Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Era big data menjadi sebuah fenomena yang menarik untuk di bahas oleh kalangan peneliti dan pengembang perangkat lunak, pengembangan aplikasi dan konsep pengelolaan data semakin banyak varian dan dukungan menjadikan kerangka big data dapat masuk kesetiap lini kehidupan, data yang tersusun baik secara singkronus maupun asingkronus, melibatkan mesin dan manusia dalam pengumpulan data menjadikan teknologi ini semakin sejalan dengan konsep Revolusi Industri 4.0 Dalam berbagai kajian di sajikan konsep dan kerangka kerja Big Data, dari kajian tersebut beberapa peneliti menyajikan lapisan dalam arsitektur Big Data, di mana masing masing lapisan memberi input bagi lapisan lain untuk dapat di olah menjadi bentuk yang siap saji di masyarakat, lapisan yang tediri dari pengumpulan data, penyimpanan data, pemrosesan data serta Analisa data, sehingga pada lapisan aplikasi penggunaan data dapat lebih maksimal di rasakan oleh pengguna. Dalam makalah ini di sajikan beberapa bahan studi literature yang di rangkum untuk mendapatkan penjelasan mengenai lapisan arsitektur Big Data yang dapat di kembagkan dan di terapkan pada bidang bidang penelitian lain

APPLYING MACHINE LEARNING FOR COP/CTP DATA FILTERING

Student Thesis (NPS NRP Project Related)Accurate tracks and targeting are key to providing decision-makers with the confidence to execute their missions. Increasingly, multiple intelligence, surveillance, and reconnaissance (ISR) assets across different intelligence sources are being used to increase the accuracy of track location, resulting in the need to develop methods to exploit heterogeneous sensor data streams for better target state estimation. One of the algorithms commonly used for target state estimation is the Kalman Filter (KF) algorithm. This algorithm performs well if its covariance matrices are accurate approximations of the uncertainty in sensor measurements. Our research complements the artificial intelligence/machine learning (AI/ML) efforts the U.S. Navy is conducting by quantitatively assessing the potential of using an ML model to predict sensor measurement noise for KF state estimation. We used a computer simulation to generate sensor tracks of a single target and trained a neural network to predict sensor error. The hybrid model (ML-KF) was able to outperform our baseline KF model that uses normalized sensor errors by approximately 20% in target position estimation. Further research in enhancing the ML model with external environment variables as inputs could potentially create an adaptive state estimation system that is capable of operating in varied environment settings.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Outstanding ThesisCaptain, Singapore ArmyApproved for public release. Distribution is unlimited

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Digital content popularity counting with Amazon Web Services

The page hit counter system processes, counts and stores page hit counts gathered from page hit events from a news media company’s websites and mobile applications. The system serves a public application interface which can be queried over the internet for page hit count information. In this thesis I will describe the process of replacing a legacy page hit counter system with a modern implementation in the Amazon Web Services ecosystem utilizing serverless technologies. The process includes the background information, the project requirements, the design and comparison of different options, the implementation details and the results. Finally, I will show how the new system implemented with Amazon Kinesis, AWS Lambda and Amazon DynamoDB has running costs that are less than half of that of the old one’s

Enhancing travel recommendations: Ai-driven personalization through user digital footprints

Esta tesis tiene como objetivo examinar la manera en que la huella digital que dejan los usuarios en internet puede utilizarse para optimizar la personalización de los servicios turísticos, mediante el uso de inteligencia artificial. El documento propone que el auge de la inteligencia artificial ha abierto un mundo de oportunidades para desarrollar nuevas herramientas para mejorar la experiencia de viaje digital. El enfoque se basa en la idea de que las huellas digitales son únicas y particulares de cada individuo y estos valiosos datos pueden dar lugar a sugerencias de viaje más inteligentes y certeras. Se consideran las actitudes de comportamiento del usuario, como la influencia del contenido generado por el usuario en las redes sociales y el boca a boca electrónico en el proceso de planificación del viaje, así como las implicaciones de este rastro de datos en la optimización de los servicios de viaje personalizados. Este modelo describe la relación entre la inteligencia artificial y la hiper personalización de servicios. Como es una tendencia creciente que está alterando nuestra realidad actual, la tesis presentada desarrolla una aplicación de viajes a medida que, con el permiso del usuario, aprovecha los datos recopilados de las redes sociales personales para construir un plan de viaje específico basado en las preferencias individuales.This thesis aims to examine the way the digital footprint users leave behind can be utilized to optimize the personalization of tourism services, through the use of artificial intelligence. The paper proposes that the surge of artificial intelligence has opened a world of opportunities to develop new tools to improve the digital travel experience. The approach is based on the idea that digital footprints are unique and particular to each individual and this valuable data can result in smarter and unerring travel suggestions. Behavioral attitudes of the user, such as the influence of user-generated content in social media and e-word of mouth in the travel planning process, are considered, as well as the implications of this data trail in the optimization of customized travel services. This model describes the relationship between artificial intelligence and hyper-personalization of services. As it is a growing trend that is disrupting our current reality, the presented thesis develops a tailor-made traveling application that, with permission of the user, leverages the data collected from personal social media to build a specific travel plan based on each user’s preferences

Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications

Tackling Lower-Resource Language Challenges: A Comparative Study of Norwegian Pre-Trained BERT Models and Traditional Approaches for Football Article Paragraph Classification

In lower-resource language settings, domain-specific tasks such as paragraph classification of football articles present significant challenges. Traditional machine learning models face difficulties in effectively capturing the linguistic complexities inherent in the paragraphs, emphasizing the need for more advanced approaches to overcome these obstacles. This thesis investigates the potential of Norwegian pre-trained BERT (Bidirectional Encoder Representations from Transformers) models for paragraph classification tasks in the context of Norwegian football articles, a domain requiring a nuanced understanding of the Norwegian language. BERT is a powerful model architecture for language-specific processing tasks, which learns from the context of words in a sentence in both directions. Specifically, this thesis compares the performance of Transformer-based BERT models with traditional machine learning models in multi-class and multi-label classification tasks. An existing dataset of about 5,500 football article paragraphs is utilized to evaluate multi-class classification results. In addition, a newly annotated multi-label dataset of just over 2,000 samples is introduced for the multi-label classification assessment. The results reveal promising performance for the Norwegian pre-trained BERT models in both classification tasks, achieving an accuracy of ∼ 0.88 and a weighted-average F1-score of ∼ 0.87 in the multi-class classification task and accuracy of ∼ 0.40 and a weighted-average F1-score of ∼ 0.58 in the multi-label classification task, significantly outperforming the results of the traditional machine learning models. This study highlights the effectiveness of Transformer-based models in lower-resource language settings. It emphasizes the need for continued research and development in Natural Language Processing for underrepresented languages

Understanding people through the aggregation of their digital footprints

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 160-172).Every day, millions of people encounter strangers online. We read their medical advice, buy their products, and ask them out on dates. Yet our views of them are very limited; we see individual communication acts rather than the person(s) as a whole. This thesis contends that socially-focused machine learning and visualization of archived digital footprints can improve the capacity of social media to help form impressions of online strangers. Four original designs are presented that each examine the social fabric of a different existing online world. The designs address unique perspectives on the problem of and opportunities offered by online impression formation. The first work, Is Britney Spears Span?, examines a way of prototyping strangers on first contact by modeling their past behaviors across a social network. Landscape of Words identifies cultural and topical trends in large online publics. Personas is a data portrait that characterizes individuals by collating heterogenous textual artifacts. The final design, Defuse, navigates and visualizes virtual crowds using metrics grounded in sociology. A reflection on these experimental endeavors is also presented, including a formalization of the problem and considerations for future research. A meta-critique by a panel of domain experts completes the discussion.by Aaron Robert Zinman.Ph.D

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges