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2nd International Conference on Energy and Environmental Protection (ICEEP 2013)

作者： Zhang, Qingqing Wang, Qingyu Zhang, Gaofeng Zhang, Yi Wu, Lanxu Shangdong Elect Power Res Inst Jinan 250002 Peoples R China Fujian Elect Power Res Inst Fuzhou 350007 Peoples R China China Titans Energy Technol Grp Co Ltd Beijing 519015 Peoples R China

ISBN: (纸本)9783037857434

Currently the massive power quality monitoring data are stored in the centralized database of the monitoring master station, the problems such as large storage space;low query retrieval speed;low reliability and poor scalability will be caused. This paper proposes a data management scheme for massive power quality monitoring data based-on the distributed-database-system. The monitoring data of different power quality indexes are stored in the distributed servers of the existing monitoring sub-stations;the server of monitoring master station is used for storing data characteristics value and data indexes, it is also used for unified management of distributed database system. The scheme takes full advantage of each server's storage space and network bandwidth, and saves the storage space and improves the access efficiency.

关键词： power quality massive data distributed database optimization storage

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distributed query optimization strategies for cloud environment

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Journal of Data, Information and Management 2021年第4期3卷 271-279页

作者： Kaseb, Mostafa R. Haytamy, Samar Sh. badry, Rasha M. Department of Information System Fayoum University Fayoum Egypt Department of Computer Science Fayoum University Fayoum Egypt

Cloud computing services are provided over the internet such as computing, servers, storage, databases, networking, software, and many more. Then, customers get services from cloud providers and pay according to their usage. Nowadays, database as a service is becoming a more popular service in cloud computing. Due to faster response and more reliability, distributed databases are used in the cloud for storing and managing data. Thus, enterprises are transferring the stored data to the cloud computing center. Although in a distributed database system via cloud environment, the required relations by a query plan may be stored at numerous sites. This exponentially increases the number of potential equivalent plan alternatives to find an optimal Query Execution Plan. Although exhaustively explore all potentials plans in large search space is not computationally reasonable. Therefore, in order to improve the performance in the cloud, query optimization mechanisms are paramount and required in order to optimize data processing time and maximize resource utilization. So, a review of the different techniques used for query optimization is conducted in this paper to provide researchers with a complete view of query processing and its optimization techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.

关键词： Amending query Cloud query processing Cloud services distributed database Query optimization

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Citus: distributed PostgreSQL for Data-Intensive Applications 21

Citus: Distributed PostgreSQL for Data-Intensive Application...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Cubukcu, Umur Erdogan, Ozgun Pathak, Sumedh Sannakkayala, Sudhakar Slot, Marco Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781450383431

Citus is an open source distributed database engine for PostgreSQL that is implemented as an extension. Citus gives users the ability to distribute data, queries, and transactions in PostgreSQL across a cluster of PostgreSQL servers to handle the needs of data-intensive applications. The development of Citus has largely been driven by conversations with companies looking to scale PostgreSQL beyond a single server and their workload requirements. This paper describes the requirements of four common workload patterns and how Citus addresses those requirements. It also shares benchmark results demonstrating the performance and scalability of Citus in each of the workload patterns and describes how Microsoft uses Citus to address one of its most challenging data problems.

关键词： postgresql distributed database relational database database extension

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Simulation and Analysis of Performance Prediction in distributed database Design using OO Approach

Simulation and Analysis of Performance Prediction in Distrib...

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3rd IEEE International Advance Computing Conference (IACC)

作者： Jagannatha, S. Reddy, S. Vijaya Prakash Kumar, T. V. Suresh Kanth, K. Rajani MSRIT Dept Comp Applicat Bangalore Karnataka India MSRIT ISE Dept Comp Applicat Bangalore Karnataka India

ISBN: (纸本)9781467345286

distributed databases have become necessity in the organizations, as applications are require data locally rather than remote locations. It is important to place data where the applications are required to process data. Data fragmentation and placement becomes a critical performance issue in distributed database design that has to be placed at different sites based on the usage of data. The performance of the application would become assessed and improved if it is properly fragment and allocate data during early design stages. In this paper, we proposed an algorithm by performing vertical fragmentation technique for the distributed database design for assessing the performance during early design stages. We simulated the algorithm with the case study. We used the proposed algorithm, Attribute Usage Matrix, and Random Number Generation technique for generating the values of Request Query, Arrival of Request, and Query generation. The Simulation results which helps the DBA to assess the performance of the proposed distributed database Design. The tool which helps the DBA to select the optimum design.

关键词： distributed database Vertical Fragmentation database Design UML and Attribute Usage Matrix

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Students Formative Assessment Framework (Faus) Using the Blockchain 3

Students Formative Assessment Framework (Faus) Using the Blo...

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3rd International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Widayanti, Riya Rahardja, Untung Oganda, Fitra Putri Hardini, Marviola Devana, Viola Tashya Esa Unggul Univ Informat Syst Greater Jakarta Indonesia Univ Raharja Informat Technol Tangerang Indonesia Univ Raharja Dept Informat Syst Tangerang Indonesia

ISBN: (纸本)9781665425803

Colleges should digitize to improve and enhance their services and make them more accessible to beneficiaries. Universities can become more productive, efficient, adaptable, and comfortable by implementing new technologies. This consideration has grown significantly due to smart universities, a concept representing higher education in the digital age, and blockchain is one of the fastest-growing technologies of this era. This article defines blockchain and smart universities and provides a conceptual framework for how smart universities can use blockchain technology to assist and provide better knowledge of their students' interactions with universities. To support this research, the researcher uses two case studies where the main problem is the impact of the assessment on the quality of education and the assessment of academic supervision in the thesis. To improve the quality of education, a seven-level formative assessment framework will be used in smart universities.

关键词： Smart University Blockchain distributed database Digitization

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Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency 49

Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-...

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49th SME North American Manufacturing Research Conference (NAMRC)

作者： Hasan, Mahmud Ogan, Kemafor Starly, Binil North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA

Blockchain based decentralized Cloud Manufacturing-as-a-Service (CMaaS) platforms enable customers to gain access to a large capacity of manufacturing nodes over cryptographically secure networks. In recent times, the Ethereum network has emerged as a popular blockchain framework for providing provenance and traceability of proprietary manufacturing data in decentralized CMaaS. However, the Ethereum ecosystem was only designed to store and transmit low volume financial transaction data and little has been done to make it an efficient repository of large manufacturing data streams in CMaaS systems. In this paper, the authors build on their previous work and report the design, implementation, and validation of middleware software architectures that allow Ethereum based distributed CMaaS platforms to harness the benefits of the secure asset models of the Ethereum ecosystem and the immutable big data storage capabilities of the decentralized BigchainDB database platform. A novel hybrid blockchain architecture enabled by efficient communication protocols and blockchain oracles is proposed. This architecture allows the transfer and immutable storage of large manufacturing data streams onto global BigchainDB nodes allowing data rich manufacturing transactions to bypass the transaction fees of the Ethereum ecosystem. Additionally, a machine learning based time series inference model is proposed which enables the forecast of Ethereum gas price into the future. This allows the CMaaS platform smart contracts to judiciously assign gas price limits and hence save on transactions ensuing from transfer or creation of assets. The outcomes of this research show that the designed hybrid architecture can lead to the reduction of significant number of computational steps and hence transaction fees on Ethereum by offloading large volume data onto BigchainDB nodes. A Random Forest regressor based time series inference model has been shown to exhibit superior performance in the prediction

关键词： blockchain smart contracts distributed database time series regression lstm gas price prediction

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An approach to finding sources of pollution to maintain stable air quality 20th

An approach to finding sources of pollution to maintain stab...

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20th IFAC Conference on Technology, Culture, and International Stability (TECIS)

作者： Lapkovsky, Roman Yu Ivashchenko, Vladimir A. Lugovoy, Nikita, V Stepanovskaya, Iraida A. Russian Acad Sci Inst Precis Mech & Control Rabochaya 24 Saratov Russia Russian Acad Sci VA Trapeznikov Inst Control Sci Profsouznaya 65 Moscow Russia

Preparation of control actions carried out at various levels to maintain a stable quality of atmospheric air includes measures to identify pollution sources and assess their cumulative impact on atmospheric air quality. We propose an approach to searching for air pollution sources based on continuous observations in a unified environmental monitoring system. The method includes primary processing, storage, and referencing large amounts of measurement information to a time grid. The process provides a more accurate binding of the measurement information to a moment in time and a point in space to provide additional filtering of data and a compact form of storage of measurement information. The proposed approach includes methods for analyzing the accumulated measurement information, making it possible to identify individual terrain areas with potential pollution sources. Data from atmospheric air quality and meteorological parameters from various terrain points, integrated into a unified environmental monitoring system, are used as the initial data. We have developed a software and hardware complex, which forms the basis of environmental monitoring systems operating in several departments and enterprises of the Russian Federation. The proposed solutions are scaled by adding additional nodes and levels. The regional network integrates already existing local systems. The approach's novelty lies in binding the measurement information to the time grid to store the data and its analysis approach. Managers could search for potential sources of pollution more effectively to further assess their cumulative impact on atmospheric air quality and determine the effects of reducing this impact on acceptable values. Copyright 2021 (C) The Authors.

关键词： Ecology Environmental Stability Monitoring Measuring distributed database Analysis of Measuring Information Air Pollution

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Empowering Patients with HIPAA Aware Personal Health Libraries 1

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10th International Conference on Health Information Science (HIS)

作者： Jamil, Hasan M. Bernitsas, Evanthia Gow, Alexandar Seyoum, Berhane Univ Idaho Dept Comp Sci Moscow ID 83843 USA Wayne State Univ Sch Med Detroit MI USA

ISBN: (数字)9783030908850

ISBN: (纸本)9783030908850;9783030908843

One of the most difficult issues that confronts clinicians and patients alike is sharing EHRs across service providers. While HIPAA grants ownership of EHR data to patients, the information is not easily shareable. At times, doctors cannot access their own patients' EHR in two different medical systems even when the record keeping systems are the same and patients grant consent. The onus of sharing falls on the patient who must physically acquire the necessary record. Such a policy hinders quality of service, causes delays and higher costs for all involved. In this paper, we introduce a new data sharing protocol aimed at reducing this hurdle, using a personal health library architecture being encouraged by National Institutes of Health in the USA.

关键词： EHR Data integration Privacy and sharing Access rights Personal health library HIPAA distributed database Middleware

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Join Query Optimization Using Genetic Ant Colony Optimization Algorithm for distributed databases 1

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2nd International Conference on Emerging Technologies in Computer Engineering - Microservices in Big Data Analytics (ICETCE)

作者： Tiwari, Preeti Chande, Swati V. Int Sch Informat & Management Jaipur Rajasthan India

ISBN: (数字)9789811383007

ISBN: (纸本)9789811383007;9789811382994

With the increase in geographical spread of data both in terms of quality and quantity, attention on the storage, retrieval and modification of this distributed data has become a prime area of research. The focus is on efficient, accurate and timely availability of information extracted from various underlying data centers. Processing of queries from these distributed database environments has become a challenging task for the database researchers because as the number of relations increases in the database, the join order complexity also increases. There are N! ways of solving a particular query where N represents the number of Relations in the join query. The success of query processed in the distributed database Environment depends largely on the search strategy implemented by the query optimizer whose task is to search an optimal Query Evaluation Plan in minimum time amongst the various query plans that can minimize the consumption of computer resources. Various search strategies beginning from Deterministic Algorithms to the most recent and modern Evolutionary Algorithms have contributed incalculably towards query optimization but they bear their own set of limitations and drawbacks. This research paper focuses on the implementation of a hybrid strategy of Evolutionary Algorithms for the optimization of join queries in DDBMS. The hybrid strategy is an integration of Ant Colony Optimization Algorithm and Genetic Algorithm and has been coined as GACO-D (Genetic Ant Colony Optimization Algorithm for distributed database). This paper focuses on the search of an optimal Join Order in minimum response time using GACO-D and also compares its performance with existing strategies.

关键词： distributed database Join query Query optimization GA ACO

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Data modelling approaches to astronomical data: Mapping large spectral line data cubes to dimensional data models

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ASTRONOMY AND COMPUTING 2022年 38卷 100539-100539页

作者： Duniam, G. Kitaeff, V. V. Wicenec, A. Univ Western Australia Int Ctr Radio Astron Res Perth WA 6009 Australia

As a new generation of large-scale telescopes are expected to produce single data products in the range of hundreds of GBs to multiple TBs, different approaches to I/O efficient data interaction and extraction need to be investigated and made available to researchers. This will become increasingly important as the downloading and distribution of TB scale data products will become unsustainable, and researchers will have to take their processing analysis to the data. We present a methodology to extract 3 dimensional spatial-spectral data from dimensionally modelled tables in Parquet format on a Hadoop system. The data is loaded into the Parquet tables from FITS cube files using a dedicated process. We compare the performance of extracting data using the Apache Spark parallel compute framework on top of the Parquet-Hadoop ecosystem with data extraction from the original source files on a shared file system. We have found that the Spark-Parquet-Hadoop solution provides significant performance benefits, particularly in a multi user environment. We present a detailed analysis of the single and multi-user experiments conducted and also discuss the benefits and limitations of the platform used for this study. (C) 2021 Elsevier B.V. All rights reserved.

关键词： distributed processing distributed database

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