Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 2017
Nowadays, there is a rapid increase in the number of sensor data produced by a wide variety of de... more Nowadays, there is a rapid increase in the number of sensor data produced by a wide variety of devices and sensors. Collections of sensor data can be semantically described using ontologies, e.g., the Semantic Sensor Network (SSN) ontology. Albeit semantically enriched, the volume of semantic sensor data is considerably larger than raw sensor data. Moreover, some measurement values can be observed several times, and a large number of repeated facts can be generated. We devise a compact or factorized representation of semantic sensor data, where repeated values are represented only once. To scale up to large datasets, tabular representation is utilized to store and manage factorized semantic sensor data using Big data technologies. We empirically study the effectiveness of the proposed factorized representation of semantic sensor data, and the impact of factorizing semantic sensor data on query processing. Furthermore, we evaluate the effects of storing RDF factorized data on state-of-the-art RDF engines and in the proposed tabular-based representation. Results suggest that factorization techniques empower storage and query processing of sensor data, and execution time can be reduced by up to two orders of magnitude.
Big biomedical data has grown exponentially during the last decades and a similar growth rate is ... more Big biomedical data has grown exponentially during the last decades and a similar growth rate is expected in the next years. Likewise, semantic web technologies have also advanced during the last years, and a great variety of tools, e.g., ontologies and query languages, have been developed by different scientific communities and practitioners. Although a rich variety of tools and big data collections are available, many challenges need to be addressed in order to discover insights from which decisions can be taken. For instance, different interoperability conflicts can exist among data collections, data may be incomplete, and entities may be dispersed across different datasets. These issues hinder knowledge exploration and discovery, being thus required data integration in order to unveil meaningful outcomes. In this chapter, we address these challenges and devise a knowledge-driven framework that relies on semantic web technologies to enable knowledge exploration and discovery. The framework receives big data sources and integrates them into a knowledge graph. Semantic data integration methods are utilized for identifying equivalent entities, i.e., entities that correspond to the same real-world elements. Fusion policies enable the merging of equivalent entities inside the knowledge graph, as well as with entities in other knowledge graphs, e.g., DBpedia and Bio2RFD. Knowledge discovery allows for the exploration of knowledge graphs in order to uncover novel patterns and relations. As proof of concept, we report on the results of applying the knowledge-driven framework in the EU funded project iASiS (http://project-iasis.eu/) in order to transform big data into actionable knowledge, paving thus the way for personalised medicine.
The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appl... more The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-[Formula: see text]emand factoriz[Formula: see text] and [Formula: see text]emantically [Formula: see text]nrich st[Formula: see text...
Longitudinal linked data are RDF descriptions of observations from related sampling frames or sen... more Longitudinal linked data are RDF descriptions of observations from related sampling frames or sensors at multiple points in time, e.g., patient medical records or climate sensor data. Observations are expressed as measurements whose values can be repeated several times in a sampling frame, resulting in a considerable increase in data volume. We devise a factorized compact representation of longitudinal linked data to reduce repetition of same measurements, and propose algorithms to generate collections of factorized longitudinal linked data that can be managed by existing RDF triple stores. We empirically study the effectiveness of the proposed factorized representation on linked observation data. We show that the total data volume can be reduced by more than 30 % on average without loss of information, as well as improve compression ratio of state-of-the-art compression techniques.
Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 2017
Nowadays, there is a rapid increase in the number of sensor data produced by a wide variety of de... more Nowadays, there is a rapid increase in the number of sensor data produced by a wide variety of devices and sensors. Collections of sensor data can be semantically described using ontologies, e.g., the Semantic Sensor Network (SSN) ontology. Albeit semantically enriched, the volume of semantic sensor data is considerably larger than raw sensor data. Moreover, some measurement values can be observed several times, and a large number of repeated facts can be generated. We devise a compact or factorized representation of semantic sensor data, where repeated values are represented only once. To scale up to large datasets, tabular representation is utilized to store and manage factorized semantic sensor data using Big data technologies. We empirically study the effectiveness of the proposed factorized representation of semantic sensor data, and the impact of factorizing semantic sensor data on query processing. Furthermore, we evaluate the effects of storing RDF factorized data on state-of-the-art RDF engines and in the proposed tabular-based representation. Results suggest that factorization techniques empower storage and query processing of sensor data, and execution time can be reduced by up to two orders of magnitude.
Big biomedical data has grown exponentially during the last decades and a similar growth rate is ... more Big biomedical data has grown exponentially during the last decades and a similar growth rate is expected in the next years. Likewise, semantic web technologies have also advanced during the last years, and a great variety of tools, e.g., ontologies and query languages, have been developed by different scientific communities and practitioners. Although a rich variety of tools and big data collections are available, many challenges need to be addressed in order to discover insights from which decisions can be taken. For instance, different interoperability conflicts can exist among data collections, data may be incomplete, and entities may be dispersed across different datasets. These issues hinder knowledge exploration and discovery, being thus required data integration in order to unveil meaningful outcomes. In this chapter, we address these challenges and devise a knowledge-driven framework that relies on semantic web technologies to enable knowledge exploration and discovery. The framework receives big data sources and integrates them into a knowledge graph. Semantic data integration methods are utilized for identifying equivalent entities, i.e., entities that correspond to the same real-world elements. Fusion policies enable the merging of equivalent entities inside the knowledge graph, as well as with entities in other knowledge graphs, e.g., DBpedia and Bio2RFD. Knowledge discovery allows for the exploration of knowledge graphs in order to uncover novel patterns and relations. As proof of concept, we report on the results of applying the knowledge-driven framework in the EU funded project iASiS (http://project-iasis.eu/) in order to transform big data into actionable knowledge, paving thus the way for personalised medicine.
The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appl... more The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-[Formula: see text]emand factoriz[Formula: see text] and [Formula: see text]emantically [Formula: see text]nrich st[Formula: see text...
Longitudinal linked data are RDF descriptions of observations from related sampling frames or sen... more Longitudinal linked data are RDF descriptions of observations from related sampling frames or sensors at multiple points in time, e.g., patient medical records or climate sensor data. Observations are expressed as measurements whose values can be repeated several times in a sampling frame, resulting in a considerable increase in data volume. We devise a factorized compact representation of longitudinal linked data to reduce repetition of same measurements, and propose algorithms to generate collections of factorized longitudinal linked data that can be managed by existing RDF triple stores. We empirically study the effectiveness of the proposed factorized representation on linked observation data. We show that the total data volume can be reduced by more than 30 % on average without loss of information, as well as improve compression ratio of state-of-the-art compression techniques.
Uploads