Дисертації з теми "Streaming Data Analysis"
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Anagnostopoulos, Christoforos. "A Statistical Framework for Streaming Data Analysis." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520838.
Повний текст джерелаPatni, Harshal Kamlesh. "Real Time Semantic Analysis of Streaming Sensor Data." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1324181415.
Повний текст джерелаFairbanks, James Paul. "Graph analysis combining numerical, statistical, and streaming techniques." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54972.
Повний текст джерелаMenglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.
Повний текст джерелаGiannini, Andrea. "Social Network Analysis: Architettura Streaming Big Data di Raccolta e Analisi Dati da Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25378/.
Повний текст джерелаKühn, Eileen [Verfasser], and A. [Akademischer Betreuer] Streit. "Online Analysis of Dynamic Streaming Data / Eileen Kühn ; Betreuer: A. Streit." Karlsruhe : KIT-Bibliothek, 2018. http://d-nb.info/1161008721/34.
Повний текст джерелаMoitra, Anindya. "Computation and Application of Persistent Homology on Streaming Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613686214764863.
Повний текст джерелаZubeir, Abdulghani Ismail. "OAP: An efficient online principal component analysis algorithm for streaming EEG data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392403.
Повний текст джерелаVigraham, Sushrutha. "Design and Analysis of a Real-time Data Monitoring Prototype for the LWA Radio Telescope." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31306.
Повний текст джерелаMaster of Science
Landford, Jordan. "Event Detection Using Correlation within Arrays of Streaming PMU Data." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3031.
Повний текст джерелаAkhmedov, Iliiazbek. "Parallelization of Push-based System for Molecular Simulation Data Analysis with GPU." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6448.
Повний текст джерелаZhou, Pu. "A dynamic approximate representation scheme for streaming time series." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/6766.
Повний текст джерелаSince we are dealing with streaming time series, the segmenting points and the corresponding approximate functions are incrementally produced. For a certain function form, we use a buffer window to find the local farthest possible segmenting point under a user specified error tolerance threshold. To achieve this goal, we define a feasible space for the coefficients of the function and show that we can indirectly find the local best segmenting point by the calculation in the coefficient space. Given the error tolerance threshold, the candidate function representing more information by unit parameter is chosen as the approximate function. Therefore, our representation scheme is more flexible and compact. We provide two dynamic algorithms, PLQS and PLQES, which involve two and three candidate functions, respectively. We also present the general strategy of function selection when more candidate functions are considered. In the experimental test, we examine the effectiveness of our algorithms with synthetic and real time series data sets. We compare our method with the piecewise linear approximation method and the experimental results demonstrate the evident superiority of our dynamic approach under the same error tolerance threshold.
Rivetti, di Val Cervo Nicolo. "Efficient Stream Analysis and its Application to Big Data Processing." Thesis, Nantes, 2016. http://www.theses.fr/2016NANT4046/document.
Повний текст джерелаNowadays stream analysis is used in many context where the amount of data and/or the rate at which it is generated rules out other approaches (e.g., batch processing). The data streaming model provides randomized and/or approximated solutions to compute specific functions over (distributed) stream(s) of data-items in worst case scenarios, while striving for small resources usage. In particular, we look into two classical and related data streaming problems: frequency estimation and (distributed) heavy hitters. A less common field of application is stream processing which is somehow complementary and more practical, providing efficient and highly scalable frameworks to perform soft real-time generic computation on streams, relying on cloud computing. This duality allows us to apply data streaming solutions to optimize stream processing systems. In this thesis, we provide a novel algorithm to track heavy hitters in distributed streams and two extensions of a well-known algorithm to estimate the frequencies of data items. We also tackle two related problems and their solution: provide even partitioning of the item universe based on their weights and provide an estimation of the values carried by the items of the stream. We then apply these results to both network monitoring and stream processing. In particular, we leverage these solutions to perform load shedding as well as to load balance parallelized operators in stream processing systems
Grupchev, Vladimir. "Improvements on Scientific System Analysis." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5851.
Повний текст джерелаAhmed, Kachkach. "Analyzing user behavior and sentiment in music streaming services." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186527.
Повний текст джерелаDe senaste åren har strömningtjänster (för musik, podcasts, TV-serier och filmer) varit i strålkastarljuset genom att förändra synen på hur vi konsumerar media. Om det tekniska impikationerna av att strömma stora mängder data är väl utforskat finns det mycket kvar i att analysera de stora datamängderna som samlas in för att förstå och förbättra tjänsterna. Genom att använda rådata om hur användarna interagerar med musiktjänsten Spotify, fokuserar den här uppsatsen på tre huvudkoncept: strömmandets kontext, användares uppmäksamhet samt sekvensiell analys av användares handlingar. Vi diskuterar betydelsen av varje koncept och föreslår en olika statistiska och maskininlärningstekniker för att modellera dem. Vi visar hur dessa modeller kan användas för att förbättra strömmningstjänster genom att antyda användares sentiment, förbättra rekommendationer, karaktärisera användarsessioner, extrahera betendemönster och ta fram användbar affärsdata.
Hilley, David B. "Temporal streams programming abstractions for distributed live stream analysis applications /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31695.
Повний текст джерелаCommittee Chair: Ramachandran, Umakishore; Committee Member: Clark, Nathan; Committee Member: Haskin, Roger; Committee Member: Pu, Calton; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Gratorp, Christina. "Bitrate smooting: a study on traffic shaping and -analysis in data networks." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10136.
Повний текст джерелаExamensarbetet bakom denna rapport utgör en undersökande studie om hur transmission av mediadata i nätverk kan göras effektivare. Det kan åstadkommas genom att viss tilläggsinformation avsedd för att jämna ut datatakten adderas i det realtidsprotokoll, Real Time Protocol, som används för strömmande media. Genom att försöka skicka lika mycket data under alla konsekutiva tidsintervall i sessionen kommer datatakten vid en godtycklig tidpunkt med större sannolikhet att vara densamma som vid tidigare klockslag. En streamingserver kan tolka, hantera och skicka data vidare enligt instruktionerna i protokollets sidhuvud. Datatakten jämnas ut genom att i förtid, under tidsintervall som innehåller mindre data, skicka även senare data i strömmen. Resultatet av detta är en utjämnad datataktskurva som i sin tur leder till en jämnare användning av nätverkskapaciteten.
Arbetet inkluderar en översiktlig analys av beteendet hos strömmande media, bakgrundsteori om filkonstruktion och nätverksteknologier samt ett förslag på hur mediafiler kan modifieras för att uppfylla syftet med examensarbetet. Resultat och diskussion kan förhoppningsvis användas som underlag för en framtida implementation av en applikation ämnad att förbättra trafikflöden över nätverk.
Aussel, Nicolas. "Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL007/document.
Повний текст джерелаWith the rise of the number of sensors and actuators in an aircraft and the development of reliable data links from the aircraft to the ground, it becomes possible to improve aircraft security and maintainability by applying real-time analysis techniques. However, given the limited availability of on-board computing and the high cost of the data links, current architectural solutions cannot fully leverage all the available resources limiting their accuracy.Our goal is to provide a distributed algorithm for failure prediction that could be executed both on-board of the aircraft and on a ground station and that would produce on-board failure predictions in near real-time under a communication budget. In this approach, the ground station would hold fast computation resources and historical data and the aircraft would hold limited computational resources and current flight's data.In this thesis, we will study the specificities of aeronautical data and what methods already exist to produce failure prediction from them and propose a solution to the problem stated. Our contribution will be detailed in three main parts.First, we will study the problem of rare event prediction created by the high reliability of aeronautical systems. Many learning methods for classifiers rely on balanced datasets. Several approaches exist to correct a dataset imbalance and we will study their efficiency on extremely imbalanced datasets.Second, we study the problem of log parsing as many aeronautical systems do not produce easy to classify labels or numerical values but log messages in full text. We will study existing methods based on a statistical approach and on Deep Learning to convert full text log messages into a form usable as an input by learning algorithms for classifiers. We will then propose our own method based on Natural Language Processing and show how it outperforms the other approaches on a public benchmark.Last, we offer a solution to the stated problem by proposing a new distributed learning algorithm that relies on two existing learning paradigms Active Learning and Federated Learning. We detail our algorithm, its implementation and provide a comparison of its performance with existing methods
Agarwal, Virat. "Algorithm design on multicore processors for massive-data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34839.
Повний текст джерелаMarkou, Ioannis. "Analysing User Viewing Behaviour in Video Streaming Services." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292095.
Повний текст джерелаAnvändarupplevelsen som erbjuds av en videostreamingtjänst spelar en grundläggande roll för kundnöjdheten. Denna upplevelse kan försämras av dålig uppspelningskvalitet och buffertproblem. Dessa problem kan orsakas av en efterfrågan från användare som är högre än videostreamingtjänstens kapacitet. Resursskalningsmetoder kan öka tillgängliga resurser för att täcka behovet. De flesta resursskalningssystem är dock reaktiva och uppskalas automatiskt när en viss behovströskel överskrids. Under populärt livestreaminginnehåll kan efterfrågan vara så hög att även genom att skala upp i sista minuten kan systemet fortfarande vara underutnyttjat tillfälligt, vilket resulterar i en dålig användarupplevelse. Lösningen på detta problem är proaktiv skalning som är händelsebaserad och använder innehållsrelaterad information för att skala upp eller ner, enligt kunskap från tidigare händelser. Som ett resultat är proaktiv resursskalning en nyckelfaktor för att tillhandahålla tillförlitliga videostreamingtjänster. Användares visningsvanor påverkar efterfrågan kraftigt. För att ge en exakt modell för proaktiva resursskalningsverktyg måste dessa vanor modelleras. Denna avhandling ger en sådan prognosmodell för användarvyer som kan användas av en proaktiv resursskalningsmekanism. Denna modell är skapad genom att använda maskininlärningsalgoritmer på data från både live-TV och streamingtjänster. För att producera en modell med tillfredsställande noggrannhet ansågs ett flertal dataattribut relaterade till användare, innehåll och innehållsleverantörer. Resultaten av den här avhandlingen visar att efterfrågan på användare kan modelleras med hög noggrannhet utan att starkt förlita sig på användarrelaterade attribut utan istället genom att analysera tidigare händelseloggar och med kunskap om innehållsleverantörens schema, vare sig det är live-tv eller tjänster för videostreaming.
Zhu, Jun. "Energy and Design Cost Efficiency for Streaming Applications on Systems-on-Chip." Licentiate thesis, Stockholm : Skolan för informations- och kommunikationsteknik, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10591.
Повний текст джерелаIegorov, Oleg. "Une approche de fouille de données pour le débogage temporel des applications embarquées de streaming." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM032/document.
Повний текст джерелаDebugging streaming applications run on multimedia embedded systems found in modern consumer electronics (e.g. in set-top boxes, smartphones, etc) is one of the most challenging areas of embedded software development. With each generation of hardware, more powerful and complex Systems-on-Chip (SoC) are released, and developers constantly strive to adapt their applications to these new platforms. Embedded software must not only return correct results but also deliver these results on time in order to respect the Quality-of-Service (QoS) properties of the entire system. The non-respect of QoS properties lead to the appearance of temporal bugs which manifest themselves in multimedia embedded systems as, for example, glitches in the video or cracks in the sound. Temporal debugging proves to be tricky as temporal bugs are not related to the functional correctness of the code, thus making traditional GDB-like debuggers essentially useless. Violations of QoS properties can stem from complex interactions between a particular application and the system or other applications; the complete execution context must be, therefore, taken into account in order to perform temporal debugging. Recent advances in tracing technology allow software developers to capture a trace of the system's execution and to analyze it afterwards to understand which particular system activity is responsible for the violations of QoS properties. However, such traces have a large volume, and understanding them requires data analysis skills that are currently out of the scope of the developers' education.In this thesis, we propose SATM (Streaming Application Trace Miner) - a novel temporal debugging approach for embedded streaming applications. SATM is based on the premise that such applications are designed under the dataflow model of computation, i.e. as a directed graph where data flows between computational units called actors. In such setting, actors must be scheduled in a periodic way in order to meet QoS properties expressed as real-time constraints, e.g. displaying 30 video frames per second. We show that an actor which does not eventually respect its period at runtime causes the violation of the application’s real-time constraints. In practice, SATM is a data analysis workflow combining statistical measures and data mining algorithms. It provides an automatic solution to the problem of temporal debugging of streaming applications. Given an execution trace of a streaming application exhibiting low QoS as well as a list of its actors, SATM firstly determines exact actors’ invocations found in the trace. It then discovers the actors’ periods, as well as parts of the trace in which the periods are not respected. Those parts are further analyzed to extract patterns of system activity that differentiate them from other parts of the trace. Such patterns can give strong hints on the origin of the problem and are returned to the developer. More specifically, we represent those patterns as minimal contrast sequences and investigate various solutions to mine such sequences from execution trace data.Finally, we demonstrate SATM’s ability to detect both an artificial perturbation injected in an open source multimedia framework, as well as temporal bugs from two industrial use cases coming from STMicroelectronics. We also provide an extensive analysis of sequential pattern mining algorithms applied on execution trace data and explain why state-of-the-art algorithms fail to efficiently mine sequential patterns from real-world traces
Ediger, David. "Analyzing hybrid architectures for massively parallel graph analysis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47659.
Повний текст джерелаZhao, Qi. "Towards Ideal Network Traffic Measurement: A Statistical Algorithmic Approach." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19821.
Повний текст джерелаCommittee Chair: Xu, Jun; Committee Member: Ammar, Mostafa; Committee Member: Feamster, Nick; Committee Member: Ma, Xiaoli; Committee Member: Zegura, Ellen.
Awodokun, Olugbenga. "Classification of Patterns in Streaming Data Using Clustering Signatures." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189.
Повний текст джерелаZamam, Mohamad. "A unified framework for real-time streaming and processing of IoT data." Thesis, Linnéuniversitetet, Institutionen för medieteknik (ME), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-66057.
Повний текст джерелаMogis, Jay D. "Transparency, technology and trust: Music metrics and cultural distortion." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/199497/1/Jay_Mogis_Thesis.pdf.
Повний текст джерелаBiswas, Ayan. "Uncertainty and Error Analysis in the Visualization of Multidimensional and Ensemble Data Sets." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480605991395144.
Повний текст джерелаBarros, Victor Perazzolo. "Big data analytics em cloud gaming: um estudo sobre o reconhecimento de padrões de jogadores." Universidade Presbiteriana Mackenzie, 2017. http://tede.mackenzie.br/jspui/handle/tede/3405.
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The advances in Cloud Computing and communication technologies enabled the concept of Cloud Gaming to become a reality. Through PCs, consoles, smartphones, tablets, smart TVs and other devices, people can access and use games via data streaming, regardless the computing power of these devices. The Internet is the fundamental way of communication between the device and the game, which is hosted and processed on an environment known as Cloud. In the Cloud Gaming model, the games are available on demand and offered in large scale to the users. The players' actions and commands are sent to servers that process the information and send the result (reaction) back to the players. The volume of data processed and stored in these Cloud environments exceeds the limits of analysis and manipulation of conventional tools, but these data contains information about the players' profile, its singularities, actions, behavior and patterns that can be valuable when analyzed. For a proper comprehension and understanding of this raw data and to make it interpretable, it is necessary to use appropriate techniques and platforms to manipulate this amount of data. These platforms belong to an ecosystem that involves the concepts of Big Data. The model known as Big Data Analytics is an effective and capable way to, not only work with these data, but understand its meaning, providing inputs for assertive analysis and predictive actions. This study searches to understand how these technologies works and propose a method capable to analyze and identify patterns in players' behavior and characteristics on a virtual environment. By knowing the patterns of different players, it is possible to group and compare information, in order to optimize the user experience, revenue for developers and raise the level of control over the environment in a way that players' actions can be predicted. The results presented are based on different analysis modeling using the Hadoop technology combined with data visualization tools and information from open data sources in a dataset of the World of Warcraft game. Fraud detection, users' game patterns, churn prevention inputs and relations with game attractiveness elements are examples of modeling used. In this research, it was possible to map and identify the players' behavior patterns and create a prediction of its frequency and tendency to evade or stay in the game.
Os avanços das tecnologias de Computacão em Nuvem (Cloud Computing) e comunicações possibilitaram o conceito de Jogos em Nuvem (Cloud Gaming) se tornar uma realidade. Por meio de computadores, consoles, smartphones, tablets, smart TVs e outros equipamentos é possível acessar via streaming e utilizar jogos independentemente da capacidade computacional destes dispositivos. Os jogos são hospedados e executados em um ambiente computacional conhecido como Nuvem, a Internet é o meio de comunicação entre estes dispositivos e o jogo. No modelo conhecido como Cloud Gaming, compreendesse que os jogos são disponibilizados sob demanda para os usuários e podem ser oferecidos em larga escala. Os comandos e ações dos jogadores são enviados para servidores que processam a informação e enviam o resultado (reação) para o jogador. A quantidade de dados que são processados e armazenados nestes ambientes em Nuvem superam os limites de análise e manipulação de plataformas convencionais, porém tais dados contém informacões sobre o perfil dos jogadores, suas particularidades, ações, comportamentos e padrões que podem ser importantes quando analisados. Para uma devida compreensão e lapidação destes dados brutos, a fim de torná-los interpretáveis, se faz necessário o uso de técnicas e plataformas apropriadas para manipulação desta quantidade de dados. Estas plataformas fazem parte de um ecossistema que envolvem os conceitos de Big Data. Arquiteturas e ferramentas de Big Data, mais especificamente, o modelo denominado Big Data Analytics, são instrumentos eficazes e capazes de não somente trabalhar com estes dados, mas entender seu significado, fornecendo insumos para análise assertiva e predição de acões. O presente estudo busca compreender o funcionamento destas tecnologias e fornecer um método capaz de identificar padrões nos comportamentos e características dos jogadores em ambiente virtual. Conhecendo os padrões de diferentes usuários é possível agrupar e comparar as informações, a fim de otimizar a experiência destes usuários no jogo, aumentar a receita para os desenvolvedores e elevar o nível de controle sobre o ambiente ao ponto que seja possível de prever ações futuras dos jogadores. Os resultados obtidos são derivados de diferentes modelagens de análise utilizando a tecnologia Hadoop combinada com ferramentas de visualização de dados e informações de fontes de dados abertas, em um dataset do jogo World of Warcraft. Detecção de fraude, padrões de jogo dos usuários, insumos para prevencão de churn e relações com elementos de atratividade no jogo, são exemplos de modelagens abordadas. Nesta pesquisa foi possível mapear e identificar os padrões de comportamento dos jogadores e criar uma previsão e tendência de assiduidade sobre evasão ou permanencia de usuários no jogo.
OUYANG, SHAO-YU, and 歐陽少佑. "Research on Live Streaming Data Analysis of University Basketball Association." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jm4sd2.
Повний текст джерела輔仁大學
體育學系碩士班
107
The research was focused on the current live streaming situation of the college basketball league in Taiwan ,and to explore the relevant factors from the data. This research was based on a total of 64 games from the rematch to the final on the 106th academic year and the 107th academic year of the University Basketball Association men’s group first level. We used secondary data analysis method. The relevant data was collected from YouTube channel managing system. The results were proceeded by descriptive ststistics and related analysis. The conclusions are as follow: 1.The channel-related data is growing positively, watching ethnic men> women, and the majority of student groups. The Chien Hsin University of Science and Technology is the best among all teams.2.The score gap is not significantly related to live streaming data and the content of the game affects the rating performance of the audience.
Hsing, Hong-Yu, and 邢弘宇. "A Performance Analysis and Estimation of the Data Stream of Spark Streaming." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/7uem9b.
Повний текст джерела國立臺中科技大學
資訊工程系碩士班
104
Since Spark Streaming handles data in a coarse-grained model (processing a micro-batch of data at a time), delays are inevitable. In the framework of Spark Streaming, data is processed after a certain amount has been collected, which aggravates the problem of delays in data processing. Such delays stem from the design of the framework. In view of that, it is worth contemplating how to calibrate the operational parameters of Spark Streaming. To put it simply, we must try to perform optimization on the processing time and the use of memory. However, it would be very time-consuming to run the program each time optimization is required. Consequently, the study focuses on the analysis and estimation of the DstreamGraph within the framework of Spark Streaming. With a view to increasing the level of parallelism, decreasing the workload of serialization and deserialization, and securing reasonable batch-processing time, the appropriate parameter configuration for an operation is figured out, so that repetitive calibrations are not necessary. The study presents a formula estimation model for transformation parameters, which is effective in analyzing and estimating the duration of a batch-processing cycle. With the model, developers can accurately and swiftly figure out the most appropriate batch-processing time, preventing the redundant restarting and testing of the program for subsequent calibration. It also serves as guidance for setting the batch interval in Spark Streaming to limit the delay within the one-second range.
Кузьо, Олег Олегович, та Oleg Kuzo. "Розробка інформаційної системи для моніторингу музичних вподобань користувачів стрімінгових сервісів". Bachelor's thesis, 2021. http://elartu.tntu.edu.ua/handle/lib/35714.
Повний текст джерелаQualification work is devoted to solving the problem of the user of the streaming service for the analysis of his musical preferences. Purpose: to create an information system for analyzing the musical preferences of users of streaming services. The first section of the qualification work describes the subject of research, identifies the main components for assessing the musical taste of users, determines the required functionality of the final product based on existing analogues, and selects the best solutions for the final product. The second section of the qualification work describes in detail the stages of designing the final product. The principle of operation of the system database and construction of the web interface for the user is considered. The user manual was also created and the minimum system requirements for the quality of the final product were derived, and functional and structural testing was performed. The third section highlights the importance of adaptation in the labor process and describes the general safety requirements for occupational safety for PC users.
ВСТУП 7 1 ПРЕДМЕТНЕ ДОСЛІЖЕННЯ ТА ВИДІЛЕННЯ ОСНОВНИХ АСПЕКТІВ РОБОТИ 8 1.1 ОПИС ПРЕДМЕТУ ДОСЛІДЖЕННЯ 8 1.2 ПОРІВНЯЛЬНИЙ АНАЛІЗ ІСНУЮЧИХ ІНСТРУМЕНТІВ ДЛЯ МОНІТОРИНГУ СТРІМІНГОВИХ СЕРВІСІВ 10 1.3 ВИЗНАЧЕННЯ ОПТИМАЛЬНИХ РІШЕНЬ ДЛЯ РОЗРОБКИ КІНЦЕВОГО ПРОДУКТУ 17 1.4 КОНЦЕПТУАЛЬНА МОДЕЛЬ 24 1.5 ВИСНОВКИ ДО РОЗДІЛУ 25 2 ПРОЄКТУВАННЯ ТА СТВОРЕННЯ КІНЦЕВОГО ПРОДУКТУ 28 2.1 ПРОЄКТУВАННЯ ТА ВІЗУАЛІЗАЦІЯ АЛГОРИТМІВ РОБОТИ КОМПОНЕНТІВ В СИСТЕМІ 28 2.2 СТВОРЕННЯ ПРОГРАМНО-АПАРАТНОГО СЕРЕДОВИЩА 32 2.3 ЕКСПЕРИМЕНТАЛЬНА ЧАСТИНА 41 2.4 ФУНКЦІОНАЛЬНЕ ТА СТРУКТУРНЕ ТЕСТУВАННЯ 46 2.5 ВИСНОВКИ ДО РОЗДІЛУ 50 3 БЕЗПЕКА ЖИТТЄДІЯЛЬНОСТІ, ОСНОВИ ХОРОНИ ПРАЦІ 53 3.1 ЗНАЧЕННЯ АДАПТАЦІЇ В ТРУДОВОМУ ПРОЦЕСІ. 53 3.2 ЗАГАЛЬНІ ВИМОГИ БЕЗПЕКИ З ОХОРОНИ ПРАЦІ ДЛЯ КОРИСТУВАЧІВ ПК 56 ВИСНОВКИ 61 СПИСОК ВИКОРИСТАНИХ ДЖЕРЕЛ 63
Ferreira, Ernesto Carlos Casanova. "Big Data Streaming Analytics." Master's thesis, 2019. http://hdl.handle.net/11110/1927.
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