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

Citation matching, or the automatic grouping of bibliographic references that refer to the same document, is a data management problem faced by automatic digital libraries for scientific literature such as CiteSeer and Google Scholar. Although several solutions have been offered for citation matching in large bibliographic databases, these solutions typically require expensive batch clustering operations that must be run offline. Large digital libraries containing citation information can reduce maintenance costs and provide new services through efficient online processing of citation data, resolving document citation relationships as new records become available. Additionally, information found in citations can be used to supplement document metadata, requiring the generation of a canonical citation record from merging variant citation subfields into a unified "best guess" from which to draw information. Citation information must be merged with other information sources in order to provide a complete document record. This paper outlines a system and algorithms for online citation matching and canonical metadata generation. A Bayesian framework is employed to build the ideal citation record for a document that carries the added advantages of fusing information from disparate sources and increasing system resilience to erroneous data.

/pdf/learning-metadata-from-the-evidence-in-an-on-line-citation-3o4wel778o.pdf

Learning metadata from the evidence in an on-line citation matching scheme

Due to the ubiquitous presence of missing values (MVs) in real-world datasets, the MV imputation problem, aiming to recover MVs, is an important and fundamental data preprocessing step for various data analytics and mining tasks to effectively achieve good performance. To impute MVs, a typical idea is to explore the correlations amongst the attributes of the data. However, those correlations are usually complex and thus difficult to identify. Accordingly, we develop a new deep learning model called MIssing Data Imputation denoising Autoencoder (MIDIA) that effectively imputes the MVs in a given dataset by exploring non-linear correlations between missing values and non-missing values. Additionally, by considering various data missing patterns, we propose two effective MV imputation approaches based on the proposed MIDIA model, namely MIDIA-Sequential and MIDIA-Batch. MIDIA-Sequential imputes the MVs attribute-by-attribute sequentially by training an independent MIDIA model for each incomplete attribute. By contrast, MIDIA-Batch imputes the MVs in one batch by training a uniform MIDIA model. Finally, we evaluate the proposed approaches by experimentation in comparison with existing MV imputation algorithms. The experimental results demonstrate that both MIDIA-Sequential and MIDIA-Batch achieve significantly higher imputation accuracy compared with existing solutions, and the proposed approaches are capable of handling various data missing patterns and data types. Specifically, MIDIA-Sequential performs better than MIDIA-Batch for data with monotone missing pattern, while MIDIA-Batch performs better than MIDIA-Sequential for data with general missing pattern.

/pdf/midia-exploring-denoising-autoencoders-for-missing-data-5frklnwa4v.pdf

MIDIA: exploring denoising autoencoders for missing data imputation

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 article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.

Significant Correlation Pattern Mining in Smart Homes

Wireless sensor networks have recently received a lot of attentions due to a wide range of applications such as object tracking, environmental monitoring, warehouse inventory, and health care [16, 29]. In these applications, physical data is continuously collected by the sensor nodes in order to facilitate application specific processing and analysis. A database-style query interface is natural for development of applications and systems on sensor networks. There are projects pursuing this research direction [14, 15, 25]. However, these existing works have not yet explored the spatial property and the dynamic characteristics of sensor networks. In this paper, we investigate how to process a window query in highly dynamic geo-sensor networks and propose several innovative ideas on enabling techniques. The networks considered are highly dynamic because the sensor nodes can move around (by self-propelling or attaching themselves to moving objects) as well as turn to sleeping mode. The geo-sensor nodes are assumed to be location-aware. There exist many research issues in executing a window query in such sensor networks. The dynamic characteristics make those issues non-trivial. A critical set of networking protocols and access methods need to be developed. In this paper, we present a location-based stateless protocol for routing a window query to its targeted area, a space-dividing algorithm for query propagation and data aggregation in the queried area, and a solution to address user mobility issue when the query result is returned.

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

Window Query Processing in Highly Dynamic Geo-Sensor Networks: Issues and Solutions

Recent research has led to the advent of software systems capable of performing query processing in sensor networks. They perform query processing in a sensor network by constructing a routing tree rooted at an access point (i.e., a base station), where the queries are submitted, parsed, and optimized. This approach to query processing is centralized in nature. Performing query optimization at a single node (base station) does not generate efficient query plans, and requires each node to report metadata to the access point. In addition, the routing tree infrastructure inefficiently aggregates data packets. To address these issues, this paper proposes several decentralized query processing systems that utilize sensor node's innate spatial and semantic characteristics. Experimental results conclude that decentralizing query processing significantly reduces energy costs. In addition, experimental results show that the spatial and semantic properties of nodes are influential in designing a decentralized query processing system.

http://www.cse.psu.edu/~wul2/Publications/wlee%20mobiquitous05a.pdf

Wang-Chien Lee

Papers

Learning metadata from the evidence in an on-line citation matching scheme

MIDIA: exploring denoising autoencoders for missing data imputation

Significant Correlation Pattern Mining in Smart Homes

Window Query Processing in Highly Dynamic Geo-Sensor Networks: Issues and Solutions

Decentralizing query processing in sensor networks