Databases and Information Systems Commons^™

Improved User News Feed Customization For An Open Source Search Engine, Timothy Chow

Master's Projects

Yioop is an open source search engine project hosted on the site of the same name.It offers several features outside of searching, with one such feature being a news feed. The current news feed system aggregates articles from a curated list of news sites determined by the owner. However in its current state, the feed list is limited in size, constrained by the hardware that the aggregator is run on. The goal of my project was to overcome this limit by improving the current storage method used. The solution was derived by making use of IndexArchiveBundles and IndexShards, both of …

Developing A Mongodb Monitoring System Using Nosql Databases For Monitored Data Management, Anjitha Karattu Thodi

Master's Projects

MongoDB is a NoSQL database, specifically used to efficiently store and access a large quantity of unstructured data over a distributed cluster of nodes. As the number of nodes in the cluster increases, it becomes difficult to manually monitor different components of the database. This poses an interesting problem of monitoring the MongoDB database to view the state of the system at any point. Although a few proprietary monitoring tools exist to monitor MongoDB clusters, they are not freely available for use in academia. Therefore, the focus of this project is to create a monitoring system that is completely built …

The Use Of Digital Millenium Copyright Act To Stifle Speech Through Non-Copyright Related Takedowns, Miller Freeman

Seattle Journal of Technology, Environmental, & Innovation Law

In 1998, Congress passed the Digital Millennium Copyright Act. This law provided new methods of protecting copyright in online media. These protections shift the normal judicial process that would stop the publication of infringing materials to private actors: the online platforms. As a result, online platforms receive notices of infringement and issue takedowns of allegedly copyrighted works without the judicial process which normally considers the purpose of the original notice of infringement. In at least one case, discussed in detail below, this has resulted in a notice and takedown against an individual for reasons not related to the purpose of …

Applications Of Digital Remote Sensing To Quantify Glacier Change In Glacier And Mount Rainier National Parks, Brianna Clark

Electronic Theses and Dissertations

Digital remote sensing and geographic information systems were employed in performing area and volume calculations on glacial landscapes. Characteristics of glaciers from two geographic regions, the Intermountain Region (between the Rocky Mountain and Cascade Ranges) and the Pacific Northwest, were estimated for the years 1985, 2000, and 2015. Glacier National Park was studied for the Intermountain Region whereas Mount Rainier National Park was representative of the glaciers in the Pacific Northwest. Within the thirty year period of the study, the glaciers in Glacier National Park decreased in area by 27.5 percent while those on Mount Rainier only decreased by 5.7 …

Vzwam Web-Based Lookup, Ruben Claudio

Masters Theses & Doctoral Dissertations

This web-based lookup will allow V employees to find territory sales rep much faster. It will simplify the process and eliminate manual processes.

At the moment, a combination of multiple manual processes is needed to find territory sales reps. The company’s CRM does not allow to find rep sales quickly. When an in-house sales representative is talking to a prospect, this sales rep has to go through a few series of steps to find an outside or territory sales rep --which is usually needed to schedule in-person meetings, that results in delays while doing transactions with the prospects. Besides, because …

Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil

Student Works (2020-2029)

One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective …

Supervised Optimal Decision Machine Learning Approach To Class- And Method-Level Data Preprocessing Towards Effective Software Defect Prediction, Felix Ebubeogu Amarachukwu

Student Works (2020-2029)

Software defect prediction provides actionable outputs to software teams while contributing to industrial success. Therefore, predicting the number of defects in a new version of software at both the class and method levels is an important goal of defect prediction studies to assist software teams in optimizing their test efforts towards improving software quality. However, despite remarkable achievements in defect prediction, the quality of the data applied in defect prediction studies has been a major concern, with related quality issues leading to numerous contradictory findings in machine learning research. In addition, a demonstrated approach for predicting the number of defects …

Classification Of Dividend News Based On The Movement Of The Share Market Prices Of Public Listed Companies In Bursa Malaysia, Vijaya Kumar Shubana

Student Works (2020-2029)

Stock market is naturally complex and plays a major role in towards the nation’s growth. However, the performance of a company in stock market varies due to many influences but not limited to economics, political and financial related news. This study attempts to classify the share market dividend news announcement in Bursa Malaysia based on the pattern of share market price. Samples including five hundred (500) observations of dividend news from forty-seven (47) listed companies in Bursa Malaysia during the period of 2000 to 2018 are used in this study. There are three (3) main objectives in this study which …

Improving Energy-Efficiency Through Smart Data Placement In Hadoop Clusters, Ahmed Mostafa

Theses and Dissertations

Hadoop, a pioneering open source framework, has revolutionized the big data world because of its ability to process vast amounts of unstructured and semi-structured data. This ability makes Hadoop the ‘go-to’ technology for many industries that generate big data, thus it also aids in being cost effective, unlike other legacy systems. Hadoop MapReduce is used in large scale data parallel applications to process massive amounts of data across a cluster and is used for scheduling, processing, and executing jobs. Basically, MapReduce is the right hand of Hadoop, as its library is needed to process these large data sets. In this …

Use Of Eye-Tracking To Identify Psychological Indicators Of Sleepiness, Debasis Roy, Fiona Fui-Hoon Nah, Matthew Thimgan

Research Collection School Of Computing and Information Systems

Sleepiness or sleep deprivation creates a serious hazard or obstacle to task execution and performance. Sleep deprivation can be life-threating (e.g., when driving or executing attention-critical tasks). We are interested to examine if eye-tracking technology can be used to assess and detect sleepiness in an online environment. In this research proposal, we will focus on examining the relationships between sleepiness and parameters involving pupil size, blinks, and saccades.

Jplink: On Linking Jobs To Vocational Interest Types, Amila Silva, Pei Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits …

Relationships Between Willingness To Share Information For Benefits And Trust, Gaurav Bansal, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

This research examines the role of willingness to share one’s information for three benefits as tradeoffs – monetary gains, personalization, and national security – and their effects on trust in online businesses. Data were gathered from MTurk and the results indicate that willingness to share information for monetary gains and personalization is marginally associated with trust in online businesses, but willingness to share information for national security has no association with trust in online businesses. The paper also discusses implications, limitations, and future research directions.

Dynamic Fraud Detection Via Sequential Modeling, Panpan Zheng

Graduate Theses and Dissertations

The impacts of information revolution are omnipresent from life to work. The web services have signicantly changed our living styles in daily life, such as Facebook for communication and Wikipedia for knowledge acquirement. Besides, varieties of information systems, such as data management system and management information system, make us work more eciently. However, it is usually a double-edged sword. With the popularity of web services, relevant security issues are arising, such as fake news on Facebook and vandalism on Wikipedia, which denitely impose severe security threats to OSNs and their legitimate participants. Likewise, oce automation incurs another challenging security issue, …

Data Breach Consequences And Responses: A Multi-Method Investigation Of Stakeholders, Hamid Reza Nikkhah

Graduate Theses and Dissertations

The role of information in today’s economy is essential as organizations that can effectively store and leverage information about their stakeholders can gain an advantage in their markets. The extensive digitization of business information can make organizations vulnerable to data breaches. A data breach is the unauthorized access to sensitive, protected, or confidential data resulting in the compromise of information security. Data breaches affect not only the breached organization but also various related stakeholders. After a data breach, stakeholders of the breached organizations show negative behaviors, which causes the breached organizations to face financial and non-financial costs. As such, the …

A 2020 Perspective On "Client Risk Informedness In Brokered Cloud Services: An Experimental Pricing Study", Di Shang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Cloud computing and the cloud services market have advanced in the past ten years. Cloud services now include most information technology (IT) services from fundamental computing services to more cutting- edge artificial intelligence (AI) services. Accordingly, opportunities have emerged for research on the design of new market features to improve the cloud services market to benefit providers and users. Based on our observation of the recent development of cloud services, in this short research commentary, we share our agenda for future studies of this important sector of IT services.

A Data-Analytics Approach For Risk Evaluation In Peer-To-Peer Lending Platforms, Feng He, Yuelei Li, Tiecheng Xu, Libo Yin, Wei Zhang, Xiaotao Zhang

Research Collection School Of Accountancy

The goal of this article is to investigate the roles of individual behavior characteristics and Internet finance industry risk in the light of bank run theory for P2P. We know that risk evaluation is clearly important for peer-to-peer (P2P) lending platforms in China, as during the last two years, the industry has experienced thousands of platform crashes. Traditional approaches to evaluate enterprise risk are increasingly ineffective in this industry, due to the difficulty of assessing the real information. In addition, the Internet business model makes it possible to record new kinds of information. By applying a data-driven analytics method, we …

Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the …

Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …

Route Choice Behaviour And Travel Information In A Congested Network: Static And Dynamic Recursive Models, Giselle De Moraes Ramos, Tien Mai, Winnie Daamen, Emma Frejinger

Research Collection School Of Computing and Information Systems

Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy. To this end, we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers, travel diaries and link travel times on the overall network. Though such combined data sets exist, these have not yet …

Platform Pricing With Strategic Buyers: The Impact Of Future Production Cost, Mei Lin, Xiajun Amy Pan, Quan Zheng

Research Collection School Of Computing and Information Systems

Two-sided platforms are often coupled with exclusive hardware products that connect two sides of users, the consumers of the hardware product (i.e., buyers) and the application developers (i.e., sellers). The hardware product in the platform business model introduces three important issues that are not yet well understood in the literature of platform pricing: potentially downward-trending production cost, product quality improvements, and consumers' strategic behaviors. Using analytical modeling, our study explicitly factors in these issues in analyzing a monopoly platform owner's two-sided pricing problem. The platform sequentially introduces and prices quality-improving hardware products, for which the costliness of quality may decrease. …

Hierarchical Reinforcement Learning With Integrated Discovery Of Salient Subgoals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals which are simpler to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is …

Privacy-Preserving Protocol For Atomic Swap Between Blockchains, Kiran Gurung

Boise State University Theses and Dissertations

Atomic swap facilitates fair exchange of cryptocurrencies without the need for a trusted authority. It is regarded as one of the prominent technologies for the cryptocurrency ecosystem, helping to realize the idea of a decentralized blockchain introduced by Bitcoin. However, due to the heterogeneity of the cryptocurrency systems, developing efficient and privacy-preserving atomic swap protocols has proven challenging. In this thesis, we propose a generic framework for atomic swap, called PolySwap, that enables fair ex-change of assets between two heterogeneous sets of blockchains. Our construction 1) does not require a trusted third party, 2) preserves the anonymity of the swap …

The Future Of Work Now: Cyber Threat Attribution At Fireeye, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbent described below is an example of this phenomenon. It’s a clear example of an existing job that’s been transformed by AI and related tools.

Retrofitting Embeddings For Unsupervised User Identity Linkage, Tao Zhou, Ee-Peng Lim, Roy Ka-Wei Lee, Feida Zhu, Jiuxin Cao

Research Collection School Of Computing and Information Systems

User Identity Linkage (UIL) is the problem of matching user identities across multiple online social networks (OSNs) which belong to the same person. The solutions to UIL problem facilitate cross-platform research on OSN users and enable many useful applications such as user profiling and recommendation. As the UIL labeled data are often lacking and costly to obtain, learning user embeddings for matching user identities using an unsupervised approach is therefore highly desired. In this paper, we propose a novel unsupervised UIL framework for enhancing existing user embedding-based UIL methods. Our proposed framework incorporates two key ideas, user-discriminative features and retrofitting …

Rankbooster: Visual Analysis Of Ranking Predictions, Abishek Puri, Bon Kyung Ku, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Ranking is a natural and ubiquitous way to facilitate decision-making in various applications. However, different rankings are often used for the same set of entities, with each ranking method placing emphasis on different factors. These factors can also be multi-dimensional in nature, compounding the problem. This complexity can make it challenging for an entity which is being ranked to understand what they can do to improve their rankings, and to analyze the effect of changes in various factors to their overall rank. In this paper, we present RankBooster, a novel visual analytics system to help users conveniently investigate ranking predictions. …

Cornac: A Comparative Framework For Multimodal Recommender Systems, Aghiles Salah, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests …

Secure And Efficient Models For Retrieving Data From Encrypted Databases In Cloud, Sultan Ahmed A Almakdi

Graduate Theses and Dissertations

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users’ fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Graduate Student Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross-sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Feature Extraction And Analysis Of Binaries For Classification, Micah Flack

Annual Research Symposium

The research project, Feature Extraction and, Analysis of Binaries for Classification, provides an in-depth examination of the features shared by unlabeled binary samples, for classification into the categories of benign or malicious software using several different methods. Because of the time it takes to manually analyze or reverse engineer binaries to determine their function, the ability to gather features and then instantly classify samples without explicitly programming the solution is incredibly valuable. It is possible to use an online service; however, this is not always viable depending on the sensitivity of the binary. With Python3 and the Pefile library, we …