Data Mining - Concepts and Techniques.

Statistics for Spatial Data.

Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

/pdf/friendship-and-mobility-user-movement-in-location-based-2u0rrxuc9l.pdf

Friendship and mobility: user movement in location-based social networks

The theory of affordances

A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks

Temporal Networks

The wide use of GPS sensors in smart phones encourages people to record their personal trajectories and share them with others in the Internet. A recommendation service is needed to help people process the large quantity of trajectories and select potentially interesting ones. The GPS trace data is a new format of information and few works focus on building user preference profiles on it. In this work we proposed a trajectory recommendation framework and developed three recommendation methods, namely, Activity-Based Recommendation (ABR), GPS-Based Recommendation (GBR) and Hybrid Recommendation. The ABR recommends trajectories purely relying on activity tags. For GBR, we proposed a generative model to construct user profiles based on GPS traces. The Hybrid recommendation combines the ABR and GBR. We finally conducted extensive experiments to evaluate these proposed solutions and it turned out the hybrid solution displays the best performance.

/pdf/mining-gps-data-for-trajectory-recommendation-9g77i8jy3s.pdf

Mining GPS Data for Trajectory Recommendation

Social network systems (SNSs) such as Facebook and Twitter have recently attracted millions of users by providing social network based services to support easy message posting, information sharing and inter-friend communication. With the rapid growth of social networks, users of SNSs may easily get overwhelmed by the excessive volume of information feeds and felt challenging to digest and find truly valuable information. In this paper, we introduce a personalized feed recommendation service for SNS users based on user interests and social network contexts. Our approach incorporates both the topical preference and topological locality of a user in determining a feed’s relevance. We propose a popularity diffusion model to propagate feeds in social networks and support our recommendation service with a set of personalized indices for feed-based information retrieval. A suite of efficient index manipulation algorithms are developed in our framework to address the need of managing the dynamics in social networks. We conduct an extensive performance evaluation to compare our proposal with alternative solutions using both real and synthetic social network data, which suggests our proposal outperforms in both efficiency and relevance.

Personalized Feed Recommendation Service for Social Networks

Caching has been an important technique for saving network traffic and reducing response time, especially in mobile environments where bandwidth is often a scarce resource. In this paper, we propose a novel approach for caching multidimensional data in a cluster of mobile devices. In particular, we focus on the most common types of multi-dimensional queries, namely range and k-nearest neighbor queries, by computing a cacheable region for every query, caching the result at the client, and indexing it in an R*-tree at the cluster gateway. Subsequent queries are first issued to the R*-tree and only remainder queries or queries that cannot be guaranteed exact answers are sent to the remote data server. To the best of our knowledge, our work is the first to study caching results from complex multi-dimensional queries (e.g., kNN query) and propose to build an R*-tree on previously fetched query results in a cluster of mobile devices. Rigorous experiments show that our approach significantly reduces network traffic and response time.

http://www.cse.psu.edu/~wul2/Publications/wlee%20MDM05.pdf

Distributed caching of multi-dimensional data in mobile environments

Parkinson's disease (PD) is the second most common neurological disorder after Alzheimer's disease. Key clinical features of PD are motor-related and are typically assessed by healthcare providers based on qualitative visual inspection of a patient's movement/gait/posture. More advanced diagnostic techniques such as computed tomography scans that measure brain function, can be cost prohibitive and may expose patients to radiation and other harmful effects. To mitigate these challenges, and open a pathway to remote patient-physician assessment, the authors of this work propose a data mining driven methodology that uses low cost, non-invasive sensors to model and predict the presence (or lack therefore) of PD movement abnormalities and model clinical subtypes. The study presented here evaluates the discriminative ability of non-invasive hardware and data mining algorithms to classify PD cases and controls. A 10-fold cross validation approach is used to compare several data mining algorithms in order to determine that which provides the most consistent results when varying the subject gait data. Next, the predictive accuracy of the data mining model is quantified by testing it against unseen data captured from a test pool of subjects. The proposed methodology demonstrates the feasibility of using non-invasive, low cost, hardware and data mining models to monitor the progression of gait features outside of the traditional healthcare facility, which may ultimately lead to earlier diagnosis of emerging neurological diseases.

A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data

A good cache management method for mobile wireless environments has to handle problems associated with limited client resources and frequent client disconnections, in addition to standard problems found in wired environments, such as variable data sizes and data updates. In this paper we propose a gain-based cache replacement policy, Min-SAUD, for wireless data dissemination when cache consistency must be enforced before a cached item is used. Min-SAUD considers several factors that affect cache performance, namely access probability, update frequency, data size, retrieval delay, and cache validation cost. Min-SAUD is optimal in terms of the stretch performance measure. Preliminary experimental results show that in most cases the Min-SAUD replacement policy substantially outperforms two existing policies, namely LRU and SAIU.

http://www.cse.psu.edu/~wul2/Publications/wlee%20ICPP%202001.pdf

Wang-Chien Lee

Papers

Mining GPS Data for Trajectory Recommendation

Personalized Feed Recommendation Service for Social Networks

Distributed caching of multi-dimensional data in mobile environments

A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data

An optimal cache replacement policy for wireless data dissemination under cache consistency