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.

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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

A massive amount of data is available in distributed fashion on various networks, including Internet, peer-to-peer networks, and wireless sensor networks. Users are often interested in monitoring interesting patterns or abnormal events hidden in these data. Transferring all the raw data from each host node to a central coordinator for processing is costly and unnecessary. In this study, we investigate the problem of monitoring changes on the data distribution in the networks (MCDN). To address this problem, we propose a technique, called wavenet, by compressing the local item set in each host node into a compact yet accurate summary, called local wavelet, for communication with the coordinator. We also propose adaptive monitoring to address the issues of local wavelet propagation in wavenet. An extensive performance evaluation has been conducted to validate our proposal and demonstrates the efficiency of wavenet.

Wavenet: A Wavelet-Based Approach to Monitor Changes on Data Distribution in Networks

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision.

Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

To study the issue of missing values (MVs), we propose the Order-Sensitive Imputation for Clustered Missing values (OSICM) framework, in which missing values are imputed sequentially such that the values filled earlier in the process are also used for later imputation of other MVs. Obviously, the order of imputations is critical to the effectiveness and efficiency of OSICM framework. We formulate the searching of the optimal imputation order as an optimization problem, and show its NP-hardness. Furthermore, we devise an algorithm to find the exact optimal solution and propose two approximate/heuristic algorithms to trade off effectiveness for efficiency. Finally, we conduct extensive experiments on real and synthetic datasets to demonstrate the superiority of our OSICM framework.

Order-Sensitive Imputation for Clustered Missing Values (Extended Abstract)

Online support centers are emerging as a cost-effective and innovative solution designed to enable end-users to resolve technical problems more effectively without relying on live support from contact center agents. However, the capacity limitation of corporate knowledge bases prevents online support centers from effectively resolving user problems. In addition, traditional textual search techniques employed by most online support centers fall short from accurately interpreting user queries due to the ambiguity of user requests and the heterogeneity of technical problems. In this paper, we present SolutionFinder, an autonomous framework, which dynamically integrates online resources to enrich the knowledge base for IT support systems. SolutionFinder provides context-aware search support to remove the textual ambiguity embedded in user queries. Furthermore, SolutionFinder transforms solution documents into solution paths to analyze their similarity to provide high-quality solution recommendations. Evaluation results suggest by leveraging our proposed algorithms, a support service can accurately locate Web solution resources and provide high-quality services.

SolutionFinder: Intelligent Knowledge Integration and Dissemination for Solution Retrieval in IT Support Services

In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q; l; w; n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings.

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Wang-Chien Lee

Papers

Wavenet: A Wavelet-Based Approach to Monitor Changes on Data Distribution in Networks

Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

Order-Sensitive Imputation for Clustered Missing Values (Extended Abstract)

SolutionFinder: Intelligent Knowledge Integration and Dissemination for Solution Retrieval in IT Support Services

Nearest window cluster queries