What Makes a Review Encouraging: Feature Analysis of User Access Logs in a Large-scale Online Movie Review Site

2021 ◽  
Author(s):  
Kakeru Ito ◽  
Yoshiyuki Shoji ◽  
Sumio Fujita ◽  
Martin J. Dürst
Keyword(s):  
Large Scale ◽  
Feature Analysis ◽  
User Access ◽  
Access Logs

scholarly journals A Data Dictionary Approach To Meeting User Requests For Accounting Information

1999 ◽  
Vol 3 (1) ◽  
pp. 53-60
Author(s):  
Kristi Yuthas ◽  
Dennis F. Togo
Keyword(s):  
Large Scale ◽  
Accounting Information ◽  
User Involvement ◽  
Massive Data ◽  
Data Dictionary ◽  
Large Scale Data ◽  
User Access ◽  
Increasing Demand ◽  
User Friendly ◽  
Scale Data

In this era of massive data accumulation, dynamic development of large-scale data-bases and interfaces intended to be user-friendly, there is still an increasing demand on analysts as actual user access to databases is still not a common practice. A data dictionary approach, that includes providing users with a list of relevant data items within the database, can expedite the analysis of information requirements and the development of user-requested information systems. Furthermore, this approach enhances user involvement and reduces the demands on the analysts for systems devel-opment projects.

scholarly journals Indexing Techniques for Web Access Logs

2004 ◽  
pp. 305-334 ◽  
Author(s):  
Yannis Manolopoulos ◽  
Mikolaj Morzy ◽  
Tadeusz Morzy ◽  
Alexandros Nanopoulos ◽  
Marek Wojciechowski ◽  
...  
Keyword(s):  
Log Data ◽  
Web Log ◽  
Indexing Techniques ◽  
Web Access ◽  
User Access ◽  
Single User ◽  
Efficient Processing ◽  
Access Logs ◽  
Web Access Logs ◽  
The Web

Access histories of users visiting a web server are automatically recorded in web access logs. Conceptually, the web-log data can be regarded as a collection of clients’ access-sequences, where each sequence is a list of pages accessed by a single user in a single session. This chapter presents novel indexing techniques that support efficient processing of so-called pattern queries, which consist of finding all access sequences that contain a given subsequence. Pattern queries are a key element of advanced analyses of web-log data, especially those concerning typical navigation schemes. In this chapter, we discuss the particularities of efficiently processing user access-sequences with pattern queries, compared to the case of searching unordered sets. Extensive experimental results are given, which examine a variety of factors and illustrate the superiority of the proposed methods over indexing techniques for unordered data adapted to access sequences.

scholarly journals Ship Classification and Detection Based on CNN Using GF-3 SAR Images

2018 ◽  
Vol 10 (12) ◽  
pp. 2043 ◽  
Author(s):  
Mengyuan Ma ◽  
Jie Chen ◽  
Wei Liu ◽  
Wei Yang
Keyword(s):  
Large Scale ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Feature Analysis ◽  
Single Shot ◽  
Target Classification ◽  
Sar Images ◽  
Different Types ◽  
Ship Classification ◽  
Fully Connected

Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.

Hybrid C&C-DMA Scheme For Multi-User Access in Large-Scale VLC Network

2017 ◽  
Author(s):  
Minglei Huang ◽  
Zhitong Huang ◽  
Yu Xiao ◽  
Yuefeng Ji
Keyword(s):  
Large Scale ◽  
User Access

Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Cory R. Schaffhausen ◽  
Timothy M. Kowalewski
Keyword(s):  
Product Development ◽  
Large Scale ◽  
Early Stage ◽  
User Needs ◽  
Critical Component ◽  
Trip Planning ◽  
Final Study ◽  
Large Populations ◽  
User Access ◽  
Shared Context

Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.

scholarly journals An analysis on the revoking mechanisms for JSON Web Tokens

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880153 ◽  
Author(s):  
László Viktor Jánoky ◽  
János Levendovszky ◽  
Péter Ekler
Keyword(s):  
Distributed Systems ◽  
Internet Of Things ◽  
Access Control ◽  
Large Scale ◽  
Low Complexity ◽  
Wide Spread ◽  
Significant Performance ◽  
User Access

JSON Web Tokens provide a scalable solution with significant performance benefits for user access control in decentralized, large-scale distributed systems. Such examples would entail cloud-based, micro-services styled systems or typical Internet of Things solutions. One of the obstacles still preventing the wide-spread use of JSON Web Token–based access control is the problem of invalidating the issued tokens upon clients leaving the system. Token invalidation presently takes a considerable processing overhead or a drastically increased architectural complexity. Solving this problem without losing the main benefits of JSON Web Tokens still remains an open challenge which will be addressed in the article. We are going to propose some solutions to implement low-complexity token revocations and compare their characteristics in different environments with the traditional solutions. The proposed solutions have the benefit of preserving the advantages of JSON Web Tokens, while also adhering to stronger security constraints and possessing a finely tuneable performance cost.

scholarly journals Analisis Kesiapan Penerapan Process Mining pada Sistem Manajemen Pembelajaran Universitas Telkom

2021 ◽  
Vol 8 (6) ◽  
pp. 1227
Author(s):  
Angelina Prima Kurniati ◽  
Gede Agung Ary Wisudiawan
Keyword(s):  
Feasibility Study ◽  
Learning Process ◽  
Data Analytics ◽  
Process Mining ◽  
Process Models ◽  
Computer Based Learning ◽  
Event Log ◽  
User Access ◽  
Learning Management ◽  
Access Logs

<p>Sistem manajemen pembelajaran (<em>Learning Management System/ LMS</em>) berbasis komputer telah banyak digunakan untuk mengelola pembelajaran dalam institusi pendidikan, termasuk universitas. LMS merekam dan mengelola akses pengguna secara otomatis dalam bentuk <em>event log</em>. Data dalam <em>event log</em> tersebut dapat dianalisis untuk mengenali pola penggunaan LMS sebagai pertimbangan pengembangan LMS. Salah satu metode yang dapat diadopsi adalah <em>process mining</em>, yaitu menganalisis data <em>event log</em> berbasis proses. Analisis data berbasis proses ini bertujuan untuk memodelkan proses yang terjadi dan terekam dalam LMS, mengecek kesesuaian pelaksanaan proses dengan prosedur, dan mengusulkan pengembangan proses di masa mendatang. Makalah ini mengeksplorasi kesiapan data penggunaan LMS di Universitas Telkom sebagai subjek penelitian untuk dianalisis dengan pendekatan <em>process mining</em>. Sepanjang pengetahuan kami, belum ada penelitian sebelumnya yang melakukan analisis data berbasis proses pada LMS ini. Kontribusi penelitian ini adalah eksplorasi peluang untuk menganalisis proses pembelajaran dan pengembangan metode pembelajaran berbasis LMS. Analisis kesiapan LMS dilakukan berdasarkan daftar pengecekan komponen yang dibutuhkan dalam <em>process mining</em>. Makalah ini mengikuti tahap-tahap utama dalam <em>Process Mining Process Methodology</em> (PM<sup>2</sup>). Studi kasus yang dieksplorasi adalah proses pembelajaran pada satu mata kuliah dalam satu semester berdasarkan <em>event log </em>yang diekstrak dari LMS. Hasil penelitian ini menunjukkan bahwa analisis data dalam LMS ini dapat digunakan untuk menganalisis performansi pembelajaran di Universitas Telkom dari kelompok pengguna yang berbeda-beda dan dapat dikembangkan untuk menganalisis data pada studi kasus yang lebih besar. Studi kelayakan ini diakhiri dengan diskusi tentang kelayakan LMS untuk dianalisis dengan <em>process mining</em>, evaluasi oleh tim ahli LMS, dan usulan pengembangan LMS di masa mendatang. <em></em></p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><em>Computer-based Learning Management Systems (LMS) are commonly used in educational institutions, including universities. An LMS records and manages user access logs in an event log. Data in an event log can be analysed to understand patterns in the LMS usage to support recommendations for improvements. One promising method is process mining, which is a process-based data analytics working on event logs. Process mining aims to discover process models as recorded in the LMS, conformance checking of process execution to the defined procedure, and suggest improvements. This paper explores the feasibility of Telkom University LMS usage data to be analysed using process mining. To the best of our knowledge, there was no previous research doing process-based data analytics on this LMS. This paper contributes to explore opportunities to analyse learning processes and enhance LMS-based learning methods. The feasibility study is based on a data component checklist for process mining. This paper is written following the main stages on the Process Mining Project Methodology (PM2). We explore a case study of the learning process of a course in a semester, based on an event log extracted from the LMS. The results show that data analytics on this LMS can be used to analyse learning process performance in Telkom University, based on different user roles. This feasibility study is concluded with a discussion on the feasibility of the LMS to be analysed using process mining, an evaluation by the representative of the LMS expert team, and a recommendation for improvements.</em></em></p>

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