Databases and Information Systems Commons^™

Modular Scheduling System For Westside School District, Tyler Bienhoff

Honors Program: Senior Projects (Public)

Westside School district offers a modular scheduling system for their high school that is more similar to a college schedule than the typical high school system. Due to the complexity of their master schedule each semester, there are no commercially available products that can assist in creating a schedule. Hence, this thesis discusses a scheduling algorithm and management system that was built specifically for Westside High School with the potential to be expanded for use by other interested schools. The first part of the paper is focused on gathering input from students and faculty for which courses and how many …

Supporting User Evaluation Of Messaging Interactions With Potential Romantic Partners Discovered Online, Douglas Zytko

Dissertations

Online dating systems have transformed the way people pursue romance. To arrive at a decision to meet for a face-to-face date, users gather information about each other online pertinent to romantic attraction. Yet sometimes they discover on the date that they made the wrong choice. One aspect of online dating system-use that may be a contributing factor, but is largely overlooked in the literature, is interaction through text-based messaging interfaces. This dissertation explores how messaging interactions inform face-to-face meeting decisions through two qualitative studies, and explores through a mixed methods field study how innovative messaging interfaces that embody theory from …

Cross-Platform Openhds Web Application, Nick Littlefield

Thinking Matters Symposium Archive

A proof of concept cross platform data collection application for Demographic and Health studies has been developed. This application allows for the collection of information about locations, individuals, births, and deaths within a region. This application can be run on both mobile devices, laptops, and desktops.

Surveying Digital Collections Stewardship In Nebraska [Original Survey Form], Jennifer L. Thoegersen, Blake Graham

University of Nebraska-Lincoln Data Repository

No abstract provided.

Feature Detection In Medical Images Using Deep Learning, Anthony Pasquarelli

Honors Projects in Information Systems and Analytics

This project explores the use of deep learning to predict age based on pediatric hand X-Rays. Data from the Radiological Society of North America’s pediatric bone age challenge were used to train and evaluate a convolutional neural network. The project used InceptionV3, a CNN developed by Google, that was pre-trained on ImageNet, a popular online image dataset. Our fine-tuned version of InceptionV3 yielded an average error of less than 10 months between predicted and actual age. This project shows the effectiveness of deep learning in analyzing medical images and the potential for even greater improvements in the future. In addition …

Environmental Restoration Database, Joao Nascimento

Graduate Theses & Non-Theses

Environmental restoration projects face many challenges. Public awareness, funding constraints, unpredictable weather, unknown biological/chemical factors and the uncertainty about how the targeted ecosystem will develop work against the planned and ideal restoration.

One way the projects’ efficiency can be improved is by using software tools for data and quality management systems, in order to share information, make field practice follow rules, keep track of maintenance tasks, measure results and, therefore, increase the rate of success by the amount of resources invested.

Since the conception of every project, all resources involved need to be focused and coherent to the final restoration …

Social Network Monitoring For Bursty Cascade Detection, Wei Xie, Feida Zhu, Jing Xiao, Jianzong Wang

Research Collection School Of Computing and Information Systems

Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we …

Detect Rumor And Stance Jointly By Neural Multi-Task Learning, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

In recent years, an unhealthy phenomenon characterized as the massive spread of fake news or unverified information (i.e., rumors) has become increasingly a daunting issue in human society. The rumors commonly originate from social media outlets, primarily microblogging platforms, being viral afterwards by the wild, willful propagation via a large number of participants. It is observed that rumorous posts often trigger versatile, mostly controversial stances among participating users. Thus, determining the stances on the posts in question can be pertinent to the successful detection of rumors, and vice versa. Existing studies, however, mainly regard rumor detection and stance classification as …

Foundations Of Health Information Technology (Undergraduate) Course Materials, Chi Zhang

Computer Science and Information Technology Ancillary Materials

This is a collection of all materials used in Health Information Technology by Dr. Chi Zhang at Kennesaw State University, including lecture slides, assignments, and assessments, including a question bank.

Topics covered include:

• Clinical Financial Records
• Evidence-Based Medicine
• e-Prescribing
• Patient Bedside Systems
• Telemedicine
• Health Information Networks
• Cryptography
• Accreditation
• HIPAA Privacy and Security

A Sliding-Window Framework For Representative Subset Selection, Yanhao Wang, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. A common approach is to model RSS as the submodular maximization problem because the utility of extracted representatives often satisfies the "diminishing returns" property. To capture the data recency issue and support different types of constraints in real-world problems, we formulate RSS as maximizing a submodular function subject to a d-knapsack constraint (SMDK) over sliding windows. Then, we propose a novel KnapWindow framework for SMDK. Theoretically, KnapWindow is 1-ε/1+d - approximate for SMDK and achieves sublinear complexity. Finally, we evaluate the efficiency and effectiveness …

Pccf: Periodic And Continual Temporal Co-Factorization For Recommender Systems, Guibing Guo, Feida Zhu, Shilin Qu, Xingwei Wang

Research Collection School Of Computing and Information Systems

Rating-only collaborative filtering has been extensively studied for decades with great improvements achieved in predicting a user’s preference on a target item at a particular time point. Yet, it remains a research challenge on how to capture users’ rating patterns which may drift over time. In this article, we propose a time-aware matrix co-factorization model, called PCCF, which considers two types of temporal effects, i.e., periodic and continual. Specifically, periodic effects refer to the impact of discrete periodic time slices with which users’ preferences may be associated, and continual effects refer to the impact of continuous gradual time over which …

Every Step You Take, I’Ll Be Watching You: Practical Stepauth-Entication Of Rfid Paths, Kai Bu, Yingjiu Li

Research Collection School Of Computing and Information Systems

Path authentication thwarts counterfeits in RFID-based supply chains. Its motivation is that tagged products taking invalid paths are likely faked and injected by adversaries at certain supply chain partners/steps. Existing solutions are path-grained in that they simply regard a product as genuine if it takes any valid path. Furthermore, they enforce distributed authentication by offloading the sets of valid paths to some or all steps from a centralized issuer. This not only imposes network and storage overhead but also leaks transaction privacy. We present StepAuth, the first step-grained path authentication protocol that is practically efficient for authenticating products with strict …

A Novel Representation And Compression For Queries On Trajectories In Road Networks, Xiaochun Yang, Bin Wang, Kai Yang, Chengfei Liu, Baihua Zheng

Research Collection School Of Computing and Information Systems

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road mbox{networks}, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the …

Octopus: An Online Topic-Aware Influence Analysis System For Social Networks, Ju Fan, Jiarong Qiu, Yuchen Li, Qingfei Meng, Dongxiang Zhang, Guoliang Li, Kian-Lee Tan, Xiaoyong Du

Research Collection School Of Computing and Information Systems

The wide adoption of social networks has brought a new demand on influence analysis. This paper presents OCTOPUS that offers social network users and analysts valuable insights through topic-aware social influence analysis services. OCTOPUS has the following novel features. First, OCTOPUS provides a user-friendly interface that allows users to employ simple and easy-to-use keywords to perform influence analysis. Second, OCTOPUS provides three powerful keyword-based topic-aware influence analysis tools: keyword-based influential user discovery, personalized influential keywords suggestion, and interactive influential paths exploration. These tools can not only discover influential users, but also provide insights on how the users influence the network. …

Augmented Keyword Search On Spatial Entity Databases, Dongxiang Zhang, Yuchen Li, Xin Cao, Jie Shao, Heng Tao Shen

Research Collection School Of Computing and Information Systems

In this paper, we propose a new type of query that augments the spatial keyword search with an additional boolean expression constraint. The query is issued against a corpus of structured or semi-structured spatial entities and is very useful in applications like mobile search and targeted location-aware advertising. We devise three types of indexing and filtering strategies. First, we utilize the hybrid IR2" role="presentation" style="display: inline; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">IR2IR2-tree and propose a novel hashing …

Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors Of Food Journalers, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek

Research Collection School Of Computing and Information Systems

Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices. However, the links between journaling and healthy eating have not been thoroughly examined. Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets? How different are their eating habits compared to those of average consumers who tend to be less conscious about health? In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal. Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and …

Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely …

Scalable Hypergraph-Based Image Retrieval And Tagging System, Lu Chen, Yunjun Gao, Yuanliang Zhang, Sibo Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Massive amounts of images textually annotated by different users are provided by social image websites, e.g., Flickr. Social images are always associated with various information, such as visual features, tags, and users. In this paper, we utilize hypergraph instead of ordinary graph to model social images, since relations among various information are more sophisticated than pairwise. Based on the hypergraph, we propose HIRT, a scalable image retrieval and tagging system, which uses Personalized PageRank to measure vertex similarity, and employs top-k search to support image retrieval and tagging. To achieve good scalability and efficiency, we develop parallel and approximate top-k …

Combination Forecasting Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Shunchang Yu, Bin Li, Steven C. H. Hoi, Shuigeng G. Zhou

Research Collection School Of Computing and Information Systems

Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, …

Location-Aware Influence Maximization Over Dynamic Social Streams, Yanhao Wang, Yuchen Li, Ju Fan, Kianlee Tan

Research Collection School Of Computing and Information Systems

Influence maximization (IM), which selects a set of k seed users (a.k.a., a seed set) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications. However, most existing IM algorithms are static and location-unaware. They fail to provide high-quality seed sets efficiently when the social network evolves rapidly and IM queries are location-aware. In this article, we first define two IM queries, namely Stream Influence Maximization (SIM) and Location-aware SIM (LSIM), to track influential users over social streams. Technically, SIM adopts the sliding window model and maintains a seed set with …