One of the specific objectives of LAMBDA (Learning, Applying, Multiplying Big Data Analytics) project is focused its efforts on formulating and delivering proper strategies and activities for dissemination and communication that will result in the best and most effective advertising of the project at local, regional and European, and international level.
One of the specific objectives of LAMBDA (Learning, Applying, Multiplying Big Data Analytics) project is focused its efforts on formulating and delivering proper strategies and activities for dissemination and communication that will result in the best and most effective advertising of the project at local, regional and European, and international level.
One of the goals of the LAMBDA project is outreach and engagement with different types of stakeholders including the wider public. In the very beginning, the LAMBDA Consortium defined a Stakeholder Engagement Strategy as a set of actions for the involvement of external stakeholders and alignment of joint activities with LAMBDA objectives and priorities defined in the Grant Agreement.
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.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 809965.
