Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> The paper discusses the Gaia metaoperating system which extends the reach of traditional operating systems to manage ubiquitous computing habitats and living spaces as integrated programmable environments. Gaia exports services to query, access, and use existing resources and context, and provides a framework to develop user-centric, resource-aware, multidevice, context-sensitive, and mobile applications. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems. <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> We present a framework for utilizing context-awareness for classifying services. We also present a novel RDF-based model for context-aware reasoning in pervasive computing environment. Our model enables a flexible way of describing context-based rules that can be used for constructing prioritized service lists and also for giving recommendations about certain available services. In addition, the model provides a way for describing context-triggered actions, e.g. notifications. We have implemented several demonstrations of the use of our model. <s> BIB004 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an up-to-date comparison of the most interesting, data-oriented approaches available in the literature. <s> BIB005 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Ontologies are a widely accepted tool for the modeling of context information. We view the identification of the benefits and challenges of ontologybased models to be an important next step to further improve the usability of ontologies in context-aware applications. We outline a set of criteria with respect to ontology engineering and context modeling and discuss some recent achievements in the area of ontology-based context modeling in order to determine the important next steps necessary to fully exploit ontologies in pervasive computing. <s> BIB006 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Context representation is a fundamental process in developing context aware systems for the pervasive world. We present a light weight XML based context representation schema called ContextML in which context information is categorized into scopes and related to different types of entities (e.g. user, device). The schema is also applied for encoding management messages in order to allow for a flexible framework supporting gradual plug & play extendibility and mobility. ContextML is tailored to be used for REST-based communication between the framework components. Explanation of the schema is provided with the help of real world examples. Moreover, the European C-CAST testbed is introduced, embracing a variety of context providers and application domains. <s> BIB007 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Object-Role Modeling ORM is an approach for modeling and querying information at the conceptual level, and for transforming ORM models and queries to or from other representations. Unlike attribute-based approaches such as Entity-Relationship ER modeling and class modeling within the Unified Modeling Language UML, ORM is fact-oriented, where all facts and rules are modeled in terms of natural sentences easily understood and validated by nontechnical business users. ORM's modeling procedure facilitates validation by verbalization and population with concrete examples. ORM's graphical notation is far more expressive than that of ER diagrams or UML class diagrams, and its attribute-free nature makes it more stable and adaptable to changing business requirements. This article explains the fundamentals of ORM, illustrates some of its advantages as a data modeling approach, and outlines some recent research to extend ORM, with special attention to mappings to deductive databases. <s> BIB008 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> This paper introduces a design pattern that allows for the OWL 2 DL representation of concepts central to social reality: roles. The work presented here is motivated by experiences in the development of the LKIF Core ontology of basic legal concepts [10,8]. This paper applies modelling steps identied in earlier work for the representation of transactions [9] to the domain of roles. This is done by building on Searle's theory of social reality [14]. We use the new features of OWL 2 to approximate a reied relation, and show how the approach of [9] can be reused to dene a pattern for capturing roles, intentional concepts and n-ary relations [13]. <s> BIB009 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL. <s> BIB010 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Semantic Web models and technologies provide information in machine-readable languages that enable computers to access the Web more intelligently and perform tasks automatically without the direction of users. These technologies are relatively recent and advancing rapidly, creating a set of unique challenges for those developing applications. ::: ::: Semantic Web for the Working Ontologist is the essential, comprehensive resource on semantic modeling, for practitioners in health care, artificial intelligence, finance, engineering, military intelligence, enterprise architecture, and more. Focused on developing useful and reusable models, this market-leading book explains how to build semantic content (ontologies) and how to build applications that access that content. ::: ::: New in this edition: ::: ::: ::: Coverage of the latest Semantic Web tools for organizing, querying, and processing information - see details in TOC below ::: ::: Detailed information on the latest ontologies used in key web applications including ecommerce, social networking, data mining, using government data, and more ::: ::: ::: ::: ::: Updated with the latest developments and advances in Semantic Web technologies for organizing, querying, and processing information, including SPARQL, RDF and RDFS, OWL 2.0, and SKOS ::: Detailed information on the ontologies used in today's key web applications, including ecommerce, social networking, data mining, using government data, and more ::: Even more illustrative examples and case studies that demonstrate what semantic technologies are and how they work together to solve real-world problems ::: ::: Table of Contents ::: ::: 1?What Is The Semantic Web? ::: 2?Semantic Modeling ::: 3?RDF - The Basis of the Semantic Web ::: 4?SPARQL - The Query Language for RDF ::: 5?Semantic Web Application Architecture ::: 6?RDF And Inferencing ::: 7?RDF Schema Language ::: 8?RDFS-Plus ::: 9?SKOS - the Simple Knowledge Organization System ::: 10?Ontologies in the Wild: Linked Open Data and the Open Graph Project ::: 11?Basic OWL ::: 12?Counting and Sets In OWL ::: 13?MORE Ontologies in the Wild: QUDT, GoodRelations, and OBO Foundry ::: 14?Good and Bad Modeling Practices ::: 15?OWL 2.0 Levels and Logic ::: 16?Conclusions ::: 17?Frequently Asked Questions <s> BIB011 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Context Modeling <s> Metadata is the data about data. With the development of information technologies, it is realized that metadata is of great importance in discovery, identification, localization and access of resources. For the context-aware middleware, it is necessary to provide the metadata of context resources connected to it for the context consumers. However, the complexity of context-aware computing results in the diversity of context resources which makes it difficult in explicitly describing the metadata. In this paper, an XML-based markup language called VirtualSensorML is proposed for the purpose of providing model and schema to describe the metadata of context resources in an understandable format. It defines fundamental elements and various sub elements to describe the common and special properties of different context resources. The metadata management of context resources is currently implemented in a multi-agent based context-aware middleware. The evaluation demonstrates that it is efficient to improve the discovery of context resources. <s> BIB012
Being associated with variant context sources, accurate representation of context with a high certainty under different conditions of measuring range and sampling methods is very important to assure the quality of contextual information. In this sense, context modeling is required to reason and interpret dynamic context representations at a high level abstraction in an unobtrusive way. A good context modeling aims at reducing complexity of applications for robustness and usability, and improving their adaptability and maintainability for future development. To be able to do that, it has to consider heterogeneity (i.e., imperfectionist dynamic nature), comparability (i.e., coexistence of similar context from different sources), and mobility (i.e., asynchronous, timeless data capture) of a large variety of context sources at any level of abstraction. It also considers relationships and dependencies among semantic entities such as accuracy in context provisioning versus remaining battery power. In this regard, many context modeling schemes have been proposed BIB002 ,  , BIB012 - BIB001 . Important ones are compared in Table I , and listed in the followings: • Key-value models use a simplest matching algorithm that defines a list of attributes and their content/values describing specific context. • Mark-up scheme based context models use a hierarchical data structure, mostly formed in XML BIB007 , that consists of markup tags along with their attributes and contents. Therefore, it allows efficient data retrieval. Also, this type of schema can be used among different application domains to store temporary data and in-out data transfer. However, it is not feasible to make context reasoning in presence of multi-markup schemes due to the lack of interoperability among different schemes. • Graphical models, such as Unified Role Modeling (UML)  and Object Role Modeling (ORM) BIB008 , make connections among context attributes and values based on relationships. Especially, this model is widely used within database managements BIB010 that allow holding a massive amount of data, and perform quick data retrieval. Also, complex context relations can be managed easily through database queries. • Object-oriented models BIB005 offer object-oriented techniques to be used in context modeling. Constructed object classes encapsulate or represent different context types, thereby reaching the context or processing its attributes are regulated with designed object-oriented class hierarchies and relationships. This model also provides re-usability, inheritance, and polymorphism features into context or inter-context relationships. However, the model is suitable to be used for a dedicated application that employs its own context reasoning structure. • Logic based models include formality based on facts, expressions and rules in order to set constraints, limitations, policies or preferences while defining context reasoning. It is powerful to manage richness in context definitions by allowing to add, remove, or update new set of rules. Therefore, it could cooperate with other context modeling techniques to enhance context reasoning efficiency. • Ontology based modeling BIB006 - BIB011 uses semantic technologies to represent context related attributes and relationships. It is very widely used and promising instrument thanks to its highly formal expressiveness and conceptualization. Therefore, there are many development tools and engines, such as Resource Description Language (RDF) BIB004 and Web Ontology Language (OWL) BIB009 , BIB003 , available to apply ontology reasoning techniques. This model aims at providing simple, flexible, extensible, generic and explicitly well defined design objectives. However, with growing data size, context reasoning could be computationally expensive. All context modeling approaches ultimately intends to provide solutions in context reasoning by seeking for capturing a variety of context types along with their relationships, dependencies, timeliness, and quality of content. They also intends to support accurate reasoning, and clear uncertainty on higherlevel context abstractions. Therefore, there might not be a single context modeling technique to be used in a standalone fashion.