing the user to express preferences that specify which data s/he is more interested in (and which not) in each specific context. This step allows us to impose a partial order among the data, and to load only the top (most preferred) portion of the data chunks. A running example is used to better illustrate the approach.

Nowadays user mobility requires that both content and services be appropriately personalized, in order for the (mobile) user to be always - and anywhere - equipped with the adequate share of data. Thus, the knowledge about the user, the adopted device and the environment, altogether called context, has to be taken into account in order to minimize the amount of information imported on mobile devices. The Context-ADDICT (Context-Aware Data Design, Integration, Customization and Tailoring) project aims at the definition of a complete framework which, starting from a methodology for the early design phases, supports mobile users through the dynamic hooking and integration of new, available information sources, so that an appropriate context-based portion of data, called data chunk, is delivered to their mobile devices. Data tailoring is needed because of two main reasons: the first is to keep the amount of information manageable, in order for the user not to be confused by too much, possibly noisy, information; the second is the frequent case when the mobile device is a small one, like a palm computer or a cellular phone, and thus only the most significant information must be kept on board. Context is, thus, key metainformation whose role becomes essential within the process of view design. Two main design-time activities are supported by our system in order to provide context-aware data filtering: 1) context design, based on a context model called context dimension tree and 2) definition of the relationship between each context and relevant portions of the application domain data.

XML is a rather verbose representation of semistructured data, which may require huge amounts of storage space. We propose a summarized representation of XML data, based on the concept of instance pattern, which can both provide succinct information and be directly queried. The physical representation of instance patterns exploits itemsets or association rules to summarize the content of XML datasets. Instance patterns may be used for (possibly partially) answering queries, either when fast and approximate answers are required, or when the actual dataset is not available, for example, it is currently unreachable. Experiments on large XML documents show that instance patterns allow a significant reduction in storage space, while preserving almost entirely the completeness of the query result. Furthermore, they provide fast query answers and show good scalability on the size of the dataset, thus overcoming the document size limitation of most current XQuery engines.