Graph database

Summary

A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data. Graph databases are commonly referred to as a NoSQL. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal

Official source

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications

Related people

Related units

Related concepts

Related courses

Related lectures

Filtering Random Graph Processes Over Random Time-Varying Graphs

Andreas Loukas

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity in both the graph topology and the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response and autoregressive moving average graph filters, when operating on random time-varying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that 1) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and 2) there are meaningful upper bounds for the variance of the filter output. We conclude this paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm and yield a (up to) four times complexity reduction, with a very little difference from the optimal solution.

Institute of Electrical and Electronics Engineers2017

Scalable Robust Graph Embedding with Spark

Karl Aberer, Chi Thang Duong, Trung-Dung Hoang, Quoc Viet Hung Nguyen

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embedding technique. Our method splits a graph into subgraphs to learn their embeddings in isolation and subsequently reconciles the embedding spaces derived for the subgraphs. We realize this idea through a novel distributed graph decomposition algorithm. In addition, we show how to implement our framework in Spark to enable efficient learning of effective embeddings. Experimental results illustrate that our approach scales well, while largely maintaining the embedding quality.

ASSOC COMPUTING MACHINERY2021

The main goal of my research is to establish guidelines for workplace design based on human biomechanics: specifically sitting workplaces and handling areas in 1/6G-1/3G (Moon, Mars) conditions. Such a workplace could be used in long-term space missions in order to maximize worker performance and minimize the risks of musculoskeletal injuries.Research objective is to formalize a baseline workplace design based on best practices limited to the tasks: operating a computer, assembling/maintenance operations.Proposed steps:1) Data collection and methods definitions2) Measurement and simulation of the human fatigue in the variation of sitting postures and handling areas (identification of typical poses, 3D simulation, graph analysis)3) Optimization of the workplace design based on human biomechanics (motion capture, factor analysis, optimization) 4) Simulation of workplace design in different environments (validation methods).Scientific & technological novelty: 1) Measurement of human muscular fatigue as a function of workload in reduced gravity conditions and integration of the results into the design model2) Modeling of the biomechanics of human movements in the workplace in reduced gravity conditions;3) Developing of sitting workplace design in reduced gravity conditions - taking into account human biomechanics.This research addresses problems revealed by workplace design in confined conditions found in Earth or near-Earth facilities in extreme environments including in underwater conditions, remote & non-remote locations and Low Earth Orbit (LEO), habitat simulators. Human-centered design strategies and biomechanics issues for creating an effective workplace design for long-term missions will also be addressed. The recommendations for design, maintenance, and usage of this workplace in different gravity conditions (1G, 1/6G,1/3G) will be provided. The design optimization of such a workplaces design based on human biomechanics will be done with the help of numerical simulations and digital human modeling. Physical experiments will validate these results by means of the suspension system, underwater experiment and possible by parabolic flights. This work may have a significant impact on workplace designs for both Earth and Space projects. Keywords: biomechanics, workplace, posture, extreme environments, human-centered design, Moon/Mars exploration, comfort conditions, task performance, reduced gravity, digital human modeling, motion capture

EPFL2022