Publications (74)
Data Quality Awareness: A Journey from Traditional Data Management to Data Science Systems
Sijie Dong, Soror Sahri, Themis Palpanas
Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI success is high-quality data. In this paper, we present a…
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
Vasilii Feofanov, Romain Ilbert, Malik Tiomoko +2
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models…
AgenticER: the next frontier in Entity Resolution
George Papadakis, Panos Korovesis, Manolis Koubarakis +1
The paper proposes Agentic Entity Resolution, a new paradigm where autonomous agents actively plan and execute sequential decision-making steps to resolve entities, gathering evide…
MESSI: In-Memory Data Series Indexing
Botao Peng, Panagiota Fatourou, Themis Palpanas
Data series similarity search is a core operation for several data series analysis applications across many different domains. However, the state-of-the-art techniques fail to deli…
Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
Adrien Petralia, Paul Boniol, Philippe Charpentier +1
Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for e…
Fast Data Series Indexing for In-Memory Data
Botao Peng, Panagiota Fatourou, Themis Palpanas
Data series similarity search is a core operation for several data series analysis applications across many different domains. However, the state-of-the-art techniques fail to deli…
ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMs
Yanlin Qi, Xinhang Chen, Huiqiang Jiang +3
KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scale. We introduce ParisKV, a drift-robus…
DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search
Manos Chatzakis, Yannis Papakonstantinou, Themis Palpanas
Approximate Nearest Neighbor Search (ANNS) presents an inherent tradeoff between performance and recall (i.e., result quality). Each ANNS algorithm provides its own algorithm-depen…
FreSh: A Lock-Free Data Series Index
Panagiota Fatourou, Eleftherios Kosmas, Themis Palpanas +1
We present FreSh, a lock-free data series index that exhibits good performance (while being robust). FreSh is based on Refresh, which is a generic approach we have developed for su…
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
Gaspard Berthelier, Mariia Baranova, Andrei-Tiberiu Pantea +4
Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance, often outperforming supervised models in zero-shot settings. Recent TSFM architectures, s…
NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
Adrien Petralia, Philippe Charpentier, Youssef Kadhi +1
Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients…
MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection
Emmanouil Sylligardos, John Paparrizos, Themis Palpanas +2
Anomaly detection is a fundamental task for time series analytics with important implications for the downstream performance of many applications. Despite increasing academic inter…
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning
Xiaotian Lin, Yanlin Qi, Yizhang Zhu +4
Instruction tuning has emerged as a critical paradigm for improving the capabilities and alignment of large language models (LLMs). However, existing iterative model-aware data sel…
Coconut: a scalable bottom-up approach for building data series indexes
Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos +1
Many modern applications produce massive amounts of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data serie…
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
Paul Boniol, Mohammed Meftah, Emmanuel Remy +1
Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led…
-Graph: A Graph Embedding for Interpretable Time Series Clustering
Paul Boniol, Donato Tiano, Angela Bonifati +1
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, of…
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series
Paul Boniol, Themis Palpanas
Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches proposed so far in the literature have…
PDET-LSH: Scalable In-Memory Indexing for High-Dimensional Approximate Nearest Neighbor Search with Quality Guarantees
Jiuqi Wei, Xiaodong Lee, Botao Peng +3
Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search with theoretical guarantees. Traditional LSH-based methods mainly focus on i…
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting
Romain Ilbert, Thai V. Hoang, Zonghua Zhang +1
Balancing the trade-off between accuracy and robustness is a long-standing challenge in time series forecasting. While most of existing robust algorithms have achieved certain subo…
The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art
Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas +1
Increasingly large data series collections are becoming commonplace across many different domains and applications. A key operation in the analysis of data series collections is si…
DumpyOS: A Data-Adaptive Multi-ary Index for Scalable Data Series Similarity Search
Zeyu Wang, Qitong Wang, Peng Wang +2
Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and app…
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
Zhuxian Guo, Qitong Wang, Henning Müller +3
In digital histopathology, entire neoplasm segmentation on Whole Slide Image (WSI) of Hepatocellular Carcinoma (HCC) plays an important role, especially as a preprocessing filter t…
VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series
Michele Linardi, Yan Zhu, Themis Palpanas +1
Data series motif discovery represents one of the most useful primitives for data series mining, with applications to many domains, such as robotics, entomology, seismology, medici…
Coconut Palm: Static and Streaming Data Series Exploration Now in your Palm
Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos +1
Many modern applications produce massive streams of data series and maintain them in indexes to be able to explore them through nearest neighbor search. Existing data series indexe…
Fast and Exact Similarity Search in less than a Blink of an Eye
Patrick Schäfer, Jakob Brand, Ulf Leser +2
Similarity search is a fundamental operation for analyzing data series (DS), which are ordered sequences of real values. To enhance efficiency, summarization techniques are employe…
Evaluating and Generating Query Workloads for High Dimensional Vector Similarity Search
Matteo Ceccarello, Alexandra Levchenko, Ioana Ileana +1
Similarity search lies at the heart of many modern applications, ranging from databases to deep learning to data series analysis. As such, a vast effort has been invested in develo…
Node Classification in Uncertain Graphs
Michele Dallachiesa, Charu Aggarwal, Themis Palpanas
In many real applications that use and analyze networked data, the links in the network graph may be erroneous, or derived from probabilistic techniques. In such cases, the node cl…
LeaFi: Data Series Indexes on Steroids with Learned Filters
Qitong Wang, Ioana Ileana, Themis Palpanas
The ever-growing collections of data series create a pressing need for efficient similarity search, which serves as the backbone for various analytics pipelines. Recent studies hav…
Odyssey: A Journey in the Land of Distributed Data Series Similarity Search
Manos Chatzakis, Panagiota Fatourou, Eleftherios Kosmas +2
This paper presents Odyssey, a novel distributed data-series processing framework that efficiently addresses the critical challenges of exhibiting good speedup and ensuring high sc…
DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search
Jiuqi Wei, Botao Peng, Xiaodong Lee +1
Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search in high-dimensional spaces due to its robust theoretical guarantee on query…
ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series
Adrien Petralia, Philippe Charpentier, Themis Palpanas
Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albe…
Schema-agnostic Progressive Entity Resolution (extended version)
Giovanni Simonini, George Papadakis, Themis Palpanas +1
Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited…
Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art
Ilias Azizi, Karima Echihabi, Themis Palpanas
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…
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov +4
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multiv…
DaiSy: A Library for Scalable Data Series Similarity Search
Francesca Del Gaudio, Manos Chatzakis, Gayathiri Ravendirane +2
Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailor…
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…
Post-Training in Time Series Foundation Models: A Unifying Framework
Shifeng Xie, Ambroise Odonnat, Zehao Xiao +7
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deploymen…
Dimensionality-Reduction Techniques for Approximate Nearest Neighbor Search: A Survey and Evaluation
Zeyu Wang, Haoran Xiong, Qitong Wang +4
Approximate Nearest Neighbor Search (ANNS) on high-dimensional vectors has become a fundamental and essential component in various machine learning tasks. Recently, with the rapid…
ProS: Data Series Progressive k-NN Similarity Search and Classification with Probabilistic Quality Guarantees
Karima Echihabi, Theophanis Tsandilas, Anna Gogolou +2
Existing systems dealing with the increasing volume of data series cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore, it…
Uncertain Time-Series Similarity: Return to the Basics
Michele Dallachiesa, Besmira Nushi, Katsiaryna Mirylenka +1
In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Servic…
Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series
Michele Linardi, Yan Zhu, Themis Palpanas +1
In the last fifteen years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, in…
Progressive Data Science: Potential and Challenges
Cagatay Turkay, Nicola Pezzotti, Carsten Binnig +7
Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iter…
TiVy: Time Series Visual Summary for Scalable Visualization
Gromit Yeuk-Yin Chan, Luis Gustavo Nonato, Themis Palpanas +2
Visualizing multiple time series presents fundamental tradeoffs between scalability and visual clarity. Time series capture the behavior of many large-scale real-world processes, f…
Generalized Supervised Meta-blocking (technical report)
Luca Gagliardelli, George Papadakis, Giovanni Simonini +2
Entity Resolution constitutes a core data integration task that relies on Blocking in order to tame its quadratic time complexity. Schema-agnostic blocking achieves very high recal…
Graphint: Graph-based Time Series Clustering Visualisation Tool
Paul Boniol, Donato Tiano, Angela Bonifati +1
With the exponential growth of time series data across diverse domains, there is a pressing need for effective analysis tools. Time series clustering is important for identifying p…
Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search
Jiuqi Wei, Xiaodong Lee, Zhenyu Liao +2
Approximate Nearest Neighbor (ANN) search in high-dimensional Euclidean spaces is a fundamental problem with a wide range of applications. However, there is currently no ANN method…
Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search
Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas +1
Data series are a special type of multidimensional data present in numerous domains, where similarity search is a key operation that has been extensively studied in the data series…
Twin Subsequence Search in Time Series
Georgios Chatzigeorgakidis, Dimitrios Skoutas, Kostas Patroumpas +3
We address the problem of subsequence search in time series using Chebyshev distance, to which we refer as twin subsequence search. We first show how existing time series indices c…
MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance
Jens E. d'Hondt, Teun Kortekaas, Odysseas Papapetrou +1
Modern applications frequently collect and analyze temporal data in the form of multivariate time series (MTS) -- time series that contain multiple channels. A common task in this…
Local Similarity Search on Geolocated Time Series Using Hybrid Indexing
Georgios Chatzigeorgakidis, Dimitrios Skoutas, Kostas Patroumpas +3
Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated…
Scalable Data Series Subsequence Matching with ULISSE
Michele Linardi, Themis Palpanas
Data series similarity search is an important operation and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data se…
ParIS+: Data Series Indexing on Multi-Core Architectures
Botao Peng, Panagiota Fatourou, Themis Palpanas
Data series similarity search is a core operation for several data series analysis applications across many different domains. Nevertheless, even state-of-the-art techniques cannot…
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…
Efficient Error-tolerant Search on Knowledge Graphs
Zhaoyang Shao, Davood Rafiei, Themis Palpanas
Edge-labeled graphs are widely used to describe relationships between entities in a database. Given a query subgraph that represents an example of what the user is searching for, w…
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Romain Ilbert, Malik Tiomoko, Cosme Louart +4
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensiona…
A Survey of Blocking and Filtering Techniques for Entity Resolution
George Papadakis, Dimitrios Skoutas, Emmanouil Thanos +1
Efficiency techniques are an integral part of Entity Resolution, since its infancy. In this survey, we organized the bulk of works in the field into Blocking, Filtering and hybrid…
SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality
Wissam Maamar Kouadri, Salima Benbernou, Mourad Ouziri +2
The opinion expressed in various Web sites and social-media is an essential contributor to the decision making process of several organizations. Existing sentiment analysis tools a…
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
Juntao Fang, Shifeng Xie, Shengbin Nie +7
The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier. However, this practice…
Local Pair and Bundle Discovery over Co-Evolving Time Series
Georgios Chatzigeorgakidis, Dimitrios Skoutas, Kostas Patroumpas +3
Time series exploration and mining has many applications across several industrial and scientific domains. In this paper, we consider the problem of detecting locally similar pairs…
TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search
Jiuqi Wei, Zhenyu Liao, Ruoyu Han +3
Approximate Nearest Neighbor Search (ANNS) in high-dimensional Euclidean spaces is a fundamental problem with broad applications. Subspace Collision is a newly proposed ANNS framew…
Dumpy: A Compact and Adaptive Index for Large Data Series Collections
Zeyu Wang, Qitong Wang, Peng Wang +2
Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and app…
Coconut: sortable summarizations for scalable indexes over static and streaming data series
Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos +1
Many modern applications produce massive streams of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data serie…
Dive into Time-Series Anomaly Detection: A Decade Review
Paul Boniol, Qinghua Liu, Mingyi Huang +2
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this re…
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
Shifeng Xie, Vasilii Feofanov, Ambroise Odonnat +7
Time series foundation models (TSFMs) have recently gained significant attention due to their strong zero-shot capabilities and widespread real-world applications. Such models typi…
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection
Paul Boniol, Ashwin K. Krishna, Marine Bruel +7
Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains…
PrivSketch: A Private Sketch-based Frequency Estimation Protocol for Data Streams
Ying Li, Xiaodong Lee, Botao Peng +2
Local differential privacy (LDP) has recently become a popular privacy-preserving data collection technique protecting users' privacy. The main problem of data stream collection un…
Appliance Detection Using Very Low-Frequency Smart Meter Time Series
Adrien Petralia, Philippe Charpentier, Paul Boniol +1
In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption…
GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
Christos Fragkathoulas, Eleni Psaroudaki, Themis Palpanas +1
Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the tr…
Automated Data Quality Validation in an End-to-End GNN Framework
Sijie Dong, Soror Sahri, Themis Palpanas +1
Ensuring data quality is crucial in modern data ecosystems, especially for training or testing datasets in machine learning. Existing validation approaches rely on computing data q…
A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms
George Papadakis, Nishadi Kirielle, Peter Christen +1
Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases. Numerous techniques have been developed to tac…
DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series
Adrien Petralia, Paul Boniol, Philippe Charpentier +1
In recent years, electricity suppliers have installed millions of smart meters worldwide to improve the management of the smart grid system. These meters collect a large amount of…
-Hardness: A Query Hardness Measure for Graph-Based ANN Indexes
Zeyu Wang, Qitong Wang, Xiaoxing Cheng +3
Graph-based indexes have been widely employed to accelerate approximate similarity search of high-dimensional vectors. However, the performance of graph indexes to answer different…
SEAnet: A Deep Learning Architecture for Data Series Similarity Search
Qitong Wang, Themis Palpanas
A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity s…
Hercules Against Data Series Similarity Search
Karima Echihabi, Panagiota Fatourou, Kostas Zoumpatianos +2
We propose Hercules, a parallel tree-based technique for exact similarity search on massive disk-based data series collections. We present novel index construction and query answer…