Data Mining - Concepts and Techniques.

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

http://www.cs.hut.fi/~framling/Publications/intelligentproducts_final.pdf

Intelligent Products: A survey

Multi-agent oriented programming with JaCaMo

Pervasive computing aims at providing services for human beings that interact with their environment (encompassing objects and people who reside in it). Pervasive computing applications must be able to take into account the context in which users evolve, for example, physical location, social or hierarchical position, current tasks as well as related information. These applications have to deal with the dynamic integration in the environment of new, and sometimes unexpected, elements (users or devices). In turn, the environment has to provide context information to newly designed applications. This requires a framework which is open, dynamic and minimal. We describe an architecture in which context information is distributed in the environment and context managers use semantic Web technologies in order to identify and characterize available resources. The components in the environment maintain their own context expressed in RDF (Resource Description Framework) and described through OWL ontologies. They may communicate this information to other components, obeying a simple protocol for identifying them and determining the information they can provide. We show how this architecture allows introducing new devices and new applications without interrupting what is working. In particular, the openness of ontology description languages makes possible the extension of context descriptions and ontology matching helps dealing with independently developed ontologies.

/pdf/dynamic-context-management-for-pervasive-applications-wsj70spunn.pdf

Dynamic context management for pervasive applications

Flexible Composition of Smart Device Services

We consider environments in which smart devices equipped with limited communication and computation capabilities have to cooperate to self-configure their state in an energy-efficient manner, as to meet user-defined requirements. Such requirements are expressed as scene rules, configured by the user using an intuitive interface that connects conditions on sensors' and actuators' states and actions on actuators. We translate this smart environment configuration problem into a constraint optimization problem. As to install distributiveness, robustness, and openness, we solve it using distributed message-passing algorithms. We illustrate our approach through a running example, and evaluate the performances of the implemented protocols on a simulated realistic environment.

Using message-passing DCOP algorithms to solve energy-efficient smart environment configuration problems

Pervasive computing environments involve a variety of smart devices, which tend to overcharge humans with complex or irrelevant interaction. Ambient Intelligence pushes forward a vision where technology is integrated into everyday objects with the intent of making users’ interaction with their surrounding environment simpler and more intuitive. In this paper, we expose how Ambient Intelligence can benefit from the coupling of a service-oriented approach and multi-agent systems towards more appropriate interactions with users. Our approach combines multi-agent techniques with semantic web services to enable dynamic, context-aware service composition, thus providing users with relevant high level services depending on their current context and activity.

/pdf/a-multi-agent-system-for-dynamic-service-composition-in-2g6s8xdufj.pdf

A Multi-Agent System for Dynamic Service Composition in Ambient Intelligence Environments

The Context Management Service (CMS) is an open infrastructure for managing context information developed in the Amigo project. The Amigo project is an IST Integrated Project which focuses on ambient intelligence for the networked home. Topics in the Amigo project include, but are not limited to, ambient intelligence, interoperability between different home related protocols and standards, and context management. One of the outputs of the Amigo project is in the form of (mostly open source) middleware for these particular topics, as well as demonstrator software to show the value of the middleware. The role of the CMS is to acquire information coming from various sources, such as physical sensors, user activities, and applications in process or internet applications and to subsequently combine or abstract these pieces of information into "context information" to be provided to context aware services. This article introduces the basic principles underlying the CMS design and implementation and illustrates its application to implementing an ambient intelligence enabled home environment, supporting users in handling daily routine tasks such as the morning waking up. The most salient features of the CMS include: . Its compliance to the web service architecture both for interfacing to context consuming applications and for integrating its sub components. . The modeling of context information using a high level language with much expressiveness. . State of the art context sources such as a context history manager and an audio based positioning system.

Fano Ramparany

Papers

Dynamic context management for pervasive applications

Flexible Composition of Smart Device Services

Using message-passing DCOP algorithms to solve energy-efficient smart environment configuration problems

A Multi-Agent System for Dynamic Service Composition in Ambient Intelligence Environments

An open context information management infrastructure the IST-amigo project