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作者： Van der Linde, Ian Daniel University of the Free State

学位级别：硕士

Sentiment analysis has seen a revival due to the advent of social media platforms such as Facebook and Twitter. The data posted on these platforms can be mined for valuable insights into customer relations, political unrest and product supply and demand. This information is embedded in typical Big Data, with very large volumes delivered at high velocity consisting of a wide variety of content and sources, and usually unstructured in nature. The challenge of analysing such data for decision support can be addressed through the use of sentiment analysis techniques in distributed environments designed to process and store large amounts of data in a horizontally-scalable fashion. The performance characteristics of these techniques have, however, hardly been studied in distributed environments, and the impact of cluster size on such environments is largely undocumented. The aim of this research was to investigate the accuracy and performance of four sentiment analysis approaches (a lexicon-based classifier, a Naïve-Bayes classifier, a Neural Network classifier, and a Support Vector Machine classifier) in a distributed environment with a cluster size of three to eight machines, while making use of a distributed nosql database backend to retrieve and store the data. The key investigations were to determine the nature of performance bottlenecks for each classifier in a distributed environment, how well each classifier scaled as more machines are added, and whether a relationship could be found between classifier accuracy and performance. It was determined that all four classifiers provide statistically significantly different accuracies, when compared pairwise and collectively. It was also found that there is no clear relationship between accuracy and resource usage (i. e. , a more performant technique does not necessarily have worse accuracy).

关键词： sentiment analysis nosql database document classification parallel computing distributed computing empirical analysis

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Vytvoření serverové aplikace v programovacím jazyce Swift

Vytvoření serverové aplikace v programovacím jazyce Swif...

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作者： Střálka, Martin Brno University of Technology

Tato práce se věnuje možnostem použití vývojářského jazyka Swift v serverových aplikacích. Popisuje výhody a nevýhody jazyka Swift oproti ostatním jazykům, zejmé... 详细信息

Tato práce se věnuje možnostem použití vývojářského jazyka Swift v serverových aplikacích. Popisuje výhody a nevýhody jazyka Swift oproti ostatním jazykům, zejména Obj-C který je předchůdcem Swiftu a Java, který je přímým konkurentem jazyka Swift. Dále popisuje rozdíly mezi jednotlivými frameworky používané pro webové aplikace. Výběr jazyka Swift je implementován v podobě jednoduché aplikace pro REST komunikaci s databází na základě již existující aplikace. Řešení bude schopno v databázi vyhledávat a následně zobrazovat výsledky vyhledávání.

关键词： Swift Linux Linux server Vapor nosql databáze Bakalářská práce VUT Brno Swift Linux Linux server Vapor nosql database Bachelors Thesis BUT Brno University of Technology Text

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MongoDB jako datové úložiště pro Google App Engine SDK

MongoDB jako datové úložiště pro Google App Engine SDK

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作者： Heller, Stanislav Brno University of Technology

Tato práce se zabývá možnostmi použití nosql databáze MongoDB jako úložiště pro data, která jsou v Google App Engine SDK ukládána databázovými stuby - rozhran... 详细信息

Tato práce se zabývá možnostmi použití nosql databáze MongoDB jako úložiště pro data, která jsou v Google App Engine SDK ukládána databázovými stuby - rozhraními pro simulaci produkčního databázového prostředí v Google App Engine. Existující stuby nejsou optimalizovány pro větší zátěž a při větším množství uložených dat značně zpomalují vývoj a testování celé aplikace. Práce analyzuje vlastnosti MongoDB a platformy Google App Engine se zaměřením na datové úložiště Google Datastore. Předmětem další analýzy je rozhraní pro implementaci databázových stubů v SDK. Výsledkem práce je návrh a implementace nového výkonnějšího stubu využívajícího MongoDB, který je plně integrovatelný do Google App Engine SDK.

关键词： Google App Engine nosql databáze Google App Engine SDK MongoDB Datastore stub Python cloud web Google App Engine nosql database Google App Engine SDK MongoDB Datastore stub Python Cloud Web Text

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ON THE GENERATION OF RANK 3 SIMPLE MATROIDS WITH AN APPLICATION TO TERAO'S FREENESS CONJECTURE

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SIAM JOURNAL ON DISCRETE MATHEMATICS 2021年第2期35卷 1201-1223页

作者： Barakat, Mohamed Behrends, Reimer Jefferson, Christopher Kuehne, Lukas Leuner, Martin Univ Siegen Dept Math D-57068 Siegen Germany Univ Kaiserslautern Dept Math D-67653 Kaiserslautern Germany Univ St Andrews Sch Comp Sci St Andrews KY16 9SX Fife Scotland Hebrew Univ Jerusalem Einstein Inst Math IL-91904 Jerusalem Israel Max Planck Inst Math Sci Leipzig Germany Rhein Westfal TH Aachen Lehrstuhl B Math Aachen Germany

In this paper we describe a parallel algorithm for generating all nonisomorphic rank 3 simple matroids with a given multiplicity vector. We apply our implementation in the high performance computing version of GAP to generate all rank 3 simple matroids with at most 14 atoms and an integrally splitting characteristic polynomial. We have stored the resulting matroids alongside with various useful invariants in a publicly available, Arango DB-powered database. As a byproduct we show that the smallest divisionally free rank 3 arrangement which is not inductively free has 14 hyperplanes and exists in all characteristics distinct from 2 and 5. Another database query proves that Terao's freeness conjecture is true for rank 3 arrangements with 14 hyperplanes in any characteristic.

关键词： rank 3 simple matroids integrally splitting characteristic polynomial Terao's freeness conjecture recursive iterator tree-iterator leaf-iterator iterator of leaves of rooted tree priority queue parallel evaluation of recursive iterator nosql database ArangoDB

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Building information modeling-based cyber-physical platform for building performance monitoring

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第2期16卷 1-21页

作者： Zhang, Yun-Yi Kang, Kai Lin, Jia-Rui Zhang, Jian-Ping Zhang, Yi Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Univ Glodon Joint Res Ctr Bldg Informat Beijing Peoples R China

Building performance management requires massive data input;however, the relevant data are separated and heterogeneous;thus, it prevents a comprehensive building performance management. Building information modeling brings a new way to capture rich information of a building, and has great potential in data interoperability for building performance management. This article presents a scalable building information modeling-based cyber-physical platform for building performance monitoring to integrate heterogeneous data from different buildings. A smart sensor network based on Arduino and standard protocol is installed for data sensing and collection. A building information modeling-based sensing information model integrating heterogeneous data in a unified structure is proposed, and a scalable nosql database is established to store data in a cloud environment. A series of RESTful web services is developed to share data for building performance management applications. The proposed platform is developed taking the advantage of horizontal scalability of nosql database, and the data schema and services are generated automatically based on the unified data model. The platform has collected data from 77 buildings in China, and the results of a case study show the platform brings a new paradigm in collecting, storing, integrating, and sharing of sensor data and building information for building performance monitoring and analytics.

关键词： Building information modeling building performance cyber-physical platform sensor network nosql database web service interoperability system automation

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Development of the Adaptive System for Tool Management

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第2期30卷 648-654页

作者： Chen, Jenn-Yih Lin, Yi-Ling Lee, Bean -Yin Natl Formosa Univ Dept Mech & Comp Aided Engn Yunlin 632 Taiwan Natl Formosa Univ Dept Power Mech Engn Yunlin 632 Taiwan Natl Formosa Univ Dept Mech & Comp Aided Engn Smart Machinery & Intelligent Mfg Res Ceneter Yunlin 632 Taiwan

Technological developments in the manufacturing industry have accelerated recently. Although many traditional manufacturing factories had adopted automated production, owing to the inability to control the status of individual tools, it is challenging for manufacturers to accurately track the real time status of production line tools. To bring smart production into existence, a robust database system needs to be ensured. New models of computer numerical control machines are equipped with a built-in tool management system (TMS), but most versions of this system are only able to set a machining time limit or a total number of workpieces limit for each tool to determine tool replacement intervals. Such system could likely cause early tool replacement, resulting in waste and increased tool costs. In order to implement green manufacturing, the prediction of tool wear is essential to reduce wastage of materials. In this study, we developed a TMS that is based on a not only SQL (nosql) database, which not only stores several different types of data, but also can store prediction models to make it suitable for the demands of varying enterprise scales. It will be more helpful to ensure processing safety and effectively reduce manufacturing costs.

关键词： group method of data handling nosql database polynomial network tool management system

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Analysis and evaluation of the riak cluster environment in distributed databases

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COMPUTER STANDARDS & INTERFACES 2020年 72卷 103452-103452页

作者： Topcu, Ahmet Ercan Rmis, Aimen Mukhtar Amer Univ Middle East Coll Engn & Technol Kuwait Kuwait Al Asmarya Islamic Univ Fac Sci Dept Comp Sci Zliten Libya Ankara Yildirim Beyazit Univ Dept Comp Engn Ankara Turkey

Many institutions and companies undergoing technological developments have been producing large amounts of structured and unstructured data. Special databases are required to deal with such data and nosql databases have thus emerged. They are widely used in cloud databases and distributed systems. In the era of big data, these databases provide a scalable solution with high availability. In this context, we need new architectures in order to store more and more different kinds of data. To obtain a good structure for large and diverse data, structures must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we test the Riak key-value database to measure its performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. The throughput and latency of the nosql database in different types of experiments and with different sizes of data are compared. As we increase the data size in the experiments, an increase in the number of threads leads to better throughput and latency factor was reduced. High performance results are obtained for only read operations for all experiments. We observed that performance advanced when there was only read operations compared with a mix of read and update operations. Moreover, our findings intensify the understanding of the distributed database and have to help future developers through the experimental results shown in this paper.

关键词： nosql database Big data riak Basho-bench Cluster Structured data

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BIM-based big data analytic system for healthcare facility management

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JOURNAL OF BUILDING ENGINEERING 2023年 64卷

作者： Demirdogen, Gokhan Isik, Zeynep Arayici, Yusuf Yildiz Tech Univ Civil Engn Fac Dept Civil Engn Istanbul Turkiye Northumbria Univ Dept Architecture & Built Environm London England

Facility management (FM) is the costliest stage in the building lifecycle of healthcare buildings. Building Information Modeling (BIM) has potential solution to enable information management for healthcare FM. However, BIM is not used at FM due to its static nature, while healthcare FM requires dynamic information handling. The FM systems in practice and literature require either implementation of WebGL-HTML5 applications that necessitates coding knowledge for updates or data analysis. Therefore, this paper aims to develop a healthcare FM system based on Big Data Analytic (BDA), BIM and Not only SQL (nosql) database to enable FM information query and FM-KPI (Key Performance Indicator) visualization. The proposed system is developed through Design Science Research methodology. Findings show that the system is effective and practical to perform FM data retrival and analysis by practioners in healthcare facilities management effec-tively.

关键词： BIM Facility management nosql database Dynamo Big data analytics

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An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques

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BMC BIOINFORMATICS 2019年第1期20卷 658-658页

作者： Teo, Bae Guan Dhillon, Sarinder Kaur Monash Univ Malaysia Sch Engn Kuala Lumpur Malaysia Univ Malaya Fac Sci Inst Biol Sci Data Sci & Bioinformat Lab Kuala Lumpur Malaysia

Background: Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch. Result: An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure. Conclusions: Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross- disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling

关键词： 3D Modelling Machine learning Landmark detection nosql database

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A transaction platform for microservices-based big data systems

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SIMULATION MODELLING PRACTICE AND THEORY 2023年 123卷

作者： Gonzalez-Aparicio, Maria Teresa Younas, Muhammad Tuya, Javier Casado, Ruben Univ Oviedo Polytech Sch Engn Gijon 33204 Spain Oxford Brookes Univ Sch Engn Comp & Math Oxford OX33 1HX Oxon England Accenture SL Sci & Technol Pk Gijon 33203 Spain

Microservices architecture has increasingly been adopted for building distributed and scalable applications. The premise is that microservices are designed as smaller software components which are easier to be preserved and which enable separation between different components. This paper proposes a new transaction platform for microservices architecture to manage processing of big data stored in a cluster of nosql databases. New asynchronous protocols are designed to execute database operations as transactions, and to maintain their correctness and consistency. A prototype system has been developed that simulates London bus service across bus routes. It is evaluated through simulation experiments using big data from 'Transport for London' data ser-vice in order to analyse effects of transaction processing on response time and throughput in microservices architecture. The transaction platform reliably processes database operations, and enables data availability and consistency in failure-free and failure-prone environments.

关键词： Microservices architecture Transaction management Big data nosql database

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