Context-Aware Service Selection with Uncertain Context Information

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. These are services whose description is enriched with context information related to the service execution environment and adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems context information is naturally dynamic, uncertain and incomplete, which represents an important issue when comparing service description and user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In order to handle uncertain and incomplete context information, we propose a mechanism inspired by graph-comparison for matching contextual service descriptions using similarity measures that allow inexact matching. Service description and requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole. We show how the proposed mechanism is integrated in MUSIC, an existing adaptation middleware, and how it enables more optimal adaptation decision making

Decision Support for the Usage of Mobile Information Service: A Context-Aware Service Selection Approach that Considers the Effects of Context Interdependencies

In mobile business, context information is utilised to select services mostly tailored to a user’s current situation and preferences. In existing context-aware service selection approaches, a service utility is determined by comparing its non-functional properties with current context information but without considering its integration in a service composition. This may cause suboptimal selection results, as context information and thus the determined utility of a certain service are usually dependent on its preceding and succeeding services. The latter we denote as context interdependencies. In this paper, we investigate how the effects of context interdependencies can be modelled for the context-aware service selection at planning time (i.e. before starting to accomplish a service composition). To develop this approach, we use the concept of states to model context information for the selection. In our evaluation, we find that our approach leads to superior results compared to current context-aware service selection approaches

Quality-of-Service-Aware Service Selection in Mobile Environments

The last decade is characterized by the rise of mobile technologies (UMTS, LTE, WLAN, Bluetooth, SMS, etc.) and devices (notebooks, tablets, mobile phones, smart watches, etc.). In this rise, mobiles phones have played a crucial role because they paved the way for mobile pervasion among the public. In addition, this development has also led to a rapid growth of the mobile service/application market (Statista 2017b). As a consequence, users of mobile devices nowadays find themselves in a mobile environment, with (almost) unlimited access to information and services from anywhere through the Internet, and can connect to other people at any time (cf. Deng et al. 2016; Newman 2015). Additionally, modern mobile devices offer the opportunity to select the services or information that best fit to a user’s current context. In this regard, mobile information services support users in retrieving context and non-context information, such as about the current traffic situation, public transport options, and flight connections, as well as about real-world entities, such as sights, museums, and restaurants (cf. Deng et al. 2016; Heinrich and Lewerenz 2015; Ventola 2014). An example of the application of mobile information services is several users planning a joint city day trip. Here, the users could utilize information retrieved about real-world entities for their planning. Such a trip constitutes a process with multiple participating users and may encompass actions such as visiting a museum and having lunch. For each action, mobile information services (e.g., Yelp, TripAdvisor, Google Places) can help locate available alternatives that differ only in attributes such as price, average length of stay (i.e., duration), or recommendations published by previous visitors. In addition, context information (e.g., business hours, distance) can be used to more effectively support the users in their decisions. Moreover, because multiple users are participating in the same trip, some users want to or must conduct certain actions together. However, decision-makers (e.g., mobile users) attempting to determine the optimal solution for such processes – meaning the best alternative for each action and each participating user – are confronted with several challenges, as shown by means of the city trip example: First, each user most likely has his or her own preferences and requirements regarding attributes such as price and duration, which all must be considered. Furthermore, for each action of the day trip, a huge number of alternatives probably exist. Thus, users might face difficulties selecting the optimal alternatives because of an information overload problem (Zhang et al. 2009). Second, taking multiple users into account may require the coordination of their actions because of potential dependencies among different users’ tours, which, for example, is the case when users prefer to conduct certain actions together. This turns the almost sophisticated decision problem at hand into a problem of high complexity. The problem complexity is increased further when considering context information, because this causes dependencies among different actions of a user that must be taken into account. For instance, the distance to cover by a user to reach a certain restaurant depends on the location of the previously visited museum. In conclusion, it might be impossible for a user to determine an optimal city trip tour for all users, making decision support by an information system necessary. Because the available alternatives for each action of the process can be denoted as (information) service objects (cf. Dannewitz et al. 2008; Heinrich and Lewerenz 2015; Hinkelmann et al. 2013), the decision problem at hand is a Quality-of-Service (QoS)-aware service selection problem. This thesis proposes novel concepts and optimization approaches for QoS-aware service selection regarding processes with multiple users and context information, focusing on scenarios in mobile environments. In this respect, the developed multi user context-aware service selection approaches are able to deal with dependencies among different users’ service compositions, which result from the consideration of multiple users, as well as dependencies within a user’s service composition, which result from the consideration of context information. Consequently, these approaches provide suitable support for decision-makers, such as mobile users

Context-aware service selection with uncertain context information

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. These are services whose description is enriched with context information related to the service execution environment and adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems context information is naturally dynamic, uncertain and incomplete, which represents an important issue when comparing service description and user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In order to handle uncertain and incomplete context information, we propose a mechanism inspired by graph-comparison for matching contextual service descriptions using similarity measures that allow inexact matching. Service description and requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole. We show how the proposed mechanism is integrated in MUSIC, an existing adaptation middleware, and how it enables more optimal adaptation decision making.status: publishe