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Articles 1 - 30 of 184
Full-Text Articles in Physical Sciences and Mathematics
The 4 X 4 Semantic Model: Exploiting Data, Functional, Non-Functional And Execution Semantics Across Business Process, Workflow, Partner Services And Middleware Services Tiers, Amit P. Sheth, Karthik Gomadam
The 4 X 4 Semantic Model: Exploiting Data, Functional, Non-Functional And Execution Semantics Across Business Process, Workflow, Partner Services And Middleware Services Tiers, Amit P. Sheth, Karthik Gomadam
Kno.e.sis Publications
Business processes in the global environment increasingly encompass multiple partners and complex, rapidly changing requirements. In this context it is critical that strategic business objectives align with and map accurately to systems that support flexible and dynamic business processes. To support the demanding requirements of global business processes, we propose a comprehensive, unifying 4 X 4 Semantic Model that uses Semantic Templates to link four tiers of implementation with four types of semantics. The four tiers are the Business Process Tier, the Workflow Enactment Tier, the Partner Services Tier, and the Middleware Services Tier. The four types of semantics are …
International Data Privacy Lawws And The Protectors Of Privacy, Ilmr Editors
International Data Privacy Lawws And The Protectors Of Privacy, Ilmr Editors
Brigham Young University International Law & Management Review
No abstract provided.
Cyber Power In The 21st Century, Joseph M. Elbaum
Cyber Power In The 21st Century, Joseph M. Elbaum
Theses and Dissertations
Historically, the United States Congress has acknowledged that a separate branch of military service is required to exert supremacy over each of the recognized Domains of Operation. Throughout the evolution of modern warfare, leading minds in military theory have come to the conclusion that due to fundamental differences inherent in the theory and tactics that must be employed in order to successfully wage war within a domain’s associated environment, a specialized force was needed - until now. With the recent inclusion of Cyberspace as an operational domain by the Department of Defense, the case should be made that it, too, …
Semantic Sensor Web, Amit P. Sheth, Cory Henson, Krishnaprasad Thirunarayan
Semantic Sensor Web, Amit P. Sheth, Cory Henson, Krishnaprasad Thirunarayan
Kno.e.sis Publications
No abstract provided.
Capturing Workflow Event Data For Monitoring, Performance Analysis, And Management Of Scientific Workflows, Matthew Valerio, Satya S. Sahoo, Roger Barga, Jared Jackson
Capturing Workflow Event Data For Monitoring, Performance Analysis, And Management Of Scientific Workflows, Matthew Valerio, Satya S. Sahoo, Roger Barga, Jared Jackson
Kno.e.sis Publications
To effectively support real-time monitoring and performance analysis of scientific workflow execution, varying levels of event data must be captured and made available to interested parties. This paper discusses the creation of an ontology-aware workflow monitoring system for use in the Trident system which utilizes a distributed publish/subscribe event model. The implementation of the publish/subscribe system is discussed and performance results are presented.
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Zhongmei Yao, Derek Leonard, Xiaoming Wang, Dmitri Loguinov
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Zhongmei Yao, Derek Leonard, Xiaoming Wang, Dmitri Loguinov
Computer Science Faculty Publications
In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1 − o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and …
On Visualizing Heterogeneous Semantic Networks From Multiple Data Sources, Maureen Maureen, Aixin Sun, Ee Peng Lim, Anwitaman Datta, Kuiyu Chang
On Visualizing Heterogeneous Semantic Networks From Multiple Data Sources, Maureen Maureen, Aixin Sun, Ee Peng Lim, Anwitaman Datta, Kuiyu Chang
Research Collection School Of Computing and Information Systems
In this paper, we focus on the visualization of heterogeneous semantic networks obtained from multiple data sources. A semantic network comprising a set of entities and relationships is often used for representing knowledge derived from textual data or database records. Although the semantic networks created for the same domain at different data sources may cover a similar set of entities, these networks could also be very different because of naming conventions, coverage, view points, and other reasons. Since digital libraries often contain data from multiple sources, we propose a visualization tool to integrate and analyze the differences among multiple social …
Cognitive Agents Integrating Rules And Reinforcement Learning For Context-Aware Decision Support, Teck-Hou Teng, Ah-Hwee Tan
Cognitive Agents Integrating Rules And Reinforcement Learning For Context-Aware Decision Support, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
While context-awareness has been found to be effective for decision support in complex domains, most of such decision support systems are hard-coded, incurring significant development efforts. To ease the knowledge acquisition bottleneck, this paper presents a class of cognitive agents based on self-organizing neural model known as TD-FALCON that integrates rules and learning for supporting context-aware decision making. Besides the ability to incorporate a priori knowledge in the form of symbolic propositional rules, TD-FALCON performs reinforcement learning (RL), enabling knowledge refinement and expansion through the interaction with its environment. The efficacy of the developed Context-Aware Decision Support (CaDS) system is …
Growing Fields Of Interest: Using An Expand And Reduce Strategy For Domain Model Extraction, Christopher Thomas, Pankaj Mehra, Roger Brooks, Amit P. Sheth
Growing Fields Of Interest: Using An Expand And Reduce Strategy For Domain Model Extraction, Christopher Thomas, Pankaj Mehra, Roger Brooks, Amit P. Sheth
Kno.e.sis Publications
Domain hierarchies are widely used as models underlying information retrieval tasks. Formal ontologies and taxonomies enrich such hierarchies further with properties and relationships associated with concepts and categories but require manual effort; therefore they are costly to maintain, and often stale. Folksonomies and vocabularies lack rich category structure and are almost entirely devoid of properties and relationships. Classification and extraction require the coverage of vocabularies and the alterability of folksonomies and can largely benefit from category relationships and other properties. With Doozer, a program for building conceptual models of information domains, we want to bridge the gap between the vocabularies …
Animated Database Courseware: Using Animations To Extend Conceptual Understanding Of Database Concepts, Meg Murray, Mario Guimaraes
Animated Database Courseware: Using Animations To Extend Conceptual Understanding Of Database Concepts, Meg Murray, Mario Guimaraes
Faculty and Research Publications
Teaching abstract concepts can be best supported with supplemental instructional materials such as software animations. Visualization and animations have been shown to increase student motivation and help students develop deeper understandings. Through an NSF funded CCLI grant, a set of animations to support the teaching of database concepts is being developed and made freely available. Current modules available cover areas such as database design, interactive SQL, stored procedures and triggers, transactions and database security. In this paper, we provide an overview of the Animated Database Courseware (ADbC) as well as provide examples of how this software might be utilized in …
Text Cube: Computing Ir Measures For Multidimensional Text Database Analysis, Cindy Xinde Lin, Bolin Ding, Jiawei Han, Feida Zhu, Bo Zhao
Text Cube: Computing Ir Measures For Multidimensional Text Database Analysis, Cindy Xinde Lin, Bolin Ding, Jiawei Han, Feida Zhu, Bo Zhao
Research Collection School Of Computing and Information Systems
Since Jim Gray introduced the concept of ”data cube” in 1997, data cube, associated with online analytical processing (OLAP), has become a driving engine in data warehouse industry. Because the boom of Internet has given rise to an ever increasing amount of text data associated with other multidimensional information, it is natural to propose a data cube model that integrates the power of traditional OLAP and IR techniques for text. In this paper, we propose a Text-Cube model on multidimensional text database and study effective OLAP over such data. Two kinds of hierarchies are distinguishable inside: dimensional hierarchy and term …
Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan
Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents iFALCON, a model of BDI (beliefdesire-intention) agents that is fully realized as a selforganizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case …
Innovation In The Programmable Web: Characterizing The Mashup Ecosystem, C. Jason Woodard, Shuli Yu
Innovation In The Programmable Web: Characterizing The Mashup Ecosystem, C. Jason Woodard, Shuli Yu
Research Collection School Of Computing and Information Systems
This paper investigates the structure and dynamics of the Web 2.0 software ecosystem by analyzing empirical data on web service APIs and mashups. Using network analysis tools to visualize the growth of the ecosystem from December 2005 to 2007, we find that the APIs are organized into three tiers, and that mashups are often formed by combining APIs across tiers. Plotting the cumulative distribution of mashups to APIs reveals a power-law relationship, although the tail is short compared to previously reported distributions of book and movie sales. While this finding highlights the dominant role played by the most popular APIs …
Robust Regularized Kernel Regression, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Robust Regularized Kernel Regression, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual form, which is then solved by some quadratic program solver consequently. In this correspondence, we propose a new formulation for robust regularized kernel regression under the theoretical framework of regularization networks and then tackle the optimization problem directly in the primal. We show that the primal and dual approaches are equivalent to achieving similar regression performance, but the primal formulation is more efficient and easier to be implemented than the dual one. Different from …
Explaining Inferences In Bayesian Networks, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Explaining Inferences In Bayesian Networks, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
While Bayesian network (BN) can achieve accurate predictions even with erroneous or incomplete evidence, explaining the inferences remains a challenge. Existing approaches fall short because they do not exploit variable interactions and cannot account for compensations during inferences. This paper proposes the Explaining BN Inferences (EBI) procedure for explaining how variables interact to reach conclusions. EBI explains the value of a target node in terms of the influential nodes in the target's Markov blanket under specific contexts, where the Markov nodes include the target's parents, children, and the children's other parents. Working back from the target node, EBI shows the …
Scaling Up Multi-Agent Reinforcement Learning In Complex Domains, Dan Xiao, Ah-Hwee Tan
Scaling Up Multi-Agent Reinforcement Learning In Complex Domains, Dan Xiao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (TD) methods for real-time reinforcement learning. In this paper, we present two strategies, i.e. policy sharing and neighboring-agent mechanism, to further improve the learning efficiency of TD-FALCON in complex multi-agent domains. Through experiments on a traffic control problem domain and the herding task, we demonstrate that those strategies enable TD-FALCON to remain functional and adaptable in complex multi-agent domains
A Fast Pruned‐Extreme Learning Machine For Classification Problem, Hai-Jun Rong, Yew-Soon Ong, Ah-Hwee Tan, Zexuan Zhu
A Fast Pruned‐Extreme Learning Machine For Classification Problem, Hai-Jun Rong, Yew-Soon Ong, Ah-Hwee Tan, Zexuan Zhu
Research Collection School Of Computing and Information Systems
Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification. One key strength of ELM is the significantly low computational time required for training new classifiers since the weights of the hidden and output nodes are randomly chosen and analytically determined, respectively. In this paper, we address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to underfitting/overfitting issues in pattern classification. In particular, we describe the proposed pruned-ELM (P-ELM) algorithm as a systematic and automated approach for designing ELM classifier network. P-ELM uses …
Networking, William Osei-Poku
Evaluating Online Health Information: Beyond Readability Formulas, Gondy Leroy, Stephen Helmreich, James Cowie, Trudi Miller '08, Wei Zheng '08
Evaluating Online Health Information: Beyond Readability Formulas, Gondy Leroy, Stephen Helmreich, James Cowie, Trudi Miller '08, Wei Zheng '08
CGU Faculty Publications and Research
Although understanding health information is important, the texts provided are often difficult to understand. There are formulas to measure readability levels, but there is little understanding of how linguistic structures contribute to these difficulties. We are developing a toolkit of linguistic metrics that are validated with representative users and can be measured automatically. In this study, we provide an overview of our corpus and how readability differs by topic and source. We compare two documents for three groups of linguistic metrics. We report on a user study evaluating one of the differentiating metrics: the percentage of function words in a …
Bias And Controversy In Evaluation Systems, Hady Wirawan Lauw, Ee Peng Lim, Ke Wang
Bias And Controversy In Evaluation Systems, Hady Wirawan Lauw, Ee Peng Lim, Ke Wang
Research Collection School Of Computing and Information Systems
Evaluation is prevalent in real life. With the advent of Web 2.0, online evaluation has become an important feature in many applications that involve information (e.g., video, photo, and audio) sharing and social networking (e.g., blogging). In these evaluation settings, a set of reviewers assign scores to a set of objects. As part of the evaluation analysis, we want to obtain fair reviews for all the given objects. However, the reality is that reviewers may deviate in their scores assigned to the same object, due to the potential bias of reviewers or controversy of objects. The statistical approach of averaging …
Beyond Semantic Search: What You Observe May Not Be What You Think, Chong-Wah Ngo, Yu-Gang Jiang, Xiaoyong Wei, Wanlei Zhao, Feng Wang, Xiao Wu, Hung-Khoon Tan
Beyond Semantic Search: What You Observe May Not Be What You Think, Chong-Wah Ngo, Yu-Gang Jiang, Xiaoyong Wei, Wanlei Zhao, Feng Wang, Xiao Wu, Hung-Khoon Tan
Research Collection School Of Computing and Information Systems
This paper presents our approaches and results of the four TRECVID 2008 tasks we participated in: high-level feature extraction, automatic video search, video copy detection, and rushes summarization
A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow
A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow
Research Collection School Of Computing and Information Systems
The architecture of the human cortex is uniform and hierarchical in nature. In this paper, we build upon works on hierarchical classification systems that model the cortex to develop a neural network representation for a hierarchical spatio-temporal memory (HST-M) system. The system implements spatial and temporal processing using neural network architectures. We have tested the algorithms developed against both the MLP and the Hierarchical Temporal Memory algorithms. Our results show definite improvement over MLP and are comparable to the performance of HTM.
Modality Mixture Projections For Semantic Video Event Detection, Jialie Shen, Dacheng Tao, Xuelong Li
Modality Mixture Projections For Semantic Video Event Detection, Jialie Shen, Dacheng Tao, Xuelong Li
Research Collection School Of Computing and Information Systems
Event detection is one of the most fundamental components for various kinds of domain applications of video information system. In recent years, it has gained a considerable interest of practitioners and academics from different areas. While detecting video event has been the subject of extensive research efforts recently, much less existing approach has considered multimodal information and related efficiency issues. In this paper, we use a subspace selection technique to achieve fast and accurate video event detection using a subspace selection technique. The approach is capable of discriminating different classes and preserving the intramodal geometry of samples within an identical …
Relationship Web: Trailblazing, Analytics And Computing For Human Experience, Amit P. Sheth
Relationship Web: Trailblazing, Analytics And Computing For Human Experience, Amit P. Sheth
Kno.e.sis Publications
This panel presentation was give at the 27th International Conference on Conceptual Modeling (ER 2008), Barcelona, Spain, October 20-23, 2008.
Pursuing The Peak Of Excellence: Wiki As A Knowledge Base, Rebecca Klein, Matthew Smith
Pursuing The Peak Of Excellence: Wiki As A Knowledge Base, Rebecca Klein, Matthew Smith
Information Technology Faculty and Staff Publications
The pursuit of excellent communication is a path not easily navigated. Challenges arise at every turn, and the greatest obstacle of all is ensuring availability and accuracy of information. Help Desk representatives are the first point of contact for customers placing technology requests and they must have a broad range of knowledge about services provided by the department. A large amount of time is spent in training staff members to achieve the desired level of expertise. At Valparaiso University these staff members are students, adding to the complexity of information sharing as these staff members are only employed for a …
Creating Pathways To Develop Student Professionalism - A New Direction, Jennifer Mcintosh-Elkins, Rebecca Klein
Creating Pathways To Develop Student Professionalism - A New Direction, Jennifer Mcintosh-Elkins, Rebecca Klein
Information Technology Faculty and Staff Publications
The 2007-2008 academic year brought a new program of student employment to our IT department called IT Fellows. This program brings together the technological skills of IT along with soft business skills to assist our student employees in becoming well-rounded individuals fully prepared for life after college. In previous years our primary area of student employment was the Help Desk in which there was a tiered system in place with opportunities for resume and interview experience, raises, and promotions. The area of training needed further development and the move to the new program provided enhanced opportunities for training at all …
Herding Cats: Governance Models For The Care And Feeding Of Enterprise Resources Planning Systems In Higher Education Institutions, Paula Russell
Herding Cats: Governance Models For The Care And Feeding Of Enterprise Resources Planning Systems In Higher Education Institutions, Paula Russell
MPA Capstone Projects 2006 - 2015
Enterprise Resources Planning systems (ERPs) have revolutionized the manner in which higher education institutions manage its resources. ERPs provide the mechanism to aggregate disparate data across the institution and deliver reports and facilitate analysis on the institution as a whole. ERPs provide a single source of truth for institutional data enabling an institution-wide view of expenditures and are powerful tool for decision support. Institutions choosing to invest in implementing ERPs will enter into a long term relationship with continual maintenance of the systems. Key to the success of maintaining ERPs effectively is the governance model an institution adopts for managing …
Spatio-Temporal Efficiency In A Taxi Dispatch System, Darshan Santani, Rajesh Krishna Balan, C. Jason Woodard
Spatio-Temporal Efficiency In A Taxi Dispatch System, Darshan Santani, Rajesh Krishna Balan, C. Jason Woodard
Research Collection School Of Computing and Information Systems
In this paper, we present an empirical analysis of the GPS-enabled taxi dispatch system used by the world’s second largest land transportation company. We first summarize the collective dynamics of the more than 6,000 taxicabs in this fleet. Next, we propose a simple method for evaluating the efficiency of the system over a given period of time and geographic zone. Our method yields valuable insights into system performance—in particular, revealing significant inefficiencies that should command the attention of the fleet operator. For example, despite the state of the art dispatching system employed by the company, we find imbalances in supply …
Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan
Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
We propose, theorize and implement the Recursive Pattern-based Hybrid Supervised (RPHS) learning algorithm. The algorithm makes use of the concept of pseudo global optimal solutions to evolve a set of neural networks, each of which can solve correctly a subset of patterns. The pattern-based algorithm uses the topology of training and validation data patterns to find a set of pseudo-optima, each learning a subset of patterns. It is therefore well adapted to the pattern set provided. We begin by showing that finding a set of local optimal solutions is theoretically equivalent, and more efficient, to finding a single global optimum …
Adapting Ranking Functions To User Preference, Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, Gordon Sun
Adapting Ranking Functions To User Preference, Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, Gordon Sun
Kno.e.sis Publications
Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, which is expensive to get for training a satisfactory ranking function. The demands for generating specific web search ranking functions tailored for different domains, such as ranking functions for different regions, have aggravated this problem. Recently, a few methods have been proposed to extract training examples from user clickthrough log. Due to the low cost of getting user preference data, it is attractive to combine these examples in training ranking functions. However, because of the …