Physical Sciences and Mathematics Commons^™

Dynamic Web Service Selection For Reliable Web Service Composition, San-Yih Hwang, Ee Peng Lim, Chien-Hsiang Lee, Cheng-Hung Chen

Research Collection School Of Computing and Information Systems

This paper studies the dynamic web service selection problem in a failure-prone environment, which aims to determine a subset of Web services to be invoked at run-time so as to successfully orchestrate a composite web service. We observe that both the composite and constituent web services often constrain the sequences of invoking their operations and therefore propose to use finite state machine to model the permitted invocation sequences of Web service operations. We assign each state of execution an aggregated reliability to measure the probability that the given state will lead to successful execution in the context where each web …

Tuning On-Air Signatures For Balancing Performance And Confidentiality, Baihua Zheng, Wang-Chien Lee, Peng Liu, Dik Lun Lee, Xuhua Ding

Research Collection School Of Computing and Information Systems

In this paper, we investigate the trade off between performance and confidentiality in signature-based air indexing schemes for wireless data broadcast. Two metrics, namely, false drop probability and false guess probability, are defined to quantify the filtering efficiency and confidentiality loss of a signature scheme. Our analysis reveals that false drop probability and false guess probability share a similar trend as the tuning parameters of a signature scheme change and it is impossible to achieve a low false drop probability and a high false guess probability simultaneously. In order to balance the performance and confidentiality, we perform an analysis to …

Computing Medoids In Large Spatial Datasets, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou

Research Collection School Of Computing and Information Systems

In this chapter, we consider a class of queries that arise in spatial decision making and resource allocation applications. Assume that a company wants to open a number of warehouses in a city. Let P be the set of residential blocks in the city. P represents customer locations to be potentially served by the company. At the same time, P also comprises the candidate warehouse locations because the warehouses themselves must be opened in some residential blocks.

Partially Materialized Digest Scheme: An Efficient Verification Method For Outsourced Databases, Kyriakos Mouratidis, Dimitris Sacharidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

In the outsourced database model, a data owner publishes her database through a third-party server; i.e., the server hosts the data and answers user queries on behalf of the owner. Since the server may not be trusted, or may be compromised, users need a means to verify that answers received are both authentic and complete, i.e., that the returned data have not been tampered with, and that no qualifying results have been omitted. We propose a result verification approach for one-dimensional queries, called Partially Materialized Digest scheme (PMD), that applies to both static and dynamic databases. PMD uses separate indexes …

Correlation Of Music Charts And Search Engine Rankings, Martin Klein, Olena Hunsicker, Michael Nelson

Computer Science Faculty Publications

We investigate the question whether expert rankings of real-world entities correlate with search engine (SE) rankings of corresponding web resources. We compare Billboards "Hot 100 Airplay" music charts with SE rankings of associated web resources. Out of nine comparisons we found two strong, two moderate, two weak and one negative correlation. The remaining two comparisons were inconclusive.

Object Reuse And Exchange, Michael L. Nelson, Carl Lagoze, Herbert Van De Sompel, Pete Johnston, Robert Sanderson, Simeon Warner, Jürgen Sieck (Ed.), Michael A. Herzog (Ed.)

Computer Science Faculty Publications

The Open Archives Object Reuse and Exchange (OAI-ORE) project defines standards for the description and exchange of aggregations of Web resources. The OAI-ORE abstract data model is conformant with the Architecture of the World Wide Web and leverages concepts from the Semantic Web, including RDF descriptions and Linked Data. In this paper we provide a brief review of a motivating example and its serialization in Atom.

Effects Of Similarity Metrics On Document Clustering, Rushikesh Veni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Document clustering or unsupervised document classification is an automated process of grouping documents with similar content. A typical technique uses a similarity function to compare documents. In the literature, many similarity functions such as dot product or cosine measures are proposed for the comparison operator.

For the thesis, we evaluate the effects a similarity function may have on clustering. We start by representing a document and a query, both as a vector of high-dimensional space corresponding to the keywords followed by using an appropriate distance measure in k-means to compute similarity between the document vector and the query vector to …

The Impact On Organizational Performance Of Contextual Factors, Strategy And Management Control Systems, Nazmi Saeb Jarrar

Theses: Doctorates and Masters

This dissertation examined antecedents of the use of contemporary management control systems (MCS) by testing the alignment of strategic and contextual variables with variables of contemporary systems of control in the organization. The study further explored the performance consequences of the implementation of these control systems, and the manner in which strategy can influence the organization’s control culture and management accounting practices. The impact of contextual factors, notably size and structural arrangements, such as decentralization and diversification, on management's choice of control systems were also examined. Hence, the study addressed the need for a better understanding of the association between …

Efficient Valid Scope Computation For Location-Dependent Spatial Queries In Mobile And Wireless Environments, Ken C. K. Lee, Wang-Chien Lee, Hong Va Leong, Brandon Unger, Baihua Zheng

Research Collection School Of Computing and Information Systems

In mobile and wireless environments, mobile clients can access information with respect to their locations by submitting Location-Dependent Spatial Queries (LDSQs) to Location-Based Service (LBS) servers. Owing to scarce wireless channel bandwidth and limited client battery life, frequent LDSQ submission from clients must be avoided. Observing that LDSQs issued from similar client positions would normally return the same results, we explore the idea of valid scope, that represents a spatial area in which a set of LDSQs will retrieve exactly the same query results. With a valid scope derived and an LDSQ result cached at the client side, a client …

Quality-Aware Collaborative Question Answering: Methods And Evaluation, Maggy Anastasia Suryanto, Ee Peng Lim, Aixin Sun, Roger Hsiang-Li Chiang

Research Collection School Of Computing and Information Systems

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this research, we address this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers. Despite their popularity, it is well known that answers in community QA portals have unequal quality. We therefore propose a quality-aware framework to design methods that select answers from a community QA portal considering answer quality in addition to …

Chaos And Uncertainty, M. Thulasidas

Research Collection School Of Computing and Information Systems

The end of 2008 in the finance industry can be summarized in two words – chaos and uncertainty. The subprime crisis, where everybody lost; the dizzying commodity price movements; the pink slip syndrome; the spectacular bank busts; and the gargantuan bail-outs all vouch for it.

Efficient Clustering Techniques For Managing Large Datasets, Vasanth Nemala

UNLV Theses, Dissertations, Professional Papers, and Capstones

The result set produced by a search engine in response to the user query is very large. It is typically the responsibility of the user to browse the result set to identify relevant documents. Many tools have been developed to assist the user to identify the most relevant documents. One such a tool is clustering technique. In this method, the closely related documents are grouped based on their contents. Hence if a document turns out to be relevant, so are the rest of the documents in the cluster. So it would be easy for a user to sift through the …

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 …

Wwi Postcard Collection: A Community-Based Model For Digital Libraries, Marc D. Bayer

Marc D Bayer

The paradigm shift of academic libraries from information purveyors to places of learning and communicating must occur virtually as well as physically. By creating informal virtual communities around digital collections , librarians can participate in the academic discussion and make more formal collections better known.

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

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

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

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

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 …

Animated Database Courseware: Using Animations To Extend Conceptual Understanding Of Database Concepts, Meg Murray, Mario Guimaraes

Faculty Articles

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 …

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 …

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

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 …

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

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 …

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 …

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 …

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 …

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

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 …