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检索条件"主题词=NoSQL Databases"

共 340 条记录，以下是251-260 订阅

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Extraction process of conceptual model from a document-oriented nosql database

Extraction process of conceptual model from a document-orien...

引用

International Conference on Knowledge and Systems Engineering

作者： Amal AIT BRAHIM Rabah TIGHILT FERHAT Gilles ZURFLUH Toulouse Institute of Computer Science Research (IRIT) Toulouse Capitole University Toulouse France

nosql systems are used to manage massive databases that verify 3V: Volume, Variety and Velocity. Generally, these systems are known by the characteristic "schema less" which means that we can create a database without defining the data schema beforehand. This property offers more flexibility and speed by allowing the evolution of the data model during the exploitation of the base. However, to formulate queries on the database, the user needs a precise knowledge of data model. In this article, we propose a process for the automatic extraction of the conceptual model of a document-oriented nosql database. To do this, we use the Model Driven Architecture (MDA) architecture that provides a formal framework for automatic model transformation. From a nosql database, we propose a set of transformation rules with QVT to generate the conceptual model in the form of a UML class diagram. An experimentation of the extraction process was carried out on an application in the medical field.

关键词： Unified modeling language nosql databases Data models Computational modeling Adaptation models Big Data

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Data Modeling for a nosql Database Service

Data Modeling for a NoSQL Database Service

引用

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Sudarshan S. Chawathe School of Computing and Information Science University of Maine Orono Maine USA

ISBN: (纸本)9781728138862

Cloud-hosted nosql database services, such as AWS DynamoDB, offer significant advantages, including low start-up costs, high performance and availability, wide scalability, and ease of deployment and management. These advantages have led to their rapid adoption and growth. However, the data storage, querying, and modification features supported by such nosql services are very rudimentary in comparison with those of relational and object database systems. Further, data modeling decisions made to map application requirements to the supported nosql model have very significant impact on not only performance but also financial cost incurred in using the services. Unlike the well developed body of work for relational-and object- database design, there is a great dearth of systematic procedures for nosql database design. This paper addresses this design problem by providing methods that map standard data models to the typically idiosyncratic and rudimentary models supported by nosql database services, using AWS DynamoDB as a specific instance.

关键词： cloud computing data models nosql databases data models Cloud Computing database design Data modelling design problems Legal Services Costs Database JAG1 gene

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Evaluation of storage and query performance of sensor based Internet of Things data with MongoDB

Evaluation of storage and query performance of sensor based ...

引用

International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Yilmaz, Nazim Alatli, Oylum Ciloglugil, Birol Erdur, Riza Cenk Ege Univ Dept Comp Engn Izmir Turkey

ISBN: (纸本)9781538668788

Internet of Things (IoT) can be defined as the communication network that connects physical devices such as sensors, actuators, electronic home appliances and smart devices with each other and other systems. As part of IoT, numerous physical devices work concurrently and produce data continuously with big volumes. In this regard, nosql technology is utilized, because relational databases are insufficient to store and query the IoT data. nosql is the database technology that allows rapid organization of different non-relational data types. nosql databases are divided into four categories, which are key-value based, column-oriented, document-oriented and graph databases. Since graph databases do not provide effective solutions for IoT data, several studies in the literature focus on the use of database technologies in the other three categories for the purpose of storing and querying IoT data more effectively. In this paper, it is aimed to evaluate the storage and query performance of MongoDB on IoT data. It has been decided to use MongoDB, which is a document-oriented nosql database technology, because of its popular usage and high performance. Yahoo Cloud Serving Benchmark (YCSB), which is an open source tool developed by Yahoo that allows database operations at different volumes, is used for performance measurements. YCSB has been made compatible with the IoT dataset that is used for performance measurements, by re-organizing its DB Interface Layer for this study. The paper is concluded with discussion of the performance measurement results.

关键词： internet of things nosql databases MongoDB performance analysis YCSB

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Ramesh Dharavath Anand Kumar Ashu Kumar Indian Institute of Technology (ISM) Dhanbad India Department of Computer Science & Engineering Indian Institute of Technology (ISM) Dhanbad Dhanbad Jharkhand India

ISBN: (数字)9781728146553

ISBN: (纸本)9781728146560

With the exponential growth of the Internet, the need of handling the sort of unstructured, messy and unpredictable data became so urgent. This makes nosql databases to be introduced to compensate for the inefficiency of traditional relational databases in dealing with these kind of data. Among those nosql databases, Cassandra gained popularity than the rest and today it is at use in over 1500 companies like Reddit, Netflix, Instagram, GitHub, eBay. The EQUALS and IN operator are the two most basic tools used in Cassandra Query Language (CQL) to compare two or more data items. However, the two operators are not capable of dealing with non-traditional data types like images as instead of looking for similarity between two images, they use identity (=) which is not always the correct method. Thus, this paper introduces similarity-aware EQUALS and IN operator which is very much capable of dealing with modern complex data types like images. These operators based on similarity can be used in various fields like Agriculture for finding regions capable of growing a particular crop. In this paper, we demonstrate the application of the two operators in Agriculture.

关键词： Soil Urban areas nosql databases Cotton Relational databases Distributed databases

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Curating Variational Data in Application Development 34

Curating Variational Data in Application Development

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34th IEEE International Conference on Data Engineering Workshops (ICDEW)

作者： Stoerl, Uta Mueller, Daniel Tekleab, Alexander Tolale, Stephane Stenzel, Julian Klettke, Meike Scherzinger, Stefanie Darmstadt Univ Appl Sci Darmstadt Germany Univ Rostock Rostock Germany OTH Regensburg Regensburg Germany

ISBN: (纸本)9781538655207

Building applications for processing data lakes is a software engineering challenge. We present Darwin, a middle-ware for applications that operate on variational data. This concerns data with heterogeneous structure, usually stored within a schema-flexible nosql database. Darwin assists application developers in essential data and schema curation tasks: Upon request, Darwin extracts a schema description, discovers the history of schema versions, and proposes mappings between these versions. Users of Darwin may interactively choose which mappings are most realistic. Darwin is further capable of rewriting queries at runtime, to ensure that queries comply with legacy data. Alternatively, Darwin can migrate legacy data to reduce the structural heterogeneity. Using Darwin, developers may thus evolve their data in sync with their code. In our hands-on demo, we curate synthetic as well as real-life datasets.

关键词： nosql databases schema evolution schema management data migration query rewriting variational data

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Key-value Pairs Allocation Strategy for Kinetic Drives 4

Key-value Pairs Allocation Strategy for Kinetic Drives

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4th IEEE International Conference on Big Data Computing Service and Applications (IEEE BigDataService)

作者： Eldakiky, Hebatalla Du, David H. C. Univ Minnesota Comp Sci & Engn Dept Minneapolis MN 55455 USA

ISBN: (纸本)9781538651193

The explosion in the amount of data with the development of internet and cloud computing prompted much research to develop systems to store and process this data efficiently. Therefore, nosql databases emerged as a solution to be considered due to their flexibility and high performance. key-value object storage, one of the nosql databases categories, is widely used in many Big Data applications, where it treats the object as a single opaque collection identified by a key. This collection may have different fields for each record. Recently, Seagate developed a new drive called "Kinetic drive". The kinetic drive is an independent active disk accessible by an Ethernet which enables the applications to directly connect to the drive via IP address without the need to pass through multiple legacy layers using the storage servers. It can also carry out key-value pair operations on its own. So in large scale data management, a set of kinetic drives can be used to exploit parallelism in satisfying user requests and solve the bottleneck caused by queuing of requests in the storage server which manages multiple HDDs/SDDs. On the other hand, Kinetic drive has a limited bandwidth and size. Therefore, in this paper, we develop a key-value pairs allocation algorithm for kinetic drives which takes into consideration the data popularity, limited size and bandwidth of kinetic drive to avoid queuing on the level of the drive, We also carry out performance evaluation that shows the performance of our approach in allocating the data in a near-optimal number of drives and minimizes the drives' index table.

关键词： Kinetic theory Servers Resource management Bandwidth Drives nosql databases

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C2Ont: An OWL Ontology Learning Approach from Apache Cassandra

C2Ont: An OWL Ontology Learning Approach from Apache Cassand...

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International Conference on Advanced Information Technologies (ICAIT)

作者： Nang Kham Soe Tin Tin Yee Ei Chaw Htoon University of Information Technology Yangon Myanmar

Big Data is a massive volume of both unstructured and structured data. It is crucial to efficiently represent big data as knowledge for data management. Ontologies provide knowledge as a formal description of a domain of interest. Therefore, the ontology learning approach is proposed for Apache Cassandra. It is composed of six mapping rules and converts OWL ontology from data in Cassandra by applying these mapping rules. NorthWind dataset is applied for demonstrating how to learn ontology from data in Cassandra. The evaluation result indicates that our approach can learn ontology in covering terminologically the modeled domain since the adequacy of extracted ontology is greater than 15%.

关键词： Ontologies Data models OWL Big Data nosql databases Data mining

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Securing Big Data in the Age of AI

Securing Big Data in the Age of AI

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Murat Kantarcioglu Fahad Shaon University of Texas at Dallas and Data Security Technologies LLC

ISBN: (数字)9781728167411

ISBN: (纸本)9781728167428

Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer nosql databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the nosql system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed SECUREDL system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.

关键词： Big Data Artificial intelligence Data models nosql databases Data privacy Data security

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DERP: A Deep Reinforcement Learning Cloud System for Elastic Resource Provisioning 10

DERP: A Deep Reinforcement Learning Cloud System for Elastic...

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10th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom)

作者： Bitsakos, Constantinos Konstantinou, Ioannis Koziris, Nectarios Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

ISBN: (纸本)9781538678992

Modern large scale computer clusters benefit significantly from elasticity. Elasticity allows a cluster to dynamically allocate computer resources, based on the user's fluctuating workload demands. Many cloud providers use threshold-based approaches, which have been proven to be difficult to configure and optimise, while others use reinforcement learning and decision-tree approaches, which struggle when having to handle large multidimensional cluster states. In this work we use Deep Reinforcement learning techniques to achieve automatic elasticity. We use three different approaches of a Deep Reinforcement learning agent, called DERP (Deep Elastic Resource Provisioning), that takes as input the current multi-dimensional state of a cluster and manages to train and converge to the optimal elasticity behaviour after a finite amount of training steps. The system automatically decides and proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a nosql cluster according to user-defined policies/rewards. We compare our agent to state-of-the-art, Reinforcement learning and decision-tree based, approaches in demanding simulation environments and show that it gains rewards up to 1.6 times better on its lifetime. We then test our approach in a real life cluster environment and show that the system resizes clusters in real-time and adapts its performance through a variety of demanding optimisation strategies, input and training loads.

关键词： Elasticity Resource Management Resource Provisioning Cloud computing Deep Reinforecement learning Double deep Q learning nosql databases DERP

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Reflection on MongoDB Database Logical and Physical Modeling

Reflection on MongoDB Database Logical and Physical Modeling

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AFRICON Conference

作者： Pieter Willem Jordaan Johann Erich Wolfgang Holm School for Electrical Electronic and Computer Engineering North-West University Potchefstroom South Africa

ISBN: (数字)9781728132891

ISBN: (纸本)9781728132907

Traditional relational database design uses conceptual, logical, and physical modeling based on Peter Chen's methods. UML (Unified Modeling Language), though being a set of software development tools, is often used to conceptually model data and relationships. This paper presents a methodical approach to logically and physically model data in MongoDB by utilizing UML conceptual modeling aids. Application of data models greatly impacts performance, scalability and flexibility of data systems. Furthermore, application life-cycle and expansion impact long-term decisions made during modeling. This paper takes into consideration application requirements, access patterns and life-cycle during logical and physical modeling in a cloud environment. The flexibility that a nosql modeling paradigm embodies makes logical and physical modeling complex, with no hard-and-fast rules. This paper presents logical and physical modeling concepts required during designing database applications with MongoDB.

关键词： Unified modeling language Data models nosql databases Relational databases Scalability Computational modeling

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