Philippe Cudre-Mauroux

• PhD
• Professor (Full) at University of Fribourg

Time series often present gaps in the data. This phenomenon, also called missing values, is so prevalent that a cottage industry of missing-value imputation algorithms exists, each with different capabilities and efficacy/efficiency tradeoffs. So far, however, there has been no way to accurately select the most appropriate approach among all algori...

Next-location prediction aims to forecast which location a user is most likely to visit given the user's historical data. As a sequence modeling problem by nature, it has been widely addressed using Recurrent Neural Networks (RNNs). To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as...

In Extreme Multi Label Completion (XMLCo), the objective is to predict the missing labels of a collection of documents. Together with XML Classification, XMLCo is arguably one of the most challenging document classification tasks, as the very high number of labels (at least ten of thousands) is generally very large compared to the number of availab...

With the emergence of the Internet of Things (IoT) and the rise of shared multimedia content on social media networks, available datasets have become increasingly heterogeneous. Several multimodal techniques for detecting events in data of different types and formats have emerged. Those techniques implement various detection algorithms and present...

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary s...

Time series database systems (TSDBs) are prevalent in many ap- plications ranging from monitoring and IoT devices to scientific research. Those systems are specifically designed to efficiently manage data indexed by time. Because of the variety of workloads, the diversity of time series features, and the sophistication of existing TSDBs, there is n...

Prompting and Multiple Choices Questions (MCQ) have become the preferred approach to assess the capabilities of Large Language Models (LLMs), due to their ease of manipulation and evaluation. Such experimental appraisals have pointed toward the LLMs' apparent ability to perform causal reasoning or to grasp uncertainty. In this paper, we investigate...

As companies store, process, and analyse bigger and bigger volumes of highly heterogeneous data, novel research and technological challenges are emerging. Traditional and rigid data integration and processing techniques become inadequate for a new class of data-intensive applications. There is a need for new architectural, software, and hardware so...

Modern cloud networks are a fundamental pillar of data-intensive applications. They provide high-speed transaction (packet) rates and low overhead, enabling, for instance, truly scalable database designs. These networks, however, are fundamentally different from conventional ones. Arguably, the two key discerning technologies are RDMA and programma...

Time series databases are essential for the large-scale deployment of many critical industrial applications. In infrastructure monitoring, for instance, a database system should be able to process large amounts of sensor data in real-time, execute continuous queries, and handle complex analytical queries such as anomaly detection or forecasting. Se...

As a fundamental problem in human mobility modeling, location prediction forecasts a user’s next location based on historical user mobility trajectories. Recurrent Neural Networks (RNNs) have been widely used to capture sequential patterns of user visited locations for solving location prediction problems. Due to the sparse nature of real-world use...

Graph Pattern Mining (GPM) is a class of algorithms that identifies given shapes within a graph, e.g., cliques of a certain size. Any area of a graph can contain a shape of interest, but in real-world graphs, these shapes tend to be concentrated in areas deemed skewed. Because mining skewed areas can dominate GPM computations, the overwhelming majo...

Modern Knowledge Graphs (KG) often suffer from an incompleteness issue (i.e., missing facts). By representing a fact as a triplet $(h,r,t)$ linking two entities $h$ and $t$ via a relation $r$ , existing KG completion approaches mostly consider a link prediction task to solve this problem, i.e., given two elements of a triplet predicting the...

Knowledge Graph (KG) embeddings have become a powerful paradigm to resolve link prediction tasks for KG completion. The widely adopted triple-based representation, where each triplet $(h,r,t)$ links two entities $h$ and $t$ through a relation $r$ , oversimplifies the complex nature of the data stored in a KG, in particular for hyper-relatio...

Modern DBMS engines can achieve unprecedented transaction processing speeds thanks to the invention of clever data structures, concurrency schemes, and improvements in CPU and memory subsystems. However, developing realistic and efficient networked clients to benchmark these systems remains daunting. Simply put, traditional client-side networking s...

DiBB (for Distributing Black-Box) is a meta-algorithm and framework that addresses the decades-old scalability issue of Black-Box Optimization (BBO), including Evolutionary Computation. Algorithmically, it does so by creating out-of-the-box a Partially Separable (PS) version of any existing black-box algorithm. This is done by leveraging expert kno...

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks. Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph structures e...

Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by the low...

Platforms such as Twitter are increasingly being used for real-world event detection. Recent work often leverages event-related keywords for training machine learning based event detection models. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation – and use...

Graph embeddings have been widely used for many graph analysis tasks. Mainstream factorization-based and graph-sampling-based embedding learning schemes both involve many hyperparameters and design choices. However, existing techniques often adopt some heuristics for these hyperparameters and design choices with little investigation into their impa...

We introduce Transaction Triaging, a set of techniques that manipulate streams of transaction requests and responses while they travel to and from a database server. Compared to normal transaction streams, the triaged ones execute faster once they reach the database. The triaging algorithms do not interfere with the transaction execution nor requir...

We propose a new method for learning compact state representations and policies separately but simultaneously for policy approximation in vision-based applications such as Atari games. Approaches based on deep reinforcement learning typically map pixels directly to actions to enable end-to-end training. Internally, however, the deep neural network...

Highly-heterogeneous and fast-arriving large amounts of data, otherwise said Big Data, induced the development of novel Data Management technologies. In this paper, the members of the IFIP Working Group 2.6 share their expertise in some of these technologies, focusing on: recent advancements in data integration, metadata management, data quality, g...

Explainability is a key requirement for text classification in many application domains ranging from sentiment analysis to medical diagnosis or legal reviews. Existing methods often rely on "attention" mechanisms for explaining classification results by estimating the relative importance of input units. However, recent studies have shown that such...

Digitized historical image collections as provided by individuals or memory institutions often suffer from limited or a complete lack of metadata In this paper, we present the City-Stories system that combines entity linking, multimedia retrieval, and crowdsourcing to make historical images searchable even across collections.

TC 2: Software: Theory and Practice

With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also comp...

Chatbots are computer programs aiming to replicate human conversational abilities through voice exchanges, textual dialogues, or both. They are becoming increasingly pervasive in many domains like customer support, e-coaching or entertainment. Yet, there is no standardised way of measuring the quality of such virtual agents. Instead, multiple indiv...

With the proliferation of increasingly powerful smartphones, location-centric social media platforms, such as Foursquare, have attracted millions of users sharing their physical activity online, resulting in an invaluable source of fine-grained, semantically rich, spatiotemporal user activity data. Such data provides us with an unprecedented opport...

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by...

Deep reinforcement learning applied to vision-based problems like Atari games maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it. By separating image processing from decision-making, one could better understand the complexity of each ta...

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by...

Missing values are very common in real-world data including time-series data. Failures in power, communication or storage can leave occasional blocks of data missing in multiple series, affecting not only real-time monitoring but also compromising the quality of data analysis. Traditional recovery (imputation) techniques often leverage the correlat...

Location-Based Social Networks (LBSNs) have been widely used as a primary data source for studying the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users’ mobility homophily and social proximity, and show that mobility and social features can help friendship and location...

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the...

Recording sensor data is seldom a perfect process. Failures in power, communication or storage can leave occasional blocks of data missing, affecting not only real-time monitoring but also compromising the quality of near- and off-line data analysis. Several recovery (imputation) algorithms have been proposed to replace missing blocks. Unfortunatel...

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expensive matrix factorization tech...

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order node proximity/adjacency matrix via computationally intensive matrix factorization techniques....

The presentation of the DAOR framework that performs graph embedding for arbitrary networks (i.e., graphs) without any manual tuning in near-linear time on the number of graph links producing interpretable embeddings robust in various metric spaces and preserving both low- and high-order proximities of the graph nodes.

Presentation slides of the paper "DAOC: Stable Clustering of Large Networks"

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant micro-posts containing the keyword -- referred to as th...

Proceedings of the 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9–12, 2019. Available open access (CC-BY) @ https://link.springer.com/book/10.1007%2F978-3-030-33220-4

Collaborative Knowledge Graph platforms allow humans and automated scripts to collaborate in creating, updating and interlinking entities and facts. To ensure both the completeness of the data as well as a uniform coverage of the different topics, it is crucial to identify underrepresented classes in the Knowledge Graph. In this paper, we tackle th...

Microblogging services such as Twitter are important, up-to-date, and live sources of information on a multitude of topics and events. An increasing number of systems use such services to detect and analyze events in real-time as they unfold. In this context, we recently proposed ArmaTweet —a system developed in collaboration among armasuisse and...

Clustering is a crucial component of many data mining systems involving the analysis and exploration of various data. Data diversity calls for clustering algorithms to be accurate while providing stable (i.e., deterministic and robust) results on arbitrary input networks. Moreover, modern systems often operate with large datasets, which implicitly...

We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to improve their combined expressive power. Our approach relies on two key components: 1) a ranking functi...

Collaborative Knowledge Graph platforms allow humans and automated scripts to collaborate in creating, updating and interlinking entities and facts. To ensure both the completeness of the data as well as a uniform coverage of the different topics, it is crucial to identify underrepresented classes in the Knowledge Graph. In this paper, we tackle th...

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix...

In many domains, high-quality data are used as a foundation for decision-making. An essential component to assess data quality lies in anomaly detection. We describe and empirically evaluate the design and implementation of a framework for data quality testing over real-world streams in a large-scale telecommunication network. This approach is both...

This paper presents Scalpel-CD, a first-of-its-kind system that leverages both human and machine intelligence to debug noisy labels from the training data of machine learning systems. Our system identifies potentially wrong labels using a deep probabilistic model, which is able to infer the latent class of a high-dimensional data instance by exploi...

Location Based Social Networks (LBSNs) have been widely used as a primary data source to study the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users' mobility homophily and social proximity, and show that mobility and social features can help friendship and location pred...

Neural networks have recently been shown to be highly effective at predicting links for constructing knowledge graphs. Existing research has mainly focused on designing 1) deep neural network models that are expressive in capturing fine-grained semantics, e.g., NTN and ConvE, but that are however less scalable; or 2) shallow models that are scalabl...

We present a novel algorithm to match GPS trajectories onto maps offline (in batch mode) using techniques borrowed from the field of force-directed graph drawing. We consider a simulated physical system where each GPS trajectory is attracted or repelled by the underlying road network via electrical-like forces. We let the system evolve under the ac...

When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a mo...

Crowdsourcing has become an integral part of many systems and services that deliver high-quality results for complex tasks such as data linkage, schema matching, and content annotation. A standard function of such crowd-powered systems is to publish a batch of tasks on a crowdsourcing platform automatically and to collect the results once the worke...

When searching for information, a human reader first glances over a document, spots relevant sections and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates to identify the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to supp...

There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking of diverse clustering algorith...

Performance of clustering algorithms is evaluated with the help of accuracy metrics. There is a great diversity of clustering algorithms, which are key components of many data analysis and exploration systems. However, there exist only few metrics for the accuracy measurement of overlapping and multi-resolution clustering algorithms on large datase...