This deliverable summarizes the activities of the Mihajlo Pupin Institute’s activities regarding the planned activities in Task 4.3 (LAMBDA Learning and Consulting Tools at PUPIN) framework. Task 4.3 (M13-M24) has three main objectives:
The goal of Task 4.2 is to manage the short term staff exchanges and on-site expert visits of PUPIN and UOXF staff. This deliverable reports the staff exchanges organized since the beginning of the project until February 2020. These staff exchanges aim to:
This report presents a plan for sustaining LAMBDA open education and training activities. One of the major knowledge transfer events of the LAMBDA project is the Belgrade Big Data Analytics Summer School, hence the activities necessary for organizing the next editions of the Belgrade Big Data Analytics Summer School are presented. Additionally, the efforts and investments needed for sustaining this event in the short and medium term (5-7 years) are presented.
The LAMBDA project started in July 2018 with a primary objective to Strengthening the Human capital and Education, Research and Development capacities of “Mihajlo Pupin” Institute, the leading Serbian R&D institution in information and communication technologies in order to serve as a Big Data & Analytics HUB that connects and integrates scientists and professionals from the West Balkans and the entire region into the European Research Area.
The goal of this chapter is to shed light on different types of big data applications needed in various industries including healthcare, transportation, energy, banking and insurance, digital media and e-commerce, environment, safety and security, telecommunications, and manufacturing. In response to the problems of analyzing large-scale data, different tools, techniques, and technologies have bee developed and are available for experimentation.
The size of knowledge graphs has reached the scale where centralised analytical approaches have become infeasible. Recent technological progress has enabled powerful distributed in-memory analytics that have been shown to work well on simple data structures. However, the application of such distributed analytics approaches on semantic knowledge graphs lags significantly behind. To advance both scalability and accuracy of large-scale knowledge graph analytics to a new level, foundational research on methods leveraging distributed in-memory computing and semantic technologies in combination w
In the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets the match might not be valid in every context.
Big data plays a relevant role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Semantic web technologies have also experienced great progress, and scientific communities and practitioners have contributed to the problem of big data management with ontological models, controlled vocabularies, linked datasets, data models, query languages, as well as tools for transforming big data into knowledge from which decisions can be made.
This Lecture introduces how Knowledge Graphs are generated. The goal is to gain
The rapid development of digital technologies, IoT products and connectivity platforms, social networking applications, video, audio and geolocation services has created opportunities for collecting/accumulating a large amount of data. While in the past corporations used to deal with static, centrally stored data collected from various sources, with the birth of the web and cloud services, cloud computing is rapidly overtaking the traditional in-house system as a reliable, scalable and cost-efective IT solution.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 809965.
