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

Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this paper, a novel system, namely, Correlation Pattern Mining System (CPMS), is developed to capture the usage patterns and correlations among appliances. With several new optimization techniques, CPMS can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.

A Novel System for Extracting Useful Correlation in Smart Home Environment

Searches for objects associated with location information and non-spatial attributes have increased significantly over the years. To address this need, a top-k query may be issued by taking into account both the location information and non-spatial attributes. This paper focuses on a distance-based top-k query which retrieves the best objects based on distance from candidate objects to a query point as well as other non-spatial attributes. In this paper, we propose a new index structure and query processing algorithms for distance-based top-k queries. This new index, called SKY R-tree, drives on the strengths of R-tree and Skyline algorithm to efficiently prune the search space by exploring both the spatial proximity and non-spatial attributes. Moreover, we propose a variant of SKY R-tree, called S2KY R-tree which incorporates a similarity measure of non-spatial attributes. We demonstrate, through extensive experimentation, that our proposals perform very well in terms of I/O costs and CPU time.

SKY R-tree: An Index Structure for Distance-Based Top-k Query

Migrating legacy systems involves replacing (either wholly or partially) existing systems and databases, and complex transformations between old and new data, processes and systems. Correctly performing these activities depends on descriptions of data, and other aspects of the legacy and new systems, and the relationships between them, i.e., metadata. Metadata repositories provide tools for capturing, transforming, storing, and manipulating metadata. They can also store information for managing the migration process itself and for (re)use in other migrations. This paper discusses some of the issues that arise when migrating legacy systems and examines how repository technology can be used to address these issues.

http://www.cse.psu.edu/~wul2/Publications/wlee%20DEB99.pdf

Repository Support for Metadata-based Legacy Migration.

We present a new access method called the path dictionary index PDI method for support ing nested queries on object oriented databases PDI supports object traversal and associative search respectively with a path dictionary and a set of attribute indexes built on top of the path dictionary We discuss issues on indexing and query processing in object oriented databases describe the operations of the new mechanism develop cost models for its storage overhead and query and update costs and compare the new mechanism to the path index method The result shows that the path dictionary index method is signi cantly better than the path index method over a wide range of parameters in terms of retrieval and update costs and that the storage overhead grows slowly with the number of indexed attributes

/pdf/path-dictionary-a-new-approach-to-query-processing-in-object-5dj3m0mkcr.pdf

Path Dictionary A New Approach to Query Processing in Object Oriented Databases

Because moving objects usually moves on spatial networks, efficient trajectory index structures are required to gain good retrieval performance on their trajectories. However, there has been little research on trajectory index structure for spatial networks, like road networks. In this paper, we propose an efficient trajectory index structure for moving objects in Location-based Services (LBS). For this, we design our access scheme for efficiently dealing with the trajectories of moving objects on road networks. In addition, we provide both an insertion algorithm to store the initial information of moving object trajectories and one to store their segment information. We also provide a retrieval algorithm to find a set of moving objects whose trajectories match the segments of a query trajectory. Finally, we show that our trajectory access scheme achieves about one order of magnitude better retrieval performance than TB-tree.

http://www.cse.psu.edu/~wul2/Publications/wlee%20OTM2005.pdf

Wang-Chien Lee

Papers

A Novel System for Extracting Useful Correlation in Smart Home Environment

SKY R-tree: An Index Structure for Distance-Based Top-k Query

Repository Support for Metadata-based Legacy Migration.

Path Dictionary A New Approach to Query Processing in Object Oriented Databases

An efficient trajectory index structure for moving objects in location-based services