Visual Facts Final Workshop [13 January 2022] (Virtual)

visual_facts_final event
The Information Management Systems Institute of Athena Research Center organizes an online workshop around the results of the VisualFacts research project "VisualFacts: Democratizing Visual Analytics, A Self-Service Platform for Big Data Exploration" funded by the Hellenic Research Foundation and by the General Secretariat for Research and Technologyin the context of the "1st Announcement of ELIDEK for the support of Postdoctoral Researchers".

The project aimed to study and produce innovative research results and tools in the field of data analysis and in particular visual exploration and analytics. Visual analytics is an important area of data science that aims to provide methods and capabilities to users to be able to analyze data visually. The main contributions of VisualFacts are techniques that scale visual analytics methods in large volumes of big data as well as visual methods for the automation of complex data analysis. To this end, the project has developed data indexing techniques adapted to the user's visual exploration and techniques for performing visual queries and optimizing visual analysis methods for different types of data, such as graph data, geospatial data and "dirty" duplicate data.

These technologies have been integrated into a prototype system and target users who wish to visually explore and analyze big raw data files of different quality (with duplicate or missing data) without requiring from them to be expert in data management technologies. The system may run on limited computing resources (e.g. the user's personal computer) and does not require the use of a database system or other specialized software. The individual technologies are open source and can be used independently or integrated into existing data management or visual analysis systems.

The event welcomes researchers, academics, executives of organizations and companies, representatives of institutions, postgraduate and undergraduate students. The results of the project will be presented, and future research directions will be discussed. The event is open to the public, but please register to attend the event.


Thursday, 13 January 2022: 12.00-13.30 (GMT+2) (Virtual)

12:00 - 12:10
Welcome – Introduction to the objectives of the project
George Papastefanatos, Senior Researcher RC ATHENA [Project Coordinator]
12:10 - 12:25
  • Prof. Minos Garofalakis – Director of IMSI\RC ATHENA
  • Prof. Dimitris Vergados - University of Piraeus, President of the General Assembly of HFRI.
  • Prof. Panos Vassiliadis – University of Ioannina [Project Team Member]
12:25 - 13:10
Presentation of the project results
  • Challenges for Visual Exploration and Analytics in Big Raw Data. George Papastefanatos, Senior Researcher RC ATHENA – Project Coordinator
  • Management of big Knowledge Graphs in Relational Databases. Dr. Marios Meimaris – Databricks - [Former Team Member]
  • Techniques for Query evaluation and optimizations in Duplicate Data. Giorgos Alexiou – ATHENA RC, Research Associate [Team Member]
  • Adaptive indexing for visual exploration and analysis of raw data. Dr. Nikos Bikakis - ATHENA RC, Research Associate [Team Member]
  • Visual Facts. A platform for the visual exploration and analysis of big raw data. Architecture and demonstration. Stavros Maroulis – ATHENA RC, Research Associate [Team Member] & Vasilis Stamatopoulos – ATHENA RC, Research Associate [Team Member]
13:10 - 13:20
Dissemination and Impact of the project
George Papastefanatos, Senior Researcher RC ATHENA [Project Coordinator]
13:20 - 13:30
Discussion-closing remarks

Datathon [23-26 June 2021] (Virtual)

The target of following challenges is to analyze the NYC Yellow Taxi Trip dataset (TAXI) and provide novel interactive visualization methods and visual analytics for the following analytical tasks. For more info see: Datathon home page.

Challenge 1: Visual Analytics for Traffic Prediction

Note. The data prediction and visualization are considered to be used in interactive applications, so real-time response is required. The focus of this task is on novel visualization methods that will allow users to use\run well-known prediction algorithms (e.g., regression) and interact with the predicted results.

Challenge 2: Visual Analytics for Dirty Mobility Data

Taxi and Other Mobility companies aggregate data from various sources, ending in having dirty\duplicate entries related to trips. A common task in such cases involves an analyst which wishes to analyze the dataset w.r.t. data quality. The use of visual techniques may reveal information (e.g., correlations, patterns) which are not easily captured by traditional (non-visual) methods. In our case, visually analyze "information" related to duplicate entries, will assist the analyst to recognize data patterns, values, or specific attributes, where duplicates records appear. Beyond the insights related to data quality, using these insights will enable the analyst to improve the effectiveness of their duplication techniques. Note. The duplicates records will be given to the participants beforehand.
Note. The data prediction and visualization are considered to be used in interactive applications, so real-time response is required.