Sort By:
Authors:Andrew C. Hill, Claire Guo, Elizabeth M. Litkowski, Ani W Manichaikul, Bing Yu, Iain R. Konigsberg, Betty A. Gorbet, Leslie A. Lange, Katherine A. Pratte, Katerina J. Kechris, Matthew DeCamp, Marilyn Coors, Victor E. Ortega, Stephen S. Rich, Jerome I. Rotter, Robert E. Gerzsten, Clary B. Clish, Jeffrey L. Curtis, Xiaowei Hu, Ma-en Obeidat, Melody Morris, Joseph Loureiro, Debby Ngo, Wanda K. O'Neal, Deborah A. Meyers, Eugene R. Bleecker, Brian D. Hobbs, Michael H. Cho, Farnoush Banaei-Kashani, Russell P. Bowler
Publication:Nature
Category: Journal
Link: TBA
Authors:Christopher D. Barrett, Yuto Suzuki, Sundos Hussein, Lohit Garg, Alexis Tumolo, Amneet Sandhu, John J. West, Matthew Zipse, Ryan Aleong, Paul Varosy, Wendy S. Tzou, Farnoush Banaei-Kashani and Michael A. Rosenberg
Publication:JAHA
Category: Journal
Link: JAHA
Authors:Yonghua Zhuang, Fuyong Xing, Debashis Ghosh, Brian D Hobbs, Craig P Hersh, Farnoush Banaei-Kashani, Russell P. Bowler, Katerina Kechris
Publication:PLOS ONE
Category: Journal
Link: TBA
Authors: Ryan Cheng, Yail J. Kim, Farnoush Banaei-Kashani
Publication:Transportation Research Board Annual Meeting
Category: Conference
Link: TBA
Authors: Tobby Lie, Kevin Rens, Farnoush Banaei-Kashani
Publication:Transportation Research Board Annual Meeting
Category: Conference
Link: TBA
Authors: Latisha Konz, Andrew Hill, Farnoush Banaei-Kashani
Publication:MDPI
Category: Journal
Link: Open Access Article
Authors: Sundous Hussein, Thao Vu, Katerina Kechis, Russel Bowler, Leslie Lange, Farnoush Banaei-Kashani
Publication:IEEE BIBM
Category: Conference
Link: TBA
Authors: Andrew Hill, Russell Bowler, Katerina Kechris, Farnoush Banaei-Kashani
Publication:IEEE Big Data
Category: Conference
Link: TBA
Authors: Ryan Cheng, Selvakumar Jayaraman, Robert Fitzgerald, Farnoush Banaei-Kashani
Publication: IWCTS
Category: Conference
Link: TBA
Authors: Mohamed Abdel-Hafiz, Mesbah Najafi, Shahab Helmi, Katherine A. Pratte, Yonghua Zhuang, Weixuan Liu, Katerina Kechris, Russell Bowler, Leslie Lange, Farnoush Banaei-Kashani
Publication:Frontiers in Big Data
Category: Journal
Link: METHODS article
Authors: Rhys Butler, Vishnu Dutt Duggirala, Farnoush Banaei-Kashani
Publication:WSDM
Category: Journal
Link: TBA
Authors: Robert Fitzgerald, Farnoush Banaei-Kashani
Publication:IEEE Big Data
Category: Conference
Link: TBA
Authors:Robert Fitzgerald, Farnoush Banaei-Kashani
Publication:SIGSPATIAL
Category: Conference
Link: TBA
Authors: Lucas A. Gillenwater, Shahab Helmi, Evan Stene, Katherine A. Pratte, Yonghua Zhuang, Ronald P. Schuyler, Leslie Lange, Peter J. Castaldi, Craig P. Hersh, Farnoush Banaei-Kashani, Russell P. Bowler, Katerina J. Kechris
Publication:PloS one
Category: Journal
Link: Plos One
Abstract: Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.
Authors: Peter J. Castaldi, Adel Boueiz, Jeong Yun, Raul San Jose Estepar, James C. Ross, George Washko, Michael H. Cho, Craig P. Hersh, Gregory L. Kinney, Kendra A. Young, Elizabeth A. Regan, David A. Lynch, Gerald J. Criner, Jennifer G. Dy, Stephen I. Rennard, Richard Casaburi, Barry J. Make, James Crapo, Edwin K. Silverman, John E. Hokanson, James D. Crapo, Edwin K. Silverman, Barry J. Make, Elizabeth A. Regan, Terri Beaty, Ferdouse Begum, Peter J. Castaldi, Michael Cho, Dawn L. DeMeo, Adel R. Boueiz, Marilyn G. Foreman, Eitan Halper-Stromberg, Lystra P. Hayden, Craig P. Hersh, Jacqueline Hetmanski, Brian D. Hobbs, John E. Hokanson, Nan Laird, Christoph Lange, Sharon M. Lutz, Merry-Lynn McDonald, Margaret M. Parker, Dmitry Prokopenko, Dandi Qiao, Elizabeth A. Regan, Phuwanat Sakornsakolpat, Edwin K. Silverman, Emily S. Wan, Sungho Won, Juan Pablo Centeno, Jean-Paul Charbonnier, Harvey O. Coxson, Craig J. Galban, MeiLan K. Han, Eric A. Hoffman, Stephen Humphries, Francine L. Jacobson, Philip F. Judy, Ella A. Kazerooni, Alex Kluiber, David A. Lynch, Pietro Nardelli, John D. Newell, Aleena Notary, Andrea Oh, Elizabeth A. Regan, James C. Ross, Raul San Jose Estepar, Joyce Schroeder, Jered Sieren, Berend C. Stoel, Juerg Tschirren, Edwin {Van Beek}, Bram {van Ginneken}, Eva {van Rikxoort}, Gonzalo Vegas Sanchez-Ferrero, Lucas Veitel, George R. Washko, Carla G. Wilson, Robert Jensen, Douglas Everett, Jim Crooks, Katherine Pratte, Matt Strand, Carla G. Wilson, John E. Hokanson, Gregory Kinney, Sharon M. Lutz, Kendra A. Young, Surya P. Bhatt, Jessica Bon, Alejandro A. Diaz, MeiLan K. Han, Barry Make, Susan Murray, Elizabeth Regan, Xavier Soler, Carla G. Wilson, Russell P. Bowler, Katerina Kechris,, Farnoush Banaei-Kashani
Publication:Chest
Category: Journal
Link: ScienceDirect
Abstract: COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.
Authors: Biu et al.
Publication: IEEE Transaction of Mobile Computing
Category: Journal
Link: TBA
Abstract: TBA
Authors: Ryan Lusk, Evan Stene, Farnoush Banaei-Kashani, Boris Tabakoff, Katerina Kechris, Laura Saba
Publication: Nature Communications
Category: Journal
Link: TBA
Abstract: TBA
Authors: Vishnu Dutt Duggirala, Rhys Sean Butler, Farnoush Banaei-Kashani
Publication: CSEDU
Category: Conference
Link: TBA
Abstract: Designed and implemented a question-answering chatbot, dubbed iTA (intelligent Teaching Assistant), which can provide detailed answers to questions by effectively identifying the most relevant answers in “long” text sources (documents or textbooks). iTA answers questions by implementing a two-stage procedure. First, the topmost relevant paragraphs are identified in the selected text source using a retrieval-based approach and scores for the retrieved paragraphs are computed. Second, using a generative model, extracted the relevant content from the top-ranked paragraph to generate the answer. Our results show that iTA is well suited to generate meaningful answers for questions posed by students.
Authors: Masoumeh Abolfathi, Zohreh Raghebi, Jafar Haadi Jafarian, Farnoush Banaei-Kashani
Publication: IWSPA
Category: Workshop
Link: TBA
Abstract: TBA
Authors: Tobby Lie, Haadi Jafarian, Stephen Hartnett, Hamilton Bean and Farnoush Banaei-Kashani
Publication: KDD TrueFact 2020
Category: Workshop
Link: TBA
Abstract: The ease of access to social media has made it extremely easy for malicious agents to disseminate divisive information on a mass scale. With the widespread reach of messages with various intents it becomes more difficult for experts to analyze such information in a timely and accurate manner to detect these intents. Within the research community, capturing online threats of this nature has been a growing area of interest. In this paper, we develop a methodology to detect intention of social polarization information shared by a single agent in social media. Toward this end, we use and study the Internet Agency Facebook ads dataset released by U.S. House Intelligence Committee. We train and evaluate a series of models for text- and image-based intention detection and show that a multi-modal model that uses both data modalities outperforms other unimodal models.
Authors: H. Truong, N. Bui, Z. Raghebi, M. Ceko, N. Pham, P. Nguyen, A. Nguyen, T. Kim, K. Siegfried, E. Stene, T. Tvrdy, L. Weinman, T. Payne, D. Burke, T. Dinh, S. D'Mello, F. Banaei-Kashani, T. Wager, P. Goldstein, T. Vu
Publication: MobiSys 2020
Category: Conference
Link: TBA
Abstract: TBA
Authors: . Truong, T. Dinh, Z. Raghebi, T. Kim, N. Bui, P. Nguyen, H. Truong, F. Banaei-Kashani, A. Halbower, T. N. Dinh, T. Vu
Publication: MobiSys 2020
Category: Conference
Link: TBA
Abstract: TBA
Authors: Shahab Helmi, Farnoush Banaei-Kashani
Publication: ICDE 2020
Category: Conference
Link: TBA
Abstract: Thanks to recent prevalence of location tracking technologies, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about the behavior of moving objects such as people, animals, and vehicles. In particular, mining patterns from "co-movements" of objects (such as movements by players of a sports team, joints of the human body while walking, and vehicles in a transportation network) can lead to the discovery of interesting patterns (e.g., offense tactics of a sports team, gait signature of a person, and driving behaviors causing heavy traffic). Various trajectory mining and frequent pattern mining techniques have been proposed to discover patterns in trajectory datasets and more generally, event sequences. However, existing approaches are inapplicable for co-movement pattern mining from multi-trajectory datasets. In this paper, we propose a novel and efficient framework for co-movement pattern mining. We also extend this framework for efficient mining of such patterns at multiple spatial scales. The performance of the proposed solutions is evaluated by conducting extensive experiments using two real datasets, a soccer game dataset and a human gait dataset. Our experimental results show that our proposed algorithms are promising.
Authors: Evan Stene, Farnoush Banaei-Kashani
Publication:DMBIH 2019
Category: Workshop
Link: TBA
Abstract: Genetic data from next-generation sequencing (NGS) technology is being produced at an ever increasing rate - already outpacing the well known Moore’s Law. Due to this pace of NGS data generation, new methods are necessary in order to facilitate rapid sequence analysis at the enormous scale required. The need for such methods is further compounded by the dropping financial cost of sequencing, leading to the normalization of large-scale genome studies spanning entire populations. A key process in the genomic data analysis pipeline, and one that is often most time consuming, is read mapping or so-called alignment. This paper introduces Sequence Alignment Memorizer (SeAlM), a technique that reduces the number of redundant alignments to enable population-scale workloads. SeAlM uses a novel method for reordering alignment queries from multiple sources to create batches with increased likelihood of containing redundant queries that can be de- duplicated before alignment, while also ordering those batches to improve the ability to cache queries effectively. We show that our technique can improve the average throughput of alignment for a single human sample by 6.5% and a population of 10 human subjects by 13.6% -18.8% depending on the type of genetic data used.
Authors: Joel Fredrickson, Michael Mannino, Omar Alqahtani, Farnoush Banaei-Kashani
Publication: Decision Support Systems
Category: Journal
Link: ScienceDirect
Abstract: We extend a similarity measure for medical event sequences (MESs) and evaluate its classification performance for retrospective mortality prediction of trauma patient outcomes. Retrospective mortality prediction is a benchmarking task used by trauma care governance bodies to assist with policy decisions. We extend the similarity measure, the Optimal Temporal Common Subsequence for MESs (OTCS-MES), by generalizing the event-matching component with a plug-in weighting element. The extended OTCS-MES uses an event prevalence weight developed in our previous study and an event severity weight developed for this study. In the empirical evaluation of classification performance, we provide a more complete evaluation than previous studies. We compare the predictive performance of the Trauma Mortality Prediction Model (TMPM), an accepted regression approach for mortality prediction in trauma data, to nearest neighbor algorithms using similarity measures for MESs. Using a data set from the National Trauma Data Bank, our results indicate improved predictive performance for an ensemble of nearest neighbor classifiers over TMPM. Our analysis reveals a superior Receiver Operating Characteristics (ROC) curve, larger AUC, and improved operating points on a ROC curve. We also study methods to adjust for uncommon class prediction: weighted voting, neighborhood size, and case base size. Results provide strong evidence that similarity measures for medical event sequences are a powerful and easily adapted method assisting with health care policy advances.
Authors: Robert J. Fitzgerald, Farnoush Banaei-Kashani
Publication: IEEE MDM 2019
Category: Conference
Link: TBA
Abstract: The existing online mapping systems process many user route queries simultaneously, yet solve each independently, using typical route guidance solutions. These route recommendations are presented as optimal, but often this is not truly the case, due to the effects of competition users experience over the resulting experienced routes, a phenomenon referred to in Game Theory as a Nash Equilibrium. Additionally, route plans of this nature can result in poor utilization of the road network from a system-optimizing perspective as well. In this paper, we introduce an enhanced approach for route guidance, motivated by the relevance of a system optimal equilibrium strategy, while also maintaining some fairness to the individual. With this approach the objective is to optimize the global road network utilization (as measured by, e.g., mobility, or global emissions) by selecting from a set of generally fair user route alternatives in a batch setting.
For the first time, we present an approximate, anytime algorithm based on Monte Carlo Tree Search and Eppstein’s Top-K Shortest Paths algorithm to solve this complex dual optimization problem in real-time. This approach attempts to identify and avoid the potentially harmful network effects of sub-optimal route combinations. Experiments show that mobility optimization over real road networks of Rye and Golden, Colorado in a microscopic traffic simulation with a network congestion-minimizing objective can achieve considerable mobility improvement for users, as observed by their effective travel time improvement up to 12% with some consideration of route fairness.
Authors: Lucas Marzec, Sridharan Raghavan, Farnoush Banaei-Kashani, Seth Creasy, Edward L. Melanson, Leslie Lange, Debashis Ghosh, Michael A. Rosenberg
Publication: PLoS ONE
Category: Journal
Link: PloS ONE
Abstract: Low levels of physical activity are associated with increased mortality risk, especially in cardiac patients, but most studies are based on self-report. Cardiac implantable electronic devices (CIEDs) offer an opportunity to collect data for longer periods of time. However, there is limited agreement on the best approaches for quantification of activity measures due to the time series nature of the data. We examined physical activity time series data from 235 subjects with CIEDs and at least 365 days of uninterrupted measures. Summary statistics for raw daily physical activity (minutes/day), including statistical moments (e.g., mean, standard deviation, skewness, kurtosis), time series regression coefficients, frequency domain components, and forecasted predicted values, were calculated for each individual, and used to predict occurrence of ventricular tachycardia (VT) events as recorded by the device. In unsupervised analyses using principal component analysis, we found that while certain features tended to cluster near each other, most provided a reasonable spread across activity space without a large degree of redundancy. In supervised analyses, we found several features that were associated with the outcome (P < 0.05) in univariable and multivariable approaches, but few were consistent across models. Using a machine-learning approach in which the data was split into training and testing sets, and models ranging in complexity from simple univariable logistic regression to ensemble decision trees were fit, there was no improvement in classification of risk over naïve methods for any approach. Although standard approaches identified summary features of physical activity data that were correlated with risk of VT, machine-learning approaches found that none of these features provided an improvement in classification. Future studies are needed to explore and validate methods for feature extraction and machine learning in classification of VT risk based on device-measured activity.
Authors: Anh Nguyen, Raghda Alqurashi, Zohreh Raghebi, Farnoush Banaei-Kashani, Ann C. Halbower, Tam Vu
Publication: CACM
Category: Journal
Link: ACM Digital Library
Abstract: Sensing physiological signals from the human head has long been used for medical diagnosis, human-computer interaction, meditation quality monitoring, among others. However, existing sensing techniques are cumbersome and not desirable for long-term studies and impractical for daily use. Due to these limitations, we explore a new form of wearable systems, called LIBS, that can continuously record biosignals such as brain wave, eye movements, and facial muscle contractions, with high sensitivity and reliability. Specifically, instead of placing numerous electrodes around the head, LIBS uses a minimal number of custom-built electrodes to record the biosignals from human ear canals. This recording is a combination of three signals of interest and unwanted noise. Therefore, we design an algorithm using a supervised Nonnegative Matrix Factorization (NMF) model to split the single-channel mixed signal into three individual signals representing electrical brain activities (EEG), eye movements (EOG), and muscle contractions (EMG). Through prototyping and implementation over a 30 day sleep experiment conducted on eight participants, our results prove the feasibility of concurrently extracting separated brain, eye, and muscle signals for fine-grained sleep staging with more than 95% accuracy. With this ability to separate the three biosignals without loss of their physiological information, LIBS has a potential to become a fundamental in-ear biosensing technology solving problems ranging from self-caring health to non-health and enabling a new form of human communication interfaces.
Authors: Joel Fredrickson, Michael Mannino, Omar Alqahtani, Farnoush Banaei-Kashani
Publication: Decision Support Systems
Category: Conference
Link: AIS eLibrary
Abstract: We extend a similarity measure for medical event sequences (MESs) and evaluate its performance on mortality prediction using a substantial trauma data set. We extend the Optimal Temporal Common Subsequence for MESs (OTCS-MES) measure by generalizing the event-matching component with user-defined weights. In the empirical evaluation of classification performance, we provide a more complete evaluation than previous studies. We compare the predictive performance of the Trauma Mortality Prediction Model (TMPM), an accepted regression approach for mortality prediction in trauma data, to nearest neighbor algorithms using similarity measures for MESs. Using a data set from the National Trauma Data Bank, our results indicate improved predictive performance for an ensemble of nearest neighbor classifiers over TMPM. Our analysis demonstrates a superior Receiver Operating Characteristics (ROC) curve, larger AUC, and improved operating points on a ROC curve. Predictive performance improves for the ensemble for a variety of sensitivity weights and false positive constraints.
Authors: Z. Raghebi, F. Banaei-Kashani
Publication: GeoRich '18, SIGMOD
Category: Workshop
Link: TBA
Abstract: With the advent of reliable positioning technologies and prevalence of location-based services, it is now feasible to accurately study the propagation of items such as infectious viruses, sensitive information pieces, and malwares through a population of moving objects, e.g., individuals, vehicles, and mobile devices. In such application scenarios, an item passes between two objects when the objects are sufficiently close (i.e., when they are, so-called, in contact), and hence once an item is initiated, it can propagate in the object population through the evolving network of contacts among objects, termed contact network. In this paper, for the first time we define and study probabilistic reachability queries in large uncertain contact networks, where propagation of items through contacts are uncertain. A probabilistic reachability query verifies whether two objects are “reachable” through the evolving uncertain contact network with a probability greater than a threshold η. For efficient processing of probabilistic queries, we propose a novel index structure, termed spatiotemporal tree cover (STC), which leverages the spatiotemporal properties of the contact network for efficient processing of the queries. Our experiments with real data demonstrate superiority of our proposed solution versus the only other existing solution (based on Monte Carlo sampling) for processing probabilistic reachability queries in generic uncertain graphs, with 300% improvement in query processing time on average.
Authors: Zachary D. Asher, Jordan A. Tunnell, David A. Baker, Robert J. Fitzgerald, Farnoush Banaei-Kashani, Sudeep Pasricha, Thomas H. Bradley
Publication: SAE International
Category: Technical Paper
Link: SAE International
Abstract: Vehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX). Two real world drive cycles were developed for analysis in the Denver, Colorado region: a city-focused drive cycle that passes through downtown as well as a highway-focused drive cycle that transitions across multiple interstates. A validated model of a 2010 Toyota Prius in Autonomie is used to determine the vehicle control fuel economy improvements that are possible from the NARX prediction. The optimal energy management control strategy is determined using dynamic programming due to its ease of use and that the solution produced is the globally optimal solution. The control strategies compared include the existing 2010 Toyota Prius control strategy as a baseline, the neural network prediction optimal energy management control strategy, and a 100% accurate prediction optimal energy management control strategy. Results show that inclusion of various sensors and signals enables a significant amount of the fuel economy improvement with respect to 100% accurate prediction. The conclusion is that prediction based optimal energy management enabled fuel economy improvements can be realized with currently available sensors and signals.
Authors: Z. Raghebi, F. Banaei-Kashani
Publication: ACM SIGSPATIAL
Category: Short Paper
Link: ACM Digital Library
Abstract: With the rapid development of location sensors, it is now possible to study how various items (such as viruses and messages) spread across populations of moving objects at scale. In such applications, two objects are considered in-contact while they are sufficiently close to each other. Such a dynamic network of objects, so-called a "contact network". In this paper, we define and study probabilistic reachability queries in uncertain contact networks, where contacts between objects are probabilistic. A probabilistic reachability query verifies whether two objects are "reachable" with a probability no less than a threshold η. We introduce Optimized Spatiotemporal Tree Cover, an index structure that leverages the spatiotemporal properties of the contact network to enable efficient processing of the reachability queries on large uncertain contact networks. With an extensive study using both real and synthetic datasets, we demonstrate superiority of our proposed solution versus a baseline solution (i.e., Monte Carlo Sampling) and the only other existing solution for reachability queries on uncertain contact networks, with 350% and 150% improvement in query processing time on average, respectively.
Authors: Z. Raghebi, F. Banaei-Kashani
Publication: IEEE BigData
Category: Short Paper
Link: TBA
Abstract: With the rapid development of location sensors, it is now possible to accurately study how various items (such as viruses or messages) spread across populations of moving objects. In such applications, an item can propagate through the object population where two objects are close. Such a dynamic network of objects called a “contact network”. In this paper, we define and study a family of probabilistic reachability queries in uncertain contact networks, where contacts between objects are probabilistic. A probabilistic reachability query verifies whether two objects are “reachable” with a probability no less than a threshold. To enable efficient processing of probabilistic reachability queries on large uncertain contact networks, first, we present a series-parallel reduction technique that significantly reduces the size of the input uncertain contact network in order to shrink the search space while maintaining accuracy and second, we introduce Optimized Spatiotemporal Tree Cover, an index structure that leverages the spatiotemporal properties of the contact network. With an extensive analytical and empirical study, we demonstrate superiority of our proposed solution versus a baseline solution (i.e., Monte Carlo sampling) and the only other existing solution with 400% and 200% improvement in query processing time on average, respectively.
Authors: Shahab Helmi, F. Banaei-Kashani
Publication: IEEE BigData '17
Category: Conference
Acceptance Rate: 18%
Link: IEEE
Abstract: Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals, and vehicles. In particular, mining patterns from co-movements of objects (such as players of a sports team, joints of a person while walking, and cars in a transportation network) can lead to the discovery of interesting patterns (e.g., offense tactics of the sports team, gait signature of the person, and driving behaviors causing heavy traffic). With our prior work, we proposed efficient algorithms to mine frequent co-movement patterns from trajectory datasets. In this paper, we focus on the problem of efficient query processing on massive co-movement pattern datasets generated by such pattern mining algorithms. Given a dataset of frequent co-movement patterns, various spatiotemporal queries can be posed to retrieve relevant patterns among all generated patterns from the pattern dataset. We term such queries “pattern queries”. Co-movement patterns are often numerous due to combinatorial complexity of such patterns, and therefore, co-movement pattern datasets grow very large, rendering naive execution of the pattern queries ineffective. In this paper, we propose novel index structures and query processing algorithms for efficient answering of two families of range pattern queries on massive co-movement pattern datasets, namely, spatial range pattern queries and temporal range pattern queries. Our extensive empirical studies with three real datasets have demonstrated the efficiency of the proposed methods.
Authors: Michael Mannino, Joel Fredrickson, Farnoush Banaei-Kashani, Iris Linck and Raghda Alqurashi
Publication: ACM Transactions on Management Information Systems (TMIS), Vol 8, Issue 2, 2017.
Category: Journal
Link: ACM Digital Library
Authors: F. Banaei-Kashani, P. Ghaemi, B. Movaqar, S. J. Kazemitabar
Publication: GeoInformatica (2017): 1-24
Category: Journal
Link: ACM Digital Library
Abstract: Given a set S of sites and a set O of objects in a metric space, the Optimal Location (OL) problem is about computing a location in the space where introducing a new site (e.g., a retail store) maximizes the number of the objects (e.g., customers) that would choose the new site as their “preferred” site among all sites. However, the existing solutions for the optimal location problem assume that there is only one criterion to determine the preferred site for each object, whereas with numerous real-world applications multiple criteria are used as preference measures. For example, while a single criterion solution might consider the metric distance between the customers and the retail store as the preference measure, a multi-criteria solution might consider the annual membership cost as well as the distance to the retail store to find an optimal location. In this paper, for the first time we develop an efficient and exact solution for the so-called Multi-Criteria Optimal Location (MCOL) problem that can scale with large datasets. Toward that end, first we formalize the MCOL problem as maximal reverse skyline query (MaxRSKY). Given a set of sites and a set of objects in a d-dimensional space, MaxRSKY query returns a location in the space where if a new site s is introduced, the size of the (bichromatic) reverse skyline set of s is maximal. To the best of our knowledge, this paper is the first to define and study MaxRSKY query. Accordingly, we propose a filter-based solution, termed EF-MaxRSKY, that effectively prunes the search space for efficient identification of the optimal location. Our extensive empirical analysis with both real and synthetic datasets show that EF-MaxRSKY is invariably efficient in computing answers for MaxRSKY queries with large datasets containing thousands of sites and objects.
Authors: S. Helmi, F. Banaei-Kashani
Publication: MATES '17, VLDB
Category: Workshop
Link: Springer
Abstract: Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals, and vehicles. In particular, mining patterns from co-movements of objects (such as movements by players of a sports team, joints of a person while walking, and cars in a transportation network) can lead to the discovery of interesting patterns (e.g., offense tactics of a sports team, gait signature of a person, and driving behaviors causing heavy traffic). Given a dataset of frequent co-movement patterns, various conventional spatial and spatiotemporal queries can be posed to retrieve relevant patterns among all generated patterns from the dataset. We term such queries, pattern queries. Co-movement patterns are often numerous due to combinatorial complexity of such patterns, and therefore, co-movement pattern datasets often grow very large in size, rendering naive execution of the pattern queries ineffective. In this paper, we propose the CPI framework, which offers a variety of index structures for efficient answering of various range pattern queries on massive co-movement pattern datasets, namely, spatial range pattern queries, temporal range (time-slice) pattern queries, and spatiotemporal range pattern queries.
Authors: S. V. Kulkarni, F. Banaei-Kashani
Publication: SSTD '17
Category: Conference
Link: PDF
Abstract: Applications focusing on analysis of multivariate spatiotemporal series (MVS) have proliferated over the past decade. Researchers in a wide array of domains ranging from action recognition to sports analytics have come forward with novel methods to classify this type of data, but well-defined benchmarks for comparative evaluation of the MVS classification methods are non-existent. We present MVSC-Bench, to target this gap.
Authors: S. Helmi, F. Banaei-Kashani
Publication: IWGS '16 Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming
Category: Workshop
Link: ACM Digital Library
Abstract: Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals and vehicles. In particular, mining patterns from interdependent co-movements of objects in a group/team (such as players of a sports team, ants of a colony in search of food, and cars in a congested downtown district) can lead to the discovery of interesting patterns (e.g., offense tactics and strategies of a sports team). Various trajectory mining, and in particular frequent episode mining (FEM), approaches have been proposed to discover such patterns from trajectory datasets. However, the existing FEM approaches neither are applicable to multivariate spatial (MVS) event sequences nor consider and leverage all spatial features of the input data. In this paper, we first introduce a Spatial Apriori property which extends the well-known Apriori property to consider the spatial properties of the input data. We present a data preprocessing technique that leverages the aforementioned Spatial Apriori to reduce the search space of our problem by filtering out irrelevant events from a given MVS event sequence. Second, we present the MVS-FEM framework which efficiently discovers co-movements patterns from MVS datasets. The efficiency of our proposed solutions is evaluated using a real dataset.
Authors: Nguyen, A., Alqurashi, R., Raghebi, Z., Banaei-Kashani, F., Halbower, A. C., & Vu, T.
Publication: SenSys '16 Proceedings of the 14th ACM Conference on Embedded Network Sensor System
Category: Conference
Link: ACM Digital Library
21 out of 119 submissions, acceptance ratio: 17.6%)
Received The Best Paper Award
Abstract: This paper introduces LIBS, a light-weight and inexpensive wearable sensing system, that can capture electrical activities of human brain, eyes, and facial muscles with two pairs of custom-built flexible electrodes each of which is embedded on an off-the-shelf foam earplug. A supervised non-negative matrix factorization algorithm to adaptively analyze and extract these bioelectrical signals from a single mixed in-ear channel collected by the sensor is also proposed. While LIBS can enable a wide class of low-cost self-care, human computer interaction, and health monitoring applications, we demonstrate its medical potential by developing an autonomous whole-night sleep staging system utilizing LIBS's outputs. We constructed a hardware prototype from off-the-shelf electronic components and used it to conduct 38 hours of sleep studies on 8 participants over a period of 30 days. Our evaluation results show that LIBS can monitor biosignals representing brain activities, eye movements, and muscle contractions with excellent fidelity such that it can be used for sleep stage classification with an average of more than 95% accuracy.
Authors: Nguyen, A., Raghebi, Z., Banaei-Kashani, F., Halbower, A. C., & Vu, T.
Publication: Proceedings of the Eighth Wireless of the Students, by the Students, and for the Students Workshop
Category: Workshop
Link: ACM Digital Library
Abstract: Bioelectrical signals representing electrical activities of human brain, eyes, and facial muscles have found widespread use both as important inputs for critical medical issues and as an invisible communication pathway between human and external devices. However, existing techniques for measuring those biosignals require attaching electrodes on the face and do not come in handy sizes for daily usage. Additionally, no study has been capable of providing all three biosignals with high fidelity simultaneously. In this paper, we present a low-cost bioelectrical sensing system, called LIBS, that can robustly collect the biosignal of good quality from inside human ears and extract all those three fundamental biosignals without loss of information. The practicality of LIBS is shown through one real world scenario of a sleep quality monitoring system. Based on preliminary results, we further propose potential healthcare applications utilizing the sensor's outputs for our future research.
Authors: Nguyen, A., Alqurashi, R., Raghebi, Z., Banaei-Kashani, F., Halbower, A. C., Dinh, T., & Vu, T.
Publication: Proceedings of the 2016 Workshop on Wearable Systems and Applications
Category: Workshop
Link: ACM Digital Library
Abstract: In this work, we present a low-cost and light-weight wearable sensing system that can monitor bioelectrical signals generated by electrically active tissues across the brain, the eyes, and the facial muscles from inside human ears. Our work presents two key aspects of the sensing, which include the construction of electrodes and the extraction of these biosignals using a supervised non-negative matrix factorization learning algorithm. To illustrate the usefulness of the system, we developed an autonomous sleep staging system using the output of our proposed in-ear sensing system. We prototyped the device and evaluated its sleep stage classification performance on 8 participants for a period of 1 month. With 94% accuracy on average, the evaluation results show that our wearable sensing system is promising to monitor brain, eyes, and facial muscle signals with reasonable fidelity from human ear canals.
Authors: Michael Mannino, Joel Fredrickson, Iris Linck, Raghda Alqurashi and Farnoush Banaei-Kashani
Publication: Workshop on Information Technologies and Systems (WITS 2015), Dallas, Texas, 2015
Category: Workshop
More: Click Here
Authors: Farnoush Banaei-Kashani, Cyrus Shahabi, Seon Ho Kim, Luciano Nocera, Giorgos Constantinou, Ying Lu, Yinghao Cai, Gérard G. Medioni, Ramakant Nevatia
Publication: Journal of Information Processing Systems (JIPS), Vol 10, No 1, pp. 1-22
Category: Journal
More: Click Here
Authors: P. Ghaemi , K. Shahabi, J. Wilson, F. Banaei-Kashani
Publication: GeoInformatica, Vol. 18, Issue 2
Category: Journal
Download: PDF
Authors: Seyed Jalal Kazemitabar, F. Banaei-Kashani, Seyed Jalil Kazemitabar, Dennis McLeod
Publication: ACMGIS
Category: Conference
Download: PDF
Ahthor(s): F. Banaei-Kashani et al.
Publication: Wireless Health
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani, M. Asghari, M Rahmani, C. Shahabi, Lisa Brenskelle
Publication: SPE Western Regional Meeting
Category: Conference
Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi
Publication: GeoInformatica, Vol. 17, Issue 1
Category: Conference
Download: PDF
Authors: P. Ghaemi , K. Shahabi, J. Wilson, F. Banaei-Kashani
Publication: ACMGIS
Category: Conference
Download: PDF
Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi
Publication: VLDB
Category: Conference
Download: PDF
Authors: H. Shirani-Mehr , F. Banaei-Kashani and C. Shahabi
Publication: W2GIS
Category: Workshop
Download: PDF
Authors: F. Banaei-Kashani, C. Shahabi and B. Pan
Publication: IWGS
Category: Workshop
Download: PDF
Authors: U. Demiryurek, F. Banaei-Kashani, C. Shahabi and Anand Ranganathan
Publication: SSTD
Category: Conference
Download: PDF
Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi
Publication: ACMGIS
Category: Conference
Download: PDF
Authors: L. Kazemi, F. Banaei-Kashani, C. Shahabi and R. Jain
Publication: DASFAA
Category: Conference
Download: PDF
Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi
Publication: DEXA
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani and C.Shahabi
Publication: CollaborateCom
Category: Conference
Download: PDF
Authors: C. Shahabi, F. Banaei Kashani, A. Khoshgozaran and S. Xing
Publication: IEEE Multimedia Magazine
Category: Journal
Download: PDF
Authors: F. Banaei-Kashani, H. Shirani-Mehr, B. Pan, N. Bopp, L. Nocera, C. Shahabi
Publication: Special Issue of IEEE Data Engineering Bulletin on Spatial and Spatiotemporal Databases
Category: Journal
Download: PDF
Authors: P. Ghaemi , K. Shahabi, J. Wilson, F. Banaei-Kashani
Publication: ACMGIS
Category: Conference
Download: PDF
Authors: B. Pan, U. Demiryurek, F. Banaei-Kashani and C. Shahabi
Publication: IWGS in conjunction with ACMGIS
Category: Workshop
Download: PDF
Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi
Publication: ICDE
Category: Conference
Download: PDF
Authors: H. Shirani-Mehr , F. Banaei-Kashani and C. Shahabi
Publication: ACMGIS
Category: Conference
Download: PDF
Authors: A. Akdogan, U. Demiryurek, F. Banaei-Kashani and C. Shahabi
Publication: IEEE CloudCom
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani and C. Shahabi
Publication: Springer
Category: Book Chapter
Download: PDF
Authors: F. Banaei-Kashani and C. Shahabi
Publication: Springer
Category: Book Chapter
Download: PDF
Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi
Publication: ACMGIS
Category: Conference
Download: PDF
Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi
Publication: GSN
Category: Conference
Download: PDF
Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi
Publication: SSTD
Category: Conference
Download: PDF
Authors: L. Nocera, A. Rihan, S. Xing, A. Khodaei, A. Khoshgozaran, C. Shahabi, F. Banaei-Kashani
Publication: ACMGIS
Category: Conference
Download: PDF
Authors: U. Demiryurek, B. Pan, F. Banaei-Kashani and C. Shahabi
Publication: IWCTS in conjunction with ACMGIS
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani and C. Shahabi
Publication: ICDE
Category: Conference
Download: PDF
Authors: C. Shahabi, F. Banaei-Kashani and K. Song
Publication: Microsoft e-Science Workshop
Workshop: Conference
Download: PDF
Authors: F. Banaei-Kashani and C. Shahabi
Publication: Journal of Computer Communications, Vol. 31, No. 2
Category: Journal
Download: PDF
Authors: C. Shahabi, M. Jahangiri and F. Banaei-Kashani
Publication: IEEE Computer, Vol. 41, No. 4
Category: Journal
Download: PDF
Authors: C. Shahabi, M. Jahangiri and F. Banaei-Kashani
Publication: IEEE Computer, Vol. 41, No. 4
Category: Conference
Download: PDF
Authors: C. Shahabi and F. Banaei-Kashani
Publication: International Journal of Computational Science and Engineering (IJCSE)
Category: Journal
Download: PDF
Authors: C. Shahabi and F. Banaei-Kashani
Publication: ICDE
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani, C. Chen and C. Shahabi
Publication: ISWS
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani, C. Chen and C. Shahabi
Publication: 3rd International Workshop on Global and Peer-to-Peer Computing (GP2PC) in conjunction with CC-Grid
Category: Workshop
Download: PDF
Authors: C. Shahabi and F. Banaei-Kashani
Publication: INFORMS Journal on Computing Special Issue on Mining Web-Based Data for e-Business Applications, Vol. 15, No. 2, Spring
Category: Journal
Download: PDF
Authors: F. Banaei-Kashani and C. Shahabi
Publication: PODC
Category: Conference
Download: PDF
Authors: F. Banaei-Kashani and C. Shahabi
Publication: International Workshop on Databases, Information Systems and Peer-to-Peer Computing (DBISP2P) in con
Category: Workshop
Download: PDF
Authors: C. Shahabi and F. Banaei-Kashani
Publication: IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), Vol. 13, No. 7
Category: Journal
Download: PDF
Authors: Book Chapter: C. Shahabi and F. Banaei-Kashani
Publication: Lecture Notes in Computer Science, Vol. 2356
Category: Book Chapter
Download: PDF
Authors: C. Shahabi, F. Banaei-Kashani and J. Faruque
Publication: Workshop on Web Mining and Web Usage Analysis (WebKDD) in conjunction with KDD Conference
Category: Workshop
Download: PDF
Authors: C. Shahabi, F. Banaei-Kashani, J. Faruque and A. Faisal
Publication: ECWeb
Category: Workshop
Download: PDF
Authors: C. Shahabi, F. Banaei-Kashani, Y. Chen and D. McLeod
Publication: CoopIS
Category: Conference
Download: PDF
