About me

My name is Dimitris Tsitsigkos and I completed my B.Sc. in Informatics and Telecommunications (DIT) in 2012, followed by an M.Sc. in Computing Systems: Software and Hardware in 2016, both at the National and Kapodistrian University of Athens (NKUA). In 2025, I finished my Ph.D. at the Computer Science and Engineering Department (CSE) of the University of Ioannina (UoI), supervised by Professor Nikos Mamoulis, Professor Panagiotis Bouros and Principal Researcher Manolis Terrovitis. I am currently a postdoctoral researcher at the Archimedes research unit , where I focus on designing and implementing indexing techniques to enhance query performance for multi-dimensional and vector data.

Earlier in my career, I was a software engineer at the Institute for the Management of Information Systems (IMIS) at the “Athena” Research and Innovation Centre (Athena RC) , where I worked with Manolis Terrovitis. During this time, I took part in several Greek and EU-funded projects, which helped me gain valuable experience in big data frameworks.

My research interests lie in the areas of data management (relational and multi-dimensional), big data management, parallel computing, distributed and real-time analytics.

Resume

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Education

  1. Ph.D., Computer Science

    Jul. 2019 – Dec. 2024

    Department of Computer Science & Engineering (CSE), University of Ioannina

    Supervisors: Prof. Nikos Mamoulis, Prof. Panagiotis Bouros, Prin. Res. Manolis Terrovitis

    Thesis: In-memory Indexing for Parallel Processing of Single and Multi-Dimensional Queries

  2. M.Sc., Computer Systems: Software and Hardware, Computer Science

    Nov. 2012 – Sep. 2016

    Department of Informatics and Telecommunications (DIT) , University of Athens

    Supervisor: Prof. Stathes Hadjiefthymiades

    Grade: 8.2/10 (≈ 3.28/4.0 GPA)

    Thesis: Complex Event Processing(CEP) for Intrusion Detection

  3. B.Sc., Computer Science

    Sep. 2006 – Jun. 2012

    Department of Informatics and Telecommunications (DIT) , University of Athens

    Supervisor: Prof. Dimitrios Gunopulos

    Grade: 7.7/10 (≈ 3,08/4.0 GPA)

    Thesis: Clustering Wikipedia resources

Experience

  1. PostDoctoral Researcher

    Feb. 2025 — Present

    Archimedes unit of Athena Research Center, Athens, Greece

  2. Doctoral Researcher

    Jan. 2024 – Dec. 2024

    Department of Computer Science & Engineering (CSE), University of Ioannina

  3. Software Engineer

    Dec. 2012 – Dec. 2023

    Information Management Systems Institute (IMIS), Athens, Greece

  4. Researcher Internship

    May. 2022 – Jul. 2022

    Institute of Computer Science, Johannes Gutenberg University Mainz, Germany

  5. Software Engineer

    Apr. 2017 – Nov. 2018

    Hellenic Army Information Technology Support Center, Athens, Greece

Technical Skills

Programming Languages
C, C++, Java, Python
Databases
MySQL, PostgreSQL, PostGIS, HBase
Parallel & Distributed Systems
Hadoop, Spark, Dask, OpenMP, SIMD, MPI
Frameworks
Spring
Web Development
JavaScript, JSP, JSF, PHP
Operating Systems
Ubuntu, Microsoft Windows, macOS

Service

Member of Program Committee
TSAS 2023, ICDE 2026, IEEE BigData 2025
External Reviewer
ICDE 2019-2025, VLDB 2019-2023, SIGMOD 2020, SIGSPATIAL 2020-2023, EDBT 2021-2025, ICDM 2025

Other

Languages
Greek (native), English (Advanced)
Awards
3rd place - Future of Database Programming Contest
(March 2025, Athens).
Volunteer
European Data Forum 2014, EDBT/ICDT 2023 Joint Conference, 6th ACM Europe Summer School on Data Science 2025, HDMS 2025

Publications

  • BS-tree: A gapped data-parallel B-tree
    D. Tsitsigkos, A. Michalopoulos, N. Mamoulis and M. Terrovitis, • IEEE International Conference on Data Engineering, (ICDE) •
    PDF CODE
  • Hecatoncheir: Scaling up and out spatial data management
    AT. Georgiadis, A. Michalopoulos, D. Dimitropoulos, D. Tsitsigkos, and N. Mamoulis, • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL) •
    PDF CODE
  • Efficient Distance Queries on Non-point Data
    A. Michalopoulos, D. Tsitsigkos, P. Bouros, N. Mamoulis, and M. Terrovitis, • ACM Transactions on Spatial Algorithms and Systems (ACM TSAS) •
    DOI PDF
  • Two-layer Space-oriented Partitioning for Non-point Data
    D. Tsitsigkos, P. Bouros, K. Lampropoulos, N. Mamoulis, and M. Terrovitis • IEEE Transactions on Knowledge and Data Engineering (TKDE) •
    DOI PDF CODE
  • Efficient Nearest Neighbor Queries on Non-point Data
    A. Michalopoulos, D. Tsitsigkos, P. Bouros, N. Mamoulis, and M. Terrovitis • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL) •
    DOI PDF
  • A Two-layer Partitioning for Non-point Spatial Data
    D. Tsitsigkos, K. Lampropoulos, P. Bouros, N. Mamoulis, and M. Terrovitis • International Conference on Data Engineering (ICDE) •
    DOI PDF CODE
  • In-Memory Interval Joins
    P. Bouros, N. Mamoulis, D. Tsitsigkos, and M. Terrovitis • The International Journal on Very Large Databases (VLDB J.) •
    DOI PDF
  • Band Joins for Interval Data
    P. Bouros, K. Lampropoulos, D. Tsitsigkos, N. Mamoulis, and M. Terrovitis • International Conference on Extending Database Technology (EDBT) •
    DOI PDF
  • Parallel In-Memory Evaluation of Spatial Joins
    D. Tsitsigkos, P. Bouros, N. Mamoulis, and M. Terrovitis • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL) •
    DOI PDF
  • Exploiting Social Networking and Mobile Data for Crisis Detection and Management
    K. Doka, I. Mytilinis, I. Giannakopoulos, I. Konstantinou, D. Tsitsigkos, M. Terrovitis, N. Koziris: • ISCRAM-med •
    DOI PDF
  • MoDisSENSE: A Distributed Spatio-Temporal and Textual Processing Platform for Social Networking Services.
    I. Mytilinis, I. Giannakopoulos, I. Konstantinou, K. Doka, D. Tsitsigkos, M. Terrovitis, L. Giampouras, N.Koziris • ACM International Conference on Management of Data (SIGMOD ) •
    DOI PDF

Projects

  • Graph Recommender

    In-memory Spatial Analytic Made Scalable (MESA)

    MThe project focuses on advancing spatial data management through several key objectives. It aims to design novel partitioning schemes for spatial data that ensure independent partitions, eliminating the need for synchronization or communication during spatial analysis. Efficient in-memory spatial indices will be developed to support essential query types, including range queries, nearest-neighbor queries, spatial joins, and distance joins. The project also seeks to propose effective strategies for managing both relatively static and highly dynamic spatial data. Customized techniques for partitioning, indexing, and querying will be created to handle various spatial data types, such as points, polylines, and polygons. Additionally, the project will leverage modern hardware capabilities, particularly multi-core parallelism, to optimize performance. Finally, all these components will be integrated into a unified spatial data management system.
    Responsibilities: Implemented novel parallel and non-parallel index structures for spatial queries, including spatial join, range queries, and k-NN. Contributed to the design and development of a prototype distributed spatial data management framework using MPI and OpenMP.
    Programming languages: C++
    Distributed/parallel Framework: MPI, OpenMP

    Site
  • Graph Recommender

    MORE: Management of Real-time Energy data

    MORE will deliver a platform to address the technical challenges in time series and stream management, focusing on the RES industry. Specifically, more’s platform will introduce an architecture incorporating edge computing and cloud computing to address responsiveness and the need for sophisticated analytics simultaneously. This architecture will be combined with time series summarization techniques, or as we more accurately term them in MORE, modeling techniques for sensor data. Models are any compressed representations that allow the reconstruction of the original data points of a time series (e.g., a linear function) within a known error-bound (possibly zero). This approach synergizes with the edge computing approach since summarization can be done at the edge, reducing the load in the whole data processing pipeline.
    Responsibilities: Implemented a continuous evaluation module of sliding window aggregations on the edge, using Java. Additionally, I implemented all the parallel and distributed versions of pattern extraction methods using Python and Dask framework.
    Programming languages: Java, Python
    Distributed/parallel Framework: Dask

    Site GitHub
  • Portfolio Website

    Amnesia: A Powerful Data Anonymization Platform

    Amnesia is a web-based platform for anonymizing data, including relational, multi-dimensional, and hierarchical data. Currently, the platform supports a variety of anonymization algorithms such as Flush, parallel Flush, k-anonymity, km-anonymity, and Incognito. Amnesia also supports the definition of custom anonymization rules and hierarchies.
    Responsibilities: Built the framework from scratch and led its development as the main software engineer until 2020.
    Programming languages: JavaScript, Java
    Other technologies/tools: Spring

    Live Site Site GitHub
  • Edge Indexing

    MoDisSENSE: A Distributed Spatio-Temporal and Textual Processing Platform for Social Networking Services.

    MoDisSENSE is an open-source distributed platform that provides personalized search for points of interest (POIs) and trending events based on the user's social graph, i.e., by combining spatio-textual user-generated data (e.g. GPS traces, check-ins, uses profiles, graph of friendship relations, user posts in social networks, etc.).
    Responsibilities: Implemented a distributed version of the DBSCAN algorithm for POI discovery, and a distributed algorithm that collects GPS traces and reconstructs the trajectories of the end-users. Both algorithms were implemented using Hadoop, Hbase and postGIS. I also developed all web services that are relevant to POI discovery and suggestion.
    Programming languages: Java
    Distributed/parallel Framework: Hadoop
    Databases: >Hbase, PostGIS

    Site GitHub