Langage de requête

Résumé

Un langage de requête est un langage informatique utilisé pour accéder aux données d'une base de données ou d'autres systèmes d'information. Il permet d'obtenir les données vérifiant certaines conditions (on parle de critères de sélection), comme toutes les personnes qui habitent une ville donnée. Les données peuvent être triées, elles peuvent également être regroupées suivant les valeurs d'une donnée particulière (par exemple on va regrouper toutes les personnes qui habitent la même rue). La grammaire d'un langage de requête est adaptée à la structure des données interrogées. Le langage de requête le plus connu et le plus utilisé est SQL. Exemples Langage de requête Par ordre alphabétique :

Datalog pour les bases de données déductives
DMX pour les modèles d'exploration de données (Data Mining)
MDX pour les bases de données multidimensionnelles OLAP
OQL pour les bases de données orientées objet
Prolog
SPARQL pour les graphes RDF
SQL pour les bases de données

Source officielle

https://fr.wikipedia.org/wiki/Langage_de_requ%C3%AAte

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Sampling-Based AQP in Modern Analytical Engines

Anastasia Ailamaki, Viktor Sanca

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often considered a second-class citizen. Modern analytical engines optimized for high bandwidth media and multi-core architectures only exacerbate existing inefficiencies, resulting in prohibitive query-time online sampling and longer preprocessing times in offline AQP systems. We demonstrate that the sampling operators can be practical in modern scale-up analytical systems. First, we evaluate three common sampling methods, identify algorithmic bottlenecks, and propose hardware-conscious optimizations. Second, we reduce the performance penalties of the added processing and sample materialization through system-aware operator design and compare the sample creation time to the matching relational operators of an in-memory JIT-compiled engine. The cost of data reduction with materialization is up to 2.5x of the equivalent group-by in the case of stratified sampling and virtually free (∼1x) for reasonable sample sizes of other strategies. As query processing starts to dominate the execution time, the gap between online and offline AQP methods diminishes.

ACM2022

Chargement

We propose to represent the "basic behaviors" of MPEG-7 description tools as a set of constructor, operator and function signatures. This way of making explicit the behavior of datatypes using function signatures is commonly used to formalize data models.A major advantage of this proposal is that, based on signatures, functions can be implemented using different algorithms and technologies, which could be considered as an MPEG-7 API. It?s important to note that the specification of signatures and their description is application independent. It therefore respects one of the major features of MPEG-7, which is "application independency". Implementations of the functions? cores are clearly out of the scope of the standard. Inevitably, the use of an MPEG-7 API makes building MPEG-7 applications easier and more effective as developers would not be totally dependent on the understanding of the MPEG-7 semantics and examples part.

2003

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Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Jie Luo, Mengshi Qi, Yiyu Wang, Yi Yang

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent the whole video, while ignoring visual pattern consistencies and intrinsic temporal relationships across different frames. Furthermore, the semantic correspondence between natural language queries and person-centric actions in videos has not been fully explored. To address these problems, we propose a novel binary representation learning framework, named Semanticsaware Spatial-temporal Binaries (S(2)Bin), which simultaneously considers spatial-temporal context and semantic relationships for cross-modal video retrieval. By exploiting the semantic relationships between two modalities, S(2)Bin can efficiently and effectively generate binary codes for both videos and texts. In addition, we adopt an iterative optimization scheme to learn deep encoding functions with attribute-guided stochastic training. We evaluate our model on three video datasets and the experimental results demonstrate that S(2)Bin outperforms the state-of-the-art methods in terms of various cross-modal video retrieval tasks.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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