Distributed Semantic Analytics

Posted on: Fri, 06/18/2021 - 14:21 By: valentina.janev

This module will cover the needs and challenges of distributed analytics and then dive into the details of scalable semantic analytics stack (SANSA) used to scalable analytics for knowledge graphs. This module will cover the setup, Apis and different layer of SANSA. At the end of this module, the audience will be able to execute examples and create programs that use SANSA APIs.


Semantic Analytics in the Palm of Your Browser

Posted on: Mon, 05/31/2021 - 15:36 By: valentina.janev

Linked open data sources and the semantic web has become a precious data source for data analytics tasks and data integration. The growing data set sizes of RDF Knowledge Graph data need scalable processing and analytics techniques. The processing power of in-memory frameworks which can perform scalable distributed semantic analytics like SANSA, make use of Apache Spark and Apache Jena to provide start-to-end extensive scalable analytics on RDF knowledge graphs.

Apache Hadoop

Posted on: Wed, 05/12/2021 - 15:05 By: valentina.janev

Ova lekcija opisuje softversko okruženje za obradu velikih količina podataka Apache Hadoop, njegove komponente koji upotpunjavaju i proširuju mogućnosti Hadoop-a, kao i njegovo korištenje u praksi. Da bismo u potpunosti razumeli prednosti Hadoop-a, potrebno je sagledati najpre razliku između paralelnog i distribuiranog računanja, načine čuvanja (HDFS arhitekturu), upravljanje resursima i procesiranja podataka, princip horizontalne skalabilnosti, itd.

Managing Knowledge in Energy Data Spaces

Posted on: Mon, 04/26/2021 - 11:39 By: valentina.janev

Data in the energy domain grows at unprecedented rates and is usually generated by heterogeneous energy systems.
Despite the great potential that big data-driven technologies can bring to the energy sector, general adoption is still lagging. Several challenges related to controlled data exchange and data integration are still not wholly achieved. As a result, fragmented applications are developed against energy data silos, and data exchange is limited to few applications.

Survey on Big Data Tools

Posted on: Thu, 06/11/2020 - 09:53 By: valentina.janev

This introductory lecture discusses the Big Data processing pipeline and the Big Data Landscape from the following perspectives

  • Big Data Frameworks
  • NoSQL Platforms and Knowledge Graphs
  • Stream Processing Data Engines
  • Big Data Preprocessing
  • Big Data Analytics
  • Big Data Visualization Tools.

Overview and Comparison of Machine Learning Algorithms

Posted on: Thu, 06/11/2020 - 09:24 By: valentina.janev

Big Data Analytics is a crucial component of the Big data paradigm and refers to the process of extracting useful knowledge from large datasets or streams of data. Due to enormity, high dimensionality, heterogeneous, and distributed nature of data, traditional techniques of data mining may be unsuitable to work with big data. 

SCADA Intrusion Detection Systems

Posted on: Thu, 06/11/2020 - 08:44 By: valentina.janev

Specific intrusion detection systems (IDSs) are needed to secure modern supervisory control and data acquisition (SCADA) systems due to their architecture, stringent real-time requirements, network traffic features and specific application layer protocols. This lecture aims to contribute to assess the state-of-the-art, identify the open issues and provide an insight for future study areas. To achieve these objectives, we start from the factors that impact the design of dedicated intrusion detection systems in SCADA networks and focus on network-based IDS solutions.

Reasoning on Financial Knowledge Graphs: The Case of Company Networks

Posted on: Fri, 06/05/2020 - 10:53 By: valentina.janev

The initial release of KGs was started on an industry scale by Google and further continued with the publication of other large-scale KGs such as Facebook, Microsoft, Amazon, DBpedia, Wikidata and many more. As an influence of the increasing hype in KG and advanced AI-based services, every individual company or organization is adapting to KG. The KG technology has immediately reached industry, and big companies have started to build their own graphs such as the industrial Knowledge Graph at Siemens.

Embedding-based Recommendations on Scholarly Knowledge Graphs

Posted on: Tue, 06/02/2020 - 09:37 By: valentina.janev

The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and the evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allows to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains.

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