Open Access. Powered by Scholars. Published by Universities.®
Databases and Information Systems Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Numerical Analysis and Scientific Computing (661)
- Social and Behavioral Sciences (342)
- Artificial Intelligence and Robotics (340)
- Graphics and Human Computer Interfaces (310)
- Software Engineering (234)
-
- Business (225)
- Communication (216)
- Engineering (192)
- Social Media (186)
- Theory and Algorithms (175)
- Computer Engineering (165)
- Information Security (142)
- OS and Networks (115)
- Programming Languages and Compilers (97)
- E-Commerce (81)
- Data Storage Systems (69)
- Medicine and Health Sciences (66)
- Public Affairs, Public Policy and Public Administration (54)
- Education (53)
- Management Information Systems (50)
- Transportation (46)
- Health Information Technology (45)
- International and Area Studies (43)
- Asian Studies (42)
- Finance and Financial Management (42)
- Digital Communications and Networking (32)
- Technology and Innovation (25)
- Keyword
-
- Social media (53)
- Machine learning (49)
- Online learning (43)
- Deep learning (40)
- Data mining (39)
-
- Artificial intelligence (34)
- Query processing (29)
- Twitter (28)
- Classification (24)
- Reinforcement learning (24)
- Algorithms (23)
- Deep Learning (23)
- Neural networks (23)
- Clustering (21)
- Graph neural networks (20)
- Machine Learning (20)
- Semantics (20)
- Task analysis (20)
- Algorithm (19)
- Visualization (19)
- Anomaly detection (18)
- Cloud computing (18)
- Image retrieval (18)
- Recommender systems (18)
- Social network (18)
- Performance (17)
- Sentiment analysis (17)
- Singapore (17)
- Natural language processing (16)
- Social networks (16)
- Publication Year
Articles 2611 - 2640 of 3436
Full-Text Articles in Databases and Information Systems
Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
We present a self-organizing neural model for creating intelligent learning agents in virtual worlds. As agents in a virtual world roam, interact and socialize with users and other agents as in real world without explicit goals and teachers, learning in virtual world presents many challenges not found in typical machine learning benchmarks. In this paper, we highlight the unique issues and challenges of building learning agents in virtual world using reinforcement learning. Specifically, a self-organizing neural model, named TD-FALCON (Temporal Difference - Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function in …
Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan
Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCON) are used to learn non-player characters' behavior rules according to recorded patterns. Behavior learning abilities of these two models are investigated by learning specific sample Bots in the Unreal Tournament game in a supervised manner. Our empirical experiments demonstrate that both SGNN and FALCON are able to recognize important behavior patterns and learn the necessary knowledge to operate in the Unreal environment. …
Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan
Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Capacitated arc routing problem (CARP) has attracted much attention due to its generality to many real world problems. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving CARP ranging from small to medium size. In this paper we propose a formal probabilistic memetic algorithm for CARP that is equipped with an adaptation mechanism to control the degree of global exploration against local exploitation while the search progresses. Experimental study on benchmark instances of CARP showed that the proposed probabilistic scheme led to improved search performances when introduced into a recently proposed state-of-the-art …
Impact Of Flow And Brand Equity In 3d Virtual Worlds, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, David Dewester, So Ra Park
Impact Of Flow And Brand Equity In 3d Virtual Worlds, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, David Dewester, So Ra Park
Research Collection School Of Computing and Information Systems
This research is a partial test of Park et al.’s (2008) model to assess the impact of flow and brand equity in 3D virtual worlds. It draws on flow theory as its main theoretical foundation to understand and empirically assess the impact of flow on brand equity and behavioral intention in 3D virtual worlds. The findings suggest that the balance of skills and challenges in 3D virtual worlds influences users’ flow experience, which in turn influences brand equity. Brand equity then increases behavioral intention. The authors also found that the impact of flow on behavioral intention in 3D virtual worlds …
Semantic Context Modeling With Maximal Margin Conditional Random Fields For Automatic Image Annotation, Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-Seng Chua, Chong-Wah Ngo
Semantic Context Modeling With Maximal Margin Conditional Random Fields For Automatic Image Annotation, Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-Seng Chua, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources, semantic context has been exploited in AIA and brings promising results. However, previous works either casted the problem into structural classification or adopted multi-layer modeling, which suffer from the problems of scalability or model efficiency. In this paper, we propose a novel discriminative Conditional Random Field (CRF) model for semantic context modeling in AIA, which is built over semantic concepts and treats an image as a whole observation without segmentation. Our model captures the interactions between semantic …
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus.
Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong Koh, Xuan Khoa Nguyen, C. Jason Woodard
Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong Koh, Xuan Khoa Nguyen, C. Jason Woodard
Research Collection School Of Computing and Information Systems
This paper reports on a preliminary study to assess the feasibility of using the Open Cirrus Cloud Computing Research testbed to provide offline and online analytical support for taxi fleet operations. In the study, we benchmarked the performance gains from distributing the offline analysis of GPS location traces over multiple virtual machines using the Apache Hadoop implementation of the MapReduce paradigm. We also explored the use of the Apache Cassandra distributed database system for online retrieval of vehicle trace data. While configuring the testbed infrastructure was straightforward, we encountered severe I/O bottlenecks in running the benchmarks due to the lack …
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Research Collection School Of Computing and Information Systems
Given a set D of trajectories, a query object q, and a query time extent Γ, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D, the set of trajectories that are among the k1 nearest neighbors (NNs) of q within Γ, and meanwhile, have q as one of their k2 NNs. This type of queries is useful in many applications such as decision making, data mining, and pattern recognition, as it considers both the proximity of the trajectories to q and the proximity of q to the trajectories. In this paper, we first formalize MNN search …
Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi Zhang, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Ee Peng Lim
Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi Zhang, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Wikipedia is one of the most successful online knowledge bases, attracting millions of visits daily. Not surprisingly, its huge success has in turn led to immense research interest for a better understanding of the collaborative knowledge building process. In this paper, we performed a (terrorism) domain-specific case study, comparing and contrasting the knowledge evolution in Wikipedia with a knowledge base created by domain experts. Specifically, we used the Terrorism Knowledge Base (TKB) developed by experts at MIPT. We identified 409 Wikipedia articles matching TKB records, and went ahead to study them from three aspects: creation, revision, and link evolution. We …
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Research Collection School Of Computing and Information Systems
Spatio-temporal data concerning the movement of individuals over space and time contains latent information on the associations among these individuals. Sources of spatio-temporal data include usage logs of mobile and Internet technologies. This article defines a spatio-temporal event by the co-occurrences among individuals that indicate potential associations among them. Each spatio-temporal event is assigned a weight based on the precision and uniqueness of the event. By aggregating the weights of events relating two individuals, we can determine the strength of association between them. We conduct extensive experimentation to investigate both the efficacy of the proposed model as well as the …
Efficient Processing Of Exact Top-K Queries Over Disk-Resident Sorted Lists, Hwee Hwa Pang, Xuhua Ding, Baihua Zheng
Efficient Processing Of Exact Top-K Queries Over Disk-Resident Sorted Lists, Hwee Hwa Pang, Xuhua Ding, Baihua Zheng
Research Collection School Of Computing and Information Systems
The top-k query is employed in a wide range of applications to generate a ranked list of data that have the highest aggregate scores over certain attributes. As the pool of attributes for selection by individual queries may be large, the data are indexed with per-attribute sorted lists, and a threshold algorithm (TA) is applied on the lists involved in each query. The TA executes in two phases--find a cut-off threshold for the top-k result scores, then evaluate all the records that could score above the threshold. In this paper, we focus on exact top-k queries that involve monotonic linear …
A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung Ku, Yangwoo Ko, Jisun An, Dongman Lee
A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung Ku, Yangwoo Ko, Jisun An, Dongman Lee
Research Collection School Of Computing and Information Systems
A social-based routing protocol for opportunistic networks considers the direct delivery as forwarding metrics. By ignoring the indirect delivery through intermediate nodes, it misses chances to find paths that are better in terms of delivery ratio and time. To overcome this limitation, we propose to incorporate transitivity, which considers the indirect delivery through intermediate nodes, as one of the forwarding metrics. We also found that some message forwards do not improve the delivery performance. To reduce the number of these useless forwards, the proposed scheme forwards messages to an encountered node when the increase of total utility value is greater …
Satrap: Data And Network Heterogeneity Aware P2p Data-Mining, Hock Kee Ang, Vivekanand Gopalkrishnan, Anwitaman Datta, Wee Keong Ng, Steven C. H. Hoi
Satrap: Data And Network Heterogeneity Aware P2p Data-Mining, Hock Kee Ang, Vivekanand Gopalkrishnan, Anwitaman Datta, Wee Keong Ng, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peers' data which can help reduce redundant communication In this paper, we examine the properties of network and data heterogeneity and propose …
Otl: A Framework Of Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi
Otl: A Framework Of Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in …
Player Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Richa Sharan, Jaideep Srivastava
Player Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Richa Sharan, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models forgame players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance …
Z-Sky: An Efficient Skyline Query Processing Framework Based On Z-Order, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Huajing Li, Yuan Tian
Z-Sky: An Efficient Skyline Query Processing Framework Based On Z-Order, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Huajing Li, Yuan Tian
Research Collection School Of Computing and Information Systems
Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and …
Visualizing And Exploring Evolving Information Networks In Wikipedia, Ee Peng Lim, Agus Trisnajaya Kwee, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Maureen Maureen
Visualizing And Exploring Evolving Information Networks In Wikipedia, Ee Peng Lim, Agus Trisnajaya Kwee, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Maureen Maureen
Research Collection School Of Computing and Information Systems
Information networks in Wikipedia evolve as users collaboratively edit articles that embed the networks. These information networks represent both the structure and content of community’s knowledge and the networks evolve as the knowledge gets updated. By observing the networks evolve and finding their evolving patterns, one can gain higher order knowledge about the networks and conduct longitudinal network analysis to detect events and summarize trends. In this paper, we present SSNetViz+, a visual analytic tool to support visualization and exploration of Wikipedia’s information networks. SSNetViz+ supports time-based network browsing, content browsing and search. Using a terrorism information network as an …
Weakly-Supervised Hashing In Kernel Space, Yadong Mu, Jialie Shen, Shuicheng Yan
Weakly-Supervised Hashing In Kernel Space, Yadong Mu, Jialie Shen, Shuicheng Yan
Research Collection School Of Computing and Information Systems
The explosive growth of the vision data motivates the recent studies on efficient data indexing methods such as locality-sensitive hashing (LSH). Most existing approaches perform hashing in an unsupervised way. In this paper we move one step forward and propose a supervised hashing method, i.e., the LAbel-regularized Max-margin Partition (LAMP) algorithm. The proposed method generates hash functions in weakly-supervised setting, where a small portion of sample pairs are manually labeled to be “similar” or “dissimilar”. We formulate the task as a Constrained Convex-Concave Procedure (CCCP), which can be relaxed into a series of convex sub-problems solvable with efficient Quadratic-Program (QP). …
Exclusive Lasso For Multi-Task Feature Selection, Yang Zhou, Rong Jin, Steven C. H. Hoi
Exclusive Lasso For Multi-Task Feature Selection, Yang Zhou, Rong Jin, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes co-varying variables in groups, the proposed exclusive lasso regularizer models the scenario when variables in the same group compete with each other. Analysis is presented to illustrate the properties of the proposed regularizer. We present a framework of kernel-based multi-task feature selection algorithm based on the proposed exclusive lasso regularizer. An efficient algorithm is derived to solve the related optimization problem. Experiments with document categorization show that our approach outperforms state-of-the-art algorithms for multi-task feature selection.
Two-View Transductive Support Vector Machines, Guangxia Li, Steven C. H. Hoi, Kuiyu Chang
Two-View Transductive Support Vector Machines, Guangxia Li, Steven C. H. Hoi, Kuiyu Chang
Research Collection School Of Computing and Information Systems
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam detection, which changes at a very brisk pace. For some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such as term frequency, while a second view may contain a small but highly-informative number of domain specific features. We thus propose a novel two-view transductive SVM that takes advantage of both the abundant amount of unlabeled data …
Exploiting Query Logs For Cross-Lingual Query Suggestions., Wei Gao, Cheng Niu, Jian-Yun Nie, Ming Zhou, Kam-Fai Wong, Hsiao-Wuen Hon
Exploiting Query Logs For Cross-Lingual Query Suggestions., Wei Gao, Cheng Niu, Jian-Yun Nie, Ming Zhou, Kam-Fai Wong, Hsiao-Wuen Hon
Research Collection School Of Computing and Information Systems
Query suggestion aims to suggest relevant queries for a given query, which helps users better specify their information needs. Previous work on query suggestion has been limited to the same language. In this article, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to the scenarios of cross-language information retrieval (CLIR) and other related cross-lingual applications. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of …
Learning User Profiles For Personalized Information Dissemination, Ah-Hwee Tan, Christine Teo
Learning User Profiles For Personalized Information Dissemination, Ah-Hwee Tan, Christine Teo
Research Collection School Of Computing and Information Systems
Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised Adaptive Resonance Theory (ART) system, known as fuzzy ARAM, can be used to learn user profiles for personalized information dissemination. ARAM learning is on-line, fast, and incremental. Acquisition of new knowledge does not require re-training on previously learned cases. ARAM integrates both user-defined and system-learned knowledge in a single framework. Therefore inconsistency between the two knowledge sources will not arise. ARAM has been used to develop a personalized news system known as PIN. Preliminary experiments …
Understanding The Values Of Mobile Technology In Education: A Value-Focused Thinking Approach, Hong Sheng, Keng Siau, Fiona Nah
Understanding The Values Of Mobile Technology In Education: A Value-Focused Thinking Approach, Hong Sheng, Keng Siau, Fiona Nah
Research Collection School Of Computing and Information Systems
Mobile technology has mobilized the human interaction in all dimensions by supporting mobile collaboration. As collaboration is key to learning in today's educational environment, mobile technology has tremendous potential in supporting and improving education and its delivery. Given that mobile technology for education is a new phenomenon that is gaining popularity, the values of using mobile technology to support education need to be further researched and better understood. In this research, we used the Value-Focused Thinking approach to interview students and instructors to identify the values of education that are enabled by mobile technology. These values are represented in the …
A Social Network Based Study Of Software Team Dynamics, Subhajit Datta, Vikrant S. Kaulgoud, Vibhu Saujanya Sharma, Nishant Kumar
A Social Network Based Study Of Software Team Dynamics, Subhajit Datta, Vikrant S. Kaulgoud, Vibhu Saujanya Sharma, Nishant Kumar
Research Collection School Of Computing and Information Systems
Members of software project teams have specific roles and responsibilities which are formally defined during project inception or at the start of a life cycle activity. Often, the team structure undergoes spontaneous changes as delivery deadlines draw near and critical tasks have to be completed. Some members -- depending on their skill or seniority -- need to take on more responsibilities, while others end up being peripheral to the project's execution. We posit that this kind of ad hoc reorganization of a team's structure can be discerned from the project's bug tracker. In this paper, we extract a social network …
Optimal Matching Between Spatial Datasets Under Capacity Constraints, Hou U Leong, Kyriakos Mouratidis, Man Lung Yiu, Nikos Mamoulis
Optimal Matching Between Spatial Datasets Under Capacity Constraints, Hou U Leong, Kyriakos Mouratidis, Man Lung Yiu, Nikos Mamoulis
Research Collection School Of Computing and Information Systems
Consider a set of customers (e.g., WiFi receivers) and a set of service providers (e.g., wireless access points), where each provider has a capacity and the quality of service offered to its customers is anti-proportional to their distance. The capacity constrained assignment (CCA) is a matching between the two sets such that (i) each customer is assigned to at most one provider, (ii) every provider serves no more customers than its capacity, (iii) the maximum possible number of customers are served, and (iv) the sum of Euclidean distances within the assigned provider-customer pairs is minimized. Although max-flow algorithms are applicable …
Generating Synonyms Based On Query Log Data, Stelios Paparizos, Tao Cheng, Hady W. Lauw
Generating Synonyms Based On Query Log Data, Stelios Paparizos, Tao Cheng, Hady W. Lauw
Research Collection School Of Computing and Information Systems
An approach is described for generating synonyms to supplement at least one information item, such as, in one case, a set of related items. The approach can involve an expansion phase, a clean-up phase, and a reduction phase. In the expansion phase, the approach identifies, for each related item, a set of initial synonym candidates. In the clean-up phase, the approach removes noise from the set of initial synonym candidates (if such noise exists), to provide a set of filtered synonym candidate items. In the reduction phase, the approach ranks and applies a threshold (or thresholds) to the set of …
Adaptive Ensemble Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng
Adaptive Ensemble Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng
Research Collection School Of Computing and Information Systems
Classification in P2P networks has become an important research problem in data mining due to the popularity of P2P computing environments. This is still an open difficult research problem due to a variety of challenges, such as non-i.i.d. data distribution, skewed or disjoint class distribution, scalability, peer dynamism and asynchronism. In this paper, we present a novel P2P Adaptive Classification Ensemble (PACE) framework to perform classification in P2P networks. Unlike regular ensemble classification approaches, our new framework adapts to the test data distribution and dynamically adjusts the voting scheme by combining a subset of classifiers/peers according to the test data …
Continuous Spatial Assignment Of Moving Users, Hou U Leong, Kyriakos Mouratidis, Nikos Mamoulis
Continuous Spatial Assignment Of Moving Users, Hou U Leong, Kyriakos Mouratidis, Nikos Mamoulis
Research Collection School Of Computing and Information Systems
Consider a set of servers and a set of users, where each server has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of users it can serve). Our task is to assign every user to one server subject to the coverage and capacity constraints. To offer the highest quality of service, we wish to minimize the average distance between users and their assigned server. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), …
Efficient Skyline Maintenance For Streaming Data With Partially-Ordered Domains, Yuan Fang, Chee-Yong Chan
Efficient Skyline Maintenance For Streaming Data With Partially-Ordered Domains, Yuan Fang, Chee-Yong Chan
Research Collection School Of Computing and Information Systems
We address the problem of skyline query processing for a count-based window of continuous streaming data that involves both totally- and partially-ordered attribute domains. In this problem, a fixed-size buffer of the N most recent tuples is dynamically maintained and the key challenge is how to efficiently maintain the skyline of the sliding window of N tuples as new tuples arrive and old tuples expire. We identify the limitations of the state-of-the-art approach STARS, and propose two new approaches, STARS+ and SkyGrid, to address its drawbacks. STARS+ is an enhancement of STARS with three new optimization techniques, while SkyGrid is …
Data Mining Based Predictive Models For Overall Health Indices, Ridhima Rajkumar, Kyong Jin Shim, Jaideep Srivastava
Data Mining Based Predictive Models For Overall Health Indices, Ridhima Rajkumar, Kyong Jin Shim, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
In this study, we infer health care indices of individuals using their pharmacy medical and prescription claims. Specifically, we focus on the widely used Charlson Index. We use data mining techniques to formulate the problem of classifying Charlson Index (CI) and build predictive models to predict individual health index score. First, we present comparative analyses of several classification algorithms. Second, our study shows that certain ensemble algorithms lead to higher prediction accuracy in comparison to base algorithms. Third, we introduce cost-sensitive learning to the classification algorithms and show that the inclusion of cost-sensitive learning leads to improved prediction accuracy. The …