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Articles 1 - 30 of 34
Full-Text Articles in Computer Engineering
Unsupervised Feature Selection For Outlier Detection By Modelling Hierarchical Value-Feature Couplings, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Unsupervised Feature Selection For Outlier Detection By Modelling Hierarchical Value-Feature Couplings, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Research Collection School Of Computing and Information Systems
Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework (CUFS for short) to filter out noisy or redundant features for subsequent outlier detection in categorical data. CUFS quantifies the outlierness (or relevance) of features by learning and integrating both the feature value couplings and feature couplings. Such value-to-feature couplings capture intrinsic data characteristics and …
Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang
Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang
Research Collection School Of Computing and Information Systems
Microblogging services allow users to create hashtags to categorize their posts. In recent years,the task of recommending hashtags for microblogs has been given increasing attention. However,most of existing methods depend on hand-crafted features. Motivated by the successful use oflong short-term memory (LSTM) for many natural language processing tasks, in this paper, weadopt LSTM to learn the representation of a microblog post. Observing that hashtags indicatethe primary topics of microblog posts, we propose a novel attention-based LSTM model whichincorporates topic modeling into the LSTM architecture through an attention mechanism. Weevaluate our model using a large real-world dataset. Experimental results show that …
From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan
From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan
Research Collection School Of Computing and Information Systems
With the booming popularity of online social networks like Twitter and Weibo, online user footprints are accumulating rapidly on the social web. Simultaneously, the question of how to leverage the large-scale user-generated social media data for personal credit scoring comes into the sight of both researchers and practitioners. It has also become a topic of great importance and growing interest in the P2P lending industry. However, compared with traditional financial data, heterogeneous social data presents both opportunities and challenges for personal credit scoring. In this article, we seek a deep understanding of how to learn users’ credit labels from social …
Towards Learning And Verifying Invariants Of Cyber-Physical Systems By Code Mutation, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Towards Learning And Verifying Invariants Of Cyber-Physical Systems By Code Mutation, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. …
Towards Concolic Testing For Hybrid Systems, Pingfan Kong, Yi Li, Xiaohong Chen, Jun Sun, Meng Sun, Jingyi Wang
Towards Concolic Testing For Hybrid Systems, Pingfan Kong, Yi Li, Xiaohong Chen, Jun Sun, Meng Sun, Jingyi Wang
Research Collection School Of Computing and Information Systems
Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate whether we can combine random sampling and symbolic execution in order to effectively verify hybrid systems. We identify a sufficient condition under which such a combination is more effective than random sampling. Furthermore, we analyze different strategies of combining random sampling and symbolic execution and propose an algorithm which allows us to dynamically switch between them so as to reduce the overall cost. Our method has been implemented as a web-based checker named …
Rapid Deployment Indoor Localization Without Prior Human Participation, Han Xu, Zimu Zhou, Longfei Shangguan
Rapid Deployment Indoor Localization Without Prior Human Participation, Han Xu, Zimu Zhou, Longfei Shangguan
Research Collection School Of Computing and Information Systems
In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSSbased methods while relieving it …
Designing Minimal Effective Normative Systems With The Help Of Lightweight Formal Methods, Jianye Hao, Eunsuk Kang, Jun Sun, Daniel Jackson
Designing Minimal Effective Normative Systems With The Help Of Lightweight Formal Methods, Jianye Hao, Eunsuk Kang, Jun Sun, Daniel Jackson
Research Collection School Of Computing and Information Systems
Normative systems are an important approach to achieving effective coordination among (often an arbitrary number of) agents in multiagent systems. A normative system should be effective in ensuring the satisfaction of a desirable system property, and minimal (i.e., not containing norms that unnecessarily over-constrain the behaviors of agents). Designing or even automatically synthesizing minimal effective normative systems is highly non-trivial. Previous attempts on synthesizing such systems through simulations often fail to generate normative systems which are both minimal and effective. In this work, we propose a framework that facilitates designing of minimal effective normative systems using lightweight formal methods. Given …
Summarization Of Egocentric Videos: A Comprehensive Survey, Ana Garcia Del Molino, Cheston Tan, Joo-Hwee Lim, Ah-Hwee Tan
Summarization Of Egocentric Videos: A Comprehensive Survey, Ana Garcia Del Molino, Cheston Tan, Joo-Hwee Lim, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
The introduction of wearable video cameras (e.g., GoPro) in the consumer market has promoted video life-logging, motivating users to generate large amounts of video data. This increasing flow of first-person video has led to a growing need for automatic video summarization adapted to the characteristics and applications of egocentric video. With this paper, we provide the first comprehensive survey of the techniques used specifically to summarize egocentric videos. We present a framework for first-person view summarization and compare the segmentation methods and selection algorithms used by the related work in the literature. Next, we describe the existing egocentric video datasets …
Repmatch: Robust Feature Matching And Pose For Reconstructing Modern Cities, Wen-Yan Lin, Siying Liu, Minh N. Do, Ping Tan, Jiangbo Lu
Repmatch: Robust Feature Matching And Pose For Reconstructing Modern Cities, Wen-Yan Lin, Siying Liu, Minh N. Do, Ping Tan, Jiangbo Lu
Research Collection School Of Computing and Information Systems
A perennial problem in recovering 3-D models from images is repeated structures common in modern cities. The problem can be traced to the feature matcher which needs to match less distinctive features (permitting wide-baselines and avoiding broken sequences), while simultaneously avoiding incorrect matching of ambiguous repeated features. To meet this need, we develop RepMatch, an epipolar guided (assumes predominately camera motion) feature matcher that accommodates both wide-baselines and repeated structures. RepMatch is based on using RANSAC to guide the training of match consistency curves for differentiating true and false matches. By considering the set of all nearest-neighbor matches, RepMatch can …
Indoor Localization Via Multi-Modal Sensing On Smartphones, Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, Yunhao Liu
Indoor Localization Via Multi-Modal Sensing On Smartphones, Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, Yunhao Liu
Research Collection School Of Computing and Information Systems
Indoor localization is of great importance to a wide range ofapplications in shopping malls, office buildings and publicplaces. The maturity of computer vision (CV) techniques andthe ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pureCV-based solutions usually involve hundreds of photos andpre-calibration to construct image database, a labor-intensiveoverhead for practical deployment. We present ClickLoc, anaccurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted insemantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the …
Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie
Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie
Research Collection School Of Computing and Information Systems
In large-scale distributed file systems, efficient metadata operations are critical since most file operations have to interact with metadata servers first. In existing distributed hash table (DHT) based metadata management systems, the lookup service could be a performance bottleneck due to its significant CPU overhead. Our investigations showed that the lookup service could reduce system throughput by up to 70%, and increase system latency by a factor of up to 8 compared to ideal scenarios. In this paper, we present MetaFlow, a scalable metadata lookup service utilizing software-defined networking (SDN) techniques to distribute lookup workload over network components. MetaFlow tackles …
A Multilingual Semi-Supervised Approach In Deriving Singlish Sentic Patterns For Polarity Detection, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth
A Multilingual Semi-Supervised Approach In Deriving Singlish Sentic Patterns For Polarity Detection, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
Due to the huge volume and linguistic variation of data shared online, accurate detection of the sentiment of a message (polarity detection) can no longer rely on human assessors or through simple lexicon keyword matching. This paper presents a semi-supervised approach in constructing essential toolkits for analysing the polarity of a localised scarce-resource language, Singlish (Singaporean English). Corpus-based bootstrapping using a multilingual, multifaceted lexicon was applied to construct an annotated testing dataset, while unsupervised methods such as lexicon polarity detection, frequent item extraction through association rules and latent semantic analysis were used to identify the polarity of Singlish n-grams before …
Outlier Detection In Complex Categorical Data By Modeling The Feature Value Couplings, Guansong Pang, Longbing Cao, Ling Chen
Outlier Detection In Complex Categorical Data By Modeling The Feature Value Couplings, Guansong Pang, Longbing Cao, Ling Chen
Research Collection School Of Computing and Information Systems
This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial …
Practitioners' Expectations On Automated Fault Localization, Pavneet Singh Kochhar, Xin Xia, David Lo, Shanping Li
Practitioners' Expectations On Automated Fault Localization, Pavneet Singh Kochhar, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Software engineering practitioners often spend significant amount of time and effort to debug. To help practitioners perform this crucial task, hundreds of papers have proposed various fault localization techniques. Fault localization helps practitioners to find the location of a defect given its symptoms (e.g., program failures). These localization techniques have pinpointed the locations of bugs of various systems of diverse sizes, with varying degrees of success, and for various usage scenarios. Unfortunately, it is unclear whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by surveying 386 practitioners from more than 30 countries …
An Adaptability-Driven Model And Tool For Analysis Of Service Profitability, Eng Lieh Ouh, Jarzabek Stan
An Adaptability-Driven Model And Tool For Analysis Of Service Profitability, Eng Lieh Ouh, Jarzabek Stan
Research Collection School Of Computing and Information Systems
Profitability of adopting Software-as-a-Service (SaaS) solutions forexisting applications is currently analyzed mostly in informal way. Informalanalysis is unreliable because of the many conflicting factors that affect costs andbenefits of offering applications on the cloud. We propose a quantitative economicmodel for evaluating profitability of migrating to SaaS that enables potentialservice providers to evaluate costs and benefits of various migration strategiesand choices of target service architectures. In previous work, we presented arudimentary conceptual SaaS economic model enumerating factors that have todo with service profitability, and defining qualitative relations among them. Aquantitative economic model presented in this paper extends the conceptualmodel with equations …
Linear Encryption With Keyword Search, Shiwei Zhang, Guomin Yang, Yi Mu
Linear Encryption With Keyword Search, Shiwei Zhang, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
Nowadays an increasing amount of data stored in the public cloud need to be searched remotely for fast accessing. For the sake of privacy, the remote files are usually encrypted, which makes them difficult to be searched by remote servers. It is also harder to efficiently share encrypted data in the cloud than those in plaintext. In this paper, we develop a searchable encryption framework called Linear Encryption with Keyword Search (LEKS) that can semi-generically convert some existing encryption schemes meeting our Linear Encryption Template (LET) to be searchable without re-encrypting all the data. For allowing easy data sharing, we …
Stpp: Spatial-Temporal Phase Profiling Based Method For Relative Rfid Tag Localization, Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, Yunhao Liu
Stpp: Spatial-Temporal Phase Profiling Based Method For Relative Rfid Tag Localization, Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
Many object localization applications need the relative locations of a set of objects as oppose to their absolute locations. Although many schemes for object localization using radio frequency identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. In this paper, we propose an approach called spatial-temporal phase profiling (STPP) to RFID-based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously interrogating …
Passively Testing Routing Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee-Xian Tan, Hwee-Pink Tan
Passively Testing Routing Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee-Xian Tan, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Smart systems are today increasingly developed with the number of wireless sensor devices that drastically increases. They are implemented within several contexts through our environment. Thus, sensed data transported in ubiquitous systems are important and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed to wireless sensor networks (WSN). However, one stage that is often neglected before their deployment, is the conformance testing process, a crucial and challenging step. Active testing techniques commonly used in wired networks are not suitable to WSN and passive approaches are needed. While some works …
Demo: Ta$Ker: Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Thivya Kandappu, Randy Tandriansyah, Archan Misra
Demo: Ta$Ker: Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Thivya Kandappu, Randy Tandriansyah, Archan Misra
Research Collection School Of Computing and Information Systems
We design and develop TA$Ker, a real-world mobile crowd- sourcing platform to empirically study the worker responses to various task recommendation and selection strategies.
Poster: Improving Communication And Communicability With Smarter Use Of Text-Based Messages On Mobile And Wearable Devices, Kenny T. W. Choo
Poster: Improving Communication And Communicability With Smarter Use Of Text-Based Messages On Mobile And Wearable Devices, Kenny T. W. Choo
Research Collection School Of Computing and Information Systems
While smartphones have undoubtedly afforded many modern conveniences such as emails, instant messaging or web search, the notifications from smartphones conversely impact our lives through a deluge of information, or stress arising from expectations that we should turn our immediate attention to them (e.g., work emails). In my latest research, we find that the glanceability of smartwatches may provide an opportunity to reduce the perceived disruption from mobile notifications. Text is a common medium for communication in smart devices, the application of natural language processing on text, together with the physical affordances of smartwatches, present exciting opportunities for research to …
Poster: Air Quality Friendly Route Recommendation System, Savina Singla, Divya Bansal, Archan Misra
Poster: Air Quality Friendly Route Recommendation System, Savina Singla, Divya Bansal, Archan Misra
Research Collection School Of Computing and Information Systems
To model the overall personal inhalation of hazardous gases through the air (both indoor and outdoor) by an individual, provide air quality friendly route recommendations, thus raising the overall quality of urban movement and living healthy life.
Efspredictor: Predicting Configuration Bugs With Ensemble Feature Selection, Bowen Xu, David Lo, Xin Xia, Ashish Sureka, Shanping Li
Efspredictor: Predicting Configuration Bugs With Ensemble Feature Selection, Bowen Xu, David Lo, Xin Xia, Ashish Sureka, Shanping Li
Research Collection School Of Computing and Information Systems
The configuration of a system determines the system behavior and wrong configuration settings can adversely impact system's availability, performance, and correctness. We refer to these wrong configuration settings as configuration bugs. The importance of configuration bugs has prompted many researchers to study it, and past studies can be grouped into three categories: detection, localization, and fixing of configuration bugs. In the work, we focus on the detection of configuration bugs, in particular, we follow the line-of-work that tries to predict if a bug report is caused by a wrong configuration setting. Automatically prediction of whether a bug is a configuration …
Dual-Server Public-Key Encryption With Keyword Search For Secure Cloud Storage, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang
Dual-Server Public-Key Encryption With Keyword Search For Secure Cloud Storage, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang
Research Collection School Of Computing and Information Systems
Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public key encryption with keyword search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside keyword guessing attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named dual-server PEKS (DS-PEKS). As another main contribution, we define a new variant of the smooth projective …
Smokey: Ubiquitous Smoking Detection With Commercial Wifi Infrastructures, Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu
Smokey: Ubiquitous Smoking Detection With Commercial Wifi Infrastructures, Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method …
Tuning By Turning: Enabling Phased Array Signal Processing For Wifi With Inertial Sensors, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu
Tuning By Turning: Enabling Phased Array Signal Processing For Wifi With Inertial Sensors, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu
Research Collection School Of Computing and Information Systems
Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate …
Improving The Sensitivity Of Unobtrusive Inactivity Detection In Sensor-Enabled Homes For The Elderly, Alvin C. Valera, Hwee-Pink Tan, Liming Bai
Improving The Sensitivity Of Unobtrusive Inactivity Detection In Sensor-Enabled Homes For The Elderly, Alvin C. Valera, Hwee-Pink Tan, Liming Bai
Research Collection School Of Computing and Information Systems
Unobtrusive in-home monitoring systems are gaining acceptability and are being deployed to enable relatives and caregivers to remotely monitor and provide timely care to their elderly loved ones or senior clients, respectively, who are living independently. Such systems can provide information about nonmovement or inactivity of the elderly resident. As prolonged inactivity could mean potential danger, several algorithms have been proposed to automatically detect unusually long durations of inactivity. Such schemes, however, suffer from low sensitivity due to their high detection latency. In this paper, we propose Dwell Time-enhanced Dynamic Threshold (DTDT), a scheme for computing adaptive alert thresholds that …
Interactive Teachable Cognitive Agents: Smart Building Blocks For Multiagent Systems, Budhitama Subagdja, Ah-Hwee Tan
Interactive Teachable Cognitive Agents: Smart Building Blocks For Multiagent Systems, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Developing a complex intelligent system by abstracting their behaviors, functionalities, and reasoning mechanisms can be tedious and time consuming. In this paper, we present a framework for developing an application or software system based on smart autonomous components that collaborate with the developer or user to realize the entire system. Inspired by teachable approaches and programming-by-demonstration methods in robotics and end-user development, we treat intelligent agents as teachable components that make up the system to be built. Each agent serves different functionalities and may have prebuilt operations to accomplish its own design objectives. However, each agent may also be equipped …
Human Activity Prediction By Mapping Grouplets To Recurrent Self-Organizing Map, Qianru Sun, Hong Liu, Mengyuan Liu, Tianwei Zhang
Human Activity Prediction By Mapping Grouplets To Recurrent Self-Organizing Map, Qianru Sun, Hong Liu, Mengyuan Liu, Tianwei Zhang
Research Collection School Of Computing and Information Systems
Human activity prediction is defined as inferring the high-level activity category with the observation of only a few action units. It is very meaningful for time-critical applications such as emergency surveillance. For efficient prediction, we represent the ongoing human activity by using body part movements and taking full advantage of inherent sequentiality, then find the best matching activity template by a proper aligning measurement.In streaming videos, dense spatio-temporal interest points (STIPs) are first extracted as low-level descriptors for their high detection efficiency. Then, sparse grouplets, i.e., clustered point groups, are located to represent body part movements, for which we propose …
Ambient And Smartphone Sensor Assisted Adl Recognition In Multi-Inhabitant Smart Environments, Nirmalya Roy, Archan Misra, Diane Cook
Ambient And Smartphone Sensor Assisted Adl Recognition In Multi-Inhabitant Smart Environments, Nirmalya Roy, Archan Misra, Diane Cook
Research Collection School Of Computing and Information Systems
Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users’ spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user …
Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan
Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan
Research Collection School Of Computing and Information Systems
We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to …