Streaming Linked Data: From Vision to Practice

A survey on semantic data management as intersection of ontology-based data access, semantic modeling and data lakes

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies. 

Semantic Data Management in Data Lakes

The future integration of different energy carriers in a single multi-energy network is fundamental for enhancing the flexibility, efficiency and sustainability of the whole energy system. This challenge is strongly associated with the digital transition of networks, as it requires an integrated management of data coming not only from several internal systems of utilities (GIS, asset management, DMS), but also related to different energy vectors. This paper presents a semantic, modular, open-source platform based on international standards to manage and study multi-energy networks: the Multi Energy Semantic Platform (MESP). The architecture, based on Kubernetes and microservices, ensures synergy between semantic web technologies and big data/machine learning algorithms. To show the potential of the platform, a real case study for the city of Milan is presented. In this example, the electricity distribution grid and the district heating network are described using a compatible semantic model (based on IEC CIM) and their exchange points are identified and geographically visualized. Both utilities and universities may benefit from this open-source platform, which supports an easy integration of innovative artificial intelligence algorithms and is adaptable according to the needs of considered use cases. Moreover, the MESP may support management operations of energy communities or small electric cooperatives, which cannot afford large investments both in economic terms and in human resources.

MESP - An Interoperable Platform for Multi-Energy Systems

RDF mapping engines enable access to existing heterogeneous data sources as RDF Knowledge Graph (KG). However, these mapping engines have two challenges: i) processing streaming data sources with changing velocity efficiently, ii) and providing a rich variety in the format of the generated KG output. To tackle these challenges, I carry out my research in 3 steps. I will first design a highly scalable data stream mapping solution to handle dynamic velocity of streaming data sources. Preliminary results indicate that our stream mapping solution outperforms state of the art engines with lower latency, constant memory usage, and higher throughput. I will then refine this architecture in a task-based fashion, aiming to be a common architecture for any kind of mapping. Finally, I will utilize the common modular mapping architecture and extend it with a component to derive an intermediate representation of the data mapping process, enabling heterogeneous to heterogeneous data mapping. The combined solution will provide a highly scalable heterogeneous to heterogeneous data stream mapping engine, enabling us to have multiple views of the underlying KG. 

A Window into the Multiple Views of Linked Data

In the last decades, Knowledge Graph (KG) empowered analytics have been used to extract advanced insights from data. Several companies integrated legacy relational databases with semantic technologies using Ontology-Based Data Access (OBDA). In practice, this approach enables the analysts to write SPARQL queries both over KGs and SQL relational data sources by making transparent most of the implementation details. However, the volume of data is continuously increasing, and a growing number of companies are adopting distributed storage platforms and distributed computing engines. There is a gap between big data and semantic technologies. Ontop, one of the reference OBDA systems, is limited to legacy relational databases, and the compatibility with the big data analytics engine Apache Spark is still missing. This paper introduces Chimera, an open-source software suite that aims at filling such a gap. Chimera enables a new type of round-tripping data science pipelines. Data Scientists can query data stored in a data lake using SPARQL through Ontop and SparkSQL while saving the semantic results of such analysis back in the data lake. This new type of pipelines semantically enriches data from Spark before saving them back.

Chimera: A Bridge Between Big Data Analytics and Semantic Technologies.

This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.

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Chimera: A Bridge Between Big Data Analytics and Semantic Technologies.

Accepted Tutorials at The Web Conference 2022