Databases and Information Systems Commons^™

Mining Coherent Anomaly Collections On Web Data, Hanbo Dai, Feida Zhu, Ee-Peng Lim, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

The recent boom of weblogs and social media has attached increasing importance to the identification of suspicious users with unusual behavior, such as spammers or fraudulent reviewers. A typical spamming strategy is to employ multiple dummy accounts to collectively promote a target, be it a URL or a product. Consequently, these suspicious accounts exhibit certain coherent anomalous behavior identifiable as a collection. In this paper, we propose the concept of Coherent Anomaly Collection (CAC) to capture this kind of collections, and put forward an efficient algorithm to simultaneously find the top-K disjoint CACs together with their anomalous behavior patterns. Compared …

Cross-View Graph Embedding, Zhiwu Huang, S. Shan, H. Zhang, S. Lao, X. Chen

Research Collection School Of Computing and Information Systems

Recently, more and more approaches are emerging to solve the cross-view matching problem where reference samples and query samples are from different views. In this paper, inspired by Graph Embedding, we propose a unified framework for these cross-view methods called Cross-view Graph Embedding. The proposed framework can not only reformulate most traditional cross-view methods (e.g., CCA, PLS and CDFE), but also extend the typical single-view algorithms (e.g., PCA, LDA and LPP) to cross-view editions. Furthermore, our general framework also facilitates the development of new cross-view methods. In this paper, we present a new algorithm named Cross-view Local Discriminant Analysis (CLODA) …

A Generalized Cluster Centroid Based Classifier For Text Categorization, Guansong Pang, Shengyi Jiang

Research Collection School Of Computing and Information Systems

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two wellknown classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is …

Google Apps For Education: Valparaiso University's Migration Experience, Rebecca Klein, Richard Orelup, Matthew Smith

Information Technology Faculty and Staff Publications

Many campuses are investigating cloud-based or hosted email solutions. This paper will cover Valparaiso University’s decision to move to the Google Apps for Education platform and our campus migration strategy. Google Apps offers significant savings in both cost of service and cost of support / maintenance while simultaneously offering functionality improvements to the campus experience over our previous system. Valparaiso University was using the GroupWise email and calendaring system and began the process of migrating all of campus to the Google Apps for Education platform in early 2011. Our process began with a student led evaluation team to select the …

Adaptive Grid Based Localized Learning For Multidimensional Data, Sheetal Saini

Doctoral Dissertations

Rapid advances in data-rich domains of science, technology, and business has amplified the computational challenges of "Big Data" synthesis necessary to slow the widening gap between the rate at which the data is being collected and analyzed for knowledge. This has led to the renewed need for efficient and accurate algorithms, framework, and algorithmic mechanisms essential for knowledge discovery, especially in the domains of clustering, classification, dimensionality reduction, feature ranking, and feature selection. However, data mining algorithms are frequently challenged by the sparseness due to the high dimensionality of the datasets in such domains which is particularly detrimental to the …

Building A Computer Program To Support Children, Parents, And Distraction During Healthcare Procedures, Kirsten Hanrahan, Ann Marie Mccarthy, Charmaine Kleiber, Kaan Ataman, W. Nick Street, M. Bridget Zimmerman, Annel L. Ersig

Business Faculty Articles and Research

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model …

Computing Perception From Sensor Data, Payam Barnaghi, Frieder Ganz, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations …

Privacy Preserving Boosting In The Cloud With Secure Half-Space Queries, Shumin Guo, Keke Chen

Kno.e.sis Publications

This paper presents a preliminary study on the PerturBoost approach that aims to provide efficient and secure classifier learning in the cloud with both data and model privacy preserved.

The Shanghai-Hongkong Team At Mediaeval2012: Violent Scene Detection Using Trajectory-Based Features, Yu-Gang Jiang, Qi Dai, Chun Chet Tan, Xiangyang Xue, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The Violent Scene Detection task offers a very practical challenge in detecting complex and diverse violent video clips in movies. In this working note paper, we will briefly describe our system and discuss the results, which achieved top performance in mAP@201 and runner-up in mAP@100, among all 35 submissions worldwide. The central component of our system is a set of features derived from the appearance and motion of local patch trajectories [2]. We use these features and SVM classifier as the baseline approach and add in a few other components to further improve the performance. Our findings indicate that the …

Understanding Engagement In Educational Computer Games, Fiona Fui-Hoon Nah, Yunjie Zhou, Adeline Boey, Hanji Li

Research Collection School Of Computing and Information Systems

This paper presents an empirical study to understand engagement in educational computer games. Engagement is defined as an experience that occupies an individual’s attention and captures one’s interest. The nature of engagement is viewed as comprising conditions, actions, and outcomes of engagement. The data collection method includes in-depth interviews with 12 educational computer game players who have experienced engagement in playing these games. We used the Grounded Theory (GT) approach to develop an understanding of user engagement in the computer game-based learning context.

Neural Modeling Of Episodic Memory: Encoding, Retrieval, And Forgetting, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan, Janusz A. Starzyk

Research Collection School Of Computing and Information Systems

This paper presents a neural model that learns episodic traces in response to a continuous stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatio-temporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Combined with a mechanism of gradual forgetting, the model is able to achieve a high level of memory performance and robustness, while controlling memory consumption over time. We present experimental studies, …

Entity Synonyms For Structured Web Search, Tao Cheng, Hady W. Lauw, Stelios Paparizos

Research Collection School Of Computing and Information Systems

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven …

A Probabilistic Graphical Model For Topic And Preference Discovery On Social Media, Lu Liu, Feida Zhu, Lei Zhang, Shiqiang Yang

Research Collection School Of Computing and Information Systems

Many web applications today thrive on offering services for large-scale multimedia data, e.g., Flickr for photos and YouTube for videos. However, these data, while rich in content, are usually sparse in textual descriptive information. For example, a video clip is often associated with only a few tags. Moreover, the textual descriptions are often overly specific to the video content. Such characteristics make it very challenging to discover topics at a satisfactory granularity on this kind of data. In this paper, we propose a generative probabilistic model named Preference-Topic Model (PTM) to introduce the dimension of user preferences to enhance the …

Influentials, Novelty, And Social Contagion: The Viral Power Of Average Friends, Close Communities, And Old News, Nicholas Harrigan, Palakorn Achananuparp, Ee Peng Lim

Research Collection School Of Computing and Information Systems

What is the effect of (1) popular individuals, and (2) community structures on the retransmission of socially contagious behavior? We examine a community of Twitter users over a five month period, operationalizing social contagion as ‘retweeting’, and social structure as the count of subgraphs (small patterns of ties and nodes) between users in the follower/following network. We find that popular individuals act as ‘inefficient hubs’ for social contagion: they have limited attention, are overloaded with inputs, and therefore display limited responsiveness to viral messages. We argue this contradicts the ‘law of the few’ and ‘influentials hypothesis’. We find that community …

Talk Versus Work: Characteristics Of Developer Collaboration On The Jazz Platform, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta

Research Collection School Of Computing and Information Systems

IBM's Jazz initiative offers a state-of-the-art collaborative development environment (CDE) facilitating developer interactions around interdependent units of work. In this paper, we analyze development data across two versions of a major IBM product developed on the Jazz platform, covering in total 19 months of development activity, including 17,000+ work items and 61,000+ comments made by more than 190 developers in 35 locations. By examining the relation between developer talk and work, we find evidence that developers maintain a reasonably high level of connectivity with peer developers with whom they share work dependencies, but the span of a developer's communication goes …

Trajectory-Based Modeling Of Human Actions With Motion Reference Points, Yu-Gang Jiang, Qi Dai, Xiangyang Xue, Wei Liu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Human action recognition in videos is a challenging problem with wide applications. State-of-the-art approaches often adopt the popular bag-of-features representation based on isolated local patches or temporal patch trajectories, where motion patterns like object relationships are mostly discarded. This paper proposes a simple representation specifically aimed at the modeling of such motion relationships. We adopt global and local reference points to characterize motion information, so that the final representation can be robust to camera movement. Our approach operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a …

The Factors Behind A Successful Implementation Of Electronic Health Records Systems, Anjee Gorkhali

Engineering Management & Systems Engineering Theses & Dissertations

This research explores the role that budget for Information System (IS) and technical expertise of healthcare service provider staff play on the successful leap from a partial to exhaustive implementation of Electronic Health Records (EHR) Systems. Technical expertise in Information Systems might not be easily measurable directly, but there are a number of indicators that could be used as a proxy, such as: Information System (IS) Department Budget, number of IS staff and the extent of technical trainings provided by the IS department to the clinical staff. This research study hypothesizes that quality technical trainings conducted by an IS department …

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 …