Automating the processing of health insurance claims to achieve "Straight-Through Processing" is one of the holy grails that all insurance companies aim to achieve. One of the major impediments to this automation is the difficulty in establishing the relationship between the underwriting exclusions that a policy has and the incoming claim's diagnosis information. Typically, policy underwriting exclusions are captured in free-text such as "Respiratory illnesses are excluded due to a pre-existing asthma condition". A medical claim coming from a hospital would have the diagnosis represented using the International Classification of Disease (ICD) codes from the World Health Organization. The complex and labour-intensive task of establishing the relationship between free-text underwriting exclusions in health insurance policies and medical diagnosis codes from health insurance claims is critical towards determining if a claim should be rejected due to underwriting exclusions. In this work, we present a novel framework that leverages both explicit and implicit domain knowledge present in medical ontologies and pre-trained language models respectively, to effectively establish the relationship between free-text describing medical conditions present in underwriting exclusions and the ICD-10CM diagnosis codes in health insurance claims. Termed KAMEL (Knowledge Aware Medical Entity Linkage), our proposed framework addresses the limitations faced by prior approaches when evaluated on real-world health insurance claims data. Our proposed framework have been deployed in several multi-national health insurance providers to automate their health insurance claims.

Clustering of large-scale vehicle trajectories is an important aspect for understanding urban traffic patterns, particularly for optimizing public transport routes and frequencies and improving the decisions made by authorities. Existing trajectory clustering schemes are not well suited to large numbers of trajectories in dense city road networks due to the difficulty in finding a representative distance measure between trajectories that can scale to very large datasets. In this paper, we propose a novel Dijkstra-based dynamic time warping distance measure, trajDTW between two trajectories, which is suitable for large numbers of overlapping trajectories in a dense road network as found in major cities around the world. We also propose a novel fast-clusiVAT algorithm that can suggest the number of clusters in a trajectory dataset and identify and visualize the trajectories belonging to each cluster. We conduct experiments on a large-scale taxi trajectory dataset consisting of 3.28 million trajectories obtained from the GPS traces of 15 061 taxis within Singapore over a period of one month. Our analysis finds 13 trajectory clusters spanning the major expressways of Singapore, each of which can be further divided into two sub-clusters based on the travel direction. For each cluster, we provide a time-based distribution of trajectories to yield insights into how urban mobility patterns change with the time of day. We compare the trajectory clusters obtained using our approach with those obtained using popular general and trajectory specific clustering frameworks: DBSCAN, OPTICS, NETSCAN, and NEAT. We demonstrate that the clusters obtained using our novel fast-clusiVAT framework are better than those obtained using other clustering schemes, evaluated based on two internal cluster validity measures: Dunn's and Silhouette indices. Moreover, our fast-clusiVAT algorithm achieves significant speedup over a comparable approach without loss of cluster quality.

Biclustering is a data mining technique that allows simultaneous clustering of two variables. A common biclustering task for categorical variables is to find ‘heavy’ biclusters, i.e., biclusters with high co-occurrence values. Although algorithms have been proposed to extract heavy biclusters, they provide little information about relative importance of each bicluster, as well as importance of the variables for each bicluster. To address these problems, there have been attempts to apply mixture models using information theory or Bayesian method. Although they are able to rank the biclusters and the variables for each bicluster, they do not target at extracting heavy biclusters. Furthermore, these models constrain the search for biclusters in such a way that every cell in the matrix must participate in some bicluster. We attempt to alleviate these limitations using dual topic models. First of all, we develop a generalized LDA topic model that extracts dual topics, i.e., topics in opposite directions – row- and column-topics. To obtain better topics, it applies mutual reinforcement, i.e., considering column-topics while constructing row-topics, and vice versa. Heavy biclusters, the high co-occurred relationship, are extracted using thresholds. We show that our model Dual Topic to Biclusters (DT2B) is effective in extracting heavy biclusters by experimenting over a simulated data, a text corpus (NIPS author-document) and a microarray gene expression data. Results show that biclusters extracted by DT2B are better.

It is critical for a large telecommunications company such as Singtel to truly understand the behavior and preference of its customers, in order to win their loyalty in a highly fragmented and competitive market. In this paper we propose a novel graph edge-clustering algorithm (DGEC) that can discover unique behavioral groups, from rich usage data sets (such as CDRs and beyond). A behavioral group is a set of nodes that share similar edge properties reflecting customer behavior, but are not necessarily connected to each other and therefore different from the usual notion of graph communities. DGEC is an optimization-based model that uses the stochastic proximal gradient method, implemented as a distributed algorithm that scales to tens of millions of nodes and edges. The performance of DGEC is satisfactory for deployment, with an execution time of 2.4 hours over a graph of 5 million nodes and 27 million edges in a 8-machine environment (32 cores and 64GB memory per machine). We evaluate the behavioral groups discovered by DGEC by combining other information such as demographics and customer profiles, and demonstrate that these behavioral groups are objective, consistent and insightful. DGEC has now been deployed in production, and also shows promising potential to extract new usage behavioral features from other data sources such as web browsing, app usage and TV consumption.

In industrial domains such as finance, telecommunications, the internet, and sensor monitoring, large volumes of unlabeled temporal data are continuously generated, such as financial transactions, sensor measurements and user activities. From a data analysis standpoint, there is significant utility to be gained by detecting and understanding changes in the data, such as physical activity recognition and content consumption behavior, or anomalies and faults in robots and sensors. However, because the data is unlabeled, it is challenging to visualize and understand in a way that produces interpretable insights, furthermore, the large volume of data imposes a scalability requirement. In the concept drift and stream mining literature, existing methods may focus on one or two, but rarely all three, of the aforementioned aspects: unlabeled data, interpretable output, scalability. Addressing this need, we propose ConTrack, an unsupervised method that tracks multiple evolving concepts in temporal data, and which is parallelized over a cluster of machines. To enhance interpretability, our method structures its output at a per-user (or actor) level, where users subscribe to one or more evolving concepts. Our method applies to problem settings (multiple concepts, unsupervised data, temporal data, user-oriented data) that cannot be handled by existing concept drift and stream mining methods, and outperforms popular unsupervised baselines from the wider Data Mining and Machine Learning literature.

Extraction of mobility patterns of people in a city helps in urban planning, traffic control, transportation management, etc. We present a demo that shows how to extract interesting individual and collective mobility patterns from mobile phone cellular data. Individual patterns are captured using a next place prediction algorithm that builds different Dynamic Bayesian Network (DBN) models and chooses the best DBN model. Collective patterns are captured by aggregating individual patterns using efficient query processing methods. Each query requires three arguments, i.e., current location cl, current time ct and query time qt. These parameters are varied to capture information at different granularity level resulting in many interesting patterns. Cellular data, although less accurate, irregular and sparser than GPS and less detailed than CDR, is cheap and abundantly available and is used by all sections of people. Demo results show that these shortcomings can be overcome, at least to some extent, by exploiting the underlying repetitive patterns. As part of individual patterns, the system predicts the next place (along with a confidence measure) for a given combination of current location, Time-of-Day and Day-of-week of a user. Collective mobility patterns can start from a planning area or a latlon (with a radius). It visually shows the major destinations along with their intensity. An interesting finding was that planning areas with higher population has higher predictability. The UI system also shows heat maps that show continuous mobility of people. These types of patterns can have significant bearing on urban planning and related applications.

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.

Trip time prediction is an important problem. Taxi passengers often want to know when they will arrive at their destinations. We design a method of predicting taxi trip time by finding historical similar trips. Trips are clustered based on origin, destination, and start time. Then similar trips are mapped to road networks to find frequent sub-trajectories that are used to model travel time of the various parts of the routes. Experimental results show this method is effective.

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.

In this paper we present methods for mapping trajectories of cell tower latitude-longitudes in mobile phone data to transport network. Compared to other forms of mobile phone data such as GPS traces, this form is the most popular and inexpensive but the least accurate and highly irregular. Unlike some earlier works on this type of data where only long trips are studied, our work is based on everyday transportation inside a city. The problems due to inaccuracy in determining position of a user and irregularity of phone events are felt even more intensely for such small distances. Our approach in processing this data takes into account its repetitive nature, i.e., A typical user makes the same trips every week. Moreover, many users follow the same transport route to go from one place to another. First of all, we defined stay region taking into consideration irregularity of phone events. Then we extracted trajectories between each pair of stay regions as trips. Then, we map these trips to existing transportation network consisting of bus services, train lines, expressways, and their combinations such as bus-train-bus. For a given origin and destination, we create a heat map that shows the most commonly used route. A round-the-clock heat map, both for weekdays and weekends, shows people's mobility at different times of a week. We validate our findings by both direct (sample survey) and indirect (comparison with available statistics) methods.

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.

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 platform's ability to deliver significant benefits in energy, resource, and query processing efficiency.

For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive task—2NEXPTIME-complete in the worst case. Hence, it is highly desirable to be able to accurately estimate classification time, especially for large and complex ontologies. Recently, machine learning techniques have been successfully applied to predicting the reasoning hardness category for a given (ontology, reasoner) pair. In this paper, we further develop predictive models to estimate actual classification time using regression techniques, with ontology metrics as features. Our large-scale experiments on 6 state-of-the-art OWL 2 DL reasoners and more than 450 significantly diverse ontologies demonstrate that the prediction models achieve high accuracy, good generalizability and statistical significance. Such prediction models have a wide range of applications. We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.

It has been shown, both theoretically and empirically, that reasoning about large and expressive ontologies is computationally hard. Moreover, due to the different reasoning algorithms and optimisation techniques employed, each reasoner may be efficient for ontologies with different characteristics. Based on recently-developed prediction models for various reasoners for reasoning performance, we present our work in developing a meta-reasoner that automatically selects from a number of state-of-the-art OWL reasoners to achieve optimal efficiency. Our preliminary evaluation shows that the meta-reasoner significantly and consistently outperforms 6 state-of-the-art reasoners and it achieves a performance close to the hypothetical gold standard reasoner.

Knowledge about population distribution of planning areas helps in making urban development decisions. Two important criteria are: "where do people live?" and "where do they work?" In this paper we propose methods to find home and workplaces from mobile network data. Home and work places are essential for discovery of mobility profiles of users. Validation of home and workplace prediction is not straight forward. We validate our methods using correlation with external data. Validation results show that even though a single cellular provider has only a portion of the entire population as its users, distribution of home and work places predicted using its mobile network data match that of government statistics. On the basis of this matching, we can have faith in distributions of more difficult statistics extracted from mobile network data which are difficult to obtain from external sources. We implemented an interactive system to show various distributions such as people living and working in different planning areas, and people working in different job sectors such as manufacturing. Interesting relationships are found by calculating joint distributions, e.g., Where do people, living in a planning area, work, and vice versa. Planning areas are ranked by the average distance traveled from home to work. Another interesting fact we extract is balance. Balance of a planning area is high if people live and work there, it is low if people living in a planning area work in other planning areas. We extend these statistics to regions which consist of many planning areas. The goal of this interactive system is to understand location semantics and mobility of users to aid in making urban development decisions. A video recording with subtitles is uploaded in http://www.youtube.com/watch?v=mo-7-DsCymw.

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.

One major problem of using location data collected from mobile cellular networks for mobility modelling is the oscillation phenomenon. An oscillation occurs when a mobile phone intermittently switches between cell towers instead of connecting to the nearest cell tower. For the purpose of mobility modeling, the location data needs to be cleansed to approximate the mobile device's actual location. However, this constitutes a challenge because the mobile device's true location is not known. In this paper, we study the oscillation resolution problem. We propose an algorithm framework called DECRE (Detect, Expand, Check, Remove) to detect and remove oscillation logs. To make informed decisions DECRE includes four steps: Detect, to identify log sequences that may contain oscillation using a few heuristics based on the concepts of stable period and moving at impossible speed, Expand, to look before and after suspicious records to gain more information, Check, to check whether a cell tower is observed repeatedly (which is a strong indication of oscillation), and Remove, resolving oscillation by selecting a cell tower to approximate the mobile device's actual location. Our experimental results on travel diaries show that our oscillation resolution approach is able to remove records that are far from mobile device's ground-truth locations, improve the quality of the location data, and performs better than an existing method. Our performance study on large scale cell tower data shows that the MapReduce implementation of our approach is able to process 1 Terabyte of cell tower data in five hours using a small cluster.

The rapid expansion of broadband mobile networks by Telecom Operators, has introduced a versatile global infrastructure that internally generates vast amounts of spatio-temporal network-level data (e.g., User id, location, device type, etc.) At the same time, mobile app vendors have nowadays at their fingertips massive amounts of app-level data collected through implicit or explicit crowd sourcing schemes with multi-sensing smartphones that have become a commodity. Mobile big data analytics refers to the discovery of previously unknown meaningful patterns and knowledge from a few dozen terabytes to many petabytes of data collected from mobile users at the network-level or the app-level. Example analytics range from high-level metrics and summaries (e.g., Through clustering, classification and association rule mining) useful to executive managers to alert-based analytics (e.g., Anomaly detection) useful to front-line engineers and users. This panel will explore how the academia and industry are tackling mobile big data analytic challenges. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management and mobile data mining communities should contribute to.

The Semantic Web represents an evolution of the World Wide Web towards one of entities and their relationships, rather than pages and links. Such a progression makes it possible to represent, integrate, query and reason about structured online data. Recent years have witnessed tremendous growth of mobile computing, represented by the widespread adoption of smart phones and tablets. The versatility of such smart devices and the capabilities of semantic technologies form a great foundation for a ubiquitous Semantic Web that will contribute to further realising the true potential of both disciplines. In this paper, the authors argue for values provided by the ubiquitous Semantic Web using a mobile service discovery scenario. They also provide a brief overview of state-of-the-art research in this emerging area. Finally, the authors conclude with a summary of challenges and important research problems.

Retrieval is a key phase in case-based reasoning (CBR), since it lays the foundation for the overall effectiveness of CBR systems. Its aim is to retrieve useful cases that can be used to solve the target problem. To perform the retrieval process, CBR systems typically exploit similarity knowledge and is called similarity-based retrieval (SBR). However, SBR tends to rely strongly on similarity knowledge, ignoring other forms of knowledge that can be further leveraged to improve the retrieval performance. This paper argues and motivates that association analysis of stored cases can significantly strengthen SBR. We propose a novel retrieval strategy USIMSCAR that substantially outperforms SBR by leveraging association knowledge, encoded via a certain form of association rules, in conjunction with similarity knowledge. We also propose a novel approach for extracting association knowledge from a given case base using various association rule mining techniques. We evaluate the significance of USIMSCAR in three application domains-medical diagnosis, IT service management, and product recommendation.

The goal in Rule Ensemble Learning (REL) is simultaneous discovery of a small set of simple rules and their optimal weights that lead to good generalization. Rules are assumed to be conjunctions of basic propositions concerning the values taken by the input features. It has been shown that rule ensembles for classification can be learnt optimally and efficiently using hierarchical kernel learning approaches that explore the exponentially large space of conjunctions by exploiting its hierarchical structure. The regularizer employed penalizes large features and thereby selects a small set of short features. In this paper, we generalize the rule ensemble learning using hierarchical kernels (RELHKL) framework to multi class structured output spaces. We build on the StructSVM model for sequence prediction problems and employ a ρ-norm hierarchical regularizer for observation features and a conventional 2-norm regularizer for state transition features. The exponentially large feature space is searched using an active set algorithm and the exponentially large set of constraints are handled using a cutting plane algorithm. The approach can be easily extended to other structured output problems. We perform experiments on activity recognition datasets which are prone to noise, sparseness and skewness. We demonstrate that our approach outperforms other approaches.

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.

This poster presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. Our framework contains simple pre- and post-classification strategies such as class-imbalance correction on the learning data using structure preserving oversampling, leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively, for improved performance. Through evaluation on recent publicly-available OPPORTUNITY activity datasets comprising of a large amount of multi-dimensional, continuous-valued sensory data, we show that our proposed strategies are effective in improving the 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 Models (HMM) and when these models are used as meta-learners.

Activity recognition focuses on inferring current user activities by leveraging sensory data available on todayÕs sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition Systems, termed StreamAR. The system incorporates incremental and active learning for mining user activities in data streams. The novel approach processes activities as clusters to build a robust classification framework. StreamAR integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition datasets have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today's 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.

This paper presents an overview of the current state-of-the-art in mobile data stream mining. This area of mobile data stream mining is significant for a number of new application domains such as mobile crowd sensing and mobile activity recognition. The paper presents the strategies and techniques for adaptation that are essential in order to perform real-time, continuous data mining on mobile devices. We present an overview of the algorithms research in this area. Finally, we discuss the key toolkits, systems and applications of mobile data stream mining.

In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing.

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.

The tutorial presents the state-of-the-art in mobile and ubiquitous data stream mining and discusses open research problems, issues, and challenges in this area.

There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analysis have been developed, these techniques tend to be computationally intensive. In this paper, we propose, develop and evaluate a resource-aware and energy-efficient time series analysis technique for real-time ECG analysis on mobile devices based on the well-known SAX (Symbolic Aggregate Approximation) representation for time series termed RA-SAX.

The case for integrating software agent and web service paradigms has been well documented, and we believe that convergence of these two paradigms, enhanced with context awareness, can enable more efficient and effective pervasive services. Software agents in service-oriented environments have traditionally been limited to either using, providing, or aggregating services. We propose that in dynamic heterogeneous environments it would be sometimes beneficial if the agent, in addition to invoking remote services, could acquire the capacity to execute functionality provided by the service and run it locally. To this end, we build a performance analysis model that compares time consumption and network load of service access with that of component use. We argue that such a model would allow an agent to dynamically select the more efficient alternative. We present a multicriteria decision-making model that helps dynamic selection, describe experiments comparing the two approaches, and discuss results and lessons learned.

This paper presents a novel framework for obtaining real-time feedback from students as well as enabling the teacher to leverage such feedback and understand the context and situation of the students. We present the iCALT system and discuss its educational rationale, its system operation and usage as well results from trial of the system in a classroom setting and a seminar setting.

There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.

In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adapta

With the emergence of high-end smart phones/PDAs there is a growing opportunity to enrich mobile/pervasive services with semantic reasoning. This article presents novel strategies for optimising semantic reasoning for realizing semantic applications and services on mobile devices. We have developed the mTableaux algorithm which optimizes the reasoning process to facilitate service selection. We present comparative experimental results which show that mTableaux improves the performance and scalability of semantic reasoning for mobile devices.

We propose the EADRM (Explanation-Aware Distributed Reputation Management) framework for sharing web services’ reputation in heterogeneous environments. This framework advocates rationale extraction for meaningful exchange of reputation and includes a decision support algorithm for combining rationale-fortified recommendations.

Software agents roaming around and accessing services is an important part of the vision of the Semantic Web. The need to engage in diverse activities in rapidly changing environments makes it essential that these agents are able to adapt to varying situations. We propose a novel approach to engineer adaptive software agents for such scenarios. Our agents have the ability to exchange their capabilities with peers, support multiple forms of adaptation, enable software reuse through a component-based infrastructure and provide fine-grained and efficient agent mobility. We describe our solution, the first implementation and identify further research issues.

This paper explores the use of rationale for understanding the context of reputation information so as to facilitate the exchange and transfer of reputation information across distributed heterogeneous reputation systems for selection of the best service for a particular user's requirements.

Today's mobile users have access to a wide range of Web-based services. This paper presents our m-Tableaux algorithm for enabling cost-efficient and optimised semantic reasoning to support pervasive service discovery. We present performance evaluation of the m-Tableaux optimisation strategies which clearly demonstrate its operational feasibility on a mobile device.

Smart spaces should not be bound by physical constraints but should transcend them in order to provide enhanced services. One way of overcoming this is by having smart virtual counterparts represent the real world entities in smart spaces. We propose that mobile agent technology is aptly suited for providing smart virtual counterparts of real-world entities in the virtual world and can be used to enhance the performance of smart spaces. We also present a prototype implementation using the Grasshopper mobile agent toolkit as proof of concept.

We present AgentUDM a mobile agent based support infrastructure for ubiquitous data mining (UDM). The focus of AgentUDM is to address the issues of resource-constraints and disconnections with respect to UDM.

A platform independent agent-based virtual educational environment (PIAVEE) is described. PIAVEE is conceptualized as a lightweight, flexible environment that links educational resources through a virtual data base and that is supported by an intelligent delivery system. PIAVEE is implemented through an evolutionary process, whose initial focus is the development of and access to, the virtual data base. The paper describes the initial implementations, which focus on proof of concept and basic level functionality.

The selection of Web services is typically based on both functional and nonfunctional attributes of the service, such as the quality of service (QoS) levels. Reputation, a widely acknowledged nonfunctional QoS attribute is currently expressed as the average of user ratings given to the service. However, this expression confines reputation to the subjective perception of the end user and is limited by the lack of an objective representation of performance history. In this paper, we address the need for a reputation mechanism that couples the subjective perception of the end user with the objective view of performance history. To represent performance history, we propose a novel QoS metric termed verity. Verity measures the degree of consistency exhibited by the service provider in delivering the quality levels laid out in the service contract, over a range of previous transactions. We express reputation as a composition of user rating, the compliance levels exhibited by the provider and the verity value. We contend that this reputation expression is a more viable attribute of quality than user rating alone.

This paper presents a hybrid distributed data mining (DDM) model for optimization of response time. The model combines a mobile agent approach with client server strategies to reduce the overall response time. The hybrid model proposes and develops accurate a priori estimates of the computation and communication components of response time as the costing strategy to support optimization. Experimental evaluation of the hybrid model is presented.

One major drawback in using a client server system in the mobile environment is the redundancy of data transmission in the event of connection failure. In this paper, we present a novel mobile agent architecture which allows minimum redundancy of data transmission in a client server database connection in a frequent disconnections environment. The system supports disconnection events caused by network failure or client power failure. Unlike current approaches, our model uses agent messaging to transfer the data. The messaging reduces the amount of migrations that the mobile agents performed. The architecture was implemented and tested in the Aglets platform.

A priori estimation of quality of service (QoS) levels is a significant issue in e-services since service level agreements (SLAs) need to specify and adhere to such estimates. Response time is an important metric for data intensive e-services such as data mining, data analysis and querying/information retrieval from large databases where the focus is on the time taken to present results to clients. A key component of response time in such data intensive services is the time taken to perform the computation, namely, the time taken to perform either data mining, analysis or retrieval. In this paper, we present an approach for accurately estimating the computation times of data intensive e-services.