Computer Sciences Commons^™

Semantic Provenance For Escience: Managing The Deluge Of Scientific Data, Satya S. Sahoo, Amit P. Sheth, Cory Andrew Henson

Kno.e.sis Publications

Provenance information in eScience is metadata that's critical to effectively manage the exponentially increasing volumes of scientific data from industrial-scale experiment protocols. Semantic provenance, based on domain-specific provenance ontologies, lets software applications unambiguously interpret data in the correct context. The semantic provenance framework for eScience data comprises expressive provenance information and domain-specific provenance ontologies and applies this information to data management. The authors' "two degrees of separation" approach advocates the creation of high-quality provenance information using specialized services. In contrast to workflow engines generating provenance information as a core functionality, the specialized provenance services are integrated into a scientific workflow …

Volume And Cost Implications Of Product Portfolio Complexity, Mark A. Jacobs

MIS/OM/DS Faculty Publications

Business leaders are concerned about the impacts of increasing levels of product portfolio complexity since many sense that complexity related costs such as order management, procurement, and inventory threaten to undermine operational efficiencies and consume profits. Even so, managers do not fully understand the extent and breadth of the impacts of product portfolio complexity. A more complete understanding of the operational effects of product portfolio complexity is lacking partially because researchers have not yet offered a robust theoretical perspective or studied it in a focused controlled way; until now. Herein, measures of product portfolio complexity are developed and related to …

Relative Searching Using An Ordered Token List, Anthony Rosequist

Inquiry: The University of Arkansas Undergraduate Research Journal

Many organizations have large amounts of information, such as consumer data, that need to be processed. Traditional searching algorithms only attempt to find exact matches to particular queries. This is undesirable when data are missing, outdated, or inaccurate. Therefore, a new type of search must be developed to locate records that are considered "interesting" to the user. This research paper examines past attempts to solve this problem and explores a new method involving ordered token lists to achieve this goal. The algorithm was developed, implemented, tested, and optimized.

Probabilistic Sales Forecasting For Small And Medium-Size Business Operations, Randall E. Duran

Research Collection School Of Computing and Information Systems

One of the most important aspects of operating a business is the forecasting of sales and allocation of resources to fulfill sales. Sales assessments are usually based on mental models that are not well defined, may be biased, and are difficult to refine and improve over time. Defining sales forecasting models for small- and medium-size business operations is especially difficult when the number of sales events is small but the revenue per sales event is large. This chapter reviews the challenges of sales forecasting in this environment and describes how incomplete and potentially suspect information can be used to produce …

Enhancing Recursive Supervised Learning Using Clustering And Combinatorial Optimization (Rsl-Cc), Kiruthika Ramanathan, Sheng Uei Guan

Research Collection School Of Computing and Information Systems

The use of a team of weak learners to learn a dataset has been shown better than the use of one single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be one of the best off-the-shelf classifiers. However, some problems still remain, including determining the optimal number of weak learners and the overfitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of genetic algorithm, weak learner and pattern distributor. In this paper, …

Face Annotation Using Transductive Kernel Fisher Discriminant, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. The main idea of …

Document Selection For Extracting Entity And Relationship Instances Of Terrorist Events, Zhen Sun, Ee Peng Lim, Kuiyu Chang, Maggy Anastasia Suryanto, Rohan Kumar Gunaratna

Research Collection School Of Computing and Information Systems

In this chapter, we study the problem of selecting documents so as to extract terrorist event information from a collection of documents. We represent an event by its entity and relation instances. Very often, these entity and relation instances have to be extracted from multiple documents. We therefore define an information extraction (IE) task as selecting documents and extracting from which entity and relation instances relevant to a user-specified event (aka domain specific event entity and relation extraction). We adopt domain specific IE patterns to extract potentially relevant entity and relation instances from documents, and develop a number of document …

Droid: The Drosophila Interactions Database, A Comprehensive Resource For Annotated Gene And Protein Interactions, Jingkai Yu, Svetlana Pacifico, Guozhen Liu, Russell L. Finley Jr

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. …

Learning Expressive Ontologies, Johanna Volker, Peter Haase, Pascal Hitzler

Computer Science and Engineering Faculty Publications

No abstract provided.

Hrests: An Html Microformat For Describing Restful Web Services, Jacek Kopecky, Karthik Gomadam, Tomas Vitvar

Kno.e.sis Publications

The Web 2.0 wave brings, among other aspects, the Programmable Web: increasing numbers of Web sites provide machine-oriented APIs and Web services. However, most APIs are only described with text in HTML documents. The lack of machine-readable API descriptions affects the feasibility of tool support for developers who use these services. We propose a microformat called hRESTS (HTML for RESTful Services) for machine-readable descriptions of Web APIs, backed by a simple service model. The hRESTS microformat describes main aspects of services, such as operations, inputs and outputs. We also present two extensions of hRESTS: SA-REST, which captures the facets of …

Joint Extraction Of Compound Entities And Relationships From Biomedical Literature, Cartic Ramakrishnan, Pablo N. Mendes, Rodrigo A.T.S. De Gama, Guilherme C.N. Ferreira, Amit P. Sheth

Kno.e.sis Publications

In this paper we identify some limitations of contemporary information extraction mechanisms in the context of biomedical literature. We present an extraction mechanism that generates structured representations of textual content. Our extraction mechanism achieves this by extracting compound entities, and relationships between them, occuring in text. A detailed evaluation of the relationship and compound entities extracted is presented. Our results show over 62% average precision across 8 relationship types tested with over 82% average precision for compound entity identification1.

Targeted Content Delivery For Social Media Content, Meenakshi Nagarajan, Kamal Baid, Amit P. Sheth, Shaojun Wang

Kno.e.sis Publications

Spotting contextually relevant keywords is fundamental to effective content suggestions on the Web. In this regard, misspellings, entity variations and off-topic discussions in content from Social Media pose unique challenges. Here, we present an algorithm that assists content delivery systems by identifying contextually relevant keywords and eliminating off-topic keywords. A preliminary user study over data from MySpace and Facebook clearly suggests the usefulness of our work in delivering more targeted content suggestions.

Traveling The Semantic Web Through Space, Theme And Time, Amit P. Sheth, Matthew Perry

Kno.e.sis Publications

In this installment of Semantics and Services, we further develop the idea of spatial, temporal, and thematic (STT) processing of semantic Web data and describe the Web infrastructure needed to support it. Starting from Ramesh Jain's vision of the EventWeb as a view of what's possible with a Web that better accommodates all three dimensions of event-related information (thematic, spatial, and temporal), we outline the architecture needed to support it and current research that aims to realize it.

Information Technology Adoption By Small Businesses In Minority And Ethnic Communities, Sajda Qureshi, Anne S. York

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

Information systems adoption by small businesses often is viewed as a basic building block of economic development. Yet, the components that impact business success and thus economic development are mostly unexplored, especially within the context of minority and ethnic communities, both domestically and internationally. Given that IT adoption in small businesses is very often the domain of the business owner, an investigation of how the attributes of individual business owners and the context in which they are embedded is essential. This paper develops an integrative model of the role that IT adoption plays in business success and the economic development …

Adoption Of Information Technology By Micro-Enterprises: Insights From A Rural Community, Sajda Qureshi, Mehruz Kamal, Travis Good

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

It appears that the growth of micro-enterprises is a key driver of economic development in underserved communities. However their growth is limited to only 20% of the economy even though they comprise 87% of businesses in Nebraska. Research has shown that IT adoption can increase their growth by 3.5% but the challenges to IT adoption by microenterprises are many. Current theoretical models on IT adoption focus on the intent to adopt IT in large organizations where employees’ attitudes and perceptions are measured in terms of their objectives within the structures of accountability. Microenterprises are unique in that the intention to …

Synergistic Ideation Through Pairing Participants In Facilitated Group Support Systems Sessions, John D. Murphy, Deepak Khazanchi

Information Systems and Quantitative Analysis Faculty Publications

Group Support Systems (GSS) have been used and studied in the support of facilitated ideation sessions for years. The norm for these sessions has been for participants to work individually at GSS workstations. A review of applicable literature suggests that pairing participants at GSS workstations could result in higher quality ideas and participant satisfaction. This paper reports the results of a lab experiment that tested for differences between paired and unpaired facilitated GSS sessions. These results suggest that pairing participants can yield higher quality ideas from facilitated ideation without negative consequences.

The Cleveland-Akron-Elyria Region Doing Well: More Persons Attending College And Getting Degrees, 2000 To 2007, Mark Salling

All Maxine Goodman Levin School of Urban Affairs Publications

Discussions of economic development and job availability in northeast Ohio often lament the unavailability of a qualified workforce in some sectors. Workforce training and attracting more educated population to the region are sited as important, even critical, objectives for the region. While a more detailed study of the regions’ workforce by The Center for Community Solutions is nearing completion, the release of new data by the Census Bureau provides some enlightening observations about college enrollments and educational attainment in the region.

Ohio Continues To Lag In Population Growth And Comments On Prospects For The Future An Analysis Of 2007 State Population Estimates, Mark Salling

All Maxine Goodman Levin School of Urban Affairs Publications

No abstract provided.

Medical Language Processing For Patient Diagnosis Using Text Classification And Negation Labelling, Brian Mac Namee, John D. Kelleher, Sarah Jane Delany

Conference papers

This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees, and negation labelling.

Application Of Blast-Based Techniques For Musical Information Retrieval, Fedor Aleksandrovich Korsakov

Honors Program Theses

Content retrieval in musical collections has been dependent on textual metadata (e.g. ID3 tags) which can present problems when the title of a piece is forgotten, misspelled, or when the search revolves around the similarity of sound. Content-based MIR (musical information retrieval) could offer an alternative. BLAST (basic local alignment search tool), an algorithm widely used in bioinformatics to search for sequences of aminoacids within longer sequences, seeks similarities and homologies, which makes it interesting for MIR, because musical information can be expected to be imprecise, and because homologies can allow to draw connections between musical pieces. Increased availability of …

Fundamental Stock Market Analysis Tool, Rakshith Varadaraju

Honors Program Theses

There are many sources for stock market information and an investor can tap into the television or online media to sync up with current market news. Popular sources are news channels such as CNBC or CNN who spend the better part of the day evaluating the stock market. The internet is filled with a wealth of data ranging from historical to current market information. Added to this information are countless websites that make predictions on what stocks should be bought or sold. The challenge with all of this information is to figure out which sources are actually valid and can …

Networks And Network Security, Michael Kuralt

Theses and Dissertations

In a world of ever-increasing security threats, companies have the difficult task of securing their networks from attack. One of the largest threats to a company is the users on its network. By educating its network administrators and employees about network threats, a company can lower its vulnerability to network attacks. Individual users also can benefit from understanding networks and network security by learning how to keep their computer and their information safe. Companies and users can stay safe by understanding how a network functions, what threats exist on the Internet, how to prevent and deal with attacks, and how …

Factors Affecting The Information Quality Of Personal Web Portfolios, P. Katerattanakul, Keng Siau

Research Collection School Of Computing and Information Systems

Personal Web portfolios have become a popular information source and an effective method for individuals to present themselves to others in cyberspace. Thus, the quality of personal Web portfolios is critical and affects the perception that others have of the individuals. But how do we measure quality of personal Web portfolios? What are the important factors affecting quality of personal Web portfolios? This study presents the development of an instrument measuring factors affecting information quality of personal Web portfolios. The proposed instrument, based on the Information Quality framework, was refined and validated to assess its construct validity, convergent validity, and …

The State Of Public Access To Federal Government Databases Detailed In Recommended New Book, Jennifer L. Behrens

Faculty Scholarship

No abstract provided.

Methods And System For Equalizing Data, Jaiganesh Balakrishnan, Richard K. Martin, C. Richard Johnson Jr.

AFIT Patents

A method for equalizing data and systems utilizing the method. The method of this invention for equalizing (by shortening the channel response) data includes minimizing a function of the data and a number of equalizer characteristic parameters, where the function utilizes auto-correlation data corresponding to equalized data. Updated equalizer characteristic parameters are then obtained from the minimization and an initial set of equalizer characteristic parameters. Finally, the received data is processed utilizing the equalizer defined by the minimization. The method of this invention can be implemented in an equalizer and the equalizer of this invention may be included in a …

A General Boosting Method And Its Application To Learning Ranking Functions For Web Search, Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun

Kno.e.sis Publications

We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach is based on optimization of quadratic upper bounds of the loss functions which allows us to present a rigorous convergence analysis of the algorithm. More importantly, this general framework enables us to use a standard regression base learner such as decision trees for fitting any loss function. We illustrate an application of the proposed method in learning ranking functions for Web search by combining both preference data and labeled data for training. We present experimental …

An Integrated Social Actor And Service Oriented Architecture (Soa) Approach For Improved Electronic Health Record (Ehr) Privacy And Confidentiality In The Us National Healthcare Information Network (Nhin), Gondy Leroy, Elliot Sloane, Steven Sheetz

CGU Faculty Publications and Research

The emerging US National Healthcare Information Network (NHIN) will improve healthcare’s efficacy, efficiency, and safety. The first-generation NHIN being developed has numerous advantages and limitations. One of the most difficult aspects of today’s NHIN is ensuring privacy and confidentiality for personal health data, because family and caregivers have multiple complex legal relationships to a patient. A Social Actor framework is suggested to organize and manage these legal roles, but the Social Actor framework would be very difficult to implement in today’s NHIN. Social Actor Security Management could, however, be effectively implemented using Service Oriented Architectures (SOAs), which are rapidly becoming …

Video On The Semantic Sensor Web, Cory Andrew Henson, Amit P. Sheth, Prateek Jain, Josh Pschorr, Terry Rapoch

Kno.e.sis Publications

Millions of sensors around the globe currently collect avalanches of data about our world. The rapid development and deployment of sensor technology is intensifying the existing problem of too much data and not enough knowledge. With a view to alleviating this glut, we propose that sensor data, especially video sensor data, can be annotated with semantic metadata to provide contextual information about videos on the Web. In particular, we present an approach to annotating video sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the W3C and Open Geospatial Consortium (OGC) and …

I’M A Virus Harming The Earth, M. Thulasidas

Research Collection School Of Computing and Information Systems

We humans plunder the raw material from our host planet with such an abandon that is only seen in viruses.

Self-Organizing Neural Architectures And Cooperative Learning In A Multiagent Environment, Dan Xiao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Temporal-Difference–Fusion Architecture for Learning, Cognition, and Navigation (TD-FALCON) is a generalization of adaptive resonance theory (a class of self-organizing neural networks) that incorporates TD methods for real-time reinforcement learning. In this paper, we investigate how a team of TD-FALCON networks may cooperate to learn and function in a dynamic multiagent environment based on minefield navigation and a predator/prey pursuit tasks. Experiments on the navigation task demonstrate that TD-FALCON agent teams are able to adapt and function well in a multiagent environment without an explicit mechanism of collaboration. In comparison, traditional Q-learning agents using gradient-descent-based feedforward neural networks, trained with the …