Shonali Krishnaswamy

Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges. In the proposed model, we consider that the check-in frequency characterizes users visiting preference and learn the factorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a stochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under both user-POI setting and user-time-POI setting have been conducted to test the effectiveness of the proposed method. Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.

MARS: A Personalised Mobile Activity Recognition System

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on todays smart phones. The state of the art in mobile activity recognition uses traditional classification techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository, ii) model building where the classification model is trained and tested using the collected data, iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables quick model adaptation. One of the stand out features of MARS is that training/updating the model takes less than 30 seconds per activity. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practice that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.

An integrated framework for human activity classification

This paper presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. We develop a novel framework that contains simple pre- and post-classification strategies to improve the overall performance. We achieve this through class-imbalance correction on the learning data using structure preserving oversampling (SPO), leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively. Through evaluation on recent publicly available activity datasets comprising of a large amount of multi-dimensional sensory data, we demonstrate that our proposed strategies are effective in improving classification performance over common techniques such as One Nearest Neighbor (1NN) and Support Vector Machines (SVM). Our framework also shows better performance over sequential probabilistic models, such as Conditional Random Field (CRF) and Hidden Markov Model (HMM) and when these models are used as meta-learners.

To Taxi or Not to Taxi? - Enabling Personalised and Real-Time Transportation Decisions for Mobile Users

We demonstrate a system that monitors the taxi availability at taxi stands by mining real-time taxi trajectory data streams. The system includes a server-side trajectory data stream processing and mining program and a client-side mobile application for Android smart phones. The server program continuously monitors for each taxi stand the numbers of taxis queueing at the taxi stand, the numbers of taxis that will pass the taxi stand, as well as the traffic conditions in the area around the stand. It delivers real time taxi and traffic information to mobile users via Restful web services. The client-side location-based mobile application consumes these services to help mobile users make informed transportation choices. For example the availability of taxis might yet be a deterrent when traffic is congested. Real world taxi trajectory data from more than 14000 taxis are used in the demo.

Mobile activity recognition using ubiquitous data stream mining

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the rich sensory data that is available on todays smart phones and other wearable sensors. The state of the art in mobile activity recognition research has focused on traditional classification learning techniques. In this paper, we propose the Mobile Activity Recognition System (MARS) where for the first time the classifier is built on-board the mobile device itself through ubiquitous data stream mining in an incremental manner. The advantages of on-board data stream mining for mobile activity recognition are: i) personalisation of models built to individual users; ii) increased privacy as the data is not sent to an external site; iii) adaptation of the model as the users activity profile changes. In our extensive experimental results using a recent benchmarking activity recognition dataset, we show that MARS can achieve similar accuracy when compared with traditional classifiers for activity recognition, while at the same time being scalable and efficient in terms of the mobile device resources consumption. MARS has been implemented on the Android platform for empirical evaluation.

Where Will You Go? Mobile Data Mining for Next Place Prediction

The technological advances in smartphones and their widespread use has resulted in the big volume and varied types of mobile data which we have today. Location prediction through mobile data mining leverages such big data in applications such as traffic planning, location-based advertising, intelligent resource allocation; as well as in recommender services including the popular Apple Siri or Google Now. This paper, focuses on the challenging problem of predicting the next location of a mobile user given data on his or her current location. In this work, we propose NextLocation - a personalised mobile data mining framework - that not only uses spatial and temporal data but also other contextual data such as accelerometer , bluetooth and call/sms log. In addition, the proposed framework represents a new paradigm for privacy-preserving next place prediction as the mobile phone data is not shared without user permission. Experiments have been performed using data from the Nokia Mobile Data Challenge MDC. The results on MDC data show large variability in predictive accuracy of about 17% across users. For example, irregular users are very difficult to predict while for more regular users it is possible to achieve more than 80% accuracy. To the best of our knowledge, our approach achieves the highest predictive accuracy when compared with existing results.

Scalable Energy-Efficient Distributed Data Analytics for Crowdsensing Applications in Mobile Environments

We are witnessing a new revolution in computing and communication involving symbiotic networks of people (social networks), intelligent devices, smart mobile computing, and communication devices that will form cyber-physical social systems. The emergence of intelligent devices with monitoring, sensing, and actuation capabilities referred to as Internet of Things and social networks have increased the popularity of novel social applications such as crowdsourcing and crowdsensing. The upsurge of such applications has fostered the need for scalable cost-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a scalable, energy-efficient, generic and extensible component-based distributed data analytics platform for mobile crowdsensing (MCS) applications. CARDAP incorporates on-the-move activity recognition and a number of energy efficient data delivery strategies using real-time mobile data stream mining. We propose and develop theoretical cost models for typical crowdsensing application scenarios. Experimental evaluations of CARDAP using a proof-of-concept MCS scenario validate the theoretical cost model estimates and demonstrate the platforms ability to deliver significant benefits in energy, resource, and query processing efficiency.

Home and Work Place Prediction for Urban Planning Using Mobile Network Data

We present methods to predict and validate home and work places of anonymized users using their mobile network data. Knowledge of home and work place of a user is essential in order to find his (and overall population) mobility profiles. There are many methods that predict home and work places using GPS data. But unlike GPS data, mobile network data using GSM do not provide the exact location of a phone event. We use a novel criterion that combines an extracted feature from mobile data (i.e., Inactivity - no phone event for a given period of time) with open source data about location category % (i.e., Streetdirectory.com) to predict home location. Results show that the new criterion gives better prediction accuracy than inactivity alone. We predict work place using the idea that one goes to her work place on most of the weekdays but rarely on weekends. We validate our methods by comparing against the ground truth obtained from open source data. Validation results show that our proposed methods are about 25% more accurate than existing methods both for home and work place predictions.

Where you Instagram?: Associating Your Instagram Photos with Points of Interest

Instagram, an online photo-sharing platform, has gained increasing popularity. It allows users to take photos, apply digital filters and share them with friends instantaneously by using mobile devices.Instagram provides users with the functionality to associate their photos with points of interest, and it thus becomes feasible to study the association between points of interest and Instagram photos. However, no previous work studies the association. In this paper, we propose to study the problem of mapping Instagram photos to points of interest. To understand the problem, we analyze Instagram datasets, and report our findings, which also characterize the challenges of the problem. To address the challenges, we propose to model the mapping problem as a ranking problem, and develop a method to learn a ranking function by exploiting the textual, visual and user information of photos. To maximize the prediction effectiveness for textual and visual information, and incorporate the users visiting preferences, we propose three subobjectives for learning the parameters of the proposed ranking function. Experimental results on two sets of Instagram data show that the proposed method substantially outperforms existing methods that are adapted to handle the problem.

Understanding Urban Mobility via Taxi Trip Clustering

Clustering of a large amount of taxi GPS mobility data helps to understand the spatio-temporal dynamics for the applications of urban planning and transportation. In this paper we cluster the origin-destination pairs of the passenger taxi rides to provide useful insight into the city mobility patterns, urban hot-spots, road network usage and general patterns of the crowd movement within the city of Singapore. We perform experiments on a large scale Singapore taxi dataset consisting of more than 10 million passenger origin-destination GPS points. We use the clusi VAT sampling scheme to obtain the sample trips which return coarse clusters describing the major crowd movement and reduce the data points that are not captured by the coarse clusters and may bring in noises during fine-grained clustering. After the sampling step we use the well known density based clustering algorithm DBSCAN to find cluster structure in the sampled data points and later extend it to the rest of the dataset using nearest prototype rule. We report 24 trip clusters from the dataset which are compact enough to draw meaningful conclusions about the city mobility patterns and the number of trips in each cluster is large enough to be representative of the general traffic movement.