Assessing Differences Between Physician's Realized And Anticipated Gains From Electronic Health Record Adoption,
2011
Cleveland State University
Assessing Differences Between Physician's Realized And Anticipated Gains From Electronic Health Record Adoption, Lori T. Peterson, Eric W. Ford, John Eberhardt, T. R. Huerta
Business Faculty Publications
Return on investment (ROI) concerns related to Electronic Health Records (EHRs) are a major barrier to the technology’s adoption. Physicians generally rely upon early adopters to vet new technologies prior to putting them into widespread use. Therefore, early adopters’ experiences with EHRs play a major role in determining future adoption patterns. The paper’s purposes are: (1) to map the EHR value streams that define the ROI calculation; and (2) to compare Current Users’ and Intended Adopters’ perceived value streams to identify similarities, differences and governing constructs. Primary data was collected by the Texas Medical Association, which surveyed 1,772 physicians on …
Comparing Twitter And Traditional Media Using Topic Models,
2011
Peking University
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …
Users Positions In Social Networks,
2011
San Jose State University
Users Positions In Social Networks, Jasim Qazi
Master's Projects
Social networks are a new phase in human interaction: using
technology to connect people online, the social nehvorks of today have
become a central part of the lives of millions of people. People use social
networks for sharing various infonrmation with their friends and family. This
information can take the forn of text, video, images, sound etc. and it is what
forms the collection of dats in social networks.
As social networks gain popularity and as more and more people start
using social networks, it has become more important now to understand the
inner structures of social networks and understand …
Recipe Suggestion Tool,
2011
San Jose State University
Recipe Suggestion Tool, Sakuntala Padmapriya Gangaraju
Master's Projects
ABSTRACT
There is currently a great need for a tool to search cooking recipes based on ingredients. Current search engines do not provide this feature. Most of the recipe search results in current websites are not efficiently clustered based on relevance or categories resulting in a user getting lost in the huge search results presented.
Clustering in information retrieval is used for higher efficiency and better presentation of information to the user. Clustering puts similar documents in the same cluster. If a document is relevant to a query, then the documents in the same cluster are also relevant.
The goal …
Association Rule Mining -- Geometry And Parallel Computing Approach,
2011
San Jose State University
Association Rule Mining -- Geometry And Parallel Computing Approach, Dongyi Jia
Master's Projects
Mining association rules is a very important aspect in data mining fields. The process to mine association rules not only take much time, but also take huge computing source. How to fast and efficiently find the large itemsets is a crucial point in the association rule algorithms. This paper will focus on two algorithms research and implementation in parallel computing environments. One is Bitmap Combination algorithm, the other is Bitmap FP-Growth algorithm. Compared to Apriori algorithm, both Bitmap Combination and Bitmap FP-Growth algorithms don’t need generate candidate items, avoids costly database scans. Both algorithms need to translate the original database …
Smart Search: A Firefox Add-On To Compute A Web Traffic Ranking,
2011
San Jose State University
Smart Search: A Firefox Add-On To Compute A Web Traffic Ranking, Vijaya Pamidi
Master's Projects
Search engines results are typically ordered according to some notion of importance of a web page as well as relevance of the content of a web page to a query. Web page importance is usually calculated based on some graph theoretic properties of the web. Another common technique to measure page importance is to make use of the traffic that goes to a particular web page as measured by a browser toolbar. Currently, there are some traffic ranking tools available like www.alexa.com, www.ranking.com, www.compete.com that give such analytic as to the number of users who visit a web site. Alexa …
Two-Layer Multiple Kernel Learning,
2011
Nanyang Technological University
Two-Layer Multiple Kernel Learning, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple Kernel Learning (MKL) aims to learn kernel machines for solving a real machine learning problem (e.g. classification) by exploring the combinations of multiple kernels. The traditional MKL approach is in general “shallow” in the sense that the target kernel is simply a linear (or convex) combination of some base kernels. In this paper, we investigate a framework of Multi-Layer Multiple Kernel Learning (MLMKL) that aims to learn “deep” kernel machines by exploring the combinations of multiple kernels in a multi-layer structure, which goes beyond the conventional MKL approach. Through a multiple layer mapping, the proposed MLMKL framework offers higher …
Medical Analysis Question And Answering Application For Internet Enabled Mobile Devices,
2011
San Jose State University
Medical Analysis Question And Answering Application For Internet Enabled Mobile Devices, Loc Nguyen
Master's Projects
Mobile devices such as smart phones, the iPhone, and the iPad have become more popular in recent years. With access to the Internet through cellular or WIFI networks, these mobile devices can make use of the great source of information available on the Internet. Unlike a desktop or laptop computer, an Internet enabled mobile device is designed to be carried around and available to the owner almost instantly at any moment of the day. Despite having such great advantage and potential, searching for information with a mobile device remains a difficult task. Mobile device users have to juggle between different …
Efficient Topological Olap On Information Networks,
2011
Peking University
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Research Collection School Of Computing and Information Systems
We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …
A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints,
2011
Nanyang Technological University
A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Previous studies of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP solvers. However, for N data examples, the time complexity of NPKL using a standard interior-point SDP solver could be as high as O(N6.5), which prohibits NPKL methods applicable to real applications, even for data sets of moderate size. In this paper, we present a family of efficient NPKL algorithms, termed "SimpleNPKL", which can learn non-parametric kernels from a large set of pairwise constraints efficiently. In particular, we propose two efficient SimpleNPKL algorithms. One …
Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation,
2011
Singapore Management University
Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou
Research Collection School Of Computing and Information Systems
Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the boosting-based ranking algorithms because it plays a contradictory role against the innate weight of boosting, namely source weight that focuses on adjusting source-domain ranking accuracy. Our experiments show that among three variants, the additive weight-based RankBoost, which dynamically balances the two types …
Predicting Item Adoption Using Social Correlation,
2011
Singapore Management University
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …
Mkboost: A Framework Of Multiple Kernel Boosting,
2011
Singapore Management University
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …
Identifying An Optimal Dining Plan System For The Entertainment Industry,
2011
Embry-Riddle Aeronautical University
Identifying An Optimal Dining Plan System For The Entertainment Industry, Joseph Crimi
Doctoral Dissertations and Master's Theses
The Disney Dining Plan (DDP) is a pre-paid meal plan guests can purchase when they make their reservation at Walt Disney World (WDW). Under the current system, the information provided to guests explaining the program is unclear which leads to confusion for guests. For example, guests are not sure what food they can purchase using the DDP or at which dining locations they can use the DDP. Given these problems, the present study evaluated a new information system for the DDP. The independent variables in this study were symbol type, the symbols used in the current DDP and new symbols …
Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection,
2011
Nanyang Technological University
Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan
Research Collection School Of Computing and Information Systems
On-line portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing on-line portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidences show that the stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel on-line portfolio selection strategy named ``Confidence Weighted Mean Reversion'' (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, …
Learning Feature Dependencies For Noise Correction In Biomedical Prediction,
2011
Institute for Infocomm Research, Singapore
Learning Feature Dependencies For Noise Correction In Biomedical Prediction, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
The presence of noise or errors in the stated feature values of biomedical data can lead to incorrect prediction. We introduce a Bayesian Network-based Noise Correction framework named BN-NC. After data preprocessing, a Bayesian Network (BN) is learned to capture the feature dependencies. Using the BN to predict each feature in turn, BN-NC estimates a feature's error rate as the deviation between its predicted and stated values in the training data, and allocates the appropriate uncertainty to its subsequent findings during prediction. BN-NC automatically generates a probabilistic rule to explain BN prediction on the class variable using the feature values …
Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection,
2011
Nanyang Technological University
Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan
Research Collection School Of Computing and Information Systems
Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CO Relation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety …
Ir-Tree: An Efficient Index For Geographic Document Search,
2011
Singapore Management University
Ir-Tree: An Efficient Index For Geographic Document Search, Zhisheng Li, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee, Xufa Wang
Research Collection School Of Computing and Information Systems
Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four …
Utility-Oriented K-Anonymization On Social Networks,
2011
Singapore Management University
Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee
Research Collection School Of Computing and Information Systems
"Identity disclosure" problem on publishing social network data has gained intensive focus from academia. Existing k-anonymization algorithms on social network may result in nontrivial utility loss. The reason is that the number of the edges modified when anonymizing the social network is the only metric to evaluate utility loss, not considering the fact that different edge modifications have different impact on the network structure. To tackle this issue, we propose a novel utility-oriented social network anonymization scheme to achieve privacy protection with relatively low utility loss. First, a proper utility evaluation model is proposed. It focuses on the changes on …
War Fighting In Cyberspace: Evolving Force Presentation And Command And Control,
2011
Air Intelligence Squadron
War Fighting In Cyberspace: Evolving Force Presentation And Command And Control, M. Bodine Birdwell, Robert F. Mills
Faculty Publications
The Department of Defense (DOD) is endeavoring to define war fighting in the global cyberspace domain. Creation of US Cyber Command (USCYBERCOM), a subunified functional combatant command (FCC) under US Strategic Command (USSTRATCOM), is a huge step in integrating and coordinating the defense, protection, and operation of DOD networks; however, this step does not mean that USCYBERCOM will perform or manage all cyberspace functions. In fact the vast majority of cyberspace functions conducted by the services and combatant commands (COCOM), although vital for maintaining access to the domain in support of their operations, are not of an active war-fighting nature. …
