Databases and Information Systems Commons^™

Semi-Supervised Ensemble Ranking, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining …

Improving The Availability Of Manufacturability Information Through Decentralization Of Process Planning, Eliab Opiyo

Tanzania Journal of Engineering and Technology (TJET)

Process planning is part of the general product development and production process that usually follows design and precedes manufacturing. Manufacturability and process planning information in general play central role in many product development and production activities, including paradoxically, conceptual and detail design - the activities that take place before process planning. The need of conducting some of the process planning activities formally before or during design is thus rather obvious. One of the main research issues is therefore the identification of the process planning activities that can be performed before the traditional process planning phase and handling of the process …

Open Source Software In Health Information Systems: Opportunities And Challenges, Hashim Twaakyondo

Tanzania Journal of Engineering and Technology (TJET)

The paper presents results of a study seeking to identify constraining and motivating factors associated with the adoption and use of Free Open Source Software to computerise health Information Systems in a developing country. The study approach is interpretive research to case study with a triangulation of several qualitative data collection methods such as interviews, group discussions and document analysis. The findings indicate that using open source software has advantages and disadvantages. The advantages are low entry cost to adopting software, possibilities of software localization, avoiding being hostage of proprietary software and foster knowledge acquisition among software developers. The disadvantages …

A Framework For Trust And Distrust Networks, Krishnaprasad Thirunarayan

Kno.e.sis Publications

In this age of internet and electronic commerce it is becoming increasingly important to have and to manipulate information about the trustworthiness of the content or service providers in order to make informed decisions. This paper explores realistic models of trust and distrust based on partially ordered discrete values and proposes a framework, which is sensitive to local, relative ordering of values rather than their magnitudes. The framework distinguishes between direct and inferred trust, preferring direct information over possibly conflicting inferred information. It also represents ambiguity or inconsistency explicitly. The framework is capable of handling general trust and belief networks …

A Forgetting-Based Approach For Reasoning With Inconsistent Distributed Ontologies, Guilin Qi, Yimin Wang, Peter Haase, Pascal Hitzler

Computer Science and Engineering Faculty Publications

In the context of multiple distributed ontologies, we are often confronted with the problem of dealing with inconsistency. In this paper, we propose an approach for reasoning with inconsistent distributed ontologies based on concept forgetting. We firstly define concept forgetting in description logics. We then adapt the notions of recoveries and preferred recoveries in propositional logic to description logics. Two consequence relations are then defined based on the preferred recoveries.

The Motivators And Benefits Of Sharing Knowledge To A Kms Repository In An Omani Organization, Kamla Al-Busaidi '05, Lorne Olfman, Terry Ryan, Gondy Leroy

CGU Faculty Publications and Research

Knowledge is a powerful resource that enables individuals and organizations to achieve several benefits such as improved learning and decisionmaking. Repository knowledge management system (KMS) assists organizations to efficiently capture their knowledge for later reuse. However, the breadth and depth of a knowledge management system depends on the magnitude of knowledge contributed to the system. This paper aimed to empirically investigate the motivators of knowledge sharing behavior and the individual benefits of such behavior in a culture where knowledge is perceived as power and private. Based on 104 employees in a major private petroleum organization in Oman and the partial …

Smartphones To Facilitate Communication And Improve Social Skills Of Children With Severe Autism Spectrum Disorder: Special Education Teachers As Proxies, Gondy Leroy, Gianluca De Leo

CGU Faculty Publications and Research

We present an overview of the approach we used and the challenges we encountered while designing software for smartphones to facilitate communication and improve social skills of children with severe autism spectrum disorder (ASD). We employed participatory design, using special education teachers of children with ASD as proxies for our target population.

Graph Summaries For Subgraph Frequency Estimation, Angela Maduko, Kemafor Anyanwu, Amit P. Sheth, Paul Schliekelman

Kno.e.sis Publications

A fundamental problem related to graph structured databases is searching for substructures. One issue with respect to optimizing such searches is the ability to estimate the frequency of substructures within a query graph. In this work, we present and evaluate two techniques for estimating the frequency of subgraphs from a summary of the data graph. In the first technique, we assume that edge occurrences on edge sequences are position independent and summarize only the most informative dependencies. In the second technique, we prune small subgraphs using a valuation scheme that blends information about their importance and estimation power. In both …

Defeasible Inference With Circumscriptive Owl Ontologies, Stephan Grimm, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The Web Ontology Language (OWL) adheres to the openworld assumption and can thus not be used for forms of nonmonotonic reasoning or defeasible inference, an acknowledged desirable feature in open Semantic Web environments. We investigate the use of the formalism of circumscriptive description logics (DLs) to realise defeasible inference within the OWL framework. By example, we demonstrate how reasoning with (restricted) circumscribed OWL ontologies facilitates various forms of defeasible inference, also in comparison to alternative approaches. Moreover, we sketch an extension to DL tableaux for handling the circumscriptive case and report on a preliminary implementation.

Empowering Translational Research Using Semantic Web Technologies, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Self-Organizing Neural Models Integrating Rules And Reinforcement Learning, Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as fusion architecture for learning, cognition and navigation (FALCON) can incorporate a priori knowledge and perform knowledge refinement and expansion through reinforcement learning. Symbolic rules are formulated based on pre-existing know-how and inserted into FALCON as a priori knowledge. The availability of knowledge enables FALCON to start performing earlier in the initial learning trials. Through a temporal-difference (TD) learning method, the inserted rules can be refined and expanded according to the evaluative feedback signals received …

Context Modeling With Evolutionary Fuzzy Cognitive Map In Interactive Storytelling, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen

Research Collection School Of Computing and Information Systems

To generate a believable and dynamic virtual world is a great challenge in interactive storytelling. In this paper, we propose a model, namely evolutionary fuzzy cognitive map (E-FCM), to model the dynamic causal relationships among different context variables. As an extension to conventional FCM, E-FCM models not only the fuzzy causal relationships among the variables, but also the probabilistic property of causal relationships, and asynchronous activity update of the concepts. With this model, the context variables evolve in a dynamic and uncertain manner with the according evolving time. As a result, the virtual world is presented more realistically and dynamically.

Wikinetviz: Visualizing Friends And Adversaries In Implicit Social Networks, Minh-Tam Le, Hoang-Vu Dang, Ee Peng Lim, Anwitaman Datta

Research Collection School Of Computing and Information Systems

When multiple users with diverse backgrounds and beliefs edit Wikipedia together, disputes often arise due to disagreements among the users. In this paper, we introduce a novel visualization tool known as WikiNetViz to visualize and analyze disputes among users in a dispute-induced social network. WikiNetViz is designed to quantify the degree of dispute between a pair of users using the article history. Each user (and article) is also assigned a controversy score by our proposed controversy rank model so as to measure the degree of controversy of a user (and an article) by the amount of disputes between the user …

Use Of Cognitive Mapping Techniques In Information Systems Development, Keng Siau, X. Tan

Research Collection School Of Computing and Information Systems

Cognitive mapping techniques as a communication tool can be used in various information systems (IS) development and implementation activities. The three major cognitive mapping techniques include causal mapping, semantic mapping, and concept mapping. A causal map represents a set of causal relationships among constructs within a belief system. Semantic mapping, also known as idea mapping, is used to explore an idea without the constraints of a superimposed structure. The result of concept mapping is a graphical representation in which nodes represent concepts and links represent the relationships between concepts. Cognitive mapping techniques have been proposed to be applied in requirements …

An Experimental Study On U-Commerce Adoption: Impact Of Personalization And Privacy Concerns, H. Sheng, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Ubiquitous commerce (u-commerce) represents “anytime, anywhere” commerce. U-commerce can provide a high level of personalization, which can bring significant benefits to customers. However, privacy is a major concern to customers and an obstacle to the adoption of u-commerce. This research examines how personalization and context can impact customers’ privacy concerns as well as intention to adopt u-commerce applications. As u-commerce is new and emerging, we used the scenario-based approach to operationalize personalization and context in an experimental study. The experimental results show that the effects of personalization on customers’ privacy concerns and adoption intention are situation dependent.

Visual Analytics For Supporting Entity Relationship Discovery On Text Data, Hanbo Dai, Ee Peng Lim, Hady W. Lauw, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

To conduct content analysis over text data, one may look out for important named objects and entities that refer to real world instances, synthesizing them into knowledge relevant to a given information seeking task. In this paper, we introduce a visual analytics tool called ER-Explorer to support such an analysis task. ER-Explorer consists of a data model known as TUBE and a set of data manipulation operations specially designed for examining entities and relationships in text. As part of TUBE, a set of interestingness measures is defined to help exploring entities and their relationships. We illustrate the use of ER-Explorer …

Capacity Constrained Assignment In Spatial Databases, Hou U Leong, Man Lung Yiu, Kyriakos Mouratidis, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

Given a point set P of customers (e.g., WiFi receivers) and a point set Q of service providers (e.g., wireless access points), where each q 2 Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M Q × P such that (i) each point q 2 Q (p 2 P) appears at most k times (at most nce) in M, (ii) the size of M is maximized (i.e., it comprises min{|P|,P q2Q q.k} pairs), and (iii) the total assignment cost (i.e., the sum of Euclidean distances within all pairs) is minimized. Thus, the CCA problem is …

Predicting Trusts Among Users Of Online Communities: An Epinions Case Study, Haifeng Liu, Ee Peng Lim, Hady W. Lauw, Minh-Tam Le, Aixin Sun, Jaideep Srivastava, Young Ae Kim

Research Collection School Of Computing and Information Systems

Trust between a pair of users is an important piece of information for users in an online community (such as electronic commerce websites and product review websites) where users may rely on trust information to make decisions. In this paper, we address the problem of predicting whether a user trusts another user. Most prior work infers unknown trust ratings from known trust ratings. The effectiveness of this approach depends on the connectivity of the known web of trust and can be quite poor when the connectivity is very sparse which is often the case in an online community. In this …

A Multimodal And Multilevel Ranking Scheme For Large-Scale Video Retrieval, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

A critical issue of large-scale multimedia retrieval is how to develop an effective framework for ranking the search results. This problem is particularly challenging for content-based video retrieval due to some issues such as short text queries, insufficient sample learning, fusion of multimodal contents, and large-scale learning with huge media data. In this paper, we propose a novel multimodal and multilevel (MMML) ranking framework to attack the challenging ranking problem of content-based video retrieval. We represent the video retrieval task by graphs and suggest a graph based semi-supervised ranking (SSR) scheme, which can learn with small samples effectively and integrate …

Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …

Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang

Research Collection School Of Computing and Information Systems

Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called “Collaborative Image Retrieval” (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called “Laplacian Regularized Metric Learning” (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both …

An Office Survival Guide, M. Thulasidas

Research Collection School Of Computing and Information Systems

In the unforgiving, dog-eat-dog corporate jungle, when you find yourself in a new corporate setting, you need to be sure of the welcome. More importantly, you need to prove yourself worthy of it.

Appropriation Of Privacy Management Within Social Networking Sites, Catherine Dwyer

Dissertations

Social networking sites have emerged as one of the most widely used types of interactive systems, with memberships numbering in the hundreds of millions around the globe. By providing tools for their members to manage an ever-changing set of relationships, social networking sites push a constant expansion of social boundaries. These sites place less emphasis on tools that limit social boundaries to enable privacy.

The rapid expansion of online social boundaries has caused privacy shockwaves. Privacy offline is enabled by constraints of time and space. Online, powerful search engines and long term digital storage means private data have no expiration …

Dynamics Of Online Chat, Mihai Moldovan

Dissertations

Millions of people use online synchronous chat networks on a daily basis for work, play and education. Despite their widespread use, little is known about their user dynamics. For example, one does not know how many users are typically co-present and actively engaged in public interaction in the individual chat rooms of any of the numerous public Internet Relay Chat (IRC) networks found on the Internet; or what are the factors that constrain the boundaries of user activity inside those chat rooms. Failure to collect and present such data means there is a lack of a good understanding of the …

Perception Gaps And The Adoption Of Information Technology In The Clinical Healthcare Environment, Karen Hare

Dissertations

Implementation of information systems has lagged in many areas of clinical healthcare for a variety of reasons. Economics, data complexity and resistance are among the often quoted roadblocks. Research suggests that physicians play a major part in the adoption, use and diffusion of information technology (IT) in clinical settings. There are also other healthcare professionals, clinical and non-clinical, who play important roles in making decisions about the acquisition of information technology. In addition to these groups there are information technology professionals providing the services required within the healthcare field. Finally within this group are those IT professionals who have sufficient …

The Impact Of Cultural Differences In Temporal Perception On Global Software Development Teams, Richard William Egan

Dissertations

This dissertation investigated the impact of cultural differences in temporal perception on globally dispersed software development teams. Literature and anecdotal evidence suggest that these temporal differences affect individual communication quality, which in turn will affect individual satisfaction and trust within global teams. Additionally, the temporal dispersion of the team was expected to affect an individual's sense of temporal disruption which, in turn, was expected to affect individual satisfaction and trust. Differences in temporal perception were expected to moderate this impact on perceived temporal disruption. A Fortune 100 Company that carried out software testing in Ireland, the United States, China and …

Sa-Rest: Using Semantics To Empower Restful Services And Smashups With Better Interoperability And Mediation, Karthik Gomadam, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Dynamic And Agile Soa Using Sawsdl, Karthik Gomadam, Kunal Verma, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

A Framework To Support Spatial, Temporal And Thematic Analytics Over Semantic Web Data, Matthew Perry, Amit P. Sheth

Kno.e.sis Publications

Spatial and temporal data are critical components in many applications. This is especially true in analytical applications ranging from scientific discovery to national security and criminal investigation. The analytical process often requires uncovering and analyzing complex thematic relationships between disparate people, places and events. Fundamentally new query operators based on the graph structure of Semantic Web data models, such as semantic associations, are proving useful for this purpose. However, these analysis mechanisms are primarily intended for thematic relationships. In this paper, we describe a framework built around the RDF data model for analysis of thematic, spatial and temporal relationships between …

Location-Based Hashing For Querying And Searching, Felix Ching

Computer Science and Computer Engineering Undergraduate Honors Theses

The rapidly growing information technology in modern days demands an efficient searching scheme to search for desired data. Locality Sensitive Hashing (LSH) is a method for searching similar data in a database. LSH achieves high accuracy and precision for locating desired data, but consumes a significant amount of memory and time. Based on LSH, this thesis presents two novel schemes for efficient and accurate data searching: Locality Sensitive Hashing-SmithWaterman (LSH-SmithWaterman) and Secure Min-wise Locality Sensitive Hashing (SMLSH). Both methods dramatically reduce the memory and time consumption and exhibit high accuracy in data searching. Simulation results demonstrate the efficiency of the …