411,204 research outputs found
Interoperating Context Discovery Mechanisms
Context-Aware applications adapt their behaviour to the current situation of the user. This information, for instance user location and user availability, is called context information. Context is delivered by distributed context sources that need to be discovered before they can be used to retrieve context. Currently, multiple context discovery mechanisms exist, exhibiting heterogeneous capabilities (e.g. communication mechanisms, and data formats), which can be available to context-aware applications at arbitrary moments during the ap-plication’s lifespan. In this paper, we discuss a middleware mechanism that en-ables a (mobile) context-aware application to interoperate transparently with different context discovery mechanisms available at run-time. The goal of the proposed mechanism is to hide the heterogeneity and availability of context discovery mechanisms for context-aware applications, thereby facilitating their development
Context Aware Middleware Architectures: Survey and Challenges
Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of
developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness,
context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected
middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data
Unstructured clinical texts contain rich health-related information. To
better utilize the knowledge buried in clinical texts, discovering synonyms for
a medical query term has become an important task. Recent automatic synonym
discovery methods leveraging raw text information have been developed. However,
to preserve patient privacy and security, it is usually quite difficult to get
access to large-scale raw clinical texts. In this paper, we study a new setting
named synonym discovery on privacy-aware clinical data (i.e., medical terms
extracted from the clinical texts and their aggregated co-occurrence counts,
without raw clinical texts). To solve the problem, we propose a new framework
SurfCon that leverages two important types of information in the privacy-aware
clinical data, i.e., the surface form information, and the global context
information for synonym discovery. In particular, the surface form module
enables us to detect synonyms that look similar while the global context module
plays a complementary role to discover synonyms that are semantically similar
but in different surface forms, and both allow us to deal with the OOV query
issue (i.e., when the query is not found in the given data). We conduct
extensive experiments and case studies on publicly available privacy-aware
clinical data, and show that SurfCon can outperform strong baseline methods by
large margins under various settings.Comment: KDD 2019 (Accepted for Oral Presentation at the Research track
Heuristics Miners for Streaming Event Data
More and more business activities are performed using information systems.
These systems produce such huge amounts of event data that existing systems are
unable to store and process them. Moreover, few processes are in steady-state
and due to changing circumstances processes evolve and systems need to adapt
continuously. Since conventional process discovery algorithms have been defined
for batch processing, it is difficult to apply them in such evolving
environments. Existing algorithms cannot cope with streaming event data and
tend to generate unreliable and obsolete results.
In this paper, we discuss the peculiarities of dealing with streaming event
data in the context of process mining. Subsequently, we present a general
framework for defining process mining algorithms in settings where it is
impossible to store all events over an extended period or where processes
evolve while being analyzed. We show how the Heuristics Miner, one of the most
effective process discovery algorithms for practical applications, can be
modified using this framework. Different stream-aware versions of the
Heuristics Miner are defined and implemented in ProM. Moreover, experimental
results on artificial and real logs are reported
A Dashboard-based Approach for Monitoring Object-Aware Processes
Data (e.g., event logs) gathered during the execution of business processes enable valuable insights into actual process performance. To leverage this knowledge, these data should be analyzed and interpreted in the context of the respective processes. Corresponding analyses allow for a comprehensive process monitoring as well as the discovery
of weaknesses and potential process improvements. This also applies to object-aware processes, where data drives process execution and, thus, is treated as first-class citizen. This paper introduces a dashboard with advanced monitoring functions for object-aware processes
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NoTube – making TV a medium for personalized interaction
In this paper, we introduce NoTube’s vision on deploying semantics in interactive TV context in order to contextualize distributed applications and lift them to a new level of service that provides context-dependent and personalized selection of TV content. Additionally, lifting content consumption from a single-user activity to a community-based experience in a connected multi-device environment is central to the project. Main research questions relate to (1) data integration and enrichment - how to achieve unified and simple access to dynamic, growing and distributed multimedia content of diverse formats? (2) user and context modeling - what is an appropriate framework for context modeling, incorporating task-, domain and device-specific viewpoints? (3) context-aware discovery of resources - how could rather fuzzy matchmaking between potentially infinite contexts and available media resources be achieved? (4) collaborative architecture for TV content personalization - how can the combined information about data, context and user be put at disposal of both content providers and end-users in the view of creating extremely personalized services under controlled privacy and security policies? Thus, with the grand challenge in mind - to put the TV viewer back in the driver's seat – we focus on TV content as a medium for personalized interaction between people based on a service architecture that caters for a variety of content metadata, delivery channels and rendering devices
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