Databases and Information Systems Commons^™

[Introduction To] Identity And Leadership In Virtual Communities: Establishing Credibility And Influence, Dona J. Hickey, Joe Essid

Bookshelf

The presence and ubiquity of the internet continues to transform the way in which we identify ourselves and others both online and offline. The development of virtual communities permits users to create an online identity to interact with and influence one another in ways that vary greatly from face-to-face interaction.

Identity and Leadership in Virtual Communities: Establishing Credibility and Influence explores the notion of establishing an identity online, managing it like a brand, and using it with particular members of a community. Bringing together a range of voices exemplifying how participants in online communities influence one another, this book serves …

Neutrosophic Theory And Its Applications : Collected Papers - Vol. 1, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic Theory means Neutrosophy applied in many fields in order to solve problems related to indeterminacy. Neutrosophy is a new branch of philosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. This theory considers every entity together with its opposite or negation and with their spectrum of neutralities in between them (i.e. entities supporting neither nor ). The and ideas together are referred to as . Neutrosophy is a generalization of Hegel's dialectics (the last one is based on and only). According to this theory every entity tends to be …

Essays On The Digital Divide, Belal Abdelfattah

Open Access Theses & Dissertations

The digital divide is a phenomenon that is globally persistent, despite rapidly decreasing costs in technology. While much of the variance in the adoption and use of information communication technology (ICT) that defines the digital divide can be explained by socioeconomic and demographic variables, there is still significant unaccounted variance that needs to be explained if the world's population is expected to be brought more fully into the digital age. The present research addresses this need with three cross-country studies. Study 1 primarily investigates the time individuals spend with traditional media sources as a likely explanation for their frequency of …

Public Social Network Sites And Social Recruiting, Abby Peters

Open Access Theses & Dissertations

Social network sites (SNSs) are an increasingly popular form of social media used by individuals and organizations. As these platforms continue to transform the way people communicate with one another, they are simultaneously revolutionizing the way individuals interact with organizations. Part of this dramatic change is apparent in the processes by which organizations are recruiting employees and job seekers are pursuing employment. To investigate these phenomena, I employed the diffusion of innovations theory in a SNS context to examine the relationship between organizations' use of their corporate career website and their use of SNSs as recruiting sources. Subsequently, I used …

Three Essays On Social/Political Structures And Icts Use, Seungeui Ryu

Open Access Theses & Dissertations

My research identifies how social structures affect the use of the Internet and/or a mobile chat application and how the Internet impacts the political structure of a nation. In my first essay of the 3-essay Dissertation, I am designing three models based on social structure theory that are used to study the Internet and a popular mobile chat application's use by managers in South Korea, with the help of a survey instrument. In my first essay, the contribution is on i) testing a model of manager's personal behavior on Information and Communication Technology (ICT) use at the individual level involving …

Diffusion Of Social Network Technology And Overuse Among Health Industry Knowledge Workers, Abdel Rahman Toure

Walden Dissertations and Doctoral Studies

Many organizations now realize the important role of social network technology (SNT) in building social capital and hence broadening their customer base. However, observations have indicated that, while working, many knowledge workers use SNT to engage in non-job related activities, potentially leading to a decrease in productivity. The purpose of this study was to examine the relationship between the usage of SNT and productivity in the health sector. The theoretical foundation of this study emanated from Rogers's theory of diffusion of innovations and Campbell, Rodney, Scott, and Christopher's theory of performance. Collection of data involved a self-administered survey designed with …

Neuroevolution And An Application Of An Agent Based Model For Financial Market, Anil Yaman

Dissertations and Theses

Market prediction is one of the most difficult problems for the machine learning community. Even though, successful trading strategies can be found for the training data using various optimization methods, these strategies usually do not perform well on the test data as expected. Therefore, selection of the correct strategy becomes problematic. In this study, we propose an evolutionary algorithm that produces a variation of trader agents ensuring that the trading strategies they use are different. We discuss that because the selection of the correct strategy is difficult, a variety of agents can be used simultaneously in order to reduce risk. …

Assessing Satellite Image Data Fusion With Information Theory Metrics, James Cross

Dissertations and Theses

A common problem in remote sensing is estimating an image with high spatial and high spectral resolution given separate sources of measurements from satellite instruments, one having each of these desirable properties. This thesis presents a survey of seven families of algorithms which have been developed to provide this common pattern of satellite image data fusion. They are all tested on artificially degraded sets of satellite data from the Moderate Resolution Imaging Spectroradiometer (“MODIS”) with known ideal results, and evaluated using the commonly accepted data fusion assessment metrics spectral angle mapper (“SAM”) and Erreur Relative Globale Adimensionelle de Synth`ese (“ERGAS”). …

Polymorphic Data Modeling, Steven R. Benson

College of Graduate Studies: Theses & Dissertations

There are currently no data modeling standards for modeling NoSQL document store databases. This work proposes a standard to fill the void. The proposed standard is based on our new data modeling pattern named The Polymorphic Table Pattern. The pattern embraces the “schemaless” nature of document store NoSQL while allowing the data modeler to use his or her existing skillsets. The concepts of our proposed modeling have been demonstrated against MongoDB.

Detecting Click Fraud In Online Advertising: A Data Mining Approach, Richard Oentaryo, Ee Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar

Research Collection School Of Computing and Information Systems

Click fraud - the deliberate clicking on advertisements with no real interest on the product or service offered - is one of the most daunting problems in online advertising. Building an elective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, opening the opportunity for participants to work on real-world fraud data from BuzzCity Pte. Ltd., a global mobile advertising company based in Singapore. In particular, the task is to identify fraudulent publishers who generate illegitimate clicks, and distinguish them from normal publishers. The competition was held from …

Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation …

Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu

Research Collection School Of Computing and Information Systems

This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve …

Wenzher: Comprehensive Vertical Search For Healthcare Domain, Liqiang Nie, Tao Li, Mohammad Akbari, Jialie Shen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Online health seeking has transformed the way of health knowledge exchange and reusability. The existing general and vertical health search engines, however, just routinely return lists of matched documents or question answer (QA) pairs, which may overwhelm the seekers or not sufficiently meet the seekers’ expectations. Instead, our multilingual system is able to return one multi-faceted answer that is well-structured and precisely extracted from multiple heterogeneous healthcare sources. Further, should the seekers not be satisfied with the returned search results, our system can automatically route the unsolved questions to the professionals with relevant expertise

Refining Computerized Physician Order Entry Initiatives In An Adult Intensive Care Unit, Chevita Fuller

Walden Dissertations and Doctoral Studies

Computerized physician order entry (CPOE) is used in healthcare organizations to improve workflow processes and transcription, as well as to prevent prescribing errors. Previous research has indicated challenges associated with CPOE for end-users that predispose patients to unsafe practices. Unsafe CPOE practices can be detrimental within the intensive care unit (ICU) setting due to the complexity of nursing care. Consequently, end-user satisfaction and understanding of CPOE and electronic health record (EHR) functionality are vital to avoid error omissions. CPOE initiatives should be refined post system implementation to improve clinical workflow, medication processes, and end-user satisfaction. The purpose of this quality …

Trustworthiness Of Web Services, Britto N. Arockiasamy

UNF Graduate Theses and Dissertations

Workflow systems orchestrate various business tasks to attain an objective. Web services can be leveraged to handle individual tasks. Before anyone intends to leverage service components, it is imperative and essential to evaluate the trustworthiness of these services. Therefore, choosing a trustworthy service has become an important decision while designing a workflow system. Trustworthiness can be defined as the likelihood of a service functioning as it is intended.

Selection of a service that satisfies business goals involves collecting relevant information such as security mechanisms, reliability, performance and availability. It is important to arrive at total trustworthiness, which incorporates all of …

A Scalable Backward Chaining-Based Reasoner For A Semantic Web, Hui Shi, Kurt Maly, Steven Zeil

Computer Science Faculty Publications

In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledge base covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Interposing a backward chaining reasoner between a knowledge base and a query manager yields an architecture that can support …

Moved But Not Gone: An Evaluation Of Real-Time Methods For Discovering Replacement Web Pages, Martin Klein, Michael L. Nelson

Computer Science Faculty Publications

Inaccessible Web pages and 404 “Page Not Found” responses are a common Web phenomenon and a detriment to the user’s browsing experience. The rediscovery of missing Web pages is, therefore, a relevant research topic in the digital preservation as well as in the Information Retrieval realm. In this article, we bring these two areas together by analyzing four content- and link-based methods to rediscover missing Web pages. We investigate the retrieval performance of the methods individually as well as their combinations and give an insight into how effective these methods are over time. As the main result of this work, …

Mini-Track Introduction: Information Economics, Competition, Regulation, Law And Society, Eric K. Clemons, Robert John Kauffman, Thomas A. Weber

Research Collection School Of Computing and Information Systems

This mini-track is informed by the most modern thinking in information economics and competitive strategy, and includes many interdisciplinary applications of IS and technology.

Coupling Graphs, Efficient Algorithms And B-Cell Epitope Prediction, Liang Zhao, Steven C. H. Hoi, Zhenhua Li, Limsoon Wong, Hung Nguyen

Research Collection School Of Computing and Information Systems

Coupling graphs are newly introduced in this paper to meet many application needs particularly in the field of bioinformatics. A coupling graph is a two-layer graph complex, in which each node from one layer of the graph complex has at least one connection with the nodes in the other layer, and vice versa. The coupling graph model is sufficiently powerful to capture strong and inherent associations between subgraph pairs in complicated applications. The focus of this paper is on mining algorithms of frequent coupling subgraphs and bioinformatics application. Although existing frequent subgraph mining algorithms are competent to identify frequent subgraphs …

Learning To Recommend Descriptive Tags For Questions In Social Forums, Liqiang Nie, Yiliang Zhao, Xiangyu Wang, Jialie Shen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can …

Inferring The Untold: Mining Software Engineering Research Publication Networks, Santonu Sarkar, Subhajit Datta

Research Collection School Of Computing and Information Systems

Since the inception of organized research publication in software engineering in 1975, the discipline has gained maturity. This journey has been guided by the synergy of ideas and interactions of individuals. In this paper, we discuss a method for aggregating the corpus of 19,000+ papers and 21,000+ authors across 16 specialized software engineering venues. We focus on the approach of data collection, processing and storage. It can be used to address questions by the software engineering research community. We evaluate three questions: patterns of research topics with time, factors influencing the contribution of individual researchers, and the interaction among the …

Loki: A Privacy-Conscious Platform For Crowdsourced Surveys, Thivya Kandappu, Vijay Sivaraman, Arik Friedman, Roksana Boreli

Research Collection School Of Computing and Information Systems

Emerging platforms such as Amazon Mechanical Turk and Google Consumer Surveys are increasingly being used by researchers and market analysts to crowdsource large-scale survey data from on-line populations at extremely low-cost. However, by participating in successive surveys, users risk being profiled and targeted, both by surveyors and by the platform itself. In this paper we propose, develop, and evaluate the design of a crowdsourcing platform, called Loki, that is privacy conscious. Our contributions are three-fold: (a) We propose Loki, a system that allows users to obfuscate their (ratings-based or multiple-choice) responses at-source based on their chosen privacy level, and gives …

A Hybrid Approach To Music Recommendation: Exploiting Collaborative Music Tags And Acoustic Features, Jaime C. Kaufman

UNF Graduate Theses and Dissertations

Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering.

Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and …

Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy

Research outputs 2014 to 2021

Spatial data mining helps to identify interesting patterns from the spatial data sets. However, geo spatial data requires substantial data pre-processing before data can be interrogated further using data mining techniques. Multi-dimensional spatial data has been used to explain the spatial analysis and SOLAP for pre-processing data. This paper examines some of the methods for pre-processing of the data using Arc GIS 10.2 and Spatial Analyst with a case study dataset of a watershed.

Decision Support System Data For Farmer Decision Making, Pornchai Taechatanasat, Leisa Armstrong

Research outputs 2014 to 2021

The capacity of farmers and agricultural scientists to be able to make in-season decisions is dependent on accurate climate, soil and plant data. This paper will provide a review of the types of environmental and crop data that can be collected by sensors which can used for decision support systems (DSS) or be further interrogated for real time data mining and analysis. This paper also presents a review of the data requirements for agricultural decision making by firstly reviewing decision support frameworks and agricultural DSSs, data acquisition, sensors for data acquisition and examples of data incorporation for agricultural DSSs.

Exploring Customer Specific Kpi Selection Strategies For An Adaptive Time Critical User Interface, Ingo Keck, Robert J. Ross

Conference papers

Rapid growth in the number of measures available to describe customer-organization relationships has presented a serious challenge for Business Intelligence (BI) interface developers as they attempt to provide business users with key customer information without requiring users to painstakingly sift through many interface windows and layers. In this paper we introduce a prototype Intelligent User Interface that we have deployed to partially address this issue. The interface builds on machine learning techniques to construct a ranking model of Key Performance Indicators (KPIs) that are used to select and present the most important customer metrics that can be made available to …