Replacing private transport in large cities with public and shared alternatives is increasingly relevant to reduce congestion during rush hours and air pollution. The activation of customized bus services is one of the possible strategies toward that goal, and automatic bus route design is needed when the amount of pickup addresses to manage is large. The approaches described in the literature are not suitable for real-world applications because they tend to generate many more lines than necessary, or they don't work if addresses do not form clusters. In this paper, we propose a novel bus line generation approach suitable for any address database.

Abstract In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.

Summary The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data‐intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper, we focus on a specific data‐intensive problem concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extent of queries and objects is continuously modified over time. To tackle this problem and significantly accelerate query processing, we devise a hybrid CPU/GPU pipeline that compresses data output and saves query processing work. The devised system relies on an ad‐hoc spatial index leading to a problem decomposition that results in a set of independent data‐parallel tasks. The index is based on a point‐region quadtree space decomposition and allows to tackle effectively a broad range of spatial object distributions, even those very skewed. Also, to deal with the architectural peculiarities and limitations of the GPUs, we adopt non‐trivial GPU data structures that avoid the need of locked memory accesses while favouring coalesced memory accesses, thus enhancing the overall memory throughput. To the best of our knowledge, this is the first work that exploits GPUs to efficiently solve repeated range queries over massive sets of continuously moving objects, possibly characterized by highly skewed spatial distributions. In comparison with state‐of‐the‐art CPU‐based implementations, our method highlights significant speedups in the order of 10 − 20×, depending on the dataset. Copyright © 2016 John Wiley & Sons, Ltd.

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. In this paper we focus on a specific data-intensive problem concerning the repeated processing of huge amounts of k nearest neighbours (k-NN) queries over massive sets of moving objects, where the spatial extents of queries and the position of objects are continuously modified over time. In particular, we propose a novel hybrid CPU/GPU pipeline that significantly accelerate query processing thanks to a combination of ad-hoc data structures and non-trivial memory access patterns.

In this paper we investigate the use of GPUs to solve a data-intensive problem that involves huge amounts of moving objects. The scenario which we focus on regards objects that continuously move in a 2D space, where a large percentage of them also issues range queries. The processing of these queries entails a large quantity of objects falling into the range queries to be returned. In order to solve this problem by maintaining a suitable throughput, we partition the time into ticks, and defer the parallel processing of all the objects events (location updates and range queries) occurring in a given tick to the next tick, thus slightly delaying the overall computation. We process in parallel all the events of each tick by adopting an hybrid approach, based on the combined use of CPU and GPU, and show the suitability of the method by discussing performance results. The exploitation of a GPU allow us to achieve a speedup of more than 20× on several datasets with respect to the best sequential algorithm solving the same problem. More importantly, we show that the adoption of new bitmap-based intermediate data structure we propose to avoid memory access contention entails a 10× speedup with respect to naive GPU based solutions.

Location based queries are increasingly common in mobile applications, and the associated privacy issues have become a hot research topic in the last years. Most of the current approaches, however, do not account for the location of potentially sensitive places and for constraints on the movement of users, such as speed limits or network contraints. In this demo we present different deployment scenarios of a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume that the sensitivity of users' positions depends on the spatial context, while the users' movement is confined to road networks and places. Further, the users are non-anonymous, as in the case of geo-social network members who agree to share their exact position whenever it does not fall within a sensitive place, e.g. a hospital. We will show that our proposal is suitable for different classes of devices and can be integrated in different kind of location based applications.

This paper presents a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume a scenario in which the sensitivity of user's positions is space-varying, and so depends on the spatial context, while the user's movement is confined to road networks and places. Typical users are the non-anonymous members of a geo-social network who agree to share their exact position whenever such position does not fall within a sensitive place, e.g. a hospital. Suspending location sharing while the user is inside a sensitive place is not an appropriate solution because the user's stopovers can be easily inferred from the user's trace. In this paper we present an extension of the semantic location cloaking model [1] originally developed for the cloaking of non-correlated positions in an unconstrained space. We investigate different algorithms for the generation of cloaked regions over the graph representing the urban setting. We also integrate methods to prevent velocity-based linkage attacks. Finally we evaluate experimentally the algorithms using a real data set.

Frequent item set mining (FIM) algorithms extract subsets of items that occurs frequently in a collection of sets. FIM is a key analysis in several data mining applications, and the FIM tools are among the most computationally intensive data mining ones. In this work we present a many-core parallel version of a state-of-the-art FIM algorithm, DCI, whose sequential version resulted, for most of the tested datasets, better than FP-Growth, one of the most efficient algorithms for FIM. We propose a couple of parallelization strategies for Graphics Processing Units (GPU) suitable for different resource availability, and we present the results of several experiments conducted on real-world and synthetic datasets.

The protection of semantic positions, for instance that an individual is inside a hospital, is a challenging privacy issue. For example, it can be shown that popular protection mechanisms, like location cloaking, can be easily defeated when certain mobility patterns are known, e.g., certain places are more or less popular than other places. To prevent this kind of attack, novel semantic location cloaking heuristics are being investigated. These methods are built on the knowledge of population distribution and sensitive locations. In this demonstration, we present SAWL (Semantics-aware Location cloaking), a tool supporting the comparison of semantic location cloaking methods over real and synthetic spatial scenarios.

Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications, e.g., GoogleLatitude. Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, etc. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR.

The problem of private location-based queries has been intensively researched in recent years. Several location protection algorithms exist, most of which use some form of location cloaking. However, existing work focuses on the analysis of privacy and performance, and less on the user's perspective on location privacy. We developed a prototype of the PROBE system with an emphasis on visualization of the location cloaking process, which improves user experience and increases privacy awareness.

Emerging models for context-aware role-based access control pose challenging requirements over policy administration. In this paper we address the issues raised by the decentralized administration of a spatially-aware access control model in a mobile setting. We present GEO-RBAC Admin, the administration model for the GEO-RBAC model. The model is based on the notion of hierarchy of spatial domains; a spatial domain is an entity grouping objects based on organizational and spatial proximity criteria. In the paper we formally define the model and introduce and prove relevant properties.