36 citations · 67 across the 14 of their papers we have counts for
14 papers
Toward Efficient and Scalable Design of In-Memory Graph-Based Vector Search
Ilias Azizi, Karima Echihab, Themis Palpanas +1
Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings. Vector data collections often reac…
A Comparative Analysis of Influence Signals for Data Debugging
Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides
Improving the quality of training samples is crucial for improving the reliability and performance of ML models. In this paper, we conduct a comparative evaluation of influence-bas…
HybEA: Hybrid Models for Entity Alignment
Nikolaos Fanourakis, Fatia Lekbour, Guillaume Renton +2
Entity Alignment (EA) aims to detect descriptions of the same real-world entities among different Knowledge Graphs (KG). Several embedding methods have been proposed to rank potent…
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
Nicolas Larue, Ngoc-Son Vu, Vitomir Struc +2
Modern deepfake detectors have achieved encouraging results, when training and test images are drawn from the same data collection. However, when these detectors are applied to ima…
A Meta-level Analysis of Online Anomaly Detectors
Antonios Ntroumpogiannis, Michail Giannoulis, Nikolaos Myrtakis +3
Real-time detection of anomalies in streaming data is receiving increasing attention as it allows us to raise alerts, predict faults, and detect intrusions or threats across indust…
Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review
Nikolaos Fanourakis, Vasilis Efthymiou, Dimitris Kotzinos +1
In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A fr…
On Predictive Explanation of Data Anomalies
Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides
Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a gi…
Benchmarking Blocking Algorithms for Web Entities
Vasilis Efthymiou, Kostas Stefanidis, Vassilis Christophides
An increasing number of entities are described by interlinked data rather than documents on the Web. Entity Resolution (ER) aims to identify descriptions of the same real-world ent…
MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities
Vasilis Efthymiou, George Papadakis, Kostas Stefanidis +1
Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity…
End-to-End Entity Resolution for Big Data: A Survey
Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas +2
One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that…
Massively-Parallel Feature Selection for Big Data
Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis +2
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the chal…
Continuous Top-k Queries over Real-Time Web Streams
Nelly Vouzoukidou, Bernd Amann, Vassilis Christophides
The Web has become a large-scale real-time information system forcing us to revise both how to effectively assess relevance of information for a user and how to efficiently impleme…
Querying Temporal Drifts at Multiple Granularities (Technical Report)
Sofia Kleisarchaki, Sihem Amer-Yahia, Ahlame Douzal-Chouakria +1
There exists a large body of work on online drift detection with the goal of dynamically finding and maintaining changes in data streams. In this paper, we adopt a query-based appr…
Diachronic Linked Data: Towards Long-Term Preservation of Structured Interrelated Information
Yannis Stavrakas, George Papastefanatos, Theodore Dalamagas +1
The Linked Data Paradigm is one of the most promising technologies for publishing, sharing, and connecting data on the Web, and offers a new way for data integration and interopera…