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

As peer-to-peer (P2P) systems receive growing acceptance, the need of identifying 'frequent items' in such systems appears in a variety of applications. In this paper, we define the problem of identifying frequent items (IFI) and propose an efficient in-network processing technique, called in-network filtering (netFilter), to address this important fundamental problem. netFilter operates in two phases: 1) candidate filtering: data items are grouped into item groups to obtain aggregates for pruning of infrequent items; and 2) candidate verification: the aggregates for the remaining candidate items are obtained to filter out false frequent items. We address various issues faced in realizing netFilter, including aggregate computation, candidate set optimization, and candidate set materialization. In addition, we analyze the performance of netFilter, derive the optimal setting analytically, and discuss how to achieve the optimal setting in practice. Finally, we validate the effectiveness of netFilter through extensive simulation.

Identifying Frequent Items in P2P Systems

Contour maps, showing topological distribution of extracted features, are crucial for many applications. Building a dynamic contour map in a wireless sensor network is a challenging task due to the constrained network resources. In this paper, we present the design of a contour mapping engine (CME) in wireless sensor networks. Our design incorporates in-network processing based on binary classification to reduce the total number of active nodes. The underlying network architecture is analyzed to derive an optimal configuration. We show, by extensive simulations, the superiority of CME over the state-of-the-art contour mapping techniques.

CME: A Contour Mapping Engine in Wireless Sensor Networks

Understanding social tie development among users is crucial for user engagement in social networking services. In this paper, we analyze the social interactions, both online and offline, of users and investigate the development of their social ties using data trail of 'how social ties grow' left in mobile and social networking services. To the best of our knowledge, this is the first research attempt on studying social tie development by considering both online and offline interactions in a heterogeneous yet realistic relationship. In this study, we aim to answer three key questions: i is there a correlation between online and offline interactions? ii how is the social tie developed via heterogeneous interaction channels? and iii would the development of social tie between two users be affected by their common friends? To achieve our goal, we develop a Social-aware Hidden Markov Model SaHMM that explicitly takes into account the factor of common friends in measure of the social tie development. Our experiments show that, comparing with results obtained using HMM and other heuristic methods, the social tie development captured by our SaHMM is significantly more consistent to lifetime profiles of users.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611973440.38

It takes two to tango: Exploring social tie development with both online and offline interactions

This paper studies the issues of energy-efficient query optimization for wireless sensor networks. Different from existing query optimization techniques that consider only query plans for extracting data from sensors at individual nodes, our approach takes into account both of the sensing and communication cost in query plans. Central to our study is a cost-based analysis, based on which the energy cost of candidate plans for a given query are estimated to determine a query plan that is likely to consume the least energy for execution. Simulation results show that the query plan chosen in our approach consumes significantly less energy than an approach that optimizes on sensing cost only.

http://www.cse.psu.edu/~wul2/Publications/wlee%20mdm07.pdf

Optimizing Energy-Efficient Query Processing in Wireless Sensor Networks

Many of sensor network applications require reliable data communication such that data packets can be delivered to the destination without loss. However, existing reliable transmission techniques either are too costly for resource-constrained sensor networks or have limited capabilities for achieving desirable reliability. In this paper, an effective coding scheme that exploits the tradeoff between redundant data transmission and encoding/decoding complexity is proposed, with an in-depth study on two key design parameters, the degree of repair packets and the number of repair packets. Furthermore, the expected probability of a destination obtaining all data packets under recoverable and permanent failure model for proactive transmission is analyzed, respectively. Simulations have been conducted to verify our theoretical results. The simulation results reveal profound insights in achieving high communication reliability in wireless sensor networks.

http://www.cse.psu.edu/~wul2/Publications/wlee%20INFOCOM07.pdf

Wang-Chien Lee

Papers

Identifying Frequent Items in P2P Systems

CME: A Contour Mapping Engine in Wireless Sensor Networks

It takes two to tango: Exploring social tie development with both online and offline interactions

Optimizing Energy-Efficient Query Processing in Wireless Sensor Networks

Analysis of A Loss-Resilient Proactive Data Transmission Protocol in Wireless Sensor Networks