Databases and Information Systems Commons^™

Active Snort Rules And The Needs For Computing Resources: Computing Resources Needed To Activate Different Numbers Of Snort Rules, Chad A. Arney, Xinli Wang

School of Technology Publications

This project was designed to discover the relationship between the number of enabled rules maintained by Snort and the amount of computing resources necessary to operate this intrusion detection system (IDS) as a sensor. A physical environment was set up to loosely simulate a network and an IDS sensor monitoring it.

The experiment was conducted in five trials. A different number of Snort rules was enabled in each trial and the corresponding utilization of computing resources was measured. Remarkable variation and a clear trend of CPU usage were observed in the experiment.

Mabic: Mobile Application Builder For Interactive Communication, Huy Manh Nguyen

Masters Theses & Specialist Projects

Nowadays, the web services and mobile technology advance to a whole new level. These technologies make the modern communication faster and more convenient than the traditional way. People can also easily share data, picture, image and video instantly. It also saves time and money. For example: sending an email or text message is cheaper and faster than a letter. Interactive communication allows the instant exchange of feedback and enables two-way communication between people and people, or people and computer. It increases the engagement of sender and receiver in communication.

Although many systems such as REDCap and Taverna are built for …

Online Adaptive Passive-Aggressive Methods For Non-Negative Matrix Factorization And Its Applications, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key …

Development And Semantic Exploitation Of A Relational Data Model For Service Delivery In South African Municipalities, Kgotatso Desmond Mogotlane, Jean Vincent Fonou Dombeu

The African Journal of Information Systems

Relational databases (RDB) are the main sources of structured data for government institutions and businesses. Since these databases are dependent on autonomous hardware and software they create problems of data integration and interoperability. Solutions have been proposed to convert RDB into ontology to enable their sharing, reuse and integration on the Semantic Web. However, the proposed methods and techniques remain highly technical and there is lack of research that focuses on the empirical application of these methods and techniques in information systems (IS) domains. This study develops and semantically exploits a relational data model of the South African Municipalities Information …

Fast And Adaptive Indexing Of Multi-Dimensional Observational Data, Sheng Wang, David Maier, Beng Chin Ooi

Computer Science Faculty Publications and Presentations

Sensing devices generate tremendous amounts of data each day, which include large quantities of multi-dimensional measurements. These data are expected to be immediately available for real-time analytics as they are streamed into storage. Such scenarios pose challenges to state-of-the-art indexing methods, as they must not only support efficient queries but also frequent updates. We propose here a novel indexing method that ingests multi-dimensional observational data in real time. This method primarily guarantees extremely high throughput for data ingestion, while it can be continuously refined in the background to improve query efficiency. Instead of representing collections of points using Minimal Bounding …

Ubiquitous Electronic Medical Record (Emr) For Developing Countries, Nasser Mohammed Alkathiri

Master's Theses (2009 -)

Around the globe, Healthcare Information Technology (HIT) has been evolved either by governments or healthcare providers. The utilization of these technologies has resulted in the improvement of healthcare services all over the world. This evolution has been characterized by availability, reliability, serviceability to patients, and has been enhanced with increased cost and time efficiency. As such, new systems and terms have been established. Electronic Medical Record (EMR), which can also be used interchangeably with Electronic Health Record (EHR) is considered to be the main transformation in healthcare information technologies. EMR has been aimed to reduce and eliminate existing paper based …

Rediscovering Physical Collections Through The Digital Archive: The Jesuit Libraries Provenance Project, Kyle Roberts

History: Faculty Publications and Other Works

Historic library collections offer a rich and underexplored resource for teaching undergraduate and graduate students about new digital approaches, methodologies, and platforms. Their scope and scale can make them difficult to analyze in their physical form, but remediated onto a digital platform, they offer valuable insights into the process of archive creation and the importance of making their content available to audiences that cannot normally access it. The Jesuit Libraries Provenance Project (JLPP) was launched by students, faculty, and library professionals in 2014 to create an online archive of marks of ownership—bookplates, stamps, inscriptions—contained within books from the original library …

Arise-Pie: A People Information Integration Engine Over The Web, Vincent W. Zheng, Tao Hoang, Penghe Chen, Yuan Fang, Xiaoyan Yang

Research Collection School Of Computing and Information Systems

Searching for people information on the Web is a common practice in life. However, it is time consuming to search for such information manually. In this paper, we aim to develop an automatic people information search system, named ARISE-PIE. To build such a system, we tackle two major technical challenges: data harvesting and data integration. For data harvesting, we study how to leverage search engine to help crawl the relevant Web pages for a target entity; then we propose a novel learning to query model that can automatically select a set of "best" queries to maximize collective utility (e.g., precision …

Plackett-Luce Regression Mixture Model For Heterogeneous Rankings, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Learning to rank is an important problem in many scenarios, such as information retrieval, natural language processing, recommender systems, etc. The objective is to learn a function that ranks a number of instances based on their features. In the vast majority of the learning to rank literature, there is an implicit assumption that the population of ranking instances are homogeneous, and thus can be modeled by a single central ranking function. In this work, we are concerned with learning to rank for a heterogeneous population, which may consist of a number of sub-populations, each of which may rank objects dierently. …

Tracking Virality And Susceptibility In Social Media, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In social media, the magnitude of information propagation hinges on the virality and susceptibility of users spreading and receiving the information respectively, as well as the virality of information items. These users' and items' behavioral factors evolve dynamically at the same time interacting with one another. Previous works however measure the factors statically and independently in a restricted case: each user has only a single adoption on each item, and/or users' exposure to items are observable. In this work, we investigate the inter-relationship among the factors and users' multiple adoptions on items to propose both new static and temporal models …

Behavior Analysis In Social Networks: Challenges, Technologies, And Trends, Meng Wang, Ee-Peng Lim, Lei Li, Mehmet Orgun

Research Collection School Of Computing and Information Systems

The research on social networks has advanced significantly, which can be attributed to the prevalence of the online social websites and instant messaging systems as well as the popularity of mobile apps that support easy access to online social networks. These social networks are usually characterized by the complex network structures and rich contextual information. They now become the key platforms for, among others, content dissemination, professional networking, recommendation, alerting, and political campaigns. As online social network users perform activities on the social networks, they leave data traces of human behavior which allow the latter to be studied at scale. …

Inferring Links Between Concerns And Methods With Multi-Abstraction Vector Space Model, Yun Zhang, David Lo, Xin Xia, Tien-Duy B. Le, Giuseppe Scanniello, Jianling Sun

Research Collection School Of Computing and Information Systems

Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that are relevant to the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose a multi-abstraction concern …

Attractiveness Versus Competition: Towards An Unified Model For User Visitation, Thanh-Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior provides useful insights about venues as well as the users visiting them. These insights can be used in urban planning and recommender system applications. Unlike previous works that focus on modeling distance effect on user’s choice of check-in venues, this paper studies check-in behaviors affected by two venue-related factors, namely, area attractiveness and neighborhood competitiveness. The former refers to the ability of an area with multiple venues to collectively attract checkins from users, while the latter represents the ability of a venue to compete with its neighbors in the same area for check-ins. We first embark …

Deep-Based Ingredient Recognition For Cooking Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Retrieving recipes corresponding to given dish pictures facilitates the estimation of nutrition facts, which is crucial to various health relevant applications. The current approaches mostly focus on recognition of food category based on global dish appearance without explicit analysis of ingredient composition. Such approaches are incapable for retrieval of recipes with unknown food categories, a problem referred to as zero-shot retrieval. On the other hand, content-based retrieval without knowledge of food categories is also difficult to attain satisfactory performance due to large visual variations in food appearance and ingredient composition. As the number of ingredients is far less than food …

Satt: Tailoring Code Metric Thresholds For Different Software Architectures, Maurício Aniche, Christoph Treude, Andy Zaidman, Arie Van Deursen, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Code metric analysis is a well-known approach for assessing the quality of a software system. However, current tools and techniques do not take the system architecture (e.g., MVC, Android) into account. This means that all classes are assessed similarly, regardless of their specific responsibilities. In this paper, we propose SATT (Software Architecture Tailored Thresholds), an approach that detects whether an architectural role is considerably different from others in the system in terms of code metrics, and provides a specific threshold for that role. We evaluated our approach on 2 different architectures (MVC and Android) in more than 400 projects. We …

Get Me To My Gate On Time: Efficiently Solving General-Sum Bayesian Threat Screening Games, Aaron Schlenker, Matthew Brown, Arunesh Sinha, Milind Tambe, Ruta Mehta

Research Collection School Of Computing and Information Systems

Threat Screening Games (TSGs) are used in domains where there is a set of individuals or objects to screen with a limited amount of screening resources available to screen them. TSGs are broadly applicable to domains like airport passenger screening, stadium screening, cargo container screening, etc. Previous work on TSGs focused only on the Bayesian zero-sum case and provided the MGA algorithm to solve these games. In this paper, we solve Bayesian general-sum TSGs which we prove are NP-hard even when exploiting a compact marginal representation. We also present an algorithm based upon a adversary type hierarchical tree decomposition and …

Probabilistic Models For Contextual Agreement In Preferences, Loc Do, Hady W. Lauw

Research Collection School Of Computing and Information Systems

The long-tail theory for consumer demand implies the need for more accurate personalization technologies to target items to the users who most desire them. A key tenet of personalization is the capacity to model user preferences. Most of the previous work on recommendation and personalization has focused primarily on individual preferences. While some focus on shared preferences between pairs of users, they assume that the same similarity value applies to all items. Here we investigate the notion of "context," hypothesizing that while two users may agree on their preferences on some items, they may also disagree on other items. To …

Soft Confidence-Weighted Learning, Jialei Wang, Peilin Zhao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorithms have been proposed, where the correlation between thefeatures is utilized to improve the learning efficiency. Among them,Confidence-Weighted (CW) learning algorithms are very effective, which assumethat the classification model is drawn from a Gaussian distribution, whichenables the model to be effectively updated with the second-order informationof the data stream. Despite being studied actively, these CW algorithms cannothandle nonseparable datasets and noisy datasets very …

Cross-Cultural User Perceptions Of Website Design And Security: Introduction To A Commentary And Response On Cyr (2013), Robert John Kauffman

Research Collection School Of Computing and Information Systems

Just as the well-known statistician, George Box, commented in a 1978 paper, “All models are wrong, but some are useful,” so are there many ways to design research inquiry approaches to explore issues in various e-commerce contexts – all useful too. In the two brief essays that follow, the reader will see a written commentary and a response that illustrates this idea. It occurred between a technology researcher who published an article on cross-cultural issues in website design in Cyr (2013), and an economist who is able to offer useful insights on the statistical work and data analytics with methods …

High Correlation Of Middle East Respiratory Syndrome Spread With Google Search And Twitter Trends In Korea, Soo-Yong Shin, Dong-Woo Seo, Jisun An, Haewoon Kwak, Sung-Han Kim, Jin Gwack, Min-Woo Jo

Research Collection School Of Computing and Information Systems

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman …