Neural Networks forBuilding Semantic Models and Knowledge Graphs

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInoopenFutia, Giusepp

Challenge of Design Data Exchange between heterogeneous Database Schema

The development of complex systems becomes increasingly difficult. The diversity and number of tools necessary to develop such a system is extensive. One solution to exchange engineering data between these tools is the Standard for Product Data Exchange (STEP), ISO 10303. It offers domain specific database schema called application protocols. This paper analyses the problems of the data exchange between tools via an application protocol. and proposes a Transformation Report, which records individual transformation steps during the exchange between two different tools. The Transformation Report supports the understanding of the data exchange process and helps correcting incomplete or incorrect transformed data

A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data

With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Scalable Data Integration for Linked Data

Linked Data describes an extensive set of structured but heterogeneous datasources where entities are connected by formal semantic descriptions. In thevision of the Semantic Web, these semantic links are extended towards theWorld Wide Web to provide as much machine-readable data as possible forsearch queries. The resulting connections allow an automatic evaluation to findnew insights into the data. Identifying these semantic connections betweentwo data sources with automatic approaches is called link discovery. We derivecommon requirements and a generic link discovery workflow based on similaritiesbetween entity properties and associated properties of ontology concepts. Mostof the existing link discovery approaches disregard the fact that in times ofBig Data, an increasing volume of data sources poses new demands on linkdiscovery. In particular, the problem of complex and time-consuming linkdetermination escalates with an increasing number of intersecting data sources.To overcome the restriction of pairwise linking of entities, holistic clusteringapproaches are needed to link equivalent entities of multiple data sources toconstruct integrated knowledge bases. In this context, the focus on efficiencyand scalability is essential. For example, reusing existing links or backgroundinformation can help to avoid redundant calculations. However, when dealingwith multiple data sources, additional data quality problems must also be dealtwith. This dissertation addresses these comprehensive challenges by designingholistic linking and clustering approaches that enable reuse of existing links.Unlike previous systems, we execute the complete data integration workflowvia a distributed processing system. At first, the LinkLion portal will beintroduced to provide existing links for new applications. These links act asa basis for a physical data integration process to create a unified representationfor equivalent entities from many data sources. We then propose a holisticclustering approach to form consolidated clusters for same real-world entitiesfrom many different sources. At the same time, we exploit the semantic typeof entities to improve the quality of the result. The process identifies errorsin existing links and can find numerous additional links. Additionally, theentity clustering has to react to the high dynamics of the data. In particular,this requires scalable approaches for continuously growing data sources withmany entities as well as additional new sources. Previous entity clusteringapproaches are mostly static, focusing on the one-time linking and clustering ofentities from few sources. Therefore, we propose and evaluate new approaches for incremental entity clustering that supports the continuous addition of newentities and data sources. To cope with the ever-increasing number of LinkedData sources, efficient and scalable methods based on distributed processingsystems are required. Thus we propose distributed holistic approaches to linkmany data sources based on a clustering of entities that represent the samereal-world object. The implementation is realized on Apache Flink. In contrastto previous approaches, we utilize efficiency-enhancing optimizations for bothdistributed static and dynamic clustering. An extensive comparative evaluationof the proposed approaches with various distributed clustering strategies showshigh effectiveness for datasets from multiple domains as well as scalability on amulti-machine Apache Flink cluster

An Optimization Based Design for Integrated Dependable Real-Time Embedded Systems

Moving from the traditional federated design paradigm, integration of mixedcriticality software components onto common computing platforms is increasingly being adopted by automotive, avionics and the control industry. This method faces new challenges such as the integration of varied functionalities (dependability, responsiveness, power consumption, etc.) under platform resource constraints and the prevention of error propagation. Based on model driven architecture and platform based design’s principles, we present a systematic mapping process for such integration adhering a transformation based design methodology. Our aim is to convert/transform initial platform independent application specifications into post integration platform specific models. In this paper, a heuristic based resource allocation approach is depicted for the consolidated mapping of safety critical and non-safety critical applications onto a common computing platform meeting particularly dependability/fault-tolerance and real-time requirements. We develop a supporting tool suite for the proposed framework, where VIATRA (VIsual Automated model TRAnsformations) is used as a transformation tool at different design steps. We validate the process and provide experimental results to show the effectiveness, performance and robustness of the approach

A methodology for automating graph construction and evaluation

Graphs and graph analytics facilitate new approaches to machine learning. They also provide the ability to extract new insights from the same datasets as used in traditional machine learning experiments. For this reason, many researchers are seeking to exploit graph databases in pursuit of better performance for their predictive models. However, the construction of a graph from relational or flat models such as CSV files is not a straightforward transformation. A careful selection of nodes and relationships is required to ensure an optimal construction of the target graph. Overly large graphs can cause performance issues for a number of graph algorithms and thus, graph compression is an important part of the construction process. This research has 2 components: the usage of graphs to integrate multiple data sources and a graph transformation methodology to create the integrated schema and populate the graph. Our approach to validation uses link prediction and community detection graph analytics to evaluate the graphs built using our methodology

Formatting and searching a massive, multi-parameter clinical information database

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 117-120).Formatting data and executing time-oriented queries on a massive, multi-parameter clinical information database poses significant computational challenges. The challenges encountered in converting high-resolution waveform and trend signals in the MIMIC II (Multi-parameter Intelligent Monitoring for Intensive Care II) database from an error-prone proprietary format to a stable open-source WFDB (Waveform Database) format is presented in the first half of this thesis. The design and implementation of a search engine that is capable of executing time-series queries on clinical information in the MIMIC II database such as lab results, medications, and nurse-verified values from bedside monitors is presented in the second half of this thesis. The search engine employs simple algorithms with little storage overhead to identify time periods in patient records that satisfy time series criteria based on thresholds and gradients of unevenly-sampled measurements. Results from queries executed on the search engine to detect physiological events of clinical interest were presented. Case studies on patient records returned as hits for queries were performed to review the strengths and limitations of the search engine.by Tin Htet Kyaw.M.Eng