Data Mining - Concepts and Techniques.

The following three articles are full versions of extended abstracts that were presented at the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) held in Paris, France, June 14–16, 2004. After the conference, the authors were invited to submit full versions of their papers for publication in JACM. The articles have been reviewed according to the standard JACM refereeing process. We wish to thank the PODS Program Committee for their help in selecting these papers.

Journal of the ACM

Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines. Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data in the features space and inherently overlooks the structural information. Graphs have been prevalently used to preserve structural information, and this raises the graph anomaly detection problem - identifying anomalous graph objects (nodes, edges, sub-graphs, and graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data. For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We also highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.

pdf/a-comprehensive-survey-on-graph-anomaly-detection-with-deep-57msnlha3o.pdf

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.

pdf/dipole-diagnosis-prediction-in-healthcare-via-attention-3b64ew2smn.pdf

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.

https://dl.acm.org/doi/pdf/10.1145/3097983.3098088

It is well known that people movement exhibits a high degree of repetition since people visit regular places and make regular contacts for their daily activities. This paper1 presents a novel framework named Jyotish2, which constructs a predictive model by exploiting the regular pattern of people movement found in real joint Wifi/Bluetooth trace. The constructed model is able to answer three fundamental questions: (1) where the person will stay, (2) how long she will stay at the location, and (3) who she will meet. In order to construct the predictive model, Jyotish includes an efficient clustering algorithm to exploit regularity of people movement and cluster Wifi access point information in Wifi trace into locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in Bluetooth trace. Next, the Bluetooth trace with assigned locations is used to construct predictive model including location predictor, stay duration predictor, and contact predictor to provide answers for three questions above. Finally, we evaluate the constructed predictors over real Wifi/Bluetooth trace collected by 50 participants in University of Illinois campus from March to August 2010. Evaluation results show that Jyotish successfully constructs a predictive model, which provides a considerably high prediction accuracy of people movement.

Jyotish: A novel framework for constructing predictive model of people movement from joint Wifi/Bluetooth trace

This paper presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system which is arguably the most popular and largest multimedia streaming p2p system today. We believe that our findings can be used to understand large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of channel population size and session length.Our studies also reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) Small PPLive overlays (as many as 500 nodes) are similar to random graphs in structure, (2) Average degree of a peer in the overlay (i.e., its out-degree) is independent of channel population size, (3) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing users, PPLive peers are impatient, (5) Session lengths (discretized, per channel) are typically geometrically distributed, (6) Channel Population Size variations are larger than in p2p file-sharing networks, yet they can be fitted with polynomial mathematical models. We conclude with a series of suggestions on how our findings can improve IPTV future design.

Measurement and modeling of a large-scale overlay for multimedia streaming

Smart environments such as smart grid, smart transportation, smart buildings are upon us because of major advances in sensor, communication, cloud and other cyber-physical system technologies. The collective name for interconnected sensors, placed on "things" within fixed cyber-physical infrastructures, is Internet of Things (IoT). IoT enables cities and rural areas to become smarter and to offer new digital services and functions to diverse groups of users. However, IoT often represents interconnection of static things, which are built-in into the physical infrastructures of users' homes, offices, roads and other physical and critical infrastructures. In this paper, we analyze things that are mobile, and explore the space of Internet of Mobile Things (IoMT). Mobility of digital devices such as phones and vehicles has been with us for some time, but as the number of sensors in mobile devices increases, the density of mobile devices increases, and users' reliance on mobile devices increases, mobile things become very much an integral fabric of our smart environment. In this paper, our goal is to discuss challenges, selective designs and implementations of IoMT. We show the impact of mobility and the care we collectively have to take when designing the next generation of smart environments with mobile things in them.

Internet of Mobile Things: Mobility-Driven Challenges, Designs and Implementations

To answer user queries, a data integration system employs a set of semantic mappings between the mediated schema and the schemas of data sources. In dynamic environments sources often undergo changes that invalidate the mappings. Hence, once the system is deployed, the administrator must monitor it over time, to detect and repair broken mappings. Today such continuous monitoring is extremely labor intensive, and poses a key bottleneck to the widespread deployment of data integration systems in practice.We describe MAVERIC, an automatic solution to detecting broken mappings. At the heart of MAVERIC is a set of computationally inexpensive modules called sensors, which capture salient characteristics of data sources (e.g., value distributions, HTML layout properties). We describe how MAVERIC trains and deploys the sensors to detect broken mappings. Next we develop three novel improvements: perturbation (i.e., injecting artificial changes into the sources) and multi-source training to improve detection accuracy, and filtering to further reduce the number of false alarms. Experiments over 114 real-world sources in six domains demonstrate the effectiveness of our sensor-based approach over existing solutions, as well as the utility of our improvements.

/pdf/mapping-maintenance-for-data-integration-systems-389ta3ua0o.pdf

Mapping maintenance for data integration systems

 While several deployed p2p le sharing overlays have been characterized in the literature, this paper shows that some of their conclusions may be false for p2p applications that stream media instead. Speci cally, we undertake a crawler-based investigation of PPLive, the largest live multimedia streaming system in the world today. It is important to understand IPTV overlays like PPLive in order to enable the building of larger-scale media streaming overlays. Our task is challenging because PPLive is proprietary. PPLive has multiple channels, each channel with its own overlay, and a large fraction of these channels stream preset movie schedules. A human user may join any given channel, but the user's client machine could be used to relay feeds for other non-subscribed channels too. Popular PPLive channels contain several thousands of nodes. We crawl the real deployed PPLive network via both machines in a cluster at UIUC, and by using PlanetLab hosts. Our major ndings are: (1) Unlike p2p le sharing users, PPLive peers are impatient, (2) Channel Size variations are larger than in p2p le sharing networks, (3) Average degree of a peer in the overlay (i.e., its out-degree) is independent of channel size, (4) Smaller PPLive overlays are similar to random graphs in structure, (5) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation. We believe these results point us towards taking seriously the nature of applications while designing and optimizing p2p overlays.

https://www.ideals.illinois.edu/bitstream/handle/2142/11240/Mapping%20the%20PPLive%20Network%20Studying%20the%20Impacts%20of%20Media%20Streaming%20on%20P2P%20Overlays.pdf?sequence=2

Mapping the PPLive Network: Studying the Impacts of Media Streaming on P2P Overlays

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