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A New Method for Time-Series Big Data Effective Storage

IEEE ACCESS 2017年 5卷 10694-10699页

作者： Tahmassebpour, Mahmoudreza Islamic Azad Univ North Tehran Branch Dept Informat Technol Engn Tehran *** Iran

Today, one of the main challenges of big data research is the processing of big time-series data. Moreover, time data analysis is of considerable importance, because previous trends are useful for predicting the future. Due to the considerable delay when the volume of the data increases, the presence of redundancy, and the innate lack of time-series structures, the traditional relational data model does not seem to be adequately able to analyze time data. Moreover, many traditional data structures do not support time operators, which results in an inefficient access to time data. Therefore, relational database management systems have difficulty in dealing with big data it may require massively parallel software that runs on many servers. This has led us to implement Chronos Software, an in-memory background-based time database for key-value pairs;this software was implemented using C++ language. An independent design has been suggested through appropriately using temporal algorithms, parallelism algorithms, and methods of data storage in RAM. Our results indicate that the employment of RAM for storing the data and of the Timeline Index algorithm for getting access to the time background of the keys in Chronos translate into an increase of about 40%-90% in the efficiency as compared with other databases, such as MySQL and MongoDB.

关键词： Timeline index nosql database Chronos database big data key-value database

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A Configuration-Independent Score-Based Benchmark for Distributed databases

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2016年第1期9卷 123-137页

作者： Ardagna, Claudio A. Damiani, Ernesto Frati, Fulvio Rebeccani, Davide Univ Milan Dipartimento Informat I-26013 Crema Italy Khalifa Univ Sci Technol & Res Etisalat British Telecom Innovat Ctr Abu Dhabi U Arab Emirates

The business potential of big data is leading to a data-driven economy, where low-cost and low-latency data analysis represents a major competitive advantage. The research community has proposed many technological solutions for big data, such as nosql databases, which are difficult to evaluate and compare via standard IT procurement procedures. In addition, lack of competences in big data domains make procurement of big data solutions a tedious and uncertain process, which might impair the success of a business. In this paper, we present a score-based benchmark for distributed databases, which supports adopters in selecting a solution that fits their needs. The proposed benchmark is independent from the configurations of the specific database and deployment environment, requires low effort on the part of end users, is extensible and can be applied to both SQL and nosql databases, can be used to evaluate databases according to different properties (e.g., performance, consistency), and can be integrated with existing benchmarks to reduce the burden of their execution. We experimentally evaluate our methodology to validate its effectiveness.

关键词： Big data cloud nosql database score-based benchmark

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Logical Level Design of nosql databases

Logical Level Design of NoSQL Databases

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IEEE Region 10 Conference

作者： Shreya Banerjee Anirban Sarkar Department of Computer Applications National Institute of Technology Durgapur India

ISBN: (纸本)9781509025985

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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Data Partition Optimization for Column-Family nosql databases

Data Partition Optimization for Column-Family NoSQL database...

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IEEE International Conference on Smart City

作者： Li-Yung Ho Meng-Ju Hsieh Jan-Jan Wu Pangfeng Liu Research Center for Information Technology Innovation Academia Sinica Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Department of Computer Science and Information Engineering Graduate Intitute of Networking and Multimedia National Taiwan University

ISBN: (纸本)9781509018949

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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JOURNAL OF SYSTEMS AND SOFTWARE 2017年 127卷 28-51页

作者： Damaiyanti, Titus Irma Imawan, Ardi Indikawati, Fitri Indra Choi, Yoon-Ho Kwon, Joonho Pusan Natl Univ Sch Comp Sci & Engn Busan 46241 South Korea Pusan Natl Univ Dept Big Data Busan 46241 South Korea

Advancements in sensing and communication technologies are enabling intelligent transportation systems (ITS) to easily acquire large volumes of road traffic big data. Querying road traffic data is a crucial task for providing citizens with more insightful information on traffic conditions. In this paper, we have developed a similarity query system for road traffic big data, called SigTrac, that runs on top of an existing MongoDB document store. The SigTrac system represents road traffic sensor data having spatio-temporal characteristics into traffic matrices and stores them into a MongoDB nosql document store by exploiting map-reduce operations of MongoDB. In addition, SigTrac efficiently processes similarity queries for traffic data with singular value decomposition (SVD)-based and incremental SVD-based algorithms. Our experimental studies with real traffic data demonstrate the efficiency of SigTrac for similarity query processing for road traffic big data. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Road traffic data Traffic matrices Similarity queries SVD-based query processing nosql database MongoDB

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A distributed group recommendation system based on extreme gradient boosting and big data technologies

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APPLIED INTELLIGENCE 2019年第12期49卷 4128-4149页

作者： Hammou, Badr Ait Lahcen, Ayoub Ait Mouline, Salma Mohammed V Univ Fac Sci Rabat IT Ctr LRITAssociated Unit CNRST URAC 29 Rabat Morocco Ibn Tofail Univ Natl Sch Appl Sci ENSA LGS Kenitra Morocco

Personalized recommendation systems have emerged as useful tools for recommending the appropriate items to individual users. However, in such situations, some items tend to be consumed by groups of users, such as tourist attractions or television programs. With this purpose in mind, Group Recommender Systems (GRSs) are tailored to help groups of users to find suitable items according to their preferences and needs. In general, these systems often confront the sparsity problem, which negatively affects their efficiency. With the increase in the number of users, items, groups, and ratings in the system. Data becomes too big to be processed efficiently by traditional systems. Thus, there is an increasing need for distributed recommendation approaches able to manage the issues related to Big Data and sparsity problem. In this paper, we propose a distributed group recommendation system, which is designed based on Apache Spark to handle large-scale data. It integrates a novel proposed recommendation method, a dimension reduction technique, with supervised and unsupervised learning for dealing efficiently with the curse of dimensionality problem, detecting the groups of users, and improving the prediction quality. Experimental results on three real-world data sets show that our proposal is significantly better than other competitors.

关键词： Big data technologies Group recommendation Clustering Machine learning nosql database Apache spark

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Data Platform Guidelines and Prototype for Microgrids and Energy Access: Matching Demand Profiles and Socio-Economic Data to Foster Project Development

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IEEE ACCESS 2023年 11卷 73218-73234页

作者： Fioriti, Davide Stevanato, Nicolo Ducange, Pietro Marcelloni, Francesco Colombo, Emanuela Poli, Davide Univ Pisa DESTEC I-56122 Pisa Italy Politecn Milan Dept Energy I-20156 Milan Italy Univ Pisa Dept Informat Engn I-56126 Pisa Italy

Energy access is a key need for socio-economic growth. Proven to be a key enabler of development and progress, access to electricity has been prioritized by governments using grid extension actions and off-grid solutions, namely microgrids and home systems technologies, fed by renewable sources. However, achieving universal access to energy remains a huge challenge given the lack of resources and the large population currently unserved. The lack of adequate socio-economic data at granular scale and of a good understanding of demand uptake led by economic growth is a barrier for efficient energy planning. Access to conjoint demand and socio-economic data at local level is crucial, yet hard to obtain: often such data are unavailable or very difficult to collect, and current data platforms often lack the ability to conjointly store variegated socio-economic and time series data. For these reasons, in this paper, we present a comprehensive methodology that, based on an extensive literature review, draws guidelines for developing data-sharing platforms in energy access, develops a proposed architecture to support the data collection of conjoint socio-economic and time-series data, and proposes a prototype of the final application. The methodology leverages on a novel extensive literature review to identify the major determinants of demand uptake and the corresponding consuming entities: villages, households, and appliances. The proposed architecture is able to capture numeric, categorical, and time series information for all consuming entities, based on state-of-the-art nosql databases. Finally, a prototype implementation with a web-based interface developed with Angular and Spring is proposed and discussed.

关键词： Access to electricity load estimation open data nosql database software platform energy access

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Evaluating Riak Key Value Cluster for Big Data

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2020年第1期27卷 157-165页

作者： Rmis, Aimen Mukhtar Topcu, Ahmet Ercan Ankara Yildirim Beyazit Univ Dept Comp Engn Gazze Cd 7 TR-06010 Etlik Kecioren Kecioren Turkey Amer Univ Middle East Coll Engn & Technol Eqaila Kuwait

nosql database has become an important alternative to traditional relational databases. Those databases are prepared by the management of large, continuously and variably changing data sets. They are widely used in cloud databases and distributed systems. With nosql databases, static schemes and many other restrictions are avoided. In the era of big data, such databases provide scalable high availability solutions. Their key-value feature allows fast retrieval of data and the ability to store a lot of it. There are many kinds of nosql databases with various performances. Therefore, comparing those different types of databases in terms of performance and verifying the relationship between performance and database type has become very important. In this paper, we test and evaluate the Riak key-value database for big data clusters using benchmark tools, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Execution times of the nosql database over different types of workloads and different sizes of data are compared. The results show that the Riak key-value is stable in execution time for both small and large amounts of data, and the throughput performance increases as the number of threads increases.

关键词： big data cluster nosql database Riak YCSB

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SRank: Guiding schema selection in nosql document stores

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DATA & KNOWLEDGE ENGINEERING 2024年 154卷

作者： Sachdeva, Shelly Bansal, Neha Bansal, Hardik Natl Inst Technol Delhi Delhi India Bennett Univ Sch Comp Sci Engn & Technol Greater Noida India

The rise of big data has led to a greater need for applications to change their schema frequently. nosql databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in nosql document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in nosql databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the

关键词： Data modeling MongoDB nosql database Schema design

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Digital Forensics and nosql databases

Digital Forensics and NoSQL Databases

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The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

作者： Man Qi Department of Computing Canterbury Christ Church University Canterbury Kent CT1 1QU UK

ISBN: (纸本)9781479951499

The rapid development of computer storage technologies has brought Big Data challenges to digital forensics where the dimensions of digital evidence supports has grown exponentially with heterogeneous data sets-structured,unstructured and *** relational database management systems(RDBMS)typically expose a query interface based on SQL(Structured Query Language).However,they are mainly employed for management of structured data and hard to scale to the ever growing size of data *** paper reviews nosql(Not Only SQL)database technologies as an alternative to RDBMS for management of Big *** introduces MongoDB and Riak as two representative nosql databases and evaluates their performance in data processing using the Amazon EC2 Cloud.

关键词： Digital forensics big data nosql database

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