47 research outputs found

    Decentralized Knowledge Graphs on the Web

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    Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

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    This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

    Knowledge hypergraph based-approach for multi-source data integration and querying : Application for Earth Observation domain

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    Early warning against natural disasters to save lives and decrease damages has drawn increasing interest to develop systems that observe, monitor, and assess the changes in the environment. Over the last years, numerous environmental monitoring systems and Earth Observation (EO) programs were implemented. Nevertheless, these systems generate a large amount of EO data while using different vocabularies and different conceptual schemas. Accordingly, data resides in many siloed systems and are mainly untapped for integrated operations, insights, and decision making situations. To overcome the insufficient exploitation of EO data, a data integration system is crucial to break down data silos and create a common information space where data will be semantically linked. Within this context, we propose a semantic data integration and querying approach, which aims to semantically integrate EO data and provide an enhanced query processing in terms of accuracy, completeness, and semantic richness of response. . To do so, we defined three main objectives. The first objective is to capture the knowledge of the environmental monitoring domain. To do so, we propose MEMOn, a domain ontology that provides a common vocabulary of the environmental monitoring domain in order to support the semantic interoperability of heterogeneous EO data. While creating MEMOn, we adopted a development methodology, including three fundamental principles. First, we used a modularization approach. The idea is to create separate modules, one for each context of the environment domain in order to ensure the clarity of the global ontology’s structure and guarantee the reusability of each module separately. Second, we used the upper-level ontology Basic Formal Ontology and the mid-level ontologies, the Common Core ontologies, to facilitate the integration of the ontological modules in order to build the global one. Third, we reused existing domain ontologies such as ENVO and SSN, to avoid creating the ontology from scratch, and this can improve its quality since the reused components have already been evaluated. MEMOn is then evaluated using real use case studies, according to the Sahara and Sahel Observatory experts’ requirements. The second objective of this work is to break down the data silos and provide a common environmental information space. Accordingly, we propose a knowledge hypergraphbased data integration approach to provide experts and software agents with a virtual integrated and linked view of data. This approach generates RML mappings between the developed ontology and metadata and then creates a knowledge hypergraph that semantically links these mappings to identify more complex relationships across data sources. One of the strengths of the proposed approach is it goes beyond the process of combining data retrieved from multiple and independent sources and allows the virtual data integration in a highly semantic and expressive way, using hypergraphs. The third objective of this thesis concerns the enhancement of query processing in terms of accuracy, completeness, and semantic richness of response in order to adapt the returned results and make them more relevant and richer in terms of relationships. Accordingly, we propose a knowledge-hypergraph based query processing that improves the selection of sources contributing to the final result of an input query. Indeed, the proposed approach moves beyond the discovery of simple one-to-one equivalence matches and relies on the identification of more complex relationships across data sources by referring to the knowledge hypergraph. This enhancement significantly showcases the increasing of answer completeness and semantic richness. The proposed approach was implemented in an open-source tool and has proved its effectiveness through a real use case in the environmental monitoring domain

    A survey of large-scale reasoning on the Web of data

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    As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

    Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids

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    Advances in software, embedded computing, sensors, and networking technologies will lead to a new generation of smart cyber-physical systems that will far exceed the capabilities of today’s embedded systems. They will be entrusted with increasingly complex tasks like controlling electric grids or autonomously driving cars. These systems have the potential to lay the foundations for tomorrow’s critical infrastructures, to form the basis of emerging and future smart services, and to improve the quality of our everyday lives in many areas. In order to solve their tasks, they have to continuously monitor and collect data from physical processes, analyse this data, and make decisions based on it. Making smart decisions requires a deep understanding of the environment, internal state, and the impacts of actions. Such deep understanding relies on efficient data models to organise the sensed data and on advanced analytics. Considering that cyber-physical systems are controlling physical processes, decisions need to be taken very fast. This makes it necessary to analyse data in live, as opposed to conventional batch analytics. However, the complex nature combined with the massive amount of data generated by such systems impose fundamental challenges. While data in the context of cyber-physical systems has some similar characteristics as big data, it holds a particular complexity. This complexity results from the complicated physical phenomena described by this data, which makes it difficult to extract a model able to explain such data and its various multi-layered relationships. Existing solutions fail to provide sustainable mechanisms to analyse such data in live. This dissertation presents a novel approach, named model-driven live analytics. The main contribution of this thesis is a multi-dimensional graph data model that brings raw data, domain knowledge, and machine learning together in a single model, which can drive live analytic processes. This model is continuously updated with the sensed data and can be leveraged by live analytic processes to support decision-making of cyber-physical systems. The presented approach has been developed in collaboration with an industrial partner and, in form of a prototype, applied to the domain of smart grids. The addressed challenges are derived from this collaboration as a response to shortcomings in the current state of the art. More specifically, this dissertation provides solutions for the following challenges: First, data handled by cyber-physical systems is usually dynamic—data in motion as opposed to traditional data at rest—and changes frequently and at different paces. Analysing such data is challenging since data models usually can only represent a snapshot of a system at one specific point in time. A common approach consists in a discretisation, which regularly samples and stores such snapshots at specific timestamps to keep track of the history. Continuously changing data is then represented as a finite sequence of such snapshots. Such data representations would be very inefficient to analyse, since it would require to mine the snapshots, extract a relevant dataset, and finally analyse it. For this problem, this thesis presents a temporal graph data model and storage system, which consider time as a first-class property. A time-relative navigation concept enables to analyse frequently changing data very efficiently. Secondly, making sustainable decisions requires to anticipate what impacts certain actions would have. Considering complex cyber-physical systems, it can come to situations where hundreds or thousands of such hypothetical actions must be explored before a solid decision can be made. Every action leads to an independent alternative from where a set of other actions can be applied and so forth. Finding the sequence of actions that leads to the desired alternative, requires to efficiently create, represent, and analyse many different alternatives. Given that every alternative has its own history, this creates a very high combinatorial complexity of alternatives and histories, which is hard to analyse. To tackle this problem, this dissertation introduces a multi-dimensional graph data model (as an extension of the temporal graph data model) that enables to efficiently represent, store, and analyse many different alternatives in live. Thirdly, complex cyber-physical systems are often distributed, but to fulfil their tasks these systems typically need to share context information between computational entities. This requires analytic algorithms to reason over distributed data, which is a complex task since it relies on the aggregation and processing of various distributed and constantly changing data. To address this challenge, this dissertation proposes an approach to transparently distribute the presented multi-dimensional graph data model in a peer-to-peer manner and defines a stream processing concept to efficiently handle frequent changes. Fourthly, to meet future needs, cyber-physical systems need to become increasingly intelligent. To make smart decisions, these systems have to continuously refine behavioural models that are known at design time, with what can only be learned from live data. Machine learning algorithms can help to solve this unknown behaviour by extracting commonalities over massive datasets. Nevertheless, searching a coarse-grained common behaviour model can be very inaccurate for cyber-physical systems, which are composed of completely different entities with very different behaviour. For these systems, fine-grained learning can be significantly more accurate. However, modelling, structuring, and synchronising many fine-grained learning units is challenging. To tackle this, this thesis presents an approach to define reusable, chainable, and independently computable fine-grained learning units, which can be modelled together with and on the same level as domain data. This allows to weave machine learning directly into the presented multi-dimensional graph data model. In summary, this thesis provides an efficient multi-dimensional graph data model to enable live analytics of complex, frequently changing, and distributed data of cyber-physical systems. This model can significantly improve data analytics for such systems and empower cyber-physical systems to make smart decisions in live. The presented solutions combine and extend methods from model-driven engineering, [email protected], data analytics, database systems, and machine learning

    Efficient Source Selection For SPARQL Endpoint Query Federation

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    The Web of Data has grown enormously over the last years. Currently, it comprises a large compendium of linked and distributed datasets from multiple domains. Due to the decentralised architecture of the Web of Data, several of these datasets contain complementary data. Running complex queries on this compendium thus often requires accessing data from different data sources within one query. The abundance of datasets and the need for running complex query has thus motivated a considerable body of work on SPARQL query federation systems, the dedicated means to access data distributed over the Web of Data. This thesis addresses two key areas of federated SPARQL query processing: (1) efficient source selection, and (2) comprehensive SPARQL benchmarks to test and ranked federated SPARQL engines as well as triple stores. Efficient Source Selection: Efficient source selection is one of the most important optimization steps in federated SPARQL query processing. An overestimation of query relevant data sources increases the network traffic, result in irrelevant intermediate results, and can significantly affect the overall query processing time. Previous works have focused on generating optimized query execution plans for fast result retrieval. However, devising source selection approaches beyond triple pattern-wise source selection has not received much attention. Similarly, only little attention has been paid to the effect of duplicated data on federated querying. This thesis presents HiBISCuS and TBSS, novel hypergraph-based source selection approaches, and DAW, a duplicate-aware source selection approach to federated querying over the Web of Data. Each of these approaches can be combined directly with existing SPARQL query federation engines to achieve the same recall while querying fewer data sources. We combined the three (HiBISCuS, DAW, and TBSS) source selections approaches with query rewriting to form a complete SPARQL query federation engine named Quetsal. Furthermore, we present TopFed, a Cancer Genome Atlas (TCGA) tailored federated query processing engine that exploits the data distribution to perform intelligent source selection while querying over large TCGA SPARQL endpoints. Finally, we address the issue of rights managements and privacy while accessing sensitive resources. To this end, we present SAFE: a global source selection approach that enables decentralised, policy-aware access to sensitive clinical information represented as distributed RDF Data Cubes. Comprehensive SPARQL Benchmarks: Benchmarking is indispensable when aiming to assess technologies with respect to their suitability for given tasks. While several benchmarks and benchmark generation frameworks have been developed to evaluate federated SPARQL engines and triple stores, they mostly provide a one-fits-all solution to the benchmarking problem. This approach to benchmarking is however unsuitable to evaluate the performance of a triple store for a given application with particular requirements. The fitness of current SPARQL query federation approaches for real applications is difficult to evaluate with current benchmarks as current benchmarks are either synthetic or too small in size and complexity. Furthermore, state-of-the-art federated SPARQL benchmarks mostly focused on a single performance criterion, i.e., the overall query runtime. Thus, they cannot provide a fine-grained evaluation of the systems. We address these drawbacks by presenting FEASIBLE, an automatic approach for the generation of benchmarks out of the query history of applications, i.e., query logs and LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases. Our evaluation results show that HiBISCuS, TBSS, TopFed, DAW, and SAFE all can significantly reduce the total number of sources selected and thus improve the overall query performance. In particular, TBSS is the first source selection approach to remain under 5% overall relevant sources overestimation. Quetsal has reduced the number of sources selected (without losing recall), the source selection time as well as the overall query runtime as compared to state-of-the-art federation engines. The LargeRDFBench evaluation results suggests that the performance of current SPARQL query federation systems on simple queries does not reflect the systems\\\'' performance on more complex queries. Moreover, current federation systems seem unable to deal with many of the challenges that await them in the age of Big Data. Finally, the FEASIBLE\\\''s evaluation results shows that it generates better sample queries than the state-of-the-art. In addition, the better query selection and the larger set of query types used lead to triple store rankings which partly differ from the rankings generated by previous works

    Approximation and relaxation of semantic web path queries

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    Given the heterogeneity of complex graph data on the web, such as RDF linked data, it is likely that a user wishing to query such data will lack full knowledge of the structure of the data and of its irregularities. Hence, providing flexible querying capabilities that assist users in formulating their information seeking requirements is highly desirable. In this paper we undertake a detailed theoretical investigation of query approximation, query relaxation, and their combination, for this purpose. The query language we adopt comprises conjunctions of regular path queries, thus encompassing recent extensions to SPARQL to allow for querying paths in graphs using regular expressions (SPARQL 1.1). To this language we add standard notions of query approximation based on edit distance, as well as query relaxation based on RDFS inference rules. We show how both of these notions can be integrated into a single theoretical framework and we provide incremental evaluation algorithms that run in polynomial time in the size of the query and the data, returning answers in ranked order of their `distance' from the original query. We also combine for the first time these two disparate notions into a single `flex' operation that simultaneously applies both approximation and relaxation to a query conjunct, providing even greater flexibility for users, but still retaining polynomial time evaluation complexity and the ability to return query answers in ranked order
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