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International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

作者： Rahmatian Jayanty Sholichah Mahmud Imrona Andry Alamsyah Telkom University Bandung Indonesia

ISBN: (数字)9781728172262

ISBN: (纸本)9781728172279

Currently, the development of data has increased rapidly, Solutions are needed to be able to manage data efficiently, one that can be offered is to utilize the database. The biggest decision in selecting a database is to select between SQL or nosql. MySQL is a database that uses SQL as a query language, consists of tables that store data in the form of columns and rows, then the new format database nosql, appeared, it is suitable for handling large amounts of data in a variety of formats. Neo4j is one of nosql that is widely used, it is a graph database which provides an easy way to visualize data by storing data in the form of nodes that are connected by edges. In this paper, we compared the performance of MySQL and Neo4j databases in terms of memory usage and execution time, also we presented the flexibility of the databases using P. The results show that MySQL has a faster execution time than Neo4j, although, both these databases have the same time complexity. It is also known that Neo4j has a higher memory usage than MySQL. But Neo4j has better flexibility than MySQL.

关键词： Structured Query Language databases nosql databases Decision making Public policy Visual databases Time complexity

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New approach for implementing big datamart using nosql key-value stores

New approach for implementing big datamart using NoSQL key-v...

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International Conference on Cloud Technologies and Applications (CloudTech)

作者： Abdelhak KHALIL Mustapha BELAISSAOUI LEFCG-SIAD Laboratory Hassan First University of Settat Settat Morocco

ISBN: (数字)9781728161754

ISBN: (纸本)9781728161761

Nowadays, nosql technologies are gaining significant ground and considered as the future of data storage, especially when it comes to huge amount of data, which is the case of data warehouse solutions. nosql databases provide high scalability and good performance among relational ones, which are really time consuming and can't handle large data volume. The growing popularity of the term nosql these days and vaguely related phrases like big data make us think about using this technology in decision support systems. The purpose of this paper is to investigate the possibility to instantiate a big data mart under one of the most popular and least complicated types of nosql databases; namely key-value store, the main challenge is to make a good correlation between the old-school approach of data warehousing based on traditional databases that favor data integrity, and interesting opportunities offered by new generation of database management systems. The paper describes the transformation process from multidimensional conceptual schema to the logical model following three approaches, and outlines a list of strengths and weaknesses for each one based on practical experience under Oracle nosql Database.

关键词： Analytical models Cloud computing nosql databases Snow Warehousing Big Data Data models

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Versioning Data During Migration Processes in Cloud Environment

Versioning Data During Migration Processes in Cloud Environm...

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International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Roman Ceresnak Karol Matiasko Faculty of Management Science and Informatics University of Zilina Univerzitna 8215/1 Zilina Slovakia

ISBN: (数字)9781665422260

ISBN: (纸本)9781665446785

Nowadays, big data influences many aspects of human life. They help in medicine with diagnosing different illnesses, in traffic with watching of traffic accidents, and of course, they have a crucial role in supporting decisions. It is appropriate to test another database, respectively, another database type, in every operation's unsatisfactory performance by using a set database. A transformation process is needed in this case. Big Data entering this database has a different structure and size, which influences the set transformation process's time difficulty. The transformation process changes the data structure, from relational to nonrelational, respectively nonrelational to a relational database, making it possible to stop the process or an error that can end up with an incomplete change of a data structure and the data this process must have been repeated. “Version” system we created in this paper is, in the case of incomplete data change, respectively failure of transformation process during the transformation of a relational database to a nonrelational or nonrelational database to relational, capable of continuing from the error point of the previous approach, and so it can erase necessity to perform whole transformation process from the very first beginning.

关键词： Electronic learning nosql databases Relational databases Big Data Data structures Market research Accidents

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Research on Low Cost Student Electronic Archives Management System in High-concurrency Environment

Research on Low Cost Student Electronic Archives Management ...

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International Conference on Electronics Information and Emergency Communication

作者： Zeyuan Cui Bo Cui Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Inner Mongolia University Hohhot China

ISBN: (数字)9781728163130

ISBN: (纸本)9781728163147

The Student Electronic Archives Management System (SEAMS) is one of the core systems used to carry out student work in colleges and universities. In recent years, with the increasing scale of Chinese universities, the existing SEAMS cannot handle the increased number of visits in their high-concurrency environments because of their outdated architecture. This paper analyzed the reasons and influences of the poor performance of the existing SEAMS, as well as the requirements of the SEAMS in colleges and universities and put forward a new SEAMS which works well in high-concurrency environments. The hybrid database and message queuing mechanism are used in the system to realize a high-performance and flexible SEAMS. The test results show that the efficiency improves significantly compared to single database and threaded system with request per second (RPS) above 9000.

关键词： Servers nosql databases Structured Query Language Task analysis Concurrent computing Computer architecture

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Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management

Big Data Analytics for Processing Real-time Unstructured Dat...

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Data Science and Its Applications (ICoDSA), International Conference on

作者： Faqih Hamami Iqbal Ahmad Dahlan Setya Widyawan Prakosa Khamal Fauzan Somantri School of Industrial and System Engineering Telkom University Bandung Indonesia Military Engineering Faculty Indonesia Defense University Bogor Indonesia Electronic and Computer Engineering National Taiwan University of Science & Technology Taipei Taiwan Faculty of Mathematics and Science Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9781728182353

ISBN: (纸本)9781728182360

Todays many devices generate data everywhere and anytime. Data grow massively and becomes complex thing that needs to be handled. Unstructured data is one type of big data that is difficult to process and consists of unstable attributes. In traffic management, CCTVs are installed to monitor the specific location in the highway. CCTV generates unstructured data in image and video format. These data are difficult to process due to the complexity of the data. This research proposes to implement big data analytics to process real-time unstructured data from CCTV into knowledge displayed in web dashboard. We implement the YOLO framework with YoloV4 Architecture and COCO dataset for traffic flow counting and detecting illegal parking which is categorized as abnormal situation. Unstructured data from CCTV then transformed into semi-structured format in JSON. Data also can be visualized in real time to facilitate local authority to understand the highway situation. Historical data are stored in the nosql database to deep more knowledge such as vehicle traffic pattern. The proposed system requires the ROI drawing line as trigger to count the passing vehicles. These experiments are conducted from open CCTV for traffic online in Bali Tower Public Streaming. The prototype result is able to detect the object with 10 fps.

关键词： Real-time systems Big Data Streaming media nosql databases Machine learning Data mining

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GPU in applications with non-relational DBs

GPU in applications with non-relational DBs

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Telecommunications Forum (TELFOR)

作者： Stefan Jovanov Vladimir Zdraveski Marjan Gusev Faculty of Computer Sciences and Engineering SS. Cyril and Methodius University Skopje North Macedonia

ISBN: (数字)9781665404990

ISBN: (纸本)9781665405003

Rapid speeds have been acquired throughout the past decade using GPUs and nosql-databases. This paper presents a concept of a GPU-extended non-relational database management system. The research focuses on implementing kernels and performing the basic aggregation functions over a JSON file. Different comparisons with the Numpy library, a CPU-counterpart and MongoDb show the importance of the concept. The hypothesis is to check if GPU can speed up nosql-database queries which we prove using our methods.

关键词： Graphics processing units databases Libraries Computer architecture Acceleration Companies nosql databases

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Performance Scaling of Cassandra on High-Thread Count Servers 19

Performance Scaling of Cassandra on High-Thread Count Server...

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10th ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Talreja, Disha Lahiri, Kanishka Kalambur, Subramaniam Raghavendra, Prakash Adv Micro Devices Inc Santa Clara CA 95054 USA PES Univ Dept Comp Sci Bengaluru Karnataka India

ISBN: (纸本)9781450362399

nosql databases are commonly used today in cloud deployments due to their ability to "scale-out" and effectively use distributed computing resources in a data center. At the same time, cloud servers are also witnessing rapid growth in CPU core counts, memory bandwidth, and memory capacity. Hence, apart from scaling out effectively, it's important to consider how such workloads "scale-up" within a single system, so that they can make the best use of available resources. In this paper, we describe our experiences studying the performance scaling characteristics of Cassandra, a popular open-source, column-oriented database, on a single high-thread count dual socket server. We demonstrate that using commonly used benchmarking practices, Cassandra does not scale well on such systems. Next, we show how by taking into account specific knowledge of the underlying topology of the server architecture, we can achieve substantial improvements in performance scalability. We report on how, during the course of our work, we uncovered an area for performance improvement in the official open-source implementation of the Java platform with respect to NUMA awareness. We show how optimizing this resulted in 27% throughput gain for Cassandra under studied configurations. As a result of these optimizations, using standard workload generators, we obtained up to 1.44x and 2.55x improvements in Cassandra throughput over baseline single and dual-socket performance measurements respectively. On wider testing across a variety of workloads, we achieved excellent performance scaling, averaging 98% efficiency within a socket and 90% efficiency at the system-level.

关键词： Performance benchmarking Cassandra nosql databases Performance scalability

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

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BIG DATA RESEARCH 2018年 11卷 33-43页

作者： Kucukkececi, Cihan Yazici, Adnan Middle East Tech Univ Dept Comp Engn Ankara Turkey

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 connected 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. (c) 2017 Elsevier Inc. All rights reserved.

关键词： Internet of things (IoT) Big graph databases nosql databases Wireless multimedia sensor networks Simulator

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Design a Data Warehouse Schema from Document-Oriented database

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Procedia Computer Science 2019年 159卷 221-230页

作者： Senda Bouaziz Ahlem Nabli Faiez Gargouri MIRACL Laboratory Faculty of Economics and Management Sfax BP 3018 Tunisia MIRACL Laboratory Faculty of Computer Sciences and Information Technologies Al-Baha University KSA MIRACL Laboratory Institute of Computer Science and Multimedia Sfax BP 3021 Tunisia

Traditional data warehouses are unable to meet the growing needs of the modern enterprise to integrate and analyze a wide variety of data generated by social, mobile and sensor sources. What is remarkable is that many companies have changed their data storage in nosql databases. Given the important role played by this type of databases, it is necessary to study nosql databases as a data source for the modeling and the implementation of data warehouses. This paper proposes a method to design the data warehouse schema from schema free databases known as nosql databases. Our proposal starts with the extraction of schemes from document-oriented databases as an example of nosql database. This extraction is performed based on the MapReduce paradigm. Then, it defines the structure identification graph from the extracted schemes. This graph will help the designer to identify the multidimensional concepts for each schema extracted in order to design the global data warehouse schema.

关键词： nosql databases Map-Reduce Data Warehouse Schema Design Document-oriented database Extraction

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Model driven reverse engineering of nosql property graph databases: The case of Neo4j

Model driven reverse engineering of NoSQL property graph dat...

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IEEE International Conference on Big Data

作者： Isabelle Comyn-Wattiau Jacky Akoka ESSEC Business School Cergy France CEDRIC-CNAM & IMT-TEM Paris France

Most nosql databases are schemaless. Although they offer some flexibility, they do not have any knowledge of the database schema, losing the benefits provided by these schemas. It is generally accepted that data modelling can have an impact on performance, consistency, usability, and maintainability. We argue that nosql databases need data models that ensure the proper storage and the relevant querying of the data. This paper seeks to present and illustrate an MDA-based approach, allowing us to achieve a reverse engineering of nosql property graph databases into an Extended Entity-Relationship schema. The approach is applied to the case of Neo4j graph database. We present an illustrative scenario and evaluate the reverse engineering approach.

关键词： Reverse engineering nosql databases Unified modeling language Data models Ciphers Big Data

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