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2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Ho, Li-Yung Hsieh, Meng-Ju Wu, Jan-Jan Liu, Pangfeng Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Inst Informat Sci Taipei Taiwan Natl Taiwan Univ Grad Intitute Networking & Multimedia Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781509018932

Data conversion has become an emerging topic in BigData era. To face the challenge of rapid data growth, legacy or existing relational databases have the need to convert into nosql column-family database in order to achieve better scalability. The conversion from SQL to nosql databases requires combining small, normalized SQL data tables into larger nosql data tables;a process called denormalization. A challenging issues in data conversion is how to group the denormalized columns in a large data table into "families" in order to ensure the performance of query processing. In this paper, we propose an efficient heuristic algorithm, GPA (Graph-based Partition Algorithm), to address this problem. We use TPC-C and TPC-H benchmarks to demonstrate that, the column-families produced by GPA is very efficient for large scale data processing.

关键词： vertical partition column partition column family nosql database

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Logical Level Design of nosql databases

Logical Level Design of NoSQL Databases

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IEEE Region 10 Conference (TENCON)

作者： Banerjee, Shreya Sarkar, Anirban Natl Inst Technol Dept Comp Applicat Durgapur India

ISBN: (纸本)9781509025978

Now-a-days nosql databases have been potentially used for web-scale applications due its supports towards dynamic and flexible schema design and scalability. However, lack of a suitable database designing method for heterogeneous nosql databases create difficulties towards application developers and database designers to choose and design such databases effectively. To overcome this challenge, this paper proposes a systematic and rule based transformation mechanism to convert conceptual level nosql data model [6] into an equivalent logical level data model specified in JSON schema (Java Script Object Notation). Further, the correctness of the proposed transformation mechanism has been verified. Moreover, the proposed mechanism is illustrated using a case study.

关键词： Logical Level Data Model nosql database database Design Methodology Validation Transformation Rules

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An Introduction of nosql databases Based on Their Categories and Application Industries

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ALGORITHMS 2019年第5期12卷 106-106页

作者： Chen, Jeang-Kuo Lee, Wei-Zhe Chaoyang Univ Technol Dept Informat Management Taichung 41349 Taiwan

The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise's database must be accessed as fast as possible, but the Relational database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a nosql database, which can meet the requirement of fast data access. However, there are more than hundreds of nosql databases. It is important to select a suitable nosql database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of nosql databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate nosql database for different industries.

关键词： big data nosql database RDB HBase MongoDB Neo4j

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DSM: A Low-Overhead, High-Performance, Dynamic Stream Mapping Approach for MongoDB

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2019年第2期35卷 447-469页

作者： Nguyen, Trong-Dat Lee, Sang-Won Sungkyunkwan Univ Coll Informat & Commun Engn Suwon 16419 South Korea

For write-intensive workloads, reclaiming free blocks in flash SSDs is expensive due to data fragmentation problem that leads to performance degradation. This paper addresses that problem in MongoDB, a popular document store in the current market, by introducing a novel stream mapping scheme that exploits unique characteristics of MongoDB and multi-streamed technology. It dynamically assigns streams for corresponding writes according to their hotness values and distinguishes writes on primary index files from writes on secondary index files. The proposed method is high-performance, lowoverhead, and independent of data models or workloads. Empirical results in Linkbench benchmark show that compared to the original WiredTiger our approach improves the throughput and reduces the 99th-percentile latency by up to 65% and 46.2% respectively. Compared to the best-performance in the prior research, our approach improves the throughput and reduces the 99th-percentile latency by up to 23% and 28.5% respectively. Distinguishing writes on primary index files from writes on secondary index files enhances the throughput and the 99th-percentile latency by up to 11.7% and 15.7% respectively. Moreover, by tuning the leaf page size in B+Tree of MongoDB, we can significantly improve the throughput by 1.6x-2.1x in Linkbench.

关键词： data fragmentation hot/cold data identification multi-streamed SSD nosql database MongoDB

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A nosql data management infrastructure for bridge monitoring

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SMART STRUCTURES AND SYSTEMS 2016年第4期17卷 669-690页

作者： Jeong, Seongwoon Zhang, Yilan O'Connor, Sean Lynch, Jerome P. Sohn, Hoon Law, Kincho H. Stanford Univ Dept Civil & Environm Engn 473 Via Ortega Stanford CA 94305 USA Univ Michigan Dept Civil & Environm Engn 2350 Hayward St Ann Arbor MI 48109 USA Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Taejon 305701 South Korea

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, nosql database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.

关键词： cyber infrastructure bridge monitoring bridge information modeling nosql database

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JackHare: a framework for SQL to nosql translation using MapReduce

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AUTOMATED SOFTWARE ENGINEERING 2014年第4期21卷 489-508页

作者： Chung, Wu-Chun Lin, Hung-Pin Chen, Shih-Chang Jiang, Mon-Fong Chung, Yeh-Ching Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300 Taiwan Is Land Syst Inc Hsinchu 300 Taiwan

As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging nosql database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.

关键词： Cloud computing Unstructured data processing MapReduce nosql database HBase JDBC Compiler

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Medical Systems Data Security and Biometric Authentication in Public Cloud Servers

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第2期12卷 572-582页

作者： Santos, Nelson Ghita, Bogdan Masala, Giovanni Luca Univ Plymouth Sch Engn Comp & Math Plymouth PL4 8AA England Univ Plymouth Ctr Secur Commun & Network Res Plymouth PL4 8AA England Univ Kent Sch Comp Canterbury CT2 7NZ England

Advances in distributed computing and virtualization allowed cloud computing to establish itself as a popular data management and storage option for organizations. However, unclear safeguards, practices, as well as the evolution of legislation around privacy and data protection, contribute to data security being one of the main concerns in adopting this paradigm. Another important aspect hindering the absolute success of cloud computing is the ability to ensure the digital identity of users and protect the virtual environment through logical access controls while avoiding the compromise of its authentication mechanism or storage medium. Therefore, this paper proposes a system that addresses data security wherein unauthorized access to data stored in a public cloud is prevented by applying a fragmentation technique and a nosql database. Moreover, a system for managing and authenticating users with multimodal biometrics is also suggested along with a mechanism to ensure the protection of biometric features. When compared with encryption, the proposed fragmentation method indicates better latency performance, highlighting its strong potential use-case in environments with lower latency requirements such as the healthcare IT infrastructure.

关键词： Cloud computing Biometrics (access control) Data security Biomedical imaging Encryption Authentication Security Data fragmentation cloud security nosql database security and protection

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A convergence of key-value storage systems from clouds to supercomputers

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第1期28卷 44-69页

作者： Li, Tonglin Zhou, Xiaobing Wang, Ke Zhao, Dongfang Sadooghi, Iman Zhang, Zhao Raicu, Ioan IIT Dept Comp Sci Chicago IL 60616 USA Argonne Natl Lab MCS Div Lemont IL USA Hortonworks Palo Alto CA USA Univ Calif Berkeley AMP Lab Berkeley CA 94720 USA

This paper presents a convergence of distributed key-value storage systems in clouds and supercomputers. It specifically presents ZHT, a zero-hop distributed key-value store system, which has been tuned for the requirements of high-end computing systems. ZHT aims to be a building block for future distributed systems, such as parallel and distributed file systems, distributed job management systems, and parallel programming systems. ZHT has some important properties, such as being lightweight, dynamically allowing nodes join and leave, fault tolerant through replication, persistent, scalable, and supporting unconventional operations such as append, compare and swap, callback in addition to the traditional insert/lookup/remove. We have evaluated ZHT's performance under a variety of systems, ranging from a Linux cluster with 64 nodes, an Amazon EC2 virtual cluster up to 96 nodes, to an IBM Blue Gene/P supercomputer with 8K nodes. We compared ZHT against other key-value stores and found it offers superior performance for the features and portability it supports. This paper also presents several real systems that have adopted ZHT, namely, FusionFS (a distributed file system), IStore (a storage system with erasure coding), MATRIX (distributed scheduling), Slurm++ (distributed HPC job launch), Fabriq (distributed message queue management);all of these real systems have been simplified because of key-value storage systems and have been shown to outperform other leading systems by orders of magnitude in some cases. It is important to highlight that some of these systems are rooted in HPC systems from supercomputers, while others are rooted in clouds and ad hoc distributed systems;through our work, we have shown how versatile key-value storage systems can be in such a variety of environments. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： nosql database distributed key-value store supercomputer cloud computing

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Atmosphere, an Open Source Measurement-Oriented Data Framework for IoT

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第3期17卷 1927-1936页

作者： Berta, Riccardo Kobeissi, Ahmad Bellotti, Francesco De Gloria, Alessandro Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit I-16145 Genoa Italy

The ever more extensive data collection from Internet of Thing (IoT) devices stresses the need for efficient application development tools. State-of-the-art IoT cloud services are powerful, but the best solutions are proprietary, and there is a growing demand for interoperability and standardization. We have investigated how to develop a nonvendor-locked framework, which exploits state of the art data management technologies, and targets effective and efficient development in this article. Focusing on the concept of measurement, we abstracted an architecture that could be applied in a variety of domains and contexts. We tested the framework and its workflow in four use cases analyzing data and enabling new services in health, automotive, and instruction. Our experience showed the benefits of the development tool, which is not tied to a commercial platform, nor requires the huge set-up times needed to start a project from scratch. The tool is released open-source, particularly supporting collaborative research.

关键词： Cloud computing Tools Atmospheric modeling Atmospheric measurements Scalability databases Application programming interface (API) autonomous driving cloud e-health e-mobility Internet of Thing (IoT) nosql database open source representational state transfer (REST)ful smart city

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Data-access performance anti-patterns in data-intensive systems

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EMPIRICAL SOFTWARE ENGINEERING 2024年第6期29卷 144页

作者： Muse, Biruk Asmare Nafi, Kawser Wazed Khomh, Foutse Antoniol, Giuliano Polytech Montreal Montreal PQ H3T 1J4 Canada

Data-intensive systems handle variable, high-volume, and high-velocity data generated by human and digital devices. Like traditional software, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems, as it determines their overall performance and functionality. While data access technical debts are getting attention from the research community, technical debts that affect performance are not well investigated. This study aims to identify, categorize, and validate data-access performance anti-patterns. We collected issues from nosql-based and polyglot persistence open-source data-intensive systems, implemented in Java programing language, and identified 14 new data access-performance anti-patterns categorized under seven high-level categories. We conducted a developer survey to evaluate the perceived relevance and criticality of the newly identified anti-patterns and found that Improper Handling of Node Failures, Using Synchronous Connection, and Inefficient Driver API performance anti-patterns are the most critical data-access performance anti-patterns. The study findings can help improve the quality of data-intensive software systems by raising awareness of practitioners about the impact of the data-access performance anti-patterns. At the same time, the findings will help quality assurance teams to prioritize the correction of performance anti-patterns based on their criticality.

关键词： Performance anti-patterns Data-intensive systems Polyglot persistence systems nosql database Data-access code

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