Big Data Management and Mining Laboratory

Publications

Sort By:  


  • 2019 - Using similarity measures for medical event sequences to predict mortality in trauma patients

      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.

  • 2018 - Device-measured Physical Activity Data for Classification of Patients with Ventricular Arrhythmia Events: A Pilot Investigation

      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.

  • 2018 - LIBS: A Bioelectrical Sensing System from Human Ears for Staging Whole-Night Sleep Study

      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.

  • 2018 - Mortality Prediction Performance using Similarity Measures for Medical Event Sequences

      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.

  • 2018 - Reach Me If You Can! Reachability Query in Uncertain Contact Networks

      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.

  • 2018 - Probabilistic reachability query in evolving spatiotemporal contact networks of moving objects

      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.

  • 2018 - Efficient Processing of Probabilistic Single and Batch Reachability Queries in Large and Evolving Spatiotemporal Contact Networks

      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.

  • 2017 - Spatiotemporal Range Pattern Queries on Large-scale Co-movement Pattern Datasets

      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.

  • 2017 - Development and Evaluation of a Similarity Measure for Medical Event Sequences

      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

  • 2017 - Efficient Maximal Reverse Skyline Query Processing

      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.

  • 2017 - Efficient Processing of Spatiotemporal Pattern Queries on Historical Frequent Co-Movement Pattern Datasets

      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.

  • 2017 - MVSC-Bench: A Tool to Benchmark Classification Methods for Multivariate Spatiotemporal (demo paper)

      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.

  • 2016 - Mining Frequent Episodes from Multivariate Spatiotemporal Event Sequences

      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.

  • 2016 - A Lightweight And Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring

      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.

  • 2016 - LIBS: A Low-Cost In-Ear Bioelectrical Sensing Solution for Healthcare Applications

      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.

  • 2016 - In-ear Biosignal Recording System: A Wearable For Automatic Whole-night Sleep Staging

      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.

  • 2015 - Development and Evaluation of Similarity Measures for Medical Event Sequences

      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

  • 2014 - Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity

      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

  • 2014 - A Comparative Study of Two Approaches for Supporting Optimal Network Location Queries

      Authors: P. Ghaemi , K. Shahabi, J. Wilson, F. Banaei-Kashani

      Publication: GeoInformatica, Vol. 18, Issue 2

      Category: Journal

      Download: PDF

  • 2013 - Efficient Batch Processing of Proximity Queries by Optimized Probing

      Authors: Seyed Jalal Kazemitabar, F. Banaei-Kashani, Seyed Jalil Kazemitabar, Dennis McLeod

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2013 - Monitoring Mobility Disorders at Home using 3D Visual Sensors and Mobile Sensors (demo paper)

      Ahthor(s): F. Banaei-Kashani et al.

      Publication: Wireless Health

      Category: Conference

      Download: PDF

  • 2013 - SDPF: A Framework for Online, Real-time Cleansing of Upstream Operating Data

      Authors: F. Banaei-Kashani, M. Asghari, M Rahmani, C. Shahabi, Lisa Brenskelle

      Publication: SPE Western Regional Meeting

      Category: Conference

      Download:

  • 2013 - Users Plan Optimization for Participatory Urban Texture Documentation

      Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi

      Publication: GeoInformatica, Vol. 17, Issue 1

      Category: Conference

      Download: PDF

  • 2012 - Continuous Maximal Reverse Nearest Query on Spatial Networks

      Authors: P. Ghaemi , K. Shahabi, J. Wilson, F. Banaei-Kashani

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2012 - Reachability Query in Large Evolving Contact Networks

      Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi

      Publication: VLDB

      Category: Conference

      Download: PDF

  • 2011 - A Case Study of Participatory Data Transfer for Urban Temperature Monitoring

      Authors: H. Shirani-Mehr , F. Banaei-Kashani and C. Shahabi

      Publication: W2GIS

      Category: Workshop

      Download: PDF

  • 2011 - Discovering Traffic Patterns in Traffic Sensor Data

      Authors: F. Banaei-Kashani, C. Shahabi and B. Pan

      Publication: IWGS

      Category: Workshop

      Download: PDF

  • 2011 - Online Computation of Fastest Path in Time-Dependent Spatial Networks

      Authors: U. Demiryurek, F. Banaei-Kashani, C. Shahabi and Anand Ranganathan

      Publication: SSTD

      Category: Conference

      Download: PDF

  • 2010 - A Case for Time-Dependent Shortest Path Computation in Spatial Networks

      Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2010 - Efficient Approximate Visibility Query in Large Dynamic Environments

      Authors: L. Kazemi, F. Banaei-Kashani, C. Shahabi and R. Jain

      Publication: DASFAA

      Category: Conference

      Download: PDF

  • 2010 - Efficient K-Nearest Neighbor Search in Time-Dependent Spatial Networks

      Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi

      Publication: DEXA

      Category: Conference

      Download: PDF

  • 2010 - Fixed-precision approximate continuous aggregate queries in peer-to-peer databases

      Authors: F. Banaei-Kashani and C.Shahabi

      Publication: CollaborateCom

      Category: Conference

      Download: PDF

  • 2010 - GeoDec: A framework to effectively visualize and query geospatial data for decision-making

      Authors: C. Shahabi, F. Banaei Kashani, A. Khoshgozaran and S. Xing

      Publication: IEEE Multimedia Magazine

      Category: Journal

      Download: PDF

  • 2010 - GeoSIM: A GeoSpatial Data Collection System for Participatory Urban Texture Documentation

      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

  • 2010 - Optimal Network Location Queries

      Authors: P. Ghaemi , K. Shahabi, J. Wilson, F. Banaei-Kashani

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2010 - Spatiotemporal Summarization of Traffic Data Streams

      Authors: B. Pan, U. Demiryurek, F. Banaei-Kashani and C. Shahabi

      Publication: IWGS in conjunction with ACMGIS

      Category: Workshop

      Download: PDF

  • 2010 - TransDec: A Spatiotemporal Query Processing Framework for Transportation Systems

      Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi

      Publication: ICDE

      Category: Conference

      Download: PDF

  • 2010 - Using Location-based Social Networks for Quality-Aware Participatory Data Transfer

      Authors: H. Shirani-Mehr , F. Banaei-Kashani and C. Shahabi

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2010 - Voronoi-based Geospatial Query Processing with MapReduce

      Authors: A. Akdogan, U. Demiryurek, F. Banaei-Kashani and C. Shahabi

      Publication: IEEE CloudCom

      Category: Conference

      Download: PDF

  • 2009 - Applications of sensor network data management

      Authors: F. Banaei-Kashani and C. Shahabi

      Publication: Springer

      Category: Book Chapter

      Download: PDF

  • 2009 - Case study: Scoop up data from peer-to-peer databases, Book chapter in Handbook of Peer-to-Peer Networks

      Authors: F. Banaei-Kashani and C. Shahabi

      Publication: Springer

      Category: Book Chapter

      Download: PDF

  • 2009 - Efficient Viewpoint Assignment for Urban Texture Documentation

      Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2009 - Efficient Viewpoint Selection for Urban Texture Documentationor query in spatial networks using Euclidian Restriction

      Authors: H. Shirani-Mehr, F. Banaei-Kashani and C. Shahabi

      Publication: GSN

      Category: Conference

      Download: PDF

  • 2009 - ER-CkNN: Efficient continuous nearest neighbor query in spatial networks using Euclidian Restriction

      Authors: U. Demiryurek, F. Banaei-Kashani and C. Shahabi

      Publication: SSTD

      Category: Conference

      Download: PDF

  • 2009 - GeoDec: A Multi-Layered Query Processing Framework for Spatiotemporal Data

      Authors: L. Nocera, A. Rihan, S. Xing, A. Khodaei, A. Khoshgozaran, C. Shahabi, F. Banaei-Kashani

      Publication: ACMGIS

      Category: Conference

      Download: PDF

  • 2009 - Temporal Modeling of Spatiotemporal Networks

      Authors: U. Demiryurek, B. Pan, F. Banaei-Kashani and C. Shahabi

      Publication: IWCTS in conjunction with ACMGIS

      Category: Conference

      Download: PDF

  • 2008 - Fixed-precision approximate continuous aggregate queries in peer-to-peer databases

      Authors: F. Banaei-Kashani and C. Shahabi

      Publication: ICDE

      Category: Conference

      Download: PDF

  • 2008 - On-the-fly Visualization of Scientific Geospatial Data Using Waveltes

      Authors: C. Shahabi, F. Banaei-Kashani and K. Song

      Publication: Microsoft e-Science Workshop

      Workshop: Conference

      Download: PDF

  • 2008 - Partial read from peer-to-peer databasesJournal of Computer Communications, Vol. 31, No. 2

      Authors: F. Banaei-Kashani and C. Shahabi

      Publication: Journal of Computer Communications, Vol. 31, No. 2

      Category: Journal

      Download: PDF

  • 2008 - ProDA: An end-to-end wavelet-based OLAP system for massive datasets

      Authors: C. Shahabi, M. Jahangiri and F. Banaei-Kashani

      Publication: IEEE Computer, Vol. 41, No. 4

      Category: Journal

      Download: PDF

  • 2008 - SWAM: A family of access methods for similarity-search in peer-to-peer data networks

      Authors: C. Shahabi, M. Jahangiri and F. Banaei-Kashani

      Publication: IEEE Computer, Vol. 41, No. 4

      Category: Conference

      Download: PDF

  • 2007 - Modeling peer-to-peer data networks under complex system theory

      Authors: C. Shahabi and F. Banaei-Kashani

      Publication: International Journal of Computational Science and Engineering (IJCSE)

      Category: Journal

      Download: PDF

  • 2006 - Partial selection query in peer-to-peer databases

      Authors: C. Shahabi and F. Banaei-Kashani

      Publication: ICDE

      Category: Conference

      Download: PDF

  • 2004 - WSPDS: Web Services Peer-to-peer Discovery Service

      Authors: F. Banaei-Kashani, C. Chen and C. Shahabi

      Publication: ISWS

      Category: Conference

      Download: PDF

  • 2003 - Criticality-based analysis and design of unstructured peer-to-peer networks as complex systems

      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

  • 2003 - Efficient and anonymous web usage mining for web personalization

      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

  • 2003 - Efficient flooding in power-law networks

      Authors: F. Banaei-Kashani and C. Shahabi

      Publication: PODC

      Category: Conference

      Download: PDF

  • 2003 - Searchable querical data networks

      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

  • 2002 - Decentralized resource management for a distributed continuous media server

      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

  • 2001 - A framework for efficient and anonymous web usage mining based on client-side tracking

      Authors: Book Chapter: C. Shahabi and F. Banaei-Kashani

      Publication: Lecture Notes in Computer Science, Vol. 2356

      Category: Book Chapter

      Download: PDF

  • 2001 - A reliable, efficient, and scalable system for web usage data acquisition

      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

  • 2001 - Feature Matrices: A model for efficient and anonymous web usage mining

      Authors: C. Shahabi, F. Banaei-Kashani, J. Faruque and A. Faisal

      Publication: ECWeb

      Category: Workshop

      Download: PDF

  • 2001 - Yoda: An accurate and scalable web-based recommendation system

      Authors: C. Shahabi, F. Banaei-Kashani, Y. Chen and D. McLeod

      Publication: CoopIS

      Category: Conference

      Download: PDF