Shared control is a form of video gaming accessibility support that allows players with disabilities to delegate inaccessible controls to another person. Through interviews involving 14 individuals with lived experience of accessible gaming in shared control, we explore the ways in which shared control technologies are adopted in practice, the accessibility challenges they address, and how the support currently provided in shared control can be automated to remove the need for a human assistant. Findings indicate that shared control is essential for enabling access to otherwise inaccessible games, but its reliance on human support is a key limitation. Participants welcomed the idea of automating the support with software agents, while also identifying limitations and design requirements. Accordingly, this work contributes insights into current practices and proposes guidelines for developing automated support systems.

The remarkable progress in deep learning (DL) show-cases outstanding results in various computer vision tasks. However, adaptation to real-time variations in data distributions remains an important challenge. Test- Time Training (TTT) was proposed as an effective solution to this issue, which increases the generalization ability of trained models by adding an auxiliary task at train time and then using its loss at test time to adapt the model. Inspired by the recent achievements of contrastive representation learning in unsupervised tasks, we propose ReC-TTT, a test-time training technique that can adapt a DL model to new un-seen domains by generating discriminative views of the input data. ReC- Ttt uses cross-reconstruction as an auxiliary task between a frozen encoder and two trainable en-coders, taking advantage of a single shared decoder. This enables, at test time, to adapt the encoders to extract features that will be correctly reconstructed by the decoder that, in this phase, is frozen on the source domain. Experimental results show that ReC- Ttt achieves better re-sults than other state-of-the-art techniques in most domain shift classification challenges. The code is available at: https://github.com/warpcut/ReC-TTT

In this paper we explore the use of Augmented Reality as a means to provide more widespread and equitable access to art venues and artworks, in particular for people with low vision. We investigate how people with low vision frame, access and interact with artworks in Augmented Reality, using a mobile app specifically designed for accessible and inclusive museum visits. Through a user study with 10 participants in real museum settings, we explore the specific challenges related to the accessibility of different artwork types: a medium size painting, a large tapestry, a statue, and a historical keyboard instrument. Results show that participants were able to access all artworks through an Augmented Reality mobile app running on the user’s own device. The system is also perceived as useful and usable. Additionally, we uncover human and environment factors that influence the way users access different artwork types, in particular considering Augmented Reality interactions.

This paper describes the development of PRACTICE, a distributed healthcare technological platform that supports various research initiatives by the University of Milan and the Angelo Bianchi Bonomi Hemophilia and Thrombosis Center, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico. PRACTICE includes three main components: a mobile app that patients can use to self-acquire ultrasound images at home, a computer-aided diagnosis web application that supports the practitioners through a set of machine learning models, and a set of web tools for image annotation, a prerequisite for training the machine learning models. Although PRACTICE was designed in the specific context of supporting the detection of joint recess blood effusions in hemophilic patients, this paper describes the main design and implementation challenges that apply to other applications of a research-oriented health platform.

Navigation assistance services for people with visual impairment pose the challenge of providing accurate guidance through non-visual navigation instructions. This paper proposes two techniques to guide the user during navigation assistance, and in particular during turns and straight paths. Both techniques adopt a combination of speech and non-speech audio with the aim of providing continuous, accurate, and unobtrusive guidance. The two solutions differ in the sonification technique used to generate the non-speech audio. The techniques were evaluated in a real-world environment with nine participants having severe visual impairment. Experimental results show that one of the proposed techniques is significantly more effective than a baseline approach adopted in existing solutions, in terms of navigation accuracy during turns and straight paths. Thus, it is a practical and effective solution to the problem of non-visual navigation assistance for people with visual impairment.

People with blindness or severe low vision access mobile devices using screen readers. However, noisy environments can impair screen reader intelligibility. During mobility, this could disorient or even endanger the user. To address this issue, we propose three screen reader speech compensation techniques based on environmental noise: speech rate slowing, adaptive volume increase, and adaptive equalization. Through a study with 12 participants in three simulated noise scenarios, we evaluate screen reader intelligibility and the perceived distraction from the soundscape, with and without compensations. Four of the proposed compensations, in particular those that pair speech rate reduction with volume or equalization adaptation, significantly improve screen reader's speech intelligibility in all the considered scenarios, and the compensations do not have a significant impact on the distraction from the soundscape.

Play Access is an Android assistive technology that replaces touchscreen interaction with alternative interfaces, enabling people with upper extremity impairments to access mobile games, and providing alternative means of playing mobile games for all. We demonstrate the use of Play Access to support physical therapy for children with haemophilia, with the goal of preventing long-term mobility impairments. To achieve this, we modified Play Access to enable the use of body movements, recognized using wearable sensors, as an alternative interface for playing games. This way, Play Access makes it possible to use existing Android games as exergames, hence better targeting patients’ interest.

Certified orientation and mobility specialists (COMS) work with clients who are blind or visually impaired (BVI) to help them travel independently with confidence. Part of this process involves creating a narrative description of a route and using specific techniques to help the client internalize it. We focus on the problem of automatically generating a narrative description of an indoor route based on a recording from a smartphone. These automatically generated narrations could be used in cases where a COMS is not available or to enable clients to independently practice routes that were originally learned with the help of a COMS. Specifically, we introduce Clew3D, a mobile app that leverages LIDAR-equipped iOS devices to identify orientation and mobility (O&M) landmarks and their relative location along a recorded route. The identified landmarks are then used to provide a spoken narration modeled after traditional O&M techniques. Our solution is co-designed with COMS and uses methods and language that they employ when creating route narrations for their clients. In addition to presenting Clew3D, we report the results of an analysis conducted with COMS regarding techniques and terminology used in traditional, in-person O&M instruction. We also discuss challenges posed by vision-based systems to achieve automatic narrations that are reliable. Finally, we provide an example of an automatically generated route description and compare it with the same route provided by a COMS.

We illustrate our experience, gained over years of involvement in multiple research and commercial projects, in developing accessible mobile apps with cross-platform development frameworks (CPDF). These frameworks allow the developers to write the app code only once and run it on both iOS and Android. However, they have limited support for accessibility features, in particular for what concerns the interaction with the system screen reader.

We present RePlay, an interaction substitution method designed to support people with upper extremity motor impairments while interacting with mobile apps. RePlay overcomes key limitations of existing approaches, allowing users to quickly access all app interface elements, even those not visible to the OS (a common situation especially with games). This is achieved through personalized mapping of interface elements to alternative inputs, such as external switches or non-verbal vocal sounds, hence adapting to users with diverse abilities. RePlay was implemented as an Android Accessibility Service, running without any changes at OS level.

This paper investigates how touchscreen exploration and verbal feedback can be used to support blind people to access visual artwork. We present two artwork exploration modalities. The first one, attribute-based exploration, extends prior work on touchscreen image accessibility, and provides fine-grained segmentation of artwork visual elements; when the user touches an element, the associated attributes are read. The second one, hierarchical exploration, is designed with domain experts and provides multi-level segmentation of the artwork; the user initially accesses a general description of the entire artwork and then explores a coarse segmentation of the visual elements with the corresponding high-level descriptions; once selected, coarse segments are subdivided into fine-grained ones, which the user can access for more detailed descriptions.

This paper reports our ongoing effort in the development of the ROSSINI system, which looks to address emergency situations in industrial plants. The user interaction design of ROSSINI described in this paper takes into account the fact that the user can be subject to situational impairment (e.g., limited sight due to smoke in the environment). As such, it is envisioned that existing solutions designed for people with disabilities can be adopted and extended for this purpose.

We demonstrate SoundLines, a mobile app designed to support children with visual impairments in exercising spatial exploration skills. This is achieved through multi-touch discovery of line segments on touchscreen, supported by sonification feedback. The approach is implemented as a game in which the child needs to guide a kitten to find its mother cat by following with a finger the line connecting them.

People with visual impairments or blindness (VIB) encounter difficulties in exploring graphical representations that are widely used for the study of STEM subjects. In particular, graphs are used to represent many different scientific notations: flowcharts, automata, cognitive maps, and more. Among these, structural chemical formulae are characterized by a complex, often 3-dimensional structure, which makes them hard to access and author with traditional assistive tools.

Mobile devices are accessible to people with visual impairments and hence they are convenient platforms to support assistive technologies. Indeed, in the last years many scientific contributions proposed assistive applications for mobile devices. However, few of these solutions were eventually delivered to end-users, depriving people with disabilities of important assistive tools. The underlying problem is that a number of challenges need to be faced for transitioning assistive mobile applications from research to market.

We present WordMelodies, an inclusive, cross platform, mobile app that supports children with visual impairments in the acquisition of basic literacy skills through 8 different exercises. WordMelodies has been designed and evaluated by three domain experts in assistive technologies and education for children with visual impairments. After three design and evaluation iterations the app is fully accessible, except for one limitation of the cross platform development toolkit used.

Independent mobility is one of the main challenges for blind or visually impaired (BVI) people. In particular, BVI people often need to identify and avoid nearby obstacles, for example a bicycle parked on the sidewalk. This is generally achieved with a combination of residual vision, hearing and haptic sensing using the white cane. However, in many cases, BVI people can only perceive obstacles at short distance (typically about 1m, i.e., the white cane detection range), in other situations obstacles are hard to detect (e.g., those elevated from the ground), while others should not be hit by the white cane (e.g., a standing person). Thus, some time and effort are required to identify the object in order to understand how to avoid it. A solution to these problems can be found in recent computer vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them to a BVI user. This contribution presents WatchOut, a sonification technique for conveying real-time information about the main characteristics of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centric approach, involving two iterations of online questionnaires with BVI participants in order to define, improve and evaluate the sonification technique. WatchOut was implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable, and can guide the users to avoid more than 85% of the obstacles.

Indoor navigation services for people with visual impairment are being researched in academia, and working systems have already been deployed in public places. While previous research mainly focuses on computing the user's position with high accuracy, providing non-visual navigation instructions is also a challenge and naive approaches can fail in helping users reach their target destination or even expose them to hazards.In this paper we investigate the problem of guiding users to rotate towards a target direction. We propose three different sonification techniques that provide continuous guidance during rotation, and we compare them to a single-impulse baseline, used in previous works. We also explore three variations that reinforce the proposed techniques by combining them with the baseline. A preliminary study with 10 blind participants highlights two dominant techniques, which we analyze through a follow-up study with 18 participants, from 2 groups with very distant cultural backgrounds. While stark differences emerge in the performance from the two groups, we highlight two clear results common to both: 1) one of the proposed techniques significantly outperforms the baseline, reducing the average rotation error by a factor of 3.5 (from 11° to 3°); 2) the interaction speed of this technique, generally slower than the baseline, significantly improves when combined with the baseline technique.

Providing city navigation instructions to people with motion disabilities requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. Our preliminary study on wheelchair users shows that an automatic crowdsourcing mechanism is needed, avoiding users' involvement.In this contribution we present a solution to crowdsource urban features from inertial sensors installed on a wheelchair. Activity recognition techniques based on decision trees are used to process the sensors data stream. Experimental results, conducted with data acquired from 10 real wheelchair users navigating in an outdoor environment show that our solution is effective in detecting urban features with precision around 0.9, while it is less reliable when classifying some fine-grained urban feature characteristics, like a step height. The experimental results also present our investigation aimed at identifying the best parameters for the given problem, which include number, position and type of inertial sensors, classifier type, segmentation parameters, etc.

Indoor navigation is an important service, currently investigated both in industry and academia. While the main focus of research is the computation of users' position, the additional challenge of conveying guidance instructions arises when the target user is blind or visually impaired (BVI). This contribution presents our ongoing research aimed at adopting sonification techniques to guide a BVI person. In particular we introduce three sonification techniques to guide the user during rotations. Preliminary results, conducted with 7 BVI people, show that some of the proposed sonification technique outperform a benchmark solution adopted in previous contributions.

Navigation assistive technologies aim to improve the mobility of blind or visually impaired people. In particular, turn-by-turn navigation assistants provide sequential instructions to enable autonomous guidance towards a destination. A problem frequently addressed in the literature is to obtain accurate position and orientation of the user during such guidance. An orthogonal challenge, often overlooked in the literature, is how precisely navigation instructions are followed by users. In particular, imprecisions in following rotation instructions lead to rotation errors that can significantly affect navigation. Indeed, a relatively small error during a turn is amplified by the following frontal movement and can lead the user towards incorrect or dangerous paths. In this contribution, we study rotation errors and their effect on turn-by-turn guidance for individuals with visual impairments. We analyze a dataset of indoor trajectories of 11 blind participants guided along three routes through a multi-story shopping mall using NavCog, a turn-by-turn smartphone navigation assistant. We find that participants extend rotations by 17º on average. The error is not proportional to the expected rotation; instead, it is accentuated for "slight turns" (22.5º-60º), while "ample turns" (60º-120º) are consistently approximated to 90º. We generalize our findings as design considerations for engineering navigation assistance in real-world scenarios.

Cross-platform developing techniques have been attracting lot of attention in the last years, especially in the field of mobile application, because they enable the developers to code apps in a same programming language for different platforms (e.g. iOS and Android). One well-known framework for cross-platform development is React Native that presents some features to support accessibility to blind or visually impaired (BVI) people. However, to the best of our knowledge, the accessibility of applications developed with this framework has not been systematically investigated. In this contribution we report our experience in the development of MathMelodies 2, an application that supports BVI children to study mathematics. The former version of MathMelodies was developed with native code for iPad only, while MathMelodies 2 was developed with React Native to run on both iOS and Android smartphones and tablets.

People with severe visual impairments usually have no way of identifying the colors of objects in their environment. While existing smartphone apps can recognize colors and speak them aloud, they require the user to center the object of interest in the camera's field of view, which is challenging for many users. We developed a smartphone app to address this problem that reads aloud the color of the object pointed to by the user's fingertip, without confusion from background colors. We evaluated the app with nine people who are blind, demonstrating the app's effectiveness and suggesting directions for improvements in the future.

In the field of assistive technology, large scale user studies are hindered by the fact that potential participants are geographically sparse and longitudinal studies are often time consuming. In this contribution, we rely on remote usage data to perform large scale and long duration behavior analysis on users of iMove, a mobile app that supports the orientation of people with visual impairments.

in urban settings. Knowing the location of crosswalks is critical for a blind person planning a trip that includes street crossing. By augmenting existing spatial databases (such as Google Maps or OpenStreetMap) with this information, a blind traveler may make more informed routing decisions, resulting in greater safety during independent travel. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm could also be complemented by a final crowdsourcing validation stage for increased accuracy.

There are several situations in which a person with visual impairment or blindness needs to extract information from an image. Examples include everyday activities, like reading a map, as well as educational activities, like exercises to develop visuospatial skills. In this contribution we propose a set of 6 sonification techniques to recognize simple shapes on touchscreen devices. The effectiveness of these sonification techniques is evaluated though Invisible Puzzle, a mobile application that makes it possible to conduct non-supervised evaluation sessions. Invisible Puzzle adopts a gamification approach and is a preliminary step in the development of a complete game that will make it possible to conduct a large scale evaluation with hundreds or thousands of blind users. With Invisible Puzzle we conducted 131 tests with sighted subjects and 18 tests with subjects with blindness. All subjects involved in the process successfully completed the evaluation session, with high engagement, hence showing the effectiveness of the evaluation procedure. Results give interesting insights on the differences among the sonification techniques and, most importantly, show that, after a short training, subjects are able to identify many different shapes.

Recent contributions proposed sonification techniques to allow people with visual impairment or blindness to extract information from images on touchscreen devices. In this contribution we introduce Invisible Puzzle Game, an application that is aimed at performing an instrumented remote evaluation of these sonification techniques. The aim is to reach a wide audience of both sighted and visually impaired users and to engage them, thanks to game elements, in playing over time, so that it is possible to evaluate how the performance of each sonification technique is affected by practice.

Autonomous mobility is a challenge for visually impaired people. In the last years, a number of solutions have been proposed in the scientific literature to support visually impaired people during road crossing. In our previous work we presented ZebraLocalizer a mobile application that detects zebra crossings and guides the user to safely cross. In this paper we present the ZebraRecognizer algorithm that improves the detection module of our solution and that applies innovative solutions in the field of zebra crossings recognition. The major contribution resides in the fact that ZebraRecognizer rectifies the ground plane hence removing the projection distortion of the extracted features. This leads to two major advantages: first, it possible to compute the relative distance between the user and the zebra crossing in meters. Second, the grouping and validation criteria specifically designed for the rectified line segments are much more effective, hence improving the accuracy of the recognition. An additional contribution consists in a significantly improved computation time. Indeed, ZebraRecognizer is 3 time faster than our previous solution, thanks to the adoption of a personalized version of the EDLines algorithm to detect the line segments.

Many solutions proposed in the literature to enforce privacy in presence of location information use, implicitly or explicitly, spatial granularities. However, most of the contributions do not describe the formal and computational properties of this tool in details. In this paper we propose three families of spatial granularities, specifically designed for privacy-aware systems. We mathematically characterize them and prove that all of them have two important formal properties. Then, for each of them, we show how to efficiently compute two fundamental operations.

Privacy protection in the deployment of location based services is a hot topic both in CS research and in the development of mobile applications. In this paper we consider a location based service that currently has hundreds of millions of users and we show how we developed a software that is able to discover their exact positions, by only using information publicly disclosed by the service. Our software does not exploit a specific limitation of the considered service. Rather this contribution shows that there is an entire class of services that is subject to the attack we present.

The aim of proximity services is to raise alerts based on the distance between moving objects. While distance can be easily computed from the objects' geographical locations, privacy concerns in revealing these locations exist, especially when proximity among users is being computed. Distance preserving transformations have been proposed to solve this problem by enabling the service provider to acquire pairwise distances while not acquiring the actual objects positions. It is known that distance preserving transformations do not provide formal privacy guarantees in presence of certain background knowledge but it is still unclear which are the practical conditions that make distance preserving transformations “vulnerable”. We study these conditions by designing and testing an attack based on public density information and on partial knowledge of distances between users. A clustering-based technique first discovers the approximate position of users located in the largest cities. Then a technique based on trilateration reduces this approximation and discovers the approximate position of the other users. Our experimental results show that partial distance information, like the one exchanged in a friend-finder service, can be sufficient to locate up to 60% of the users in an area smaller than a city.

Proximity services alert users about the presence of other users or moving objects based on their distance. Distance preserving transformations are among the techniques that may be used to avoid revealing the actual position of users while still effectively providing these services. Some of the proposed transformations have been shown to actually guarantee location privacy with the assumption that users are uniformly distributed in the considered geographical region, which is unrealistic assumption when the region extends to a county, a state or a country.

Smartphones provide new exciting opportunities to visually impaired users because these devices can support new assistive technologies that cannot be deployed on desktops or laptops. Some devices, like the iPhone, are rapidly gaining popularity among the visually impaired since the use of pre-installed screenreader applications renders these devices accessible. However, there are still some operations that require a longer time or higher mental workload to be completed by a visually impaired user. In this contribution we present a novel application for text entry, called TypeInBraille, that is based on the Braille code and hence is specifically designed for blind users.

Independent mobility in unfamiliar environments is a significant challenge for people with severe vision impairment. Among other problems, one specific issue concerns the identification of those road signs that can be recognized by sight only. In this paper we present ZebraLocalizer, an application for mobile devices that identifies zebra crossings and guides the user towards them. Two main problems are discussed in this contribution: the identification and localization of the crosswalks, performed by processing data acquired both from the camera and the accelerometers, and the design of an interaction paradigm specifically addressed to blind users. Experimental results, conducted both on a dataset of images and with blind users, validate the applicability of the proposed solution.

Online social networks often involve very large numbers of users who share very large volumes of content. This content is increasingly being tagged with geo-spatial and temporal coordinates that may then be used in services. For example, a service may retrieve photos taken in a certain region. The resulting geo-aware social networks (GeoSNs) pose privacy threats beyond those found in location-based services. Content published in a GeoSN is often associated with references to multiple users, without the publisher being aware of the privacy preferences of those users. Moreover, this content is often accessible to multiple users. This renders it difficult for GeoSN users to control which information about them is available and to whom it is available. This paper addresses two privacy threats that occur in GeoSNs: location privacy and absence privacy. The former concerns the availability of information about the presence of users in specific locations at given times, while the latter concerns the availability of information about the absence of an individual from specific locations during given periods of time. The challenge addressed is that of supporting privacy while still enabling useful services. We believe this is the first paper to formalize these two notions of privacy and to propose techniques for enforcing them. The techniques offer privacy guarantees, and the paper reports on empirical performance studies of the techniques.

Proximity services are a particular class of location-based services (LBS) in which a subscriber is alerted when other participants (called buddies) are nearby. Existing works in the field of privacy preservation in LBS propose techniques specifically designed for this type of service. The objective of this demo is to present the Pcube system that makes it possible to visually show the different performances of these techniques in terms of privacy protection and precision of the service. The system includes a server component providing the proximity service, a web-based client application and a client application for mobile devices. Four different privacy-preserving techniques existing in literature have been implemented and will be compared during the demo, with a particular focus on the evaluation of the recently proposed Longitude protocol.

Proximity based services are location based services (LBS) in which the service adaptation depends on the comparison between a given threshold value and the distance between a user and other (possibly moving) entities. While privacy preservation in LBS has lately received much attention, very limited work has been done on privacy-aware proximity based services. This paper describes the main privacy threats that the usage of these services can lead to, and proposes original privacy preservation techniques offering different trade-offs between quality of service and privacy preservation. The properties of the proposed algorithms are formally proved, and an extensive experimental work illustrates the practicality of the approach.

One of the privacy threats recognized in the use of LBS is represented by an adversary having information about the presence of individuals in certain locations, and using this information together with an (anonymous) LBS request to re-identify the issuer of the request associating her to the requested service. Several papers have proposed techniques to prevent this, assuming that the use of the service is considered sensitive. In this paper we investigate the more general case in which the adversary is also able to recognize traces of LBS requests by the same anonymous user, so that the identification of the issuer of one request can lead to the disclosure of the same user being in other possibly sensitive locations at different times or using sensitive services.Using the notion of "historical k-anonymity", this paper provides the first formalization of this class of privacy threats. Through extensive experiments based on realistic simulations, and runs of an optimal algorithm, we show some negative results for the defenses based on spatial generalization against these attacks under very conservative assumptions. Under more realistic location knowledge assumptions, we propose two defense algorithms, based on a strategy of changing and reusing of pseudo-identifiers, whose correctness is formally proved. Our experiments show that, among all the proposed algorithms, the ProvidentHider algorithm is particularly effective in protecting privacy for reasonably long sequences of requests.

A general consensus is that the proliferation of location- aware devices will result in a diffusion of location-based services. Privacy preservation is a challenging research issue for this kind of service. A possible solution consists of ensuring users' anonymity, i.e., ensuring that the user issuing a request is indistinguishable, among a group of users, by any attacker who has access to the service requests. In this paper we propose a formal framework to model the problem of guaranteeing anonymity when requiring location-based services. The proposed framework extends existing approaches by allowing to model different kinds of knowledge that may be available to the attacker. We show application examples of our framework, modeling both known scenarios and new ones. From a practical point of view, the framework makes it possible to define anonymity-preserving techniques that best suite the system assumptions as derived from the applicative context, and the level of privacy protection defined by the user.

Spatial generalization has been recently proposed as a technique for the anonymization of requests in location based services. This paper presents the results of an extensive experimental study, considering known generalization algorithms as well as new ones proposed by the authors.

In the recent years several research efforts have focused on the concept of time granularity and its applications. A first stream of research investigated the mathematical models behind the notion of granularity and the algorithms to manage temporal data based on those models. A second stream of research investigated symbolic formalisms providing a set of algebraic operators to define granularities in a compact and compositional way. However, only very limited manipulation algorithms have been proposed to operate directly on the algebraic representation making it unsuitable to use the symbolic formalisms in applications that need manipulation of granularities. This paper aims at filling the gap between the results from these two streams of research, by providing an efficient conversion from the algebraic representation to the equivalent low-level representation based on the mathematical models. In addition, the conversion returns a minimal representation in terms of period length. Our results have a major practical impact: users can more easily define arbitrary granularities in terms of algebraic operators, and then access granularity reasoning and other services operating efficiently on the equivalent, minimal low-level representation. As an example, we illustrate the application to temporal constraint reasoning with multiple granularities. From a technical point of view, we propose an hybrid algorithm that interleaves the conversion of calendar subexpressions into periodical sets with the minimization of the period length. The algorithm returns set-based granularity representations having minimal period length, which is the most relevant parameter for the performance of the considered reasoning services. Extensive experimental work supports the techniques used in the algorithm, and shows the efficiency and effectiveness of the algorithm.

In this paper we extend the notion of k-anonymity in the context of databases with timestamped information in order to naturally define k-anonymous views of temporal data. We also investigate the problem of obtaining these views. We show that known generalization techniques, despite being applicable under certain conditions, have some limitations, and propose a new generalization algorithm based on the hierarchy of time granularities.

This paper addresses the technical problem of efficiently reducing the periodic representation of a time granularity to its minimal form. The minimization algorithm presented in the paper has an immediate practical application: it allows users to intuitively define granularities (and more generally, recurring events) with algebraic expressions that are then internally translated to mathematical characterizations in terms of minimal periodic sets. Minimality plays a crucial role, since the value of the recurring period has been shown to dominate the complexity when processing periodic sets.

An effort has been devoted in the recent years to study and formalize the concept of time granularity and to design applications and services using the formalization. Among other proposals, a calendar algebra has been defined to facilitate the specification of new granularities and to perform conversions among them. This paper shows how granularities defined as algebraic calendar expressions can be represented as periodical sets of instants. More precisely the paper shows how each algebraic operator changes the periodical structure of the granularities given as operands. These results have an immediate application enabling users to easily specify new granularities and using them in the only constraint solver supporting time granularities that is currently available.