260,658 research outputs found

    VARDA (VARved sediments DAtabase) – providing and connecting proxy data from annually laminated lake sediments

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    Varved lake sediments provide long climatic records with high temporal resolution and low associated age uncertainty. Robust and detailed comparison of well-dated and annually laminated sediment records is crucial for reconstructing abrupt and regionally time-transgressive changes as well as validation of spatial and temporal trajectories of past climatic changes. The VARved sediments DAtabase (VARDA) presented here is the first data compilation for varve chronologies and associated palaeoclimatic proxy records. The current version 1.0 allows detailed comparison of published varve records from 95 lakes. VARDA is freely accessible and was created to assess outputs from climate models with high-resolution terrestrial palaeoclimatic proxies. VARDA additionally provides a technical environment that enables to explore the database of varved lake sediments using a connected data-model and can generate a state-of-the-art graphic representation of multi-site comparison. This allows to reassess existing chronologies and tephra events to synchronize and compare even distant varved lake records. Furthermore, the present version of VARDA permits to explore varve thickness data. In this paper, we report in detail on the data mining and compilation strategies for the identification of varved lakes and assimilation of high-resolution chronologies as well as the technical infrastructure of the database. Additional paleoclimate proxy data will be provided in forthcoming updates. The VARDA graph-database and user interface can be accessed online at https://varve.gfz-potsdam.de, all datasets of version 1.0 are available at http://doi.org/10.5880/GFZ.4.3.2019.003 (Ramisch et al., 2019)

    Estudio y comparación de bases de datos orientadas a grafos

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    90 p.Un grafo es básicamente un conjunto de puntos (vértices) en el espacio, que están conectados por un conjunto de líneas (aristas). Como una de las formas más generales de modelado de datos, un grafo permite representar fácilmente entidades, sus atributos y sus relaciones.Las Bases de Datos Orientadas a Grafos (BDOG) se caracterizan porque las estructuras de datos para el esquema e instancia se basan en modelos de datos de grafo.Estos modelos se iniciaron en los años ochenta y a principios de los noventa, junto con modelos orientados a objetos. Su influencia decayó poco a poco con la aparición de nuevos modelos de bases de datos. Recientemente, la necesidad de gestionar la informacióon a través de una estructura de grafo y las limitaciones de las bases de datos tradicionales (en particular el modelo relacional), para cubrir las necesidades de las aplicaciones actuales ha llevado al desarrollo de nuevas tecnologías, y por consiguiente ha restablecido la importancia de esta área. El objetivo principal de este estudio es realizar una comparación sistematica de bases de datos de grafo.En este trabajo se presenta una revisión de las bases de datos de grafo actuales y su comparación de acuerdo a algunas caracterìsticas de modelado de datos. Entre las características evaluadas se incluyen: almacenamiento de datos, representación de entidades y relaciones, operación y manipulación de datos (lenguajes de consulta de grafos e interfaces de programación), y restricciones de integridad. Adicionalmente,se presenta una evaluaciçon empírica basada en pruebas de carga y consulta de datos. Este trabajo permite conocer y comparar, de manera teórica y práctica,las capacidades de modelado y ejecución entregadas por cada base de datos de grafo./ABSTRACT: A graph is basically a set of points (vertices) in space, which are connected by a set of lines (edges). As one of the most general forms of data modeling, a graph easily allows the representation of entities, their attributes and their relationships. Graph-oriented Databases (GODB) are characterized because their data structures for the scheme and instance are based on graph data models. These models began in the eighties and early nineties, along with object-oriented models. Their influence gradually faded with the emergence of new models of databases. Recently, the need to manage information through a graph structure and the limitations of traditional databases (in particular the relational model), to meet the needs of current applications has led to the development of new technologies, and therefore restored the importance of this area. The main objective of this study is to perform a systematic comparison of graph databases. This work presents a review of the current graph databases and their comparison according to well-defined data modeling features. Among the evaluated features we include: data storage, representation of entities and relationships, data operation and manipulation (graph query languages and application programming interfaces), and integrity constraints. Additionally, we present an empirical evaluation based on load and query data testing. This work allows to know and compare, from a theoretical and practical point of view, the modeling and execution capabilities provided by each graph database

    Biochemical network matching and composition

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    This paper looks at biochemical network matching and compositio

    Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

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    Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable

    Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

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    Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed
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