The MASSIF platform : a modular and semantic platform for the development of flexible IoT services

In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated

Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing

Recent years have seen the utilisation of Semantic Web Service descriptions for automating a wide range of service-related activities, with a primary focus on service discovery, composition, execution and mediation. An important area which so far has received less attention is service validation, whereby advertised services are proven to conform to required behavioural specifications. This paper proposes a method for validation of service-oriented systems through automated functional testing. The method leverages ontology-based and rule-based descriptions of service inputs, outputs, preconditions and effects (IOPE) for constructing a stateful EFSM specification. The specification is subsequently utilised for functional testing and validation using the proven Stream X-machine (SXM) testing methodology. Complete functional test sets are generated automatically at an abstract level and are then applied to concrete Web services, using test drivers created from the Web service descriptions. The testing method comes with completeness guarantees and provides a strong method for validating the behaviour of Web services

Context Aware Computing for The Internet of Things: A Survey

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.Comment: IEEE Communications Surveys & Tutorials Journal, 201

WSMO-lite annotations for web services

and other research output

Enabling IoT stream management in multi-cloud environment by orchestration

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Every-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.Peer ReviewedPostprint (author's final draft

A topic modeling approach for web service annotation

The actual implementation of semantic-based mechanisms for service retrieval has been restricted, given the resource-intensive procedure involved in the formal specification of services, which generally comprises associating semantic annotations to their documentation sources. Typically, developer performs such a procedure by hand, requiring specialized knowledge on models for semantic description of services (e.g. OWL-S, WSMO, SAWSDL), as well as formal specifications of knowledge. Thus, this semantic-based service description procedure turns out to be a cumbersome and error-prone task. This paper introduces a proposal for service annotation, based on processing web service documentation for extracting information regarding its offered capabilities. By uncovering the hidden semantic structure of such information through statistical analysis techniques, we are able to associate meaningful annotations to the services operations/resources, while grouping those operations into non-exclusive semantic related categories. This research paper belongs to the TelComp 2.0 project, which Colciencas and University of Cauca founded in cooperation.En términos prácticos, la implementación de mecanismos de recuperación de servicios basados en semántica ha sido limitada, debido al costoso procedimiento que involucra la especificación formal de servicios. Este procedimiento comprende una tarea dispendiosa de anotación semántica, la cual se lleva a cabo manualmente por desarrolladores de servicios, quienes, además, deben conocer modelos para la descripción semántica de este tipo de recursos (p. ej. OWL-S, WSMO, SAWSDL). Para superar esta limitación, este artículo introduce una propuesta para la anotación de servicios web, basada en el procesamiento de su documentación disponible, para extraer la información relacionada con las capacidades que estos ofrecen. Al descubrir la estructura semántica oculta de dicha información, a través de técnicas de análisis estadístico, el mecanismo propuesto es capaz de asociar anotaciones relevantes a las operaciones/recursos de los servicios, así como agruparlos en categorías semánticas no exclusivas. Este artículo de investigación está enmarcado en del proyecto TelComp 2.0, financiado por Colciencias y la Universidad del Cauca

Java API-Aware Code Generation Engine: A Prototype

Software reuse enhances a programmer\u27s productivity and reduces programming errors. Improving software reuse through libraries and frameworks is a vast problem area. This thesis offers an approach to solve two sub-problems within the problem area- to identify the right library components, and to offer code snippets that use the components correctly. The Java API-Aware Code Generation Engine, or JAGE for short, is a prototype system that demonstrates the feasibility of generating semantically valid code snippets consisting of method calls to classes in the J2SDK library. Developers often search for sample code snippets that describe how to use the library. This thesis describes the design and implementation of JAGE, which allows software developers to use an English sentence to generate helpful code snippets in Java. This thesis also discusses the related concepts in natural-language processing including ontology, Wordnet, and object-orientation in the area of automatic code snippet generation

Un enfoque basado en modelos temáticos para la anotación semántica de servicios

The actual implementation of semantic-based mechanisms for service retrieval has been restricted, given the resource-intensive procedure involved in the formal specification of services, which generally comprises associating semantic annotations to their documentation sources. Typically, developer performs such a procedure by hand, requiring specialized knowledge on models for semantic description of services (e.g. OWL-S, WSMO, SAWSDL), as well as formal specifications of knowledge. Thus, this semantic-based service description procedure turns out to be a cumbersome and error-prone task. This paper introduces a proposal forservice annotation, based on processing web service documentation for extracting information regarding its offered capabilities. By uncovering the hidden semantic structure of such information through statistical analysis techniques, we are able to associate meaningful annotations to the services operations/resources, while grouping those operations into non-exclusive semantic related categories. This research paper belongs to the TelComp 2.0 project, which Colciencas and University of Cauca founded in cooperation.En términos prácticos, la implementación de mecanismos de recuperación de servicios basados en semántica ha sido limitada, debido al costoso procedimiento que involucra la especificación formal de servicios. Este procedimiento comprende una tarea dispendiosa de anotación semántica, la cual se lleva a cabo manualmente por desarrolladores de servicios, quienes, además, deben conocer modelos para la descripción semántica de este tipo de recursos (p. ej. OWL-S, WSMO, SAWSDL). Para superaresta limitación, este artículo introduce una propuesta para la anotación de servicios web, basada en el procesamiento de su documentación disponible, para extraer la información relacionada con las capacidades que estos ofrecen. Al descubrir la estructura semántica oculta de dicha información, a través de técnicas de análisis estadístico, el mecanismo propuesto es capaz de asociar anotaciones relevantes a las operaciones/recursos de losservicios, así como agruparlos en categorías semánticas no exclusivas. Este artículo de investigación está enmarcado en del proyecto TelComp 2.0, financiado por Colciencias y la Universidad del Cauca