Linked data and linked APIs: similarities, differences, and challenges

In an often retweeted Twitter post, entrepreneur and software architect Inge Henriksen described the relation of Web 1.0 to Web 3.0 as: “Web 1.0 connected humans with machines. Web 2.0 connected humans with humans. Web 3.0 connects machines with machines.” On the one hand, an incredible amount of valuable data is described by billions of triples, machine-accessible and interconnected thanks to the promises of Linked Data. On the other hand, rest is a scalable, resourceoriented architectural style that, like the Linked Data vision, recognizes the importance of links between resources. Hypermedia apis are resources, too—albeit dynamic ones—and unfortunately, neither Linked Data principles, nor the rest-implied self-descriptiveness of hypermedia apis sufficiently describe them to allow for long-envisioned realizations like automatic service discovery and composition. We argue that describing inter-resource links—similarly to what the Linked Data movement has done for data—is the key to machine-driven consumption of apis. In this paper, we explain how the description format restdesc captures the functionality of apis by explaining the effect of dynamic interactions, effectively complementing the Linked Data vision.Peer ReviewedPostprint (author's final draft

Linked data wrapper curation: A platform perspective

131 p.Linked Data Wrappers (LDWs) turn Web APIs into RDF end-points, leveraging the LOD cloud with current data. This potential is frequently undervalued, regarding LDWs as mere by-products of larger endeavors, e.g. developing mashup applications. However, LDWs are mainly data-driven, not contaminated by application semantics, hence with an important potential for reuse. If LDWs could be decoupled from their breakout projects, this would increase the chances of LDWs becoming truly RDF end-points. But this vision is still under threat by LDW fragility upon API upgrades, and the risk of unmaintained LDWs. LDW curation might help. Similar to dataset curation, LDW curation aims to clean up datasets but, in this case, the dataset is implicitly described by the LDW definition, and ¿stains¿ are not limited to those related with the dataset quality but also include those related to the underlying API. This requires the existence of LDW Platforms that leverage existing code repositories with additional functionalities that cater for LDW definition, deployment and curation. This dissertation contributes to this vision through: (1) identifying a set of requirements for LDW Platforms; (2) instantiating these requirements in SYQL, a platform built upon Yahoo's YQL; (3) evaluating SYQL through a fully-developed proof of concept; and (4), validating the extent to which this approach facilitates LDW curation

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Dynamic Interaction and Manipulation of Web Resources

In this thesis we join methods for evaluating queries over interlinked resources via link traversal with approaches for the integration of data over interlinked schemata via reasoning. Our approach allows for the on-the-fly alignment and processing of dynamically retrieved data in a streaming fashion including incremental query answering. We go beyond the simple consumption of exposed information by enabling manipulations of remote resources in a parallel execution system

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

An investigation into applying ontologies to the UK railway industry

The uptake of ontologies in the Semantic Web and Linked Data has proven their excellence in managing mass data. Referring to the movements of Linked Data, ontologies are applied to large complex systems to facilitate better data management. Some industries, e.g., oil and gas, have at-tempted to use ontologies to manage its internal data structure and man-agement. Researchers have dedicated to designing ontologies for the rail system, and they have discussed the potential benefits thereof. However, despite successful establishment in some industries and effort made from some research, plus the interest from major UK rail operation participants, there has not been evidence showing that rail ontologies are applied to the UK rail system. This thesis will analyse factors that hinder the application of rail ontolo-gies to the UK rail system. Based on concluded factors, the rest of the the-sis will present corresponding solutions. The demonstrations show how ontologies can fit in a particular task with improvements, aiming to pro-vide inspiration and insights for the future research into the application of ontology-based system in the UK rail system