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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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Comparison and evaluation of state-of-the-art LSM merge policies

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VLDB JOURNAL 2021年第3期30卷 361-378页

作者： Mao, Qizhong Jacobs, Steven Amjad, Waleed Hristidis, Vagelis Tsotras, Vassilis J. Young, Neal E. Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Modern nosql database systems use log-structured merge (LSM) storage architectures to support high write throughput. LSM architectures aggregate writes in a mutable MemTable (stored in memory), which is regularly flushed to disk, creating a new immutable file called an SSTable. Some of the SSTables are chosen to be periodically merged-replaced with a single SSTable containing their union. A mergepolicy (a.k.a. compaction policy) specifies when to do merges and which SSTables to combine. A bounded depth merge policy is one that guarantees that the number of SSTables never exceeds a given parameter k, typically in the range 3-10. Bounded depth policies are useful in applications where low read latency is crucial, but they and their underlying combinatorics are not yet well understood. This paper compares several bounded depth policies, including representative policies from industrial nosql databases and two new ones based on recent theoretical modeling, as well as the standard Tiered policy and Leveled policy. The results validate the proposed theoretical model and show that, compared to the existing policies, the newly proposed policies can have substantially lower write amplification with comparable read amplification.

关键词： nosql database LSM Merge policy Compaction

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A Novel Global Key-Value Storage System Based on Kinetic Drives

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ALGORITHMS 2020年第10期13卷 247-247页

作者： Cao, Xiang Li, Cheng Grand Valley State Univ Sch Comp & Informat Syst Allendale MI 49401 USA

nosql databases are flexible and efficient for many data intensive applications, and the key-value store is one of them. In recent years, a new Ethernet accessed disk drive called the "Kinetic Drive" was developed by Seagate. This new Kinetic Drive is specially designed for key-value stores. Users can directly access data with a Kinetic Drive via its IP address without going through a storage server/layer. With this new innovation, the storage stack and architectures of key-value store systems have been greatly changed. In this paper, we propose a novel global key-value store system based on Kinetic Drives. We explore data management issues including data access, key indexing, data backup, and recovery. We offer scalable solutions with small storage overhead. The performance evaluation shows that our location-aware design and backup approach can reduce the average distance traveled for data access requests.

关键词： nosql database key-value store kinetic drive key indexing global scale

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Relax with CouchDB - Into the non-relational DBMS era of bioinformatics

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GENOMICS 2012年第1期100卷 1-7页

作者： Manyam, Ganiraju Payton, Michelle A. Roth, Jack A. Abruzzo, Lynne V. Coombes, Kevin R. Univ Texas MD Anderson Canc Ctr Dept Bioinformat & Computat Biol Unit 1410 Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Hematopathol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Thorac & Cardiovasc Surg Houston TX 77030 USA

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. (C) 2012 Elsevier Inc. All rights reserved.

关键词： nosql database Copy number variation Drug-target interaction Data integration

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Benchmarking management techniques for massive IIoT time series in a fog architecture

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2021年第2期12卷 113-125页

作者： Martino, Sergio Di Peron, Adriano Riccabone, Alberto Vitale, Vincenzo Norman Univ Naples Federico II DIETI I-80127 Naples Italy Avio Aero I-80038 Pomigliano Darco NA Italy

Within the Industrial Internet of Things (IIoT) scenario, the online availability of a growing number of assets in factories enables the collection of vast amounts of data. Each asset produces time-series collections that must be handled with proper techniques while providing effective ingestion and retrieval performance in complex architectures, maintaining compliance with company and infrastructure boundaries. In this paper, we describe an experience in the management of massive time-series, conducted in a plant of Avio Aero. Firstly, we propose a fog-based architecture to ease the collection and analysis of these massive datasets. Then, we present the results of an empirical comparison of four DBMSs (PostgreSQL, Cassandra, MongoDB, and InfluxDB) in the ingestion and retrieval of gigabytes of real IIoT data. In particular, we tested different DBMS features under different types of queries. Results show that InfluxDB provides very good performance, but PostgreSQL can still be an interesting alternative.

关键词： big data time series IIoT fog architecture TSMS nosql database relational database benchmarking

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An information modeling framework for bridge monitoring

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ADVANCES IN ENGINEERING SOFTWARE 2017年 114卷 11-31页

作者： Jeong, Seongwoon Hou, Rui 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 Daejeon 305701 South Korea

Bridge management involves a variety of information from different data sources, including geometric model, analysis model, bridge management system (BMS) and structural health monitoring (SHM) system. Current practice of bridge management typically handles these diverse types of data using isolated systems and operates with limited use of the data. Sharing and integration of such information would facilitate meaningful use of the information and improve bridge management, as well as enhance bridge operation and maintenance and public safety. In many industries, information models and interoperability standards have been developed and employed to facilitate information sharing and collaboration. Given the success of building information modeling (BIM) in the Architecture, Engineering and Construction (AEC) industry, efforts have been initiated to develop frameworks and standards for bridge information modeling (BrIM). Current developments of BrIM focus primarily on the physical descriptions of bridge structures, such as geometry and material properties. This paper presents an information modeling framework for supporting bridge monitoring applications. The framework augments and extends the prior work on the OpenBrIM standards to further capture the information relevant to engineering analysis and sensor network. Implementation of the framework employs an open-source nosql database system for scalability, flexibility and performance. The framework is demonstrated using bridge information and sensor data collected from the Telegraph Road Bridge located in Monroe, Michigan. The results show that the bridge information modeling framework can potentially facilitate the integration of information involved in bridge monitoring applications, and effectively support and provide services to retrieve and utilize the information. (C) 2017 Published by Elsevier Ltd.

关键词： Bridge information modeling Bridge management Bridge monitoring nosql database

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Schema generation for document stores using workload-driven approach

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JOURNAL OF SUPERCOMPUTING 2024年第3期80卷 4000-4048页

作者： Bansal, Neha Sachdeva, Shelly Awasthi, Lalit K. Natl Inst Technol Delhi Delhi India Natl Inst Technol Hamirpur Hamirpur India

Although there are numerous data modeling tools for relational databases, data modeling for nosql databases has seen another perspective. These databases (a) do not define any explicit schema, (b) store data in a denormalized manner, and (c) give many structure alternatives. The decision to structure the data always relies on rules of thumb, which do not guarantee an optimal structural solution. Based on this motivation, this paper offers a workload-driven model for the logical schema design of a nosql document database. It consists of Model input, Intermediate transformation, and Final schema generation. The proposed model takes the conceptual schema (EER model) and application workload (queries and anticipated data volume) as input and describes a procedure to convert it into a logical model for nosql document stores. The conversion process initially converts the application queries into query graphs. The query graphs, along with the anticipated data volume, are used to generate the query labels. The resulting query labels are assigned on the schema graph designed from the EER model. The schema graph and labels are used to transform the EER model into the appropriate logical schema model based on the actions defined for each label. We evaluate the model using a case study in the eCommerce application domain. The experimental evaluation shows the proposed model outperforms the existing conventional, optimized, and query path graphs models in multiple aspects, including query performance, storage space efficiency, aggregate pipeline efficiency, read-write latency, collection-wise performance, scalability, throughput and latency. By effectively addressing the challenges associated with managing the variety and volume of big data through a well-designed schema, our proposed model significantly reduces the time, cost, and effort required for schema development and repair.

关键词： nosql database Data modelling Workload-driven approach MongoDB Schema design

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MetaStore: an adaptive metadata management framework for heterogeneous metadata models

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DISTRIBUTED AND PARALLEL databaseS 2018年第1期36卷 153-194页

作者： Prabhune, Ajinkya Stotzka, Rainer Sakharkar, Vaibhav Hesser, Jurgen Gertz, Michael Karlsruhe Inst Technol Eggenstein Leopoldshafen Germany Heidelberg Univ Dept Radiat Oncol Heidelberg Germany Heidelberg Univ Inst Comp Sci Heidelberg Germany

In this paper, we present MetaStore, a metadata management framework for scientific data repositories. Scientific experiments are generating a deluge of data, and the handling of associated metadata is critical, as it enables discovering, analyzing, reusing, and sharing of scientific data. Moreover, metadata produced by scientific experiments are heterogeneous and subject to frequent changes, demanding a flexible data model. Existing metadata management systems provide a broad range of features for handling scientific metadata. However, the principal limitation of these systems is their architecture design that is restricted towards either a single or at the most a few standard metadata models. Support for handling different types of metadata models, i.e., administrative, descriptive, structural, and provenance metadata, and including community-specific metadata models is not possible with these systems. To address this challenge, we present MetaStore, an adaptive metadata management framework based on a nosql database and an RDF triple store. MetaStore provides a set of core functionalities to handle heterogeneous metadata models by automatically generating the necessary software code (services) and on-the-fly extends the functionality of the framework. To handle dynamic metadata and to control metadata quality, MetaStore also provides an extended set of functionalities such as enabling annotation of images and text by integrating the Web Annotation Data Model, allowing communities to define discipline-specific vocabularies using Simple Knowledge Organization System, and providing advanced search and analytical capabilities by integrating the ElasticSearch. To maximize the utilization of the data models supported by nosql databases, MetaStore automatically segregates the different categories of metadata in their corresponding data models. Complex provenance graphs and dynamic metadata are modeled and stored in an RDF triple store, whereas the static metadata is sto

关键词： MetaStore nosql database Automated code generation Annotations

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The potential of semantic paradigm in warehousing of big data

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AUTOMATIKA 2019年第4期60卷 393-403页

作者： Pticek, Marina Vrdoljak, Boris Gulic, Marko Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia Univ Rijeka Fac Maritime Studies Rijeka Rijeka Croatia

Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as nosql databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of nosql databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the nosql gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. nosql databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich nosql database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating nosql data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases.

关键词： Semantic Web technologies data warehouse nosql database document store

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A user-friendly nosql framework for managing agricultural field trial data

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Wu, Steven H. Mueller, Tristan A. Natl Taiwan Univ Dept Agron Taipei Taiwan Pivot Bio Sheboygan WI USA

Field trials are one of the essential stages in agricultural product development, enabling the validation of products in real-world environments rather than controlled laboratory or greenhouse settings. With the advancement in technologies, field trials often collect a large amount of information with diverse data types from various sources. Managing and organizing extensive datasets can impose challenges for small research teams, especially with constantly evolving data collection processes with multiple collaborators and introducing new data types between studies. A practical database needs to be able to incorporate all these changes seamlessly. We present DynamoField, a flexible database framework for collecting and analyzing field trial data. The backend database for DynamoField is powered by Amazon Web Services DynamoDB, a nosql database, and DynamoField also provides a front-end interactive web interface. With the flexibility of the nosql database, researchers can modify the database schema based on the data provided by various collaborators and contract research organizations. This framework includes functions for non-technical users, including importing and exporting data, data integration and manipulation, and performing statistical analysis. Researchers can utilize cloud computing to establish a secure nosql database with minimum maintenance, this also enables collaboration with others worldwide and adapt to different data-collecting strategies as their research progresses. DynamoField is implemented in Python, and it is publicly available at https://***/ComputationalAgronomy/DynamoField.

关键词： Field trial nosql database Web application DynamoDB Amazon Web Services (AWS)

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