Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes in several different application domains. Among them, the representation and management of health and clinical processes have been attracting a growing interest. Such processes are in general related to the way each health organization provides the required healthcare services. Health and clinical processes underlie the specification and application of clinical protocols, clinical guidelines, clinical pathways, and the most common clinical/administrative procedures. Current workflow systems are lacking in effective management of three general key aspects that are common (not only) in the clinical/health context: data dependencies, exception handling, and temporal constraints. For example, a laparoscopic intervention may need the results of the concurrent bioptic analysis to be properly concluded while exceptional recovery activities have to be performed in case of emergency evidence during standard treatment; however, the successful application of a fibrinolytic therapy requires a maximum delay of 30 min after the admission into the emergency department. In this paper, we propose TNest, a new advanced, structured, and highly modular workflow modeling language that allows one to easily express data dependencies and time constraints during process design, in addition to exception handling and compensation activities. As for temporal constraints, we focus here on temporal controllability which is the capability of executing a workflow for all possible durations of all tasks satisfying all temporal constraints. Moreover, we analyze the computational complexity of the temporal controllability problem in TNest, and we propose a general algorithm to check the controllability. All the features of TNest that have been considered to model clinical pathways from classical clinical guidelines, i.e., those features for the management of STEMI patients, published by the American College of Cardiology/American Heart Association, will be used throughout the paper as a motivating scenario.

Layer 2 solutions have emerged in recent years as a valuable alternative to increase the throughput and scalability of blockchain-based architectures. The three primary types of Layer 2 solutions are state channels, sidechains, and rollups. The rollups are particularly promising, allowing significant improvements in transaction throughput, security, and efficiency, and have been adopted by many real-world projects, such as Polygon and Optimistic. However, the adoption of Layer 2 solutions has led to other challenges, such as the data availability problem, where transaction data processed off-chain must be posted back on the main chain. This is crucial to prevent data withholding attacks and ensure all participants can independently verify the blockchain state. This paper provides a comprehensive survey of existing rollup-based Layer 2 solutions with a focus on the data availability problem and discusses the major advantages and disadvantages of them. Finally, an analysis of open challenges and future research directions is provided.

Blockchain technology has been successfully applied in recent years to promote the immutability, traceability, and authenticity of previously collected and stored data. However, the amount of data stored in the blockchain is usually limited for economic and technological issues. Namely, the blockchain usually stores only a fingerprint of data, such as the hash of data, while full, raw information is stored off-chain. This is generally enough to guarantee immutability and traceability, but misses to support another important property, that is, data availability. This is particularly true when a traditional, centralized database is chosen for off-chain storage. For this reason, many proposals try to properly combine blockchain with decentralized IPFS storage. However, the storage of data on IPFS could pose some privacy problems. This paper proposes a solution that properly combines blockchain, IPFS, and encryption techniques to guarantee immutability, traceability, availability, and data privacy.

Developing Recommender Systems (RSs) is particularly interesting in the tourist domain, where one or more attractions have to be suggested to users based on preferences, contextual dimensions, and several other constraints. RSs usually rely on the availability of a vast amount of historical information about users’ past activities. However, this is not usually the case in the tourist domain, where acquiring complete and accurate information about the user’s behavior is complex, and providing personalized suggestions is frequently practically impossible. Moreover, even though most available Touristic RSs (T-RSs) are user-focused, the touristic domain also requires the development of systems that can promote a more sustainable form of tourism. The concept of sustainable tourism covers many aspects, from economic, social, and environmental issues to the attention to improving tourists’ experience and the needs of host communities. In this regard, one of the most important aspects is the prevention of overcrowded situations in attractions or locations (over-tourism). For this reason, this paper proposes a different kind of T-RS, which focuses more on the tourists’ impact on the destinations, trying to improve their experiences by offering better visit conditions. Moreover, instead of suggesting the next Point of Interest (PoI) to visit in a given situation, it provides a suggestion about a complete sequence of PoIs (tourist itinerary) that covers an entire day or vacation period. The proposed technique is based on the application of Deep Reinforcement Learning, where the tourist’s reward depends on the specific spatial and temporal context in which the itinerary has to be performed. The solution has been evaluated with a real-world dataset regarding the visits conducted by tourists in Verona (Italy) from 2014 to 2023 and compared with three baselines.

Traveling is part of many people leisure activities and an increasing fraction of the economy comes from the tourism. Given a destination, the information about the different attractions, or points of interest (POIs), can be found on many sources. Among these attractions, finding the ones that could be of interest for a specific user represents a challenging task. Travel recommendation systems deal with this type of problems. Most of the solution in the literature does not take into account the impact of the suggestions on the level of crowding of POIs. This paper considers the trip planning problem focusing on user balancing among the different POIs. To this aim, we consider the effects of the previous recommendations, as well as estimates based on historical data, while devising a new recommendation. The problem is formulated as a multi-objective optimization problem, and a recommendation engine has been designed and implemented for exploring the solution space in near real-time, through a distributed version of the Simulated Annealing approach. We test our solution using a real dataset of users visiting the POIs of a touristic city, and we show that we are able to provide high quality recommendations, yet maintaining the attractions not overcrowded.