Databases and Information Systems Commons^™

Recent Advances In Integrating Owl And Rules, Matthias Knorr, David Carral Martinez, Pascal Hitzler, Adila A. Krisnadhi, Frederick Maier, Cong Wang

Computer Science and Engineering Faculty Publications

As part of the quest for a unifying logic for the Semantic Web Technology Stack, a central issue is finding suitable ways of integrating description logics based on the Web Ontology Language (OWL) with rule-based approaches based on logic programming. Such integration is difficult since naive approaches typically result in the violation of one or more desirable design principles. For example, while both OWL 2 DL and RIF Core (a dialect of the Rule Interchange Format RIF) are decidable, their naive union is not, unless carefully chosen syntactic restrictions are applied.

We report on recent advances and ongoing work by …

A Tableau Algorithm For Description Logics With Nominal Schema, Adila Krisnadhi, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We present a tableau algorithm for the description logic ALCOV. This description logic is obtained by extending the description logic ALCO with the expressive nominal schema construct that enables DL-safe datalog with predicates of arbitrary arity to be covered within the description logic framework. The tableau algorithm provides a basis to implement a delayed grounding strategy which was not facilitated by earlier versions of decision procedures for satisfiability in expressive description logics with nominal schemas.

Principles For Conducting Critical Realist Case Study Research In Information Systems, Donald E. Wynn, Clay K. Williams

MIS/OM/DS Faculty Publications

Critical realism is emerging as a viable philosophical paradigm for conducting social science research, and has been proposed as an alternative to the more prevalent paradigms of positivism and interpretivism. Few papers, however, have offered clear guidance for applying this philosophy to actual research methodologies. Under critical realism, a causal explanation for a given phenomenon is inferred by explicitly identifying the means by which structural entities and contextual conditions interact to generate a given set of events. Consistent with this view of causality, we propose a set of methodological principles for conducting and evaluating critical realism-based explanatory case study research …

Bduol: Double Updating Online Learning On A Fixed Budget, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Kernel-based online learning often exhibits promising empirical performance for various applications according to previous studies. However, it often suffers a main shortcoming, that is, the unbounded number of support vectors, making it unsuitable for handling large-scale datasets. In this paper, we investigate the problem of budget kernel-based online learning that aims to constrain the number of support vectors by a predefined budget when learning the kernel-based prediction function in the online learning process. Unlike the existing studies, we present a new framework of budget kernel-based online learning based on a recently proposed online learning method called “Double Updating Online Learning” …

Guest Editors’ Introduction: Methods Innovations For The Empirical Study Of Technology Adoption And Diffusion, Robert John Kauffman, Angsana A. Techatassanasoontorn

Research Collection School Of Computing and Information Systems

The literature on technology adoption and diffusion is ahighly mature area of Information Systems (IS) research,which requires a deft hand in research to support the creationof new contributions of knowledge. In this specialissue, we focus on the application of various methods,including new ones, to shed light on research questions thathave not been understood fully in prior research. In particular,we will showcase research that involves theapplication of event history analysis and spatial econometrics,as well as count data models to study frequencyrelatedphenomena for changes and development in technologyadoption and diffusion. We also include an articlethat employs game theory, as well as another …

In-Game Action List Segmentation And Labeling In Real-Time Strategy Games, Wei Gong, Ee-Peng Lim, Palakorn Achananuparp, Feida Zhu, David Lo, Freddy Chong-Tat Chua

Research Collection School Of Computing and Information Systems

In-game actions of real-time strategy (RTS) games are extremely useful in determining the players' strategies, analyzing their behaviors and recommending ways to improve their play skills. Unfortunately, unstructured sequences of in-game actions are hardly informative enough for these analyses. The inconsistency we observed in human annotation of in-game data makes the analytical task even more challenging. In this paper, we propose an integrated system for in-game action segmentation and semantic label assignment based on a Conditional Random Fields (CRFs) model with essential features extracted from the in-game actions. Our experiments demonstrate that the accuracy of our solution can be as …

Reconciling Owl And Non-Monotonic Rules For The Semantic Web, Matthias Knorr, Pascal Hitzler, Frederick Maier

Computer Science and Engineering Faculty Publications

We propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the 'look and feel' of a description logic …

Analysis Of Median Household Income Differences Between Election Day-Vbm And Eip Voters, Mark Salling, Norman Robbins

All Maxine Goodman Levin School of Urban Affairs Publications

Analysis of early in-person (EIP) voting in 2008 in Cuyahoga County shows that African-American, white, and Hispanic voters who used EIP voting had significantly lower incomes than members of those same groups who voted on election day or by mail. This result applies to those voting EIP on weekdays, extended weekday hours, weekends, and the three days before election day.

Using Attribute Behavior Diversity To Build Accurate Decision Tree Committees For Microarray Data, Qian Han, Guozhu Dong

Kno.e.sis Publications

DNA microarrays (gene chips), frequently used in biological and medical studies, measure the expressions of thousands of genes per sample. Using microarray data to build accurate classifiers for diseases is an important task. This paper introduces an algorithm, called Committee of Decision Trees by Attribute Behavior Diversity (CABD), to build highly accurate ensembles of decision trees for such data. Since a committee's accuracy is greatly influenced by the diversity among its member classifiers, CABD uses two new ideas to "optimize" that diversity, namely (1) the concept of attribute behavior–based similarity between attributes, and (2) …

Ethical Considerations For Virtual Worlds, Alanah Mitchell, Deepak Khazanchi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

Metaverses, like Second Life and Teleplace, and the inherent technology capabilities that they offer continue to be of interest for researchers, practitioners, and educators. Due to this trend, and the uncertainty regarding immersive virtual experiences as contrasted with face-to-face experiences, there is a need to further understand the ethical challenges associated with this virtual context. This paper presents a starting point for discussing ethics in virtual worlds. Specifically, we review virtual worlds and their unique technology capabilities as well as the ethical considerations that arise due to these unique capabilities.

Boosting Multi-Kernel Locality-Sensitive Hashing For Scalable Image Retrieval, Hao Xia, Steven C. H. Hoi, Pengcheng Wu, Rong Jin

Research Collection School Of Computing and Information Systems

Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) is the most popular one, which recently has been extended to Kernelized Locality-Sensitive Hashing (KLSH) by exploiting kernel similarity for better retrieval efficacy. Typically, KLSH works only with a single kernel, which is often limited in real-world multimedia applications, where data may originate from multiple resources or can be represented in several different forms. For example, in content-based multimedia retrieval, a variety of features can be extracted to represent …

A Secure And Efficient Discovery Service System In Epcglobal Network, Jie Shi, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

In recent years, the Internet of Things (IOT) has drawn considerable attention from the industrial and research communities. Due to the vast amount of data generated through IOT devices and users, there is an urgent need for an effective search engine to help us make sense of this massive amount of data. With this motivation, we begin our initial works on developing a secure and efficient search engine (SecDS) based on EPC Discovery Services (EPCDS) for EPCglobal network, an integral part of IOT. SecDS is designed to provide a bridge between different partners of supply chains to share information while …

Modeling Concept Dynamics For Large Scale Music Search, Jialie Shen, Hwee Hwa Pang, Meng Wang, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over …

Collective Churn Prediction In Social Network, Richard J. Oentaryo, Ee-Peng Lim, David Lo, Feida Zhu, Philips K. Prasetyo

Research Collection School Of Computing and Information Systems

In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our …

Online Feature Selection For Mining Big Data, Steven C. H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin

Research Collection School Of Computing and Information Systems

Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner is only allowed to maintain a classifier involved a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. …

Confidence-Aware Graph Regularization With Heterogeneous Pairwise Features, Yuan Fang, Bo-June Paul Hsu, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

Conventional classification methods tend to focus on features of individual objects, while missing out on potentially valuable pairwise features that capture the relationships between objects. Although recent developments on graph regularization exploit this aspect, existing works generally assume only a single kind of pairwise feature, which is often insufficient. We observe that multiple, heterogeneous pairwise features can often complement each other and are generally more robust in modeling the relationships between objects. Furthermore, as some objects are easier to classify than others, objects with higher initial classification confidence should be weighed more towards classifying related but more ambiguous objects, an …

Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu

Research Collection School Of Computing and Information Systems

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location …

A Non-Parametric Visual-Sense Model Of Images: Extending The Cluster Hypothesis Beyond Text, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia

Research Collection School Of Computing and Information Systems

The main challenge of a search engine is to find information that are relevant and appropriate. However, this can become difficult when queries are issued using ambiguous words. Rijsbergen first hypothesized a clustering approach for web pages wherein closely associated pages are treated as a semantic group with the same relevance to the query (Rijsbergen 1979). In this paper, we extend Rijsbergen’s cluster hypothesis to multimedia content such as images. Given a user query, the polysemy in the return image set is related to the many possible meanings of the query. We develop a method to cluster the polysemous images …

Presynaptic Learning And Memory With A Persistent Firing Neuron And A Habituating Synapse: A Model Of Short Term Persistent Habituation, Kiruthika Ramanathan, Ning Ning, Dhiviya Dhanasekar, Guoqi Li, Luping Shi, Prahlad Vadakkepat

Research Collection School Of Computing and Information Systems

Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show …

(Hidden) Social Influences In Switching Mobile Service Platforms, Virpi K. Tuunainen, Tuure Tuunanen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

During the past few years, the mobile industry has gone through a radical change from business focusing on excellence in device manufacturing and supply chain management to ecosystems around successful focal players, such as Apple and Google, controlling these service platforms. In order to compete in this environment, these firms need to understand what makes a consumer switch between these mobile service platforms. To that end, we conducted an inductive qualitative study with university students from Finland and USA as subjects (142 altogether), delving into how and why consumers switch mobile phones, and what are the factors affecting their decisions. …

Data Mining Of Protein Databases, Christopher Assi

School of Computing: Dissertations, Theses, and Student Research

Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …

Twitris+: Social Media Analytics Platform For Effective Coordination, Gary Alan Smith, Amit P. Sheth, Ashutosh Sopan Jadhav, Hemant Purohit, Lu Chen, Michael Cooney, Pavan Kapanipathi, Pramod Anantharam, Pramod Koneru, Wenbo Wang

Kno.e.sis Publications

Twitris+ is a Semantic Social Media analytics platform to provide technologies for analyzing large-scale social media streams across Spatio-Temporal-Thematic (STT) and People-Content-Network (PCN) dimensions. It provides holistic situational awareness from one interface and enables organizational actors to engage in well-coordinated ways for desired tasks during emergency response.

Embracing Analytics For A Better Competitive Edge, Tin Seong Kam

Research Collection School Of Computing and Information Systems

No abstract provided.

Prosody-Based Query-By-Example Spoken Utterance Retrieval, Steven D. Werner, Nigel G. Ward

COURI Symposium Abstracts, Summer 2012

Current spoken dialogue retrieval systems are almost entirely lexically based, using automatic transcription by imperfect speech recognition technology. Once audio is reduced to text, little more than existing text search methods are used to retrieve results. However, spoken dialogue contains rich latent prosodic information about dialogue activities - such as agreeing, arguing, deciding, planning, storytelling, showing surprise - and this extra information can often be relevant to the searchers aims. Searching for similar speech acts rather then similar words enables similar contextual results, returning points where the speaker had the same sentiment during a topic. Previous research yielded 76 dimensions …

The Latent Maximum Entropy Principle, Shaojun Wang, Dale Schuurmans, Yunxin Zhao

Kno.e.sis Publications

We present an extension to Jaynes’ maximum entropy principle that incorporates latent variables. The principle of latent maximum entropy we propose is different from both Jaynes’ maximum entropy principle and maximum likelihood estimation, but can yield better estimates in the presence of hidden variables and limited training data. We first show that solving for a latent maximum entropy model poses a hard nonlinear constrained optimization problem in general. However, we then show that feasible solutions to this problem can be obtained efficiently for the special case of log-linear models---which forms the basis for an efficient approximation to the latent maximum …

What Kind Of #Communication Is Twitter? A Psycholinguistic Perspective On Communication In Twitter For The Purpose Of Emergency Coordination, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach

Kno.e.sis Publications

The present research aims to detect coordinated citizen response within social media traffic to assist emergency response. We use domain-independent linguistic properties as the first step in narrowing the candidate set of messages for domain-dependent and computationally intensive analysis.

Exploring The Impact Of Knowledge And Social Environment On Influenza Prevention And Transmission In Midwestern United States High School Students, William L. Romine, Tanvi Banerjee, William S. Barrow, William R. Folk

Kno.e.sis Publications

We used data from a convenience sample of 410 Midwestern United States students from six secondary schools to develop parsimonious models for explaining and predicting precautions and illness related to influenza. Scores for knowledge and perceptions were obtained using two-parameter Item Response Theory (IRT) models. Relationships between outcome variables and predictors were verified using Pearson and Spearman correlations, and nested [student within school] fixed effects multinomial logistic regression models were specified from these using Akaike’s Information Criterion (AIC). Neural network models were then formulated as classifiers using 10-fold cross validation to predict precautions and illness. Perceived barriers against taking precautions …

Determinants In Sustaining A Local Information System In The Philippines: The Case Of The Barangay Management Information System (Bmis), Charina P. Maneja, Nancy A. Tandang, Merlyne M. Paunlagui

Journal of Public Affairs and Development

Information is important for the executive and legislative functions of local officials. The study determined the institutional and individual factors that contributed in sustaining a Barangay Management Information System (BMIS). The study was done in five provinces covering 90 randomly selected continuing barangays and 68 randomly selected non-continuing barangays. Chi-square Test of Independence was used to determine factors associated with whether the barangay will continue to sustain BMIS or not. Logistic regression analysis was also performed to determine factors that may influence barangay's decision to sustain BMIS. The identified significant individual factors that influenced the barangays' decision to sustain BMIS …

K-Partite Graph Reinforcement And Its Application In Multimedia Information Retrieval, Yue Gao, Meng Wang, Rongrong Ji, Zheng-Jun Zha, Jialie Shen

Research Collection School Of Computing and Information Systems

In many example-based information retrieval tasks, example query actually contains multiple sub-queries. For example, in 3D object retrieval, the query is an object described by multiple views. In content-based video retrieval, the query is a video clip that contains multiple frames. Without prior knowledge, the most intuitive approach is to treat the sub-queries equally without difference. In this paper, we propose a k-partite graph reinforcement approach to fuse these sub-queries based on the to-be-retrieved database. The approach first collects the top retrieved results. These results are regarded as pseudo-relevant samples and then a k-partite graph reinforcement is performed on these …

Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li

Research Collection School Of Computing and Information Systems

Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments …