Databases and Information Systems Commons^™

Ibm Altocumulus: A Cross-Cloud Middleware And Platform, E. Michael Maximilien, Ajith Harshana Ranabahu, Roy Engehausen, Laura Anderson

Kno.e.sis Publications

Cloud computing has become the new face of computing and promises to offer virtually unlimited, cheap, readily available, "utility type" computing resources. Many vendors have entered this market with different offerings ranging from infrastructure-as-a-service such as Amazon, to fully functional platform services such as Google App Engine. However, as a result of this heterogeneity, deploying applications to a cloud and managing them needs to be done using vendor specific methods. This "lock in" is seen as a major hurdle in adopting cloud technologies to the enterprise. IBM Altocumulus, the cloud middleware platform from IBM Almaden Services Research, aims to solve …

Paraconsistent Reasoning For Owl 2, Yue Ma, Pascal Hitzler

Computer Science and Engineering Faculty Publications

A four-valued description logic has been proposed to reason with description logic based inconsistent knowledge bases. This approach has a distinct advantage that it can be implemented by invoking classical reasoners to keep the same complexity as under the classical semantics. However, this approach has so far only been studied for the basid description logic ALC. In this paper, we further study how to extend the four-valued semantics to the more expressive description logic SROIQ which underlies the forthcoming revision of the Web Ontology Language, OWL 2, and also investigate how it fares when adapated to tractable description logics including …

A Preferential Tableaux Calculus For Circumscriptive Alco, Stephan Grimm, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Nonmonotonic extensions of description logics (DLs) allow for default and local closed-world reasoning and are an acknowledged desired feature for applications, e.g. in the Semantic Web. A recent approach to such an extension is based on McCarthy's circumscription, which rests on the principle of minimising the extension of selected predicates to close off dedicated parts of a domain model. While decidability and complexity results have been established in the literature, no practical algorithmisation for circumscriptive DLs has been proposed so far. In this paper, we present a tableaux calculus that can be used as a decision procedure for concept satisfiability …

A Best Practice Model For Cloud Middleware Systems, Ajith Harshana Ranabahu, E. Michael Maximilien

Kno.e.sis Publications

Cloud computing is the latest trend in computing where the intention is to facilitate cheap, utility type computing resources in a service-oriented manner. However, the cloud landscape is still maturing and there are heterogeneities between the clouds, ranging from the application development paradigms to their service interfaces,and scaling approaches. These differences hinder the adoption of cloud by major enterprises. We believe that a cloud middleware can solve most of these issues to allow cross-cloud inter-operation. Our proposed system is Altocumulus, a cloud middleware that homogenizes the clouds. In order to provide the best use of the cloud resources and make …

Context And Domain Knowledge Enhanced Entity Spotting In Informal Text, Daniel Gruhl, Meena Nagarajan, Jan Pieper, Christine Robson, Amit P. Sheth

Kno.e.sis Publications

This paper explores the application of restricted relationship graphs (RDF) and statistical NLP techniques to improve named entity annotation in challenging Informal English domains. We validate our approach using on-line forums discussing popular music. Named entity annotation is particularly difficult in this domain because it is characterized by a large number of ambiguous entities, such as the Madonna album “Music” or Lilly Allen’s pop hit “Smile”.

We evaluate improvements in annotation accuracy that can be obtained by restricting the set of possible entities using real-world constraints. We find that constrained domain entity extraction raises the annotation accuracy significantly, making an …

Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu

Research Collection School Of Computing and Information Systems

The Bag-of-Words (BoW) model is a promising image representation for annotation. One critical limitation of existing BoW models is the semantic loss during the codebook generation process, in which BoW simply clusters visual words in Euclidian space. However, distance between two visual words in Euclidean space does not necessarily reflect the semantic distance between the two concepts, due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme for learning a codebook such that semantically related features will be mapped to the same visual word. In particular, we consider the distance between …

Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Following the exponential growth of social media, there now exist huge repositories of videos online. Among the huge volumes of videos, there exist large numbers of near-duplicate videos. Most existing techniques either focus on the fast retrieval of full copies or near-duplicates, or consider localization in a heuristic manner. This paper considers the scalable detection and localization of partial near-duplicate videos by jointly considering visual similarity and temporal consistency. Temporal constraints are embedded into a network structure as directed edges. Through the structure, partial alignment is novelly converted into a network flow problem where highly efficient solutions exist. To precisely …

Parallel Sets In The Real World: Three Case Studies, Robert Kosara, Caroline Ziemkiewicz, F. Joseph Iii Mako, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Parallel Sets are a visualization technique for categorical data. We recently released an implementation to the public in an effort to make our research useful to real users. This paper presents three case studies of Parallel Sets in use with real data.

Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang

Research Collection School Of Computing and Information Systems

Query-to-concept mapping plays one of the keys to concept-based video retrieval. Conventional approaches try to find concepts that are likely to co-occur in the relevant shots from the lexical or statistical aspects. However, the high probability of co-occurrence alone cannot ensure its effectiveness to distinguish the relevant shots from the irrelevant ones. In this paper, we propose distribution-based concept selection (DBCS) for query-to-concept mapping by analyzing concept score distributions of within and between relevant and irrelevant sets. In view of the imbalance between relevant and irrelevant examples, two variants of DBCS are proposed respectively by considering the two-sided and onesided …

Continuous Monitoring Of Spatial Queries In Wireless Broadcast Environments, Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias

Research Collection School Of Computing and Information Systems

Wireless data broadcast is a promising technique for information dissemination that leverages the computational capabilities of the mobile devices in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may then tune in the broadcast channel and process their queries locally without contacting the server. Previous work on spatial query processing for wireless broadcast systems has only considered snapshot queries over static data. In this paper, we propose an air indexing framework that 1) outperforms the existing (i.e., snapshot) techniques in …

Distance Metric Learning From Uncertain Side Information With Application To Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu

Research Collection School Of Computing and Information Systems

Automated photo tagging is essential to make massive unlabeled photos searchable by text search engines. Conventional image annotation approaches, though working reasonably well on small testbeds, are either computationally expensive or inaccurate when dealing with large-scale photo tagging. Recently, with the popularity of social networking websites, we observe a massive number of user-tagged images, referred to as "social images", that are available on the web. Unlike traditional web images, social images often contain tags and other user-generated content, which offer a new opportunity to resolve some long-standing challenges in multimedia. In this work, we aim to address the challenge of …

Unsupervised Face Alignment By Robust Nonrigid Mapping, Jianke Zhu, Luc Van Gool, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

We propose a novel approach to unsupervised facial image alignment. Differently from previous approaches, that are confined to affine transformations on either the entire face or separate patches, we extract a nonrigid mapping between facial images. Based on a regularized face model, we frame unsupervised face alignment into the Lucas-Kanade image registration approach. We propose a robust optimization scheme to handle appearance variations. The method is fully automatic and can cope with pose variations and expressions, all in an unsupervised manner. Experiments on a large set of images showed that the approach is effective.

Streaming 3d Meshes Using Spectral Geometry Images, Ying He, Boon Seng Chew, Dayong Wang, Steven C. H. Hoi, Lap Pui Chau

Research Collection School Of Computing and Information Systems

The transmission of 3D models in the form of Geometry Images (GI) is an emerging and appealing concept due to the reduction in complexity from R3 to image space and wide availability of mature image processing tools and standards. However, geometry images often suffer from the artifacts and error during compression and transmission. Thus, there is a need to address the artifact reduction, error resilience and protection of such data information during the transmission across an error prone network. In this paper, we introduce a new concept, called Spectral Geometry Images (SGI), which naturally combines the powerful spectral analysis with …

First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King

Research Collection School Of Computing and Information Systems

No abstract provided.

First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King

Research Collection School Of Computing and Information Systems

The ACM SIGMM International Workshop on Social Media(WSM’09) is the first workshop held in conjunction withthe ACM International Multimedia Conference (MM’09) atBejing, P.R. China, 2009. This workshop provides a forumfor researchers and practitioners from all over the world toshare information on their latest investigations on social mediaanalysis, exploration, search, mining, and emerging newsocial media applications.

Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose. However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only. Also, the few algorithms that find frequent subgraphs in a single labeled graph typically identify subgraphs appearing regionally in the input graph. In contrast, for real-world applications, it is commonly required that the identified frequent subgraphs in a single labeled graph should also be globally distributed. This paper thus fills this crucial void by proposing a new measure, termed G-Measure, to find globally …

Analyzing The Video Popularity Characteristics Of Large-Scale User Generated Content Systems, Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, Sue Moon

Research Collection School Of Computing and Information Systems

User generated content (UGC), now with millions of video producers and consumers, is re-shaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world's largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show …

Domain Adaptive Semantic Diffusion For Large Scale Context-Based Video Annotation, Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Learning to cope with domain change has been known as a challenging problem in many real-world applications. This paper proposes a novel and efficient approach, named domain adaptive semantic diffusion (DASD), to exploit semantic context while considering the domain-shift-of-context for large scale video concept annotation. Starting with a large set of concept detectors, the proposed DASD refines the initial annotation results using graph diffusion technique, which preserves the consistency and smoothness of the annotation over a semantic graph. Different from the existing graph learning methods which capture relations among data samples, the semantic graph treats concepts as nodes and the …

Context Is Highly Contextual!, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Temporal Data Classification Using Linear Classifiers, Peter Revesz, Thomas Triplet

School of Computing: Conference and Workshop Papers

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality.

Why Quants Fail, M. Thulasidas

Research Collection School Of Computing and Information Systems

Mathematical finance is built on a couple of assumptions. The most fundamental of them is the one on ma ket efficiency. It states that the market prices every asset fairly, and that the prices contain all the information available in the market.

Robust Lifetime Measurement In Large-Scale P2p Systems With Non-Stationary Arrivals, Xiaoming Wang, Zhongmei Yao, Yueping Zhang, Dmitri Loguinov

Computer Science Faculty Publications

Characterizing user churn has become an important topic in studying P2P networks, both in theoretical analysis and system design. Recent work has shown that direct sampling of user lifetimes may lead to certain bias (arising from missed peers and round-off inconsistencies) and proposed a technique that estimates lifetimes based on sampled residuals. In this paper, however, we show that under non-stationary arrivals, which are often present in real systems, residual-based sampling does not correctly reconstruct user lifetimes and suffers a varying degree of bias, which in some cases makes estimation completely impossible. We overcome this problem using two contributions: a …

Self-Authentication Of Audio Signals By Chirp Coding, Jonathan Blackledge, Eugene Coyle

Conference papers

This paper discusses a new approach to ‘watermarking’ digital signals using linear frequency modulated or ‘chirp’ coding. The principles underlying this approach are based on the use of a matched filter to provide a reconstruction of a chirped code that is uniquely robust in the case of signals with very low signal-to-noise ratios. Chirp coding for authenticating data is generic in the sense that it can be used for a range of data types and applications (the authentication of speech and audio signals, for example). The theoretical and computational aspects of the matched filter and the properties of a chirp …

Visible Reverse K-Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Wang-Chien Lee, Ken C. K. Lee, Qing Li

Research Collection School Of Computing and Information Systems

Reverse nearest neighbor (RNN) queries have a broad application base such as decision support, profile-based marketing, resource allocation, etc. Previous work on RNN search does not take obstacles into consideration. In the real world, however, there are many physical obstacles (e.g., buildings) and their presence may affect the visibility between objects. In this paper, we introduce a novel variant of RNN queries, namely, visible reverse nearest neighbor (VRNN) search, which considers the impact of obstacles on the visibility of objects. Given a data set P, an obstacle set O, and a query point q in a 2D space, a VRNN …

Localized Matching Using Earth Mover's Distance Towards Discovery Of Common Patterns From Small Image Samples, Hung-Khoon Tan, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper proposes a new approach for the discovery of common patterns in a small set of images by region matching. The issues in feature robustness, matching robustness and noise artifact are addressed to delve into the potential of using regions as the basic matching unit. We novelly employ the many-to-many (M2M) matching strategy, specifically with the Earth Mover's Distance (EMD), to increase resilience towards the structural inconsistency from improper region segmentation. However, the matching pattern of M2M is dispersed and unregulated in nature, leading to the challenges of mining a common pattern while identifying the underlying transformation. To avoid …

A Latent Model For Visual Disambiguation Of Keyword-Based Image Search, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia, Sujoy Roy

Research Collection School Of Computing and Information Systems

The problem of polysemy in keyword-based image search arises mainly from the inherent ambiguity in user queries. We propose a latent model based approach that resolves user search ambiguity by allowing sense specific diversity in search results. Given a query keyword and the images retrieved by issuing the query to an image search engine, we first learn a latent visual sense model of these polysemous images. Next, we use Wikipedia to disambiguate the word sense of the original query, and issue these Wiki-senses as new queries to retrieve sense specific images. A sense-specific image classifier is then learnt by combining …

Accelerating Sequence Searching: Dimensionality Reduction Method, Guojie Song, Bin Cui, Baihua Zheng, Kunqing Xie, Dongqing Yang

Research Collection School Of Computing and Information Systems

Similarity search over long sequence dataset becomes increasingly popular in many emerging applications, such as text retrieval, genetic sequences exploring, etc. In this paper, a novel index structure, namely Sequence Embedding Multiset tree (SEM − tree), has been proposed to speed up the searching process over long sequences. The SEM-tree is a multi-level structure where each level represents the sequence data with different compression level of multiset, and the length of multiset increases towards the leaf level which contains original sequences. The multisets, obtained using sequence embedding algorithms, have the desirable property that they do not need to keep the …

Batch Mode Active Learning With Applications To Text Categorization And Image Retrieval, Steven C. H. Hoi, Rong Jin, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most informative examples for manual labeling in these learning tasks. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient, since the classification model has to be retrained for every acquired labeled example. It is also inappropriate for the setup of information retrieval tasks where the user's relevance feedback is often provided for the top K retrieved items. In …

Communication-Efficient Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Distributed classification aims to learn with accuracy comparable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent environment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asynchronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR—a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust …

In-Group / Out-Group Dynamics And Effectiveness In Partially Distributed Teams, Faina Privman

Dissertations

When organizations collaborate they often do so using partially distributed teams (PDTs). In a Partially Distributed Team there exist at least two distinct sub-groups. In addition, at least one of the sub-groups has two or more members that are geographically co-located. Co-located members can meet face to face; chat in the hallway; have lunch together; and otherwise socialize with one another. On the other hand, remote members must rely on technology to communicate and work together. This distinct characteristic of partially distributed teams makes them especially susceptible to the In-Group / Out Group dynamic (Huang and Ocker, 2006). This dynamic …