Big data | EPFL Graph Search

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Thus a fourth concept, v

Marseille: habiter la densité (Quartier Saint-Charles, Marseille, F)

Laurent Eller, Luc Larnaudie

Le site de la porte d'Aix est un site fragmenté. Il se caractérise par sa forme triangulaire avec à chacun de ses angles un composant essentiel des flux: la gare Saint-Charles, l'Autoroute A7 et la porte d'Aix connectée au port industriel. Ce site a perdu au fil du temps son identité de porte d'entrée en centre-ville. À la fois bretelle d'autoroute et entrée de ville, la place Jules Guesde acquiert alors un statut ambigu entre échangeur autoroutier et espace public majeur de la ville. La rue Bernard Dubois qui relie le port à la gare est devenue une zone d'accueil des populations en transit. Cette situation favorise la persistance d'une véritable vie sociale mais aussi les conditions de la précarité. La porte d'Aix révèle aujourd'hui les traces de son histoire à travers la diversité morphologique de son tissu. Les remparts ont apporté du tissu intra-muros, le développement extra-muros le long de l'axe impérial du tissu orienté sur la façade, le début du siècle des îlots-bâtiments de service, et la construction de l'autoroute des barres modernistes libérant le sol grâce à l'empilement. Le projet ne vise pas à résoudre la densification du site en proposant une solution globale, mais en offrant à chaque tissu existant son développement: une "greffe". Par la densité et la mixité d'usage, nous proposons de construire l'identité de ce quartier. Logements, espaces publics et équipements restent nos outils qui ont fait la richesse de la ville de Marseille au cours de son histoire.

2006

Automatic L2 Regularization for Multiple Generalized Additive Models

Yousra El Bachir

Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expansions, whose coefficients are the regression parameters. The smoothness is induced by a quadratic roughness penalty on the functionsâ curvature, which is equivalent to a weighted

L_2

regularization controlled by smoothing parameters. Regression fitting relies on maximum penalized likelihood estimation for the regression coefficients, and smoothness selection relies on maximum marginal likelihood estimation for the smoothing parameters. Owing to their nonlinearity, flexibility and interpretability, generalized additive models are widely used in statistical modeling, but despite recent advances, reliable and fast methods for automatic smoothing in massive datasets are unavailable. Existing approaches are either reliable, complex and slow, or unreliable, simpler and fast, so a compromise must be made. A bridge between these categories is needed to extend use of multiple generalized additive models to settings beyond those possible in existing software. This thesis is one step in this direction. We adopt the marginal likelihood approach to develop approximate expectation-maximization methods for automatic smoothing, which avoid evaluation of expensive and unstable terms. This results in simpler algorithms that do not sacrifice reliability and achieve state-of-the-art accuracy and computational efficiency. We extend the proposed approach to big-data settings and produce the first reliable, high-performance and distributed-memory algorithm for fitting massive multiple generalized additive models. Furthermore, we develop the underlying generic software libraries and make them accessible to the open-source community.

EPFL2019

Clouseau: Blockchain-based Data Integrity for HDFS Clusters

Ioannis Mytilinis

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are accessed by multiple independent, untrusted entities, which need to interact with shared data. In such scenarios, the potential conflicts of interest incentivize malicious parties to act in a dishonest way and tamper the data to their own benefit. The decentralized nature of the systems renders verifiable data integrity a strenuous but necessary task: The various parties should be able to audit changes and detect tampering when it happens. In this work, we focus on HDFS, the most common storage substrate for Big Data analytics. HDFS is vulnerable to malicious users and participating nodes and does not provide a trustful lineage mechanism, thus jeopardizing the integrity of stored data and the credibility of extracted insights. As a remedy, we present Clouseau, a blockchain-based system that provides verifiable integrity over HDFS, while it does not incur significant overhead at the critical path of read/write operations. During the demonstration, the attendees will have the chance to interact with Clouseau, corrupt data themselves, and witness how Clouseau detects malicious actions.

IEEE COMPUTER SOC2021

Automatic L2 Regularization for Multiple Generalized Additive Models

Yousra El Bachir

L_2

EPFL2019