Medical visualization often contain colors that highlight important information. When printing medical scoring boards in grayscale, the semantic information of colors is lost. Hence, visualizations relies on text-based information for the reader to comprehend images in grayscale. Common grayscale mapping methods do not preserve the special semantics of colors. In medical scoring boards, colors that are used are red for critical, yellow for warning, green for harmless, and blue for without any concerns. To overcome problem of information loss, we present PRECOSE, a method for the color to grayscale conversion for medical scoring boards, which enables to PREserve COlor SEmantics in a grayscale color space. With PRECOSE medical scoring boards do not longer have the need for textual features. They can be printed in grayscale without the loss of colors' semantics.

In Germany, mHealth applications can be prescribed to patients since December 19, 2019 [1]. A meaningful taxonomy for the selection of a suitable mHealth application is not available in current mobile application stores. Additionally, the stores lack reliable content and quality controls. An adequate approach to determine the quality of mHealth applications has been missing since then. Presently, these facts prevent physicians from determining which mHealth applications are suitable for prescribing them to their patients. Following that, the question arises how existing technical approaches and regulations can be conjoined to provide a quality assured and low-distortion approach for the evaluation of mHealth applications. In this paper, we present mHealthAtlas, a multidisciplinary expert-based approach for the evaluation of the quality of mHealth applications, based on [2]. First, mHealthAtlas addresses the problems of undifferentiated mHealth taxonomies and proposes a solution for the differentiated classification of mHealth applications. Second, a score is proposed for a comparable quality assessment of mHealth applications. Third, the mHealthAtlas system and its mode of operation is presented.

In veterinary medicine, as in human medicine, infections during a clinical visit are a critical risk factor that must be avoided. While the registration of infectious diseases in human medicine is already highly standardized today, there is still considerable need to catch up in veterinary medicine, especially in the area of hygiene. This paper presents a software system for the standardized registration, surveillance, and evaluation, including cause-of-effect research, of (nosocomial) infections in veterinary medicine, called VISKIT - Veterinary Infection Surveillance Kit.

Thousands of posts are generated constantly by millions of users in social media, with an increasing portion of this content being geotagged. Keeping track of the whole stream of this spatio-textual content can easily become overwhelming for the user. In this paper, we address the problem of selecting a small, representative and diversified subset of posts, which is continuously updated over a sliding window. Each such subset can be considered as a concise summary of the stream's contents within the respective time interval, being dynamically updated every time the window slides to reflect newly arrived and expired posts. We define the criteria for selecting the contents of each summary, and we present several alternative strategies for summary construction and maintenance that provide different trade-offs between information quality and performance. Furthermore, we optimize the performance of our methods by partitioning the newly arriving posts spatio-textually and computing bounds for the coverage and diversity of the posts in each partition. The proposed methods are evaluated experimentally using real-world datasets containing geotagged tweets and photos.

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An increasing amount of user-generated content on the Web is geotagged. This often results in the formation of user trails, e.g., sequences of photos, check-ins, or text messages, that users generate while visiting various locations. In this paper, we introduce and study the problem of identifying sets of locations that are strongly associated under social and textual criteria. We say that a location set is associated with a set of keywords if there exists a user with posts around these locations whose textual descriptions cover all keywords. We measure the strength of this association by the number of users with posts that support it. Although the problem reminisces frequent itemset mining, we show that our support measure does not satisfy the necessary anti-monotonicity property, which is used to effectively prune the search space. Nonetheless, by studying the characteristics of the support measure, we are able to devise an efficient approach. We present a basic and two optimized algorithms, exploiting an inverted or a spatio-textual index to increase efficiency. Finally, we conduct an experimental evaluation using geotagged Flickr photos in three major cities. From a qualitative perspective, the results indicate that the introduced type of query returns meaningful and interesting location sets, which are not discovered by other existing approaches. Furthermore, the proposed optimizations and the use of appropriate indexes significantly reduce computation time.

In this paper, we address the problem of continuously maintaining a concise, diversified summary of the contents of a sliding window over a stream of geotagged posts. Selecting posts to include in the summary takes into account both the criteria of coverage and diversity, and the summary is updated dynamically when the window slides. Our proposed strategy provides a trade-off between information quality and performance. An experimental evaluation of our method is presented using two real-world datasets containing spatio-textual posts from Twitter and Flickr.

Large amounts of user-generated content are posted daily on the Web, including textual, spatial and temporal information. Exploiting this content to detect, analyze and monitor events and topics that have a potentially large span in space and time requires efficient retrieval and ranking based on criteria including all three dimensions. In this paper, we introduce a novel type of spatial-temporal-keyword query that combines keyword search with the task of maximizing the spatio-temporal coverage and diversity of the returned top-f results. We first describe a baseline algorithm based on related search results diversification problems. Then, we develop an efficient approach which exploits a hybrid spatial-temporal-keyword index to drastically reduce query execution time. To that end, we extend two state-of-the- art indices for top-f spatio-textual queries and describe how our proposed approach can be applied on top of them. We evaluate the efficiency of our algorithms by conducting experiments on two large, real-world datasets containing geotagged tweets and photos.

Many applications involve queries that combine spatial, temporal and textual filters. In this paper, we address the problem of efficient evaluation of queries that perform spatial, temporal and keyword-based filtering on historical movement data of objects which are additionally associated with textual information in the form of keywords. Our work combines and builds upon concepts and techniques for spatio-temporal and spatio-textual queries, proposing two hybrid indexes for this purpose. An experimental evaluation of the proposed approaches is presented, using real-world datasets from two different types of sources.

Extreme weather conditions often cause considerable damage. Much of this damage could be mitigated if people were better prepared for disasters to come. Therefore, public authorities and private enterprises have been investing in Early Warning Systems (EWS) in the past few years. The EWS SAFE (Sensor-Actuator based Early warning system for extreme weather conditions) provides an integrated platform which combines (i) distributed weather sensor networks, (ii) efficient location-specific weather prognosis modules, and (iii) an alerting system. The alerting system is used to supply the general public and emergency services with personalized, situation-dependent information on upcoming extreme weather conditions. Furthermore, SAFE is able to steer remote-controlled actuators that induce automated counter measures, such as disconnecting sensitive electronic equipment from the power grid. This paper describes the deployment of SAFE by an industrial consortium in the town of Mering in Germany, with focus on its performance when alerting the public during a severe thunderstorm.

The essential elements of any navigation system are a shortest-path algorithm and accurate map data. The contribution of this work is two-fold. First, the HBA* algorithm, an efficient shortest-path algorithm is presented that mimics human driving behavior by exploiting road network hierarchies. Second, in a thorough performance study dynamic travel times are introduced to replace the unreliable static speed types currently used in connection with road network datasets.

In many application fields such as disaster management or personal planning, efficient mobile services should capture users' context in order to provide them with the most appropriate information at a given time. Situation-based services aim towards this direction. They go beyond location-based services as they take a user's current situation into account. Situations include user profiles together with dimensions such as their location or current activity. Depending on the application but also within the same application the time interval of interest can be of various granularities. It is crucial to handle time granularity efficiently and in a well-defined manner. In this paper, we investigate the handling of various granularities for situations and situation sequences through coarsening methods and we propose an algorithm that we used in concrete projects.

In applications that need meteorological forecast data, for instance for planning purposes (such as construction or environmental applications), each weather service is currently an effort-intensive result of domain-dependent programming. Instead, such applications need weather information platforms that would combine new forecast technologies and new information and communication technologies in a flexible manner. In this context, we introduced SITUMET, a platform for situation-based meteorological services. The core of the platform is based on (i) open sensor integration, (ii) a flexible forecast module, and (iii) situation-based service generation. In this paper we focus on the later, which is based on the notion of users' context that remains constant during a time interval. We introduce a situation-centric approach to dynamic weather service provision in push and pull mode. It is based on a common model for current and planned activity sequences and for weather information. We illustrate our approach with a particular up-and-running mobile application defined in the SITUMET framework.

Applications deployed in mobile peer-to-peer systems have to cope with mobility and fluctuation of nodes. These dynamics restrict the reliability of communication links as well as the availability of certain nodes, services, and resources. Exploring the context of nodes can be used to anticipate these shortages. Context-awareness enables improvement of performance of systems and enables adaptation of applications in order to make their behavior more relevant with respect to their current situation. Preevaluation of context can be used to estimate future availability of crucial resources and reliability of communication links. Which information is considered to be relevant context information varies according to the application scenario. In this paper, we present a model for context information along with a mobile service that provides a local view of surrounding nodes in a Mobile Ad-hoc NETwork (MANET). The service is designed to enable efficient prediction of future stability of the vicinity of a node.

Routing plays an ever-important role in a society that relies heavily on individual means of transportation. Although efficient algorithmic solutions for navigation exist, an accurate and reliable weight database that forms the basis of an acceptable algorithmic solution is missing. This work defines algorithms and data management techniques that allow the derivation of dynamic weights from collected floating car data (FCD). Weights reflect the speed associated with a piece of road at a certain time. A collection of such historical data is used to capture trends in travel time behavior according to temporal variations. Based on large amounts of travel time data, edge-based weights in the form of time-varying characteristic travel times are derived. Since the available tracking data does not cover the entire road network, several methods are defined to compensate for the lack of data and to guarantee complete coverage. A dynamic weight database, the dynamic travel time map (DTTM) is defined and implemented as a spatio-temporal data warehouse to manage the characteristic travel times and to compute dynamic weights efficiently. An experimental evaluation establishes not only the efficiency of the proposed approach but also shows its applicability in a realistic context, using actual GPS vehicle tracking data for the road network of Athens, Greece

This work addresses the problem of efficient reactive management of topological predicates in MOD (Moving Objects Databases) settings. Detecting the satisfiability of such predicates in mobile and dynamic environments requires management of continuous and persistent conditions. We introduce two dynamical topological predicates: moving-along and moving-towards and we present efficient algorithmic solutions for their processing. Based on this, we subsequently take a deeper insight in the behavioral aspects of a MOD which manages them and we argue that the traditional ECA (Event Condition Action) paradigm, while it may ensure correct behavior, is not well-suited for enabling the users to declaratively specify some of the parameters that may affect the efficiency aspect of the reactive behavior. Towards this end, we introduce the (ECA)2 (Evolving and Context-Aware Event-Condition-Action) paradigm as a tool for specification of the triggers used by a MOD that handles requests which span over a time-interval in dynamic environments.

Event notification services are used in various applications such as digital libraries, stock tickers, traffic control, or facility management. However to our knowledge, a common semantics of events in event notification services has not been defined so far. We propose a parameterized event algebra which describes the semantics of composite events for event notification systems. The parameters serve as a basis for flexible handling of duplicates in both primitive and composite events.

We study the representation and manipulation of geospatial information in a database management system (DBMS). The geospatial data model that we use as a basis hinges on a complex object model, whose set and tuple constructors make it efficient for defining not only collections of geographic objects but also relationships among these objects. In addition, it allows easy manipulation of nonbasic types, such as spatial data types. We investigate the mapping of our reference model onto major commercial DBMS models, namely a relational model extended to abstract data types (ADT) and an object-oriented model. Our analysis shows the strengths and limits of the two model types for handling highly structured data with spatial components.

With the advent of distributed computing and the increasing trend towards the reuse of geographical data, a new generation of geographical information systems (GIS) is currently being specified. The key characteristics of these interoperable GIS are modularity and extensibility, and they are composed of existing software systems such as database management systems, traditional GIS, statistics packages and simulation models. (We use the terms interoperable GIS and open GIS interchangeably, however Open GIS is a trademark of the Open GIS Consortium (OGC)). They can be defined in terms of generic frameworks, which facilitates both information exchange between participating systems and the addition of new functionalities. Even though the idea of defining open GIS is not new, it is crucial that the steps necessary to design such a complex system be clearly decomposed. In this paper, we propose a layer decomposition for the design of an open GIS. Each layer corresponds to a different level of abstraction, starting with the application or user level down to the invocation of systems services. In addition, each such level can be specified by the same set of concepts: operation, data and session (ODS concepts). Orthogonally, the metadata needed for the interaction between levels is indispensable to achieve openness. The declarative style of specifying both user interaction and implementation on different levels of abstraction facilitates the incorporation of new software components. Therefore, the framework introduced in this paper supports the design and the evolution of interoperable GIS.