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Shaham S, Ghinita G, Ahuja R, Krumm J, Shahabi C. HTF: Homogeneous Tree Framework for Differentially-Private Release of Large Geospatial Datasets with Self-Tuning Structure Height. ACM Trans Spat Algorithms Syst 2023; 9:25. [PMID: 38384746 PMCID: PMC10881200 DOI: 10.1145/3569087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 10/19/2022] [Indexed: 02/23/2024]
Abstract
Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. In addition, they use ad-hoc criteria to decide the depth of the partitioning. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We propose a self-tuning approach to decide the depth of the partitioning structure that optimizes the use of privacy budget. Furthermore, we provide an optimization that scales the proposed split approach to large datasets while maintaining accuracy. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.
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Affiliation(s)
| | - Gabriel Ghinita
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Qatar
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Xie T, He C, Ren X, Shahabi C, Kuo CCJ. L-BGNN: Layerwise Trained Bipartite Graph Neural Networks. IEEE Trans Neural Netw Learn Syst 2023; 34:10711-10723. [PMID: 35544501 DOI: 10.1109/tnnls.2022.3171199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Learning low-dimensional representations of bipartite graphs enables e-commerce applications, such as recommendation, classification, and link prediction. A layerwise-trained bipartite graph neural network (L-BGNN) embedding method, which is unsupervised, efficient, and scalable, is proposed in this work. To aggregate the information across and within two partitions of a bipartite graph, a customized interdomain message passing (IDMP) operation and an intradomain alignment (IDA) operation are adopted by the proposed L-BGNN method. Furthermore, we develop a layerwise training algorithm for L-BGNN to capture the multihop relationship of large bipartite networks and improve training efficiency. We conduct extensive experiments on several datasets and downstream tasks of various scales to demonstrate the effectiveness and efficiency of the L-BGNN method as compared with state-of-the-art methods. Our codes are publicly available at https://github.com/TianXieUSC/L-BGNN.
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Zhang M, Lin H, Takagi S, Cao Y, Shahabi C, Xiong L. CSGAN: Modality-Aware Trajectory Generation via Clustering-based Sequence GAN. IEEE Int Conf Mob Data Manag 2023; 2023:148-157. [PMID: 37965426 PMCID: PMC10644148 DOI: 10.1109/mdm58254.2023.00032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Human mobility data is useful for various applications in urban planning, transportation, and public health, but collecting and sharing real-world trajectories can be challenging due to privacy and data quality issues. To address these problems, recent research focuses on generating synthetic trajectories, mainly using generative adversarial networks (GANs) trained by real-world trajectories. In this paper, we hypothesize that by explicitly capturing the modality of transportation (e.g., walking, biking, driving), we can generate not only more diverse and representative trajectories for different modalities but also more realistic trajectories that preserve the geographical density, trajectory, and transition level properties by capturing both cross-modality and modality-specific patterns. Towards this end, we propose a Clustering-based Sequence Generative Adversarial Network (CSGAN) that simultaneously clusters the trajectories based on their modalities and learns the essential properties of real-world trajectories to generate realistic and representative synthetic trajectories. To measure the effectiveness of generated trajectories, in addition to typical density and trajectory level statistics, we define several new metrics for a comprehensive evaluation, including modality distribution and transition probabilities both globally and within each modality. Our extensive experiments with real-world datasets show the superiority of our model in various metrics over state-of-the-art models.
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Zeighami S, Shahabi C. On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing. Proc Mach Learn Res 2023; 202:40669-40680. [PMID: 37933246 PMCID: PMC10627073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
A fundamental problem in data management is to find the elements in an array that match a query. Recently, learned indexes are being extensively used to solve this problem, where they learn a model to predict the location of the items in the array. They are empirically shown to outperform non-learned methods (e.g., B-trees or binary search that answer queries in O ( l o g n ) time) by orders of magnitude. However, success of learned indexes has not been theoretically justified. Only existing attempt shows the same query time of O ( l o g n ) , but with a constant factor improvement in space complexity over non-learned methods, under some assumptions on data distribution. In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in O ( l o g l o g n ) expected query time. We also show that allowing for slightly larger but still near-linear space overhead, a learned index can achieve O ( 1 ) expected query time. Our results theoretically prove learned indexes are orders of magnitude faster than non-learned methods, theoretically grounding their empirical success.
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Shaham S, Ghinita G, Shahabi C. Models and Mechanisms for Spatial Data Fairness. Proceedings VLDB Endowment 2022; 16:167-179. [PMID: 37220471 PMCID: PMC10201928 DOI: 10.14778/3565816.3565820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Fairness in data-driven decision-making studies scenarios where individuals from certain population segments may be unfairly treated when being considered for loan or job applications, access to public resources, or other types of services. In location-based applications, decisions are based on individual whereabouts, which often correlate with sensitive attributes such as race, income, and education. While fairness has received significant attention recently, e.g., in machine learning, there is little focus on achieving fairness when dealing with location data. Due to their characteristics and specific type of processing algorithms, location data pose important fairness challenges. We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries. We devise a novel building block to achieve fairness in the form of fair polynomials. Next, we propose two mechanisms based on fair polynomials that achieve individual spatial fairness, corresponding to two common location-based decision-making types: distance-based and zone-based. Extensive experimental results on real data show that the proposed mechanisms achieve spatial fairness without sacrificing utility.
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Affiliation(s)
- Sina Shaham
- Viterbi School of Engineering University of Southern California Los Angeles, California, USA
| | - Gabriel Ghinita
- College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation, Doha, Qatar
| | - Cyrus Shahabi
- Viterbi School of Engineering University of Southern California Los Angeles, California, USA
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Zheng K, Li Y, Shahabi C, Yin H. Introduction to the Special Issue on Intelligent Trajectory Analytics: Part II. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3510021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Kai Zheng
- University of Electronic Science and Technology of China, China
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Zaidi A, Ahuja R, Shahabi C. Differentially Private Occupancy Monitoring from WiFi Access Points. IEEE Int Conf Mob Data Manag 2022; 2022:361-366. [PMID: 36345435 PMCID: PMC9637410 DOI: 10.1109/mdm55031.2022.00081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Accurately monitoring the number of individuals inside a building is vital to limiting COVID-19 transmission. Low adoption of contact tracing apps due to privacy concerns has increased pervasiveness of passive digital tracking alternatives. Large arrays of WiFi access points can conveniently track mobile devices on university and industry campuses. The CrowdMap system employed by the University of Southern California enables such tracking by collecting aggregate statistics from connections to access points around campus. However, since these devices can be used to infer the movement of individuals, there is still a significant risk that even aggregate occupancy statistics will violate the location privacy of individuals. We examine the use of Differential Privacy in reporting statistics from this system as measured using point and range count queries. We propose discretization schemes to model the positions of users given only user connections to WiFi access points. Using this information we are able to release accurate counts of occupants in areas of campus buildings such as labs, hallways, and large discussion halls with minimized risk to individual users' privacy.
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Affiliation(s)
- Abbas Zaidi
- USC Information Laboratory, University of Southern California, Los Angeles, USA
| | - Ritesh Ahuja
- USC Information Laboratory, University of Southern California, Los Angeles, USA
| | - Cyrus Shahabi
- USC Information Laboratory, University of Southern California, Los Angeles, USA
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8
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Zheng K, Li Y, Shahabi C, Yin H. Introduction to the Special Issue on Intelligent Trajectory Analytics: Part I. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3495230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Kai Zheng
- University of Electronic Science and Technology of China, China
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9
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Nilanon T, Nocera LP, Martin AS, Kolatkar A, May M, Hasnain Z, Ueno NT, Yennu S, Alexander A, Mejia AE, Boles RW, Li M, Lee JSH, Hanlon SE, Cozzens Philips FA, Quinn DI, Newton PK, Broderick J, Shahabi C, Kuhn P, Nieva JJ. Use of Wearable Activity Tracker in Patients With Cancer Undergoing Chemotherapy: Toward Evaluating Risk of Unplanned Health Care Encounters. JCO Clin Cancer Inform 2021; 4:839-853. [PMID: 32970482 PMCID: PMC7531613 DOI: 10.1200/cci.20.00023] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Unplanned health care encounters (UHEs) such as emergency room visits can occur commonly during cancer chemotherapy treatments. Patients at an increased risk of UHEs are typically identified by clinicians using performance status (PS) assessments based on a descriptive scale, such as the Eastern Cooperative Oncology Group (ECOG) scale. Such assessments can be bias prone, resulting in PS score disagreements between assessors. We therefore propose to evaluate PS using physical activity measurements (eg, energy expenditure) from wearable activity trackers. Specifically, we examined the feasibility of using a wristband (band) and a smartphone app for PS assessments. METHODS We conducted an observational study on a cohort of patients with solid tumor receiving highly emetogenic chemotherapy. Patients were instructed to wear the band for a 60-day activity-tracking period. During clinic visits, we obtained ECOG scores assessed by physicians, coordinators, and patients themselves. UHEs occurring during the activity-tracking period plus a 90-day follow-up period were later compiled. We defined our primary outcome as the percentage of patients adherent to band-wear ≥ 80% of 10 am to 8 pm for ≥ 80% of the activity-tracking period. In an exploratory analysis, we computed hourly metabolic equivalent of task (MET) and counted 10 am to 8 pm hours with > 1.5 METs as nonsedentary physical activity hours. RESULTS Forty-one patients completed the study (56.1% female; 61.0% age 40-60 years); 68% were adherent to band-wear. ECOG score disagreement between assessors ranged from 35.3% to 50.0%. In our exploratory analysis, lower average METs and nonsedentary hours, but not higher ECOG scores, were associated with higher 150-day UHEs. CONCLUSION The use of a wearable activity tracker is generally feasible in a similar population of patients with cancer. A larger randomized controlled trial should be conducted to confirm the association between lower nonsedentary hours and higher UHEs.
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Affiliation(s)
- Tanachat Nilanon
- Department of Computer Science, University of Southern California, Los Angeles, CA.,Integrated Media Systems Center, University of Southern California, Los Angeles, CA
| | - Luciano P Nocera
- Integrated Media Systems Center, University of Southern California, Los Angeles, CA
| | - Alexander S Martin
- Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Anand Kolatkar
- Bridge Institute, University of Southern California, Los Angeles, CA.,Department of Biological Sciences, University of Southern California, Los Angeles, CA
| | - Marcella May
- Dornsife Center for Self-Report Science, University of Southern California, Los Angeles, CA
| | - Zaki Hasnain
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, CA
| | - Naoto T Ueno
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sriram Yennu
- Department of Palliative, Rehabilitation, & Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Angela Alexander
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aaron E Mejia
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA
| | - Roger Wilson Boles
- Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Ming Li
- Keck School of Medicine, University of Southern California, Los Angeles, CA.,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA
| | - Jerry S H Lee
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, MD
| | - Sean E Hanlon
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, MD
| | | | - David I Quinn
- Keck School of Medicine, University of Southern California, Los Angeles, CA.,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA.,Division of Medical Oncology, University of Southern California, Los Angeles, CA
| | - Paul K Newton
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, CA.,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA.,Department of Mathematics, University of Southern California, Los Angeles, CA
| | - Joan Broderick
- Dornsife Center for Self-Report Science, University of Southern California, Los Angeles, CA
| | - Cyrus Shahabi
- Department of Computer Science, University of Southern California, Los Angeles, CA.,Integrated Media Systems Center, University of Southern California, Los Angeles, CA
| | - Peter Kuhn
- Keck School of Medicine, University of Southern California, Los Angeles, CA.,Department of Biological Sciences, University of Southern California, Los Angeles, CA.,Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, CA.,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA.,Department of Biomedical Engineering, University of Southern California, Los Angeles, CA
| | - Jorge J Nieva
- Keck School of Medicine, University of Southern California, Los Angeles, CA.,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA.,Division of Medical Oncology, University of Southern California, Los Angeles, CA
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Ghinita G, Nguyen K, Maruseac M, Shahabi C. A secure location-based alert system with tunable privacy-performance trade-off. Geoinformatica 2020; 24:951-985. [PMID: 32837253 PMCID: PMC7297513 DOI: 10.1007/s10707-020-00410-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 03/18/2020] [Accepted: 04/17/2020] [Indexed: 06/11/2023]
Abstract
Monitoring location updates from mobile users has important applications in many areas, ranging from public health (e.g., COVID-19 contact tracing) and national security to social networks and advertising. However, sensitive information can be derived from movement patterns, thus protecting the privacy of mobile users is a major concern. Users may only be willing to disclose their locations when some condition is met, for instance in proximity of a disaster area or an event of interest. Currently, such functionality can be achieved using searchable encryption. Such cryptographic primitives provide provable guarantees for privacy, and allow decryption only when the location satisfies some predicate. Nevertheless, they rely on expensive pairing-based cryptography (PBC), of which direct application to the domain of location updates leads to impractical solutions. We propose secure and efficient techniques for private processing of location updates that complement the use of PBC and lead to significant gains in performance by reducing the amount of required pairing operations. We implement two optimizations that further improve performance: materialization of results to expensive mathematical operations, and parallelization. We also propose an heuristic that brings down the computational overhead through enlarging an alert zone by a small factor (given as system parameter), therefore trading off a small and controlled amount of privacy for significant performance gains. Extensive experimental results show that the proposed techniques significantly improve performance compared to the baseline, and reduce the searchable encryption overhead to a level that is practical in a computing environment with reasonable resources, such as the cloud.
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Affiliation(s)
| | - Kien Nguyen
- University of Southern California, Los Angeles, CA USA
| | | | - Cyrus Shahabi
- University of Southern California, Los Angeles, CA USA
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11
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Hasnain Z, Nilanon T, Li M, Mejia A, Kolatkar A, Nocera L, Shahabi C, Cozzens Philips FA, Lee JS, Hanlon SE, Vaidya P, Ueno NT, Yennu S, Newton PK, Kuhn P, Nieva J. Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic. JCO Clin Cancer Inform 2020; 4:583-601. [PMID: 32598179 PMCID: PMC7328110 DOI: 10.1200/cci.20.00010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/13/2020] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy. METHODS Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor. RESULTS Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 ± 0.029) and physical activity (area under the curve, 0.830 ± 0.080). Chair-to-table acceleration of the nonpivoting knee (t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration (t = -2.95; P = .006) and left arm angular velocity (t = -2.4; P = .025). CONCLUSION Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up-and-walk kinematics are good predictors of low physical activity.
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Affiliation(s)
- Zaki Hasnain
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Tanachat Nilanon
- Department of Computer Science, University of Southern California, Los Angeles, CA
| | - Ming Li
- Keck School of Medicine, University of Southern California, Los Angeles, CA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Aaron Mejia
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Anand Kolatkar
- The Bridge Institute, University of Southern California, Los Angeles, CA
| | - Luciano Nocera
- Department of Computer Science, University of Southern California, Los Angeles, CA
| | - Cyrus Shahabi
- Department of Computer Science, University of Southern California, Los Angeles, CA
| | | | - Jerry S.H. Lee
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, MD
| | - Sean E. Hanlon
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, MD
| | - Poorva Vaidya
- Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Naoto T. Ueno
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sriram Yennu
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Paul K. Newton
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
- Keck School of Medicine, University of Southern California, Los Angeles, CA
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, MD
- Department of Mathematics, University of Southern California, Los Angeles, CA
| | - Peter Kuhn
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
- Keck School of Medicine, University of Southern California, Los Angeles, CA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA
- The Bridge Institute, University of Southern California, Los Angeles, CA
- Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Jorge Nieva
- Keck School of Medicine, University of Southern California, Los Angeles, CA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA
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Tran L, Li Y, Nocera L, Shahabi C, Xiong L. MultiFusionNet: Atrial Fibrillation Detection With Deep Neural Networks. AMIA Jt Summits Transl Sci Proc 2020; 2020:654-663. [PMID: 32477688 PMCID: PMC7233068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia as well as a significant risk factor in heart failure and coronary artery disease. AF can be detected by using a short ECG recording. However, discriminating atrial fibrillation from normal sinus rhythm, other arrhythmia and strong noise, given a short ECG recording, is challenging. Towards this end, we propose MultiFusionNet, a deep learning network that uses a multiplicative fusion method to combine two deep neural networks trained on different sources of knowledge, i.e., extracted features and raw data. Thus, MultiFusionNet can exploit the relevant extracted features to improve upon the utilization of the deep learning model on the raw data. Our experiments show that this approach offers the most accurate AF classification and outperforms recently published algorithms that either use extracted features or raw data separately. Finally, we show that our multiplicative fusion method for combining the two sub-networks outperforms several other combining methods.
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Affiliation(s)
- Luan Tran
- University of Southern California, Los Angeles, CA, USA
| | - Yanfang Li
- University of Southern California, Los Angeles, CA, USA
| | | | - Cyrus Shahabi
- University of Southern California, Los Angeles, CA, USA
| | - Li Xiong
- Emory University, Atlanta, GA, USA
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13
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Broderick JE, May M, Schwartz JE, Li M, Mejia A, Nocera L, Kolatkar A, Ueno NT, Yennu S, Lee JSH, Hanlon SE, Cozzens Philips FA, Shahabi C, Kuhn P, Nieva J. Patient reported outcomes can improve performance status assessment: a pilot study. J Patient Rep Outcomes 2019; 3:41. [PMID: 31313047 PMCID: PMC6635569 DOI: 10.1186/s41687-019-0136-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 06/19/2019] [Indexed: 12/14/2022] Open
Abstract
Background Patient performance status is routinely used in oncology to estimate physical functioning, an important factor in clinical treatment decisions and eligibility for clinical trials. However, validity and reliability data for ratings of performance status have not been optimal. This study recruited oncology patients who were about to begin emetogenic palliative or adjuvant chemotherapy for treatment of solid tumors. We employed actigraphy as the gold standard for physical activity level. Correspondences between actigraphy and oncologists’ and patients’ ratings of performance status were examined and compared with the correspondences of actigraphy and several patient reported outcomes (PROs). The study was designed to determine feasibility of the measurement approaches and if PROs can improve the accuracy of assessment of performance status. Methods Oncologists and patients made performance status ratings at visit 1. Patients wore an actigraph and entered weekly PROs on a smartphone app. Data for days 1–14 after visit 1 were analyzed. Chart reviews were conducted to tabulate all unexpected medical events across days 1–150. Results Neither oncologist nor patient ratings of performance status predicted steps/hour (actigraphy). The PROMIS® Physical Function PRO (average of Days 1, 7, 14) was associated with steps/hour at high (for men) and moderate (for women) levels; the PROMIS® Fatigue PRO predicted steps for men, but not for women. Unexpected medical events occurred in 57% of patients. Only body weight in female patients predicted events; oncologist and patient performance status ratings, steps/hour, and other PROs did not. Conclusions PROMIS® Physical Function and Fatigue PROs show good correspondence with steps/hour making them easy, useful tools for oncologists to improve their assessment of performance status, especially for male patients. Female patients had lower levels of steps/hour than males and lower correlations among the predictors, suggesting the need for further work to improve performance status assessment in women. Assessment of pre-morbid sedentary behavior alongside current Physical Functioning and Fatigue PROs may allow for a more valid determination of disease-related activity level and performance status.
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Affiliation(s)
- Joan E Broderick
- Dornsife Center for Self-Report Science, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA.
| | - Marcella May
- Dornsife Center for Self-Report Science, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA
| | | | - Ming Li
- Norris Cancer Center, University of Southern California, Los Angeles, USA
| | - Aaron Mejia
- Norris Cancer Center, University of Southern California, Los Angeles, USA
| | - Luciano Nocera
- Department of Computer Sciences, University of Southern California, Los Angeles, USA
| | - Anand Kolatkar
- Department of Biological Sciences, University of Southern California, Los Angeles, USA
| | - Naoto T Ueno
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.,Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Sriram Yennu
- Department of Palliative Care Medicine, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Jerry S H Lee
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, USA
| | - Sean E Hanlon
- Center for Strategic Scientific Initiatives, National Cancer Institute, Bethesda, USA
| | | | - Cyrus Shahabi
- Department of Computer Sciences, University of Southern California, Los Angeles, USA
| | - Peter Kuhn
- Norris Cancer Center, University of Southern California, Los Angeles, USA.,Department of Biological Sciences, University of Southern California, Los Angeles, USA
| | - Jorge Nieva
- Norris Cancer Center, University of Southern California, Los Angeles, USA
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Abrishami MS, Nocera L, Mert M, Trujillo-Priego IA, Purushotham S, Shahabi C, Smith BA. Identification of Developmental Delay in Infants Using Wearable Sensors: Full-Day Leg Movement Statistical Feature Analysis. IEEE J Transl Eng Health Med 2019; 7:2800207. [PMID: 30800535 PMCID: PMC6375381 DOI: 10.1109/jtehm.2019.2893223] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/01/2018] [Accepted: 12/17/2018] [Indexed: 01/19/2023]
Abstract
This paper examines how features extracted from full-day data recorded by wearable sensors are able to differentiate between infants with typical development and those with or at risk for developmental delays. Wearable sensors were used to collect full-day (8-13 h) leg movement data from infants with typical development ([Formula: see text]) and infants at risk for developmental delay ([Formula: see text]). At 24 months, at-risk infants were assessed as having good ([Formula: see text]) or poor ([Formula: see text]) developmental outcomes. With this limited size dataset, our statistical analysis indicated that accelerometer features collected earlier in infancy differentiated between at-risk infants with poor and good outcomes at 24 months, as well as infants with typical development. This paper also tested how these features performed on a subset of the data for which the infant movement was known, i.e., 5-min intervals more representative of clinical observations. Our results on this limited dataset indicated that features for full-day data showed more group differences than similar features for the 5-min intervals, supporting the usefulness of full-day movement monitoring.
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Affiliation(s)
| | - Luciano Nocera
- Department of Information SystemsThe University of Maryland at BaltimoreBaltimoreMD21250USA
| | - Melissa Mert
- Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCA90033USA
| | - Ivan A. Trujillo-Priego
- Division of Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCA90033USA
| | - Sanjay Purushotham
- Department of Information SystemsThe University of Maryland at BaltimoreBaltimoreMD21250USA
| | - Cyrus Shahabi
- Department of Information SystemsThe University of Maryland at BaltimoreBaltimoreMD21250USA
| | - Beth A. Smith
- Division of Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCA90033USA
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15
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Hasnain Z, Li M, Dorff T, Quinn D, Ueno NT, Yennu S, Kolatkar A, Shahabi C, Nocera L, Nieva J, Kuhn P, Newton PK. Low-dimensional dynamical characterization of human performance of cancer patients using motion data. Clin Biomech (Bristol, Avon) 2018; 56:61-69. [PMID: 29803824 PMCID: PMC7519623 DOI: 10.1016/j.clinbiomech.2018.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 05/04/2018] [Accepted: 05/08/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Biomechanical characterization of human performance with respect to fatigue and fitness is relevant in many settings, however is usually limited to either fully qualitative assessments or invasive methods which require a significant experimental setup consisting of numerous sensors, force plates, and motion detectors. Qualitative assessments are difficult to standardize due to their intrinsic subjective nature, on the other hand, invasive methods provide reliable metrics but are not feasible for large scale applications. METHODS Presented here is a dynamical toolset for detecting performance groups using a non-invasive system based on the Microsoft Kinect motion capture sensor, and a case study of 37 cancer patients performing two clinically monitored tasks before and after therapy regimens. Dynamical features are extracted from the motion time series data and evaluated based on their ability to i) cluster patients into coherent fitness groups using unsupervised learning algorithms and to ii) predict Eastern Cooperative Oncology Group performance status via supervised learning. FINDINGS The unsupervised patient clustering is comparable to clustering based on physician assigned Eastern Cooperative Oncology Group status in that they both have similar concordance with change in weight before and after therapy as well as unexpected hospitalizations throughout the study. The extracted dynamical features can predict physician, coordinator, and patient Eastern Cooperative Oncology Group status with an accuracy of approximately 80%. INTERPRETATION The non-invasive Microsoft Kinect sensor and the proposed dynamical toolset comprised of data preprocessing, feature extraction, dimensionality reduction, and machine learning offers a low-cost and general method for performance segregation and can complement existing qualitative clinical assessments.
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Affiliation(s)
- Zaki Hasnain
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, USA,Correspondingauthor at: University of Southern California, 854 Downey Way, Los Angeles, CA 90089, USA, (Z. Hasnain)
| | - Ming Li
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Tanya Dorff
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - David Quinn
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Naoto T. Ueno
- Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sriram Yennu
- Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anand Kolatkar
- The Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Cyrus Shahabi
- Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Luciano Nocera
- Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Jorge Nieva
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Peter Kuhn
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, USA,Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA,Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA,Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Paul K. Newton
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, USA,Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA,Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA
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Abstract
Short-term traffic forecasting is a vital part of intelligent transportation systems. Recently, the combination of unprecedented data availability and the repaid development of machine learning techniques have brought on immense advancement in this field. In this paper, we aim to provide a brief overview of machine learning approaches for short-term traffic forecasting to facilitate research in related fields. We first introduce traffic forecasting and the challenges, and then introduce different approaches for modeling the temporal and/or spatial dependencies. Finally, we discuss several important directions for the future research.
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Abstract
Spatial Crowdsourcing (SC)
is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task (e.g., reporting the precipitation level at their area and time). In this setting, there is often a
budget
constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as
budget-per-time-period
vs.
budget-per-campaign
and
binary-utility
vs.
distance-based-utility
. We study the hardness of the task assignment problem in the
offline
setting and propose
online
heuristics which exploit the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool, and extensive results show the effectiveness and efficiency of our proposed solutions.
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Affiliation(s)
| | - Hien To
- University of Southern California
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18
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Martin AS, Boles RW, Nocera L, Kolatkar A, May M, Hasnain Z, Ueno NT, Yennu S, Alexander A, Mejia A, Li M, Cozzens Philips FA, Newton PK, Broderick J, Shahabi C, Kuhn P, Nieva JJ. Objective metrics of patient activity: Use of wearable trackers and patient reported outcomes in predicting unexpected healthcare events in cancer patients undergoing highly emetogenic chemotherapy. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.15_suppl.6519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | | | | | | | - Marcella May
- University of Southern California, Los Angeles, CA
| | - Zaki Hasnain
- University of Southern California, Los Angeles, CA
| | - Naoto T. Ueno
- Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sriram Yennu
- University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Aaron Mejia
- University of Southern California, Los Angeles, CA
| | | | | | | | | | | | - Peter Kuhn
- University of Southern California, Los Angeles, CA
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Abstract
The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We first, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a predefined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology.
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Affiliation(s)
- Roshanak Alialy
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Sasan Tavakkol
- Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Elham Tavakkol
- Telemedicine Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Ghorbani-Aghbologhi
- Department of Pathology and Laboratory Medicine, Davis Medical Center, University of California, Sacramento, CA, USA
| | - Alireza Ghaffarieh
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Seon Ho Kim
- Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Cyrus Shahabi
- Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
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Pan B, Demiryurek U, Gupta C, Shahabi C. Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems. Knowl Inf Syst 2014. [DOI: 10.1007/s10115-014-0783-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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Shahabi C, Kim SH, Nocera L, Constantinou G, Lu Y, Cai Y, Medioni G, Nevatia R, Banaei-Kashani F. Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity. Journal of Information Processing Systems 2014. [DOI: 10.3745/jips.2014.10.1.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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23
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Parvini F, Shahabi C. An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics. Int J Bioinform Res Appl 2007; 3:4-23. [PMID: 18048170 DOI: 10.1504/ijbra.2007.011832] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We propose a novel approach for recognising static and dynamic hand gestures by analysing the raw data streams generated by the sensors attached to the human hands. We utilise the concept of 'range of motion' in the movement of fingers and exploit this characteristic to analyse the acquired data for recognising hand signs. Our approach for hand gesture recognition addresses two major problems: user-dependency and device-dependency. Furthermore, we show that our approach neither requires calibration nor involves training. We apply our approach for recognising American Sign Language (ASL) signs and show that more than 75% accuracy in sign recognition can be achieved.
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Affiliation(s)
- Farid Parvini
- Computer Science Department, University of Southern California, Los Angeles, CA 90089-0781, USA.
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24
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Khoshgozaran A, Shahabi C. Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy. Advances in Spatial and Temporal Databases 2007. [DOI: 10.1007/978-3-540-73540-3_14] [Citation(s) in RCA: 140] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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26
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Yao SYD, Shahabi C, Zimmermann R. BroadScale: Efficient scaling of heterogeneous storage systems. Int J Digit Libr 2006. [DOI: 10.1007/s00799-005-0118-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Abstract
Recent sensor networks research has produced a class of data storage and query processing techniques called Data-Centric Storage that leverages locality-preserving distributed indexes to efficiently answer multi-dimensional range and range-aggregate queries. These distributed indexes offer a rich design space of a) logical decompositions of sensor relation schema into indexes, as well as b) physical mappings of these indexes onto sensors. In this paper, we discuss this space for energy-efficient data organizations (logical and physical mappings of tuples and attributes to sensor nodes) and examine the performance of purely local query optimization techniques for processing queries that span such decomposed relations.-
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Affiliation(s)
- Ramakrishna Gummadi
- Computer Science Department, University of Southern California, Los Angeles, CA
| | - Xin Li
- Computer Science Department, University of Southern California, Los Angeles, CA
| | - Ramesh Govindan
- Computer Science Department, University of Southern California, Los Angeles, CA
| | - Cyrus Shahabi
- Computer Science Department, University of Southern California, Los Angeles, CA
| | - Wei Hong
- Intel Research at Berkeley, Berkeley, CA
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28
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Yang K, Yoon H, Shahabi C. CLe Ver: A Feature Subset Selection Technique for Multivariate Time Series. Advances in Knowledge Discovery and Data Mining 2005. [DOI: 10.1007/11430919_60] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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29
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Sharifzadeh M, Azmoodeh F, Shahabi C. Change Detection in Time Series Data Using Wavelet Footprints. Advances in Spatial and Temporal Databases 2005. [DOI: 10.1007/11535331_8] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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30
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Hunt L, Zuckerman B, Shahabi C. Prototypical extensions to the paradigm of spatial search. The Journal of Academic Librarianship 2000. [DOI: 10.1016/s0099-1333(00)00160-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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