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.

Access control is an important component of any database management system. Several access control models have been proposed for conventional databases. However, these models do not seem adequate for geographical databases, due to the peculiarities of geographical data. Previous work on access control models for geographical data mainly concerns raster maps (images). In this paper, we present a discretionary access control model for geographical maps. We assume that each map is composed of a set of features. Each feature is represented in one or more maps by spatial objects, described by means of different spatial properties: geometric properties, describing the shape, extension and location of the objects, and topological properties, describing the topological relationships existing among objects. The proposed access control model allows the security administrator to define authorizations against map objects at a very fine granularity level, taking into account the various spatial representations and the object dimension. The model also supports both positive and negative authorizations as well as different propagation rules that make access control very flexible.

This demonstration presents a web-based generator for spatial data. This generator allows users to choose from a wide range of spatial data distributions and configure the cardinality of the data and the distribution parameters. It then provides three functionalities. First, it provides a visualization of how the data will look like. Second, it allows users to download this data in several standard formats including CSV and GeoJSON. Third, it provides a permalink that users can bookmark or share with their team members to reproduce the same dataset later. This service is a step towards standardized benchmarking for spatial data systems.

This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the development of big spatial data frameworks. These systems need to partition the data across machines to be able to scale out the computation. Unfortunately, there is no current method to automatically choose an appropriate partitioning technique based on the input data distribution. This article addresses this problem by using deep learning to train a model that captures the relationship between the data distribution and the quality of the partitioning techniques. We propose a solution that runs in two phases, training and application. The offline training phase generates synthetic data based on diverse distributions, partitions them using six different partitioning techniques, and measures their quality using four quality metrics. At the same time, it summarizes the datasets using a histogram and well-designed skewness measures. The data summaries and the quality metrics are then use to train a deep learning model. The second phase uses this model to predict the best partitioning technique given a new dataset that needs to be partitioned. We run an extensive experimental evaluation on big spatial data, and we experimentally show the applicability of the proposed technique. We show that the proposed model outperforms the baseline method in terms of accuracy for choosing the best partitioning technique by only analyzing the summary of the datasets.

Several application contexts require the ability to use together and compare different geographic datasets (maps) concerning the same or overlapping areas. This is for example the case of mediator systems, integrating distinct data sources for query processing, and GISs dealing with multi-resolution maps. In both cases, distinct maps may represent the same geographic feature with different geometry type (a road can be a region in one map and a line in another one). An important issue is therefore determining whether two multi-resolution maps are consistent, i.e., they represent the same area without contradictions, and, if not, if they are at least similar. In this paper we consider consistency and similarity of multi-resolution maps with respect to topological information. Existing approaches do not take feature geometry type into account. In this paper, we extend them with two notions of topological consistency, the first requiring the same topological relation between pairs of common features, the second `relaxing' the first one by considering similarity between topological relations. A similarity function for multi-resolution maps is then provided, taking into account both feature geometry types and topological relations of map objects. We finally discuss how the proposed consistency and similarity concepts can be significantly used in GIS applications. Some experimental results are also reported to show the effectiveness of the proposed approach.