Gestion des données de référence

Résumé

La gestion des données de référence ou gestion des données maîtres (GDR, plus connue sous le vocable anglais de master data management ou MDM) est une branche des technologies de l'information qui définit un ensemble de concepts et de processus visant à définir, stocker, maintenir, distribuer et imposer une vue complète, fiable et à jour des données référentielles au sein d’un système d’information, indépendamment des canaux de communications, du secteur d'activité ou des subdivisions métiers ou géographiques. Les données référentielles sous-tendent l’ensemble du système d’information, ce qui explique pourquoi leur gestion est devenue un enjeu crucial dans toutes les organisations depuis une dizaine d’années. Classiquement, trois types de données référentielles sont la cible de la GDR : les données « clients/fournisseurs », les données « produits » et les données « financières ». La gestion des données de référence est considérée comme une brique de l'architecture d'un système d'

Source officielle

https://fr.wikipedia.org/wiki/Gestion_des_donn%C3%A9es_de_r%C3%A9f%C3%A9rence

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Cours associés (8)

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Séances de cours associées (16)

Séances de cours associées

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Practical Private Range Search Revisited

Ioannis Demertzis, Odysseas Papapetrou

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on "practical" private range search (mainly in Databases venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees, or permit unacceptable privacy leakages. In this paper, we take an interdisciplinary approach, which combines the rigor of Security formulations and proofs with efficient Data Management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE) primarily proposed for keyword search. We reduce range search to multi-keyword search using range covering techniques with tree-like indexes. We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure, and (ii) minimizing false positives incurred by some schemes under heavy data skew. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

2016

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Practical Private Range Search in Depth

Ioannis Demertzis, Odysseas Papapetrou

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on “practical” private range search (mainly in database venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees or permit unacceptable privacy leakages. In this article, we take an interdisciplinary approach, which combines the rigor of security formulations and proofs with efficient data management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE), primarily proposed for keyword search. We reduce range search to multi-keyword search using range-covering techniques with tree-like indexes, and formalize the problem as Range Searchable Symmetric Encryption (RSSE). We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure and (ii) minimizing false positives incurred by some schemes under heavy data skew. We also explain an important concept in the recent SSE bibliography, namely locality, and design generic and specialized ways to attribute locality to our RSSE schemes. Moreover, we are the first to devise secure schemes for answering range aggregate queries, such as range sums and range min/max. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

2018

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Using the Generic Product Model for storing and sharing ERP data

Rémy Glardon, Souleïman Naciri, Michel Pouly

Nowadays, all product life cycle processes are investigated deeply in order to get an advantage over competitors. To support these processes, several software applications are available. However this wide range of heterogeneous applications leads to a large variety of data formats that hinder data integration. To tackle this problem, the Hitachi company proposed a modeling language called GPM (Generic Product Model) into which they propose to translate data coming from several domain applications. This research proposes to broaden the range of application data able to be stored in the GPM data warehouse by developing translators between ERPs and GPM. Methodologies are given for the translation of mass production and customized products' data.

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