Databases and Information Systems Commons^™

Demonstration: Secure - Semantics Empowered Rescue Environment, Pratikkumar Desai, Cory Andrew Henson, Pramod Anantharam, Amit P. Sheth

Kno.e.sis Publications

This paper demonstrates a Semantic Web enabled system for collecting and processing sensor data within a rescue environment. The real-time system collects heterogeneous raw sensor data from rescue robots through a wireless sensor network. The raw sensor data is converted to RDF using the Semantic Sensor Network (SSN) ontology and further processed to generate abstractions used for event detection in emergency scenarios.

Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The emergence of dynamic information sources – including sensor networks – has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. With this coming data explosion, real-time analytics software must either adapt or die [2]. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated …

Identifying Social Influence In Networks Using Randomized Experiments, Sinan Aral, Dylan Walker

Business Faculty Articles and Research

The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previously possible.1'2 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral contagions spread in human social networks. More precisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and …

Sempush: Privacy-Aware And Scalable Broadcasting For Semantic Microblogging, Pavan Kapanipathi, Julia Anaya, Alexandre Passant

Kno.e.sis Publications

Users of traditional microblogging platforms such as Twitter face drawbacks in terms of (1) Privacy of status updates as a followee - reaching undesired people (2) Information overload as a follower - receiving uninteresting microposts from followees. In this paper we demonstrate distributed and user-controlled dissemination of microposts using SMOB (semantic microblogging framework) and Semantic Hub (privacy-aware implementation of PuSH3 protocol) . The approach leverages users' Social Graph to dynamically create group of followers who are eligible to receive micropost. The restrictions to create the groups are provided by the followee based on the hastags in the micropost. Both SMOB …

Semantic Annotation And Search For Resources In The Next Generation Web With Sa-Rest, Ajith H. Ranabahu, Amit P. Sheth, Maryam Panahiazar, Sanjaya Wijeratne

Kno.e.sis Publications

SA-REST, the W3C member submission, can be used for supporting a wide variety of Plain Old Semantic HTML (POSH) annotation capabilities on any type of Web resource. Kino framework and tools provide support of capabilities to realize SA-RESTs promised value. These tools include (a) a browser-plugin to support annotation of a Web resource (including services) with respect to an ontology, domain model or vocabulary, (b) an annotation aware indexing engine and (c) faceted search and selection of the Web resources. At one end of the spectrum, we present Kino^E (aka Kino for Enterprise) which uses NCBO formal ontologies and …

A Survey Of Information Diffusion Models And Relevant Problems, Minh Duc Luu, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

There has been tremendous interest in diffusion of innovations or information in a social system. Nowadays, social networks (offline as well as online) are considered as important medium for diffusion and large amount of research has been conducted to understand the dynamics of diffusion in social networks. In this work, we review some of the models proposed for diffusion in social networks. We also highlight the major features of these models by dividing the surveyed models into two categories: non-network and network diffusion models. The former refers to user communities without any knowledge about the user relationship network and the …

Efficient Evaluation Of Continuous Text Seach Queries, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Consider a text filtering server that monitors a stream of incoming documents for a set of users, who register their interests in the form of continuous text search queries. The task of the server is to constantly maintain for each query a ranked result list, comprising the recent documents (drawn from a sliding window) with the highest similarity to the query. Such a system underlies many text monitoring applications that need to cope with heavy document traffic, such as news and email monitoring.In this paper, we propose the first solution for processing continuous text queries efficiently. Our objective is to …

On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu

Research Collection School Of Computing and Information Systems

Twitter is a popular microblogging site where users can easily use mobile phones or desktop machines to generate short messages to be shared with others in realtime. Twitter has seen heavy usage in many recent international events including Japan earthquake, Iran election, etc. In such events, many tweets may become viral for different reasons. In this paper, we study the virality of socio-political tweet content in the Singapore’s 2011 general election (GE2011). We collected tweet data generated by about 20K Singapore users from 1 April 2011 till 12 May 2011, and the follow relationships among them. We introduce several quantitative …

A Domain Specific Language For Enterprise Grade Cloud-Mobile Hybrid Applications, Ajith H. Ranabahu, E. Michael Maximilien, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

Cloud computing has changed the technology landscape by offering flexible and economical computing resources to the masses. However, vendor lock-in makes the migration of applications and data across clouds an expensive proposition. The lock-in is especially serious when considering the new technology trend of combining cloud with mobile devices.

In this paper, we present a domain specific language (DSL) that is purposely created for generating hybrid applications spanning across mobile devices as well as computing clouds. We propose a model-driven development process that makes use of a DSL to provide sufficient programming abstractions over both cloud and mobile features. We …

Personalized Filtering Of The Twitter Stream, Pavan Kapanipathi, Fabrizio Orlandi, Amit P. Sheth, Alexandre Passant

Kno.e.sis Publications

With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss …

Active Multiple Kernel Learning For Interactive 3d Object Retrieval Systems, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. …

Mining Direct Antagonistic Communities In Explicit Trust Networks, David Lo, Didi Surian, Zhang Kuan, Ee Peng Lim

Research Collection School Of Computing and Information Systems

There has been a recent increase of interest in analyzing trust and friendship networks to gain insights about relationship dynamics among users. Many sites such as Epinions, Facebook, and other social networking sites allow users to declare trusts or friendships between different members of the community. In this work, we are interested in extracting direct antagonistic communities (DACs) within a rich trust network involving trusts and distrusts. Each DAC is formed by two subcommunities with trust relationships among members of each sub-community but distrust relationships across the sub-communities. We develop an efficient algorithm that could analyze large trust networks leveraging …

Direction-Based Surrounder Queries For Mobile Recommendations, Xi Guo, Baihua Zheng, Yoshiharu Ishikawa, Yunjun Gao

Research Collection School Of Computing and Information Systems

Location-based recommendation services recommend objects to the user based on the user’s preferences. In general, the nearest objects are good choices considering their spatial proximity to the user. However, not only the distance of an object to the user but also their directional relationship are important. Motivated by these, we propose a new spatial query, namely a direction-based surrounder (DBS) query, which retrieves the nearest objects around the user from different directions. We define the DBS query not only in a two-dimensional Euclidean space E">EE but also in a road network R">RR . In the Euclidean space E" …

Location-Dependent Spatial Query Containment, Ken C. K. Lee, Brandon Unger, Baihua Zheng, Wang-Chien Lee

Research Collection School Of Computing and Information Systems

Nowadays, location-related information is highly accessible to mobile users via issuing Location-Dependent Spatial Queries (LDSQs) with respect to their locations wirelessly to Location-Based Service (LBS) servers. Due to the limited mobile device battery energy, scarce wireless bandwidth, and heavy LBS server workload, the number of LDSQs submitted over wireless channels to LBS servers for evaluation should be minimized as appropriate. In this paper, we exploit query containment techniques for LDSQs (called LDSQ containment) to enable mobile clients to determine whether the result of a new LDSQ Q′ is completely covered by that of another LDSQ Q previously answered by a …

Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design ahyper-graph based index structure to support approximated CANN search efficiently.

Using Social Annotations For Trend Discovery In Scientific Publications, Meiqun Hu, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform …

Active Multiple Kernel Learning For Interactive 3d Object Retrieval Systems, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. …

Cooperative Reinforcement Learning In Topology-Based Multi-Agent Systems, Dan Xiao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Topology-based multi-agent systems (TMAS), wherein agents interact with one another according to their spatial relationship in a network, are well suited for problems with topological constraints. In a TMAS system, however, each agent may have a different state space, which can be rather large. Consequently, traditional approaches to multi-agent cooperative learning may not be able to scale up with the complexity of the network topology. In this paper, we propose a cooperative learning strategy, under which autonomous agents are assembled in a binary tree formation (BTF). By constraining the interaction between agents, we effectively unify the state space of individual …

Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain

Research Collection School Of Computing and Information Systems

We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last …

The Niddk Central Repository At 8 Years—Ambition, Revision, Use And Impact, Charles F. Turner, Huaqin Pan, Gregg W. Silk, Mary-Anne Ardini, Vesselina Bakalov, Stephanie Bryant, Susanna Cantor, Kung-Yen Chang, Michael Delatte, Paul Eggers, Laxminarayana Ganapathi, Sujatha Lakshmikanthan, Joshua Levy, Sheping Li, Joseph Pratt, Norma Pugh, Ying Qin, Rebekah Rasooly, Helen Ray, Jean E. Richardson, Amanda Flynn Riley, Susan M. Rogers, Charlotte Scheper, Sylvia Tan, Stacie White, Philip C. Cooley

Publications and Research

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository makes data and biospecimens from NIDDK-funded research available to the broader scientific community. It thereby facilitates: the testing of new hypotheses without new data or biospecimen collection; pooling data across several studies to increase statistical power; and informative genetic analyses using the Repository’s well-curated phenotypic data. This article describes the initial database plan for the Repository and its revision using a simpler model. Among the lessons learned were the trade-offs between the complexity of a database design and the costs in time and money of implementation; the …