OpenRaiser/Intern-Atlas huggingface dataset:
Real-time risk control system for CNP (card not present)
Authors: Tai Hsu
Venue: KDD
Year: 2011

AliExpress is an online e-commerce platform for wholesale products. Credit card is one of its various payment methods. An online transaction using credit cards is called a "card not present" (CNP) transaction where the physical card has not been swiped into a reader. It's also the major type of credit card frauds causing a great overhead of the online operation, sellers, and buyers. To protect customers on our platform, we developed a real-time credit card fraud detection system, using the machine learning technologies which allows us to achieve a precision of 97%, at a recall of 80%. With the system, we can provide the best online shopping experience for our customers, without the high risk of online transactions which always result a high operational cost. We will briefly share our experience and practice in the expo.
Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network
Authors: Shuai Huang, Jing Li, Jieping Ye, Adam Fleisher, Kewei Chen, Teresa Wu, Eric Reiman
Venue: KDD
Year: 2011

Recent studies have shown that Alzheimer's disease (AD) is related to alteration in brain connectivity networks. One type of connectivity, called effective connectivity, defined as the directional relationship between brain regions, is essential to brain function. However, there have been few studies on modeling the effective connectivity of AD and characterizing its difference from normal controls (NC). In this paper, we investigate the sparse Bayesian Network (BN) for effective connectivity modeling. Specifically, we propose a novel formulation for the structure learning of BNs, which involves one L1-norm penalty term to impose sparsity and another penalty to ensure the learned BN to be a directed acyclic graph - a required property of BNs. We show, through both theoretical analysis and extensive experiments on eleven moderate and large benchmark networks with various sample sizes, that the proposed method has much improved learning accuracy and scalability compared with ten competing algorithms. We apply the proposed method to FDG-PET images of 42 AD and 67 NC subjects, and identify the effective connectivity models for AD and NC, respectively. Our study reveals that the effective connectivity of AD is different from that of NC in many ways, including the global-scale effective connectivity, intra-lobe, interlobe, and inter-hemispheric effective connectivity distributions, as well as the effective connectivity associated with specific brain regions. These findings are consistent with known pathology and clinical progression of AD, and will contribute to AD knowledge discovery.
Bounded coordinate-descent for biological sequence classification in high dimensional predictor space
Authors: Georgiana Ifrim, Carsten Wiuf
Venue: KDD
Year: 2011

We present a framework for discriminative sequence classification where linear classifiers work directly in the explicit high-dimensional predictor space of all subsequences in the training set (as opposed to kernel-induced spaces). This is made feasible by employing a gradient-bounded coordinate-descent algorithm for efficiently selecting discriminative subsequences without having to expand the whole space. Our framework can be applied to a wide range of loss functions, including binomial log-likelihood loss of logistic regression and squared hinge loss of support vector machines. When applied to protein remote homology detection and remote fold recognition, our framework achieves comparable performance to the state-of-the-art (e.g., kernel support vector machines). In contrast to state-of-the-art sequence classifiers, our models are simply lists of weighted discriminative subsequences and can thus be interpreted and related to the biological problem -- a crucial requirement for the bioinformatics and medical communities.
Accelerating large-scale data mining using in-database analytics
Authors: Mario E. Inchiosa
Venue: KDD
Year: 2011

In more and more industries, competitive advantage hinges on exploiting the largest quantity of data in the shortest possible time - and doing so cost-effectively. Data volumes are growing exponentially, while businesses are striving to deploy sophisticated and computationally intensive predictive analytics. Often, massive data is stored in a data warehouse running on dedicated parallel hardware, but advanced analytics is performed on a separate compute platform. Moving data from the data warehouse to the compute environment can constitute a significant bottleneck. Organizations resort to considering only a fraction of their data or refreshing their analyses infrequently. To address the data movement bottleneck and take full advantage of parallel data warehouse platforms, vendors are offering new in-database analytics capabilities. They are opening up their platforms, allowing users to run their own user-defined functions and statistical models as well as vendor- and partner-supplied advanced analytics on the database platform, close to the data, in parallel, without transporting the data through a host node or corporate network. In this talk, we will present the need for in-database analytics and discuss a number of the new solutions available, highlighting case studies where solution times have been reduced from hours to minutes or seconds.
Ranking-based classification of heterogeneous information networks
Authors: Ming Ji, Jiawei Han, Marina Danilevsky
Venue: KDD
Year: 2011

It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world. Both classification and ranking of the nodes (or data objects) in such networks are essential for network analysis. However, so far these approaches have generally been performed separately. In this paper, we combine ranking and classification in order to perform more accurate analysis of a heterogeneous information network. Our intuition is that highly ranked objects within a class should play more important roles in classification. On the other hand, class membership information is important for determining a quality ranking over a dataset. We believe it is therefore beneficial to integrate classification and ranking in a simultaneous, mutually enhancing process, and to this end, propose a novel ranking-based iterative classification framework, called RankClass. Specifically, we build a graph-based ranking model to iteratively compute the ranking distribution of the objects within each class. At each iteration, according to the current ranking results, the graph structure used in the ranking algorithm is adjusted so that the sub-network corresponding to the specific class is emphasized, while the rest of the network is weakened. As our experiments show, integrating ranking with classification not only generates more accurate classes than the state-of-art classification methods on networked data, but also provides meaningful ranking of objects within each class, serving as a more informative view of the data than traditional classification.
Ontology enhancement and concept granularity learning: keeping yourself current and adaptive
Authors: Shan Jiang, Lidong Bing, Bai Sun, Yan Zhang, Wai Lam
Venue: KDD
Year: 2011

As a well-known semantic repository, WordNet is widely used in many applications. However, due to costly edit and maintenance, WordNet's capability of keeping up with the emergence of new concepts is poor compared with on-line encyclopedias such as Wikipedia. To keep WordNet current with folk wisdom, we propose a method to enhance WordNet automatically by merging Wikipedia entities into WordNet, and construct an enriched ontology, named as WorkiNet. WorkiNet keeps the desirable structure of WordNet. At the same time, it captures abundant information from Wikipedia. We also propose a learning approach which is able to generate a tailor-made semantic concept collection for a given document collection. The learning process takes the characteristics of the given document collection into consideration and the semantic concepts in the tailor-made collection can be used as new features for document representation. The experimental results show that the adaptively generated feature space can outperform a static one significantly in text mining tasks, and WorkiNet dominates WordNet most of the time due to its high coverage.
Anomaly localization for network data streams with graph joint sparse PCA
Authors: Ruoyi Jiang, Hongliang Fei, Jun Huan
Venue: KDD
Year: 2011

Determining anomalies in data streams that are collected and transformed from various types of networks has recently attracted significant research interest. Principal Component Analysis (PCA) has been extensively applied to detecting anomalies in network data streams. However, none of existing PCA based approaches addresses the problem of identifying the sources that contribute most to the observed anomaly, or anomaly localization. In this paper, we propose novel sparse PCA methods to perform anomaly detection and localization for network data streams. Our key observation is that we can localize anomalies by identifying a sparse low dimensional space that captures the abnormal events in data streams. To better capture the sources of anomalies, we incorporate the structure information of the network stream data in our anomaly localization framework. We have performed comprehensive experimental studies of the proposed methods, and have compared our methods with the state-ofthe-art using three real-world data sets from different application domains. Our experimental studies demonstrate the utility of the proposed methods.
Discovering highly reliable subgraphs in uncertain graphs
Authors: Ruoming Jin, Lin Liu, Charu C. Aggarwal
Venue: KDD
Year: 2011

In this paper, we investigate the highly reliable subgraph problem, which arises in the context of uncertain graphs. This problem attempts to identify all induced subgraphs for which the probability of connectivity being maintained under uncertainty is higher than a given threshold. This problem arises in a wide range of network applications, such as protein-complex discovery, network routing, and social network analysis. Since exact discovery may be computationally intractable, we introduce a novel sampling scheme which enables approximate discovery of highly reliable subgraphs with high probability. Furthermore, we transform the core mining task into a new frequent cohesive set problem in deterministic graphs. Such transformation enables the development of an efficient two-stage approach which combines novel peeling techniques for maximal set discovery with depth-first search for further enumeration. We demonstrate the effectiveness and efficiency of the proposed algorithms on real and synthetic data sets.
LikeMiner: a system for mining the power of 'like' in social media networks
Authors: Xin Jin, Chi Wang, Jiebo Luo, Xiao Yu, Jiawei Han
Venue: KDD
Year: 2011

Social media is becoming increasingly ubiquitous and popular on the Internet. Due to the huge popularity of social media websites, such as Facebook, Twitter, YouTube and Flickr, many companies or public figures are now active in maintaining pages on those websites to interact with online users, attracting a large number of fans/followers by posting interesting objects, e.g., (product) photos/videos and text messages. 'Like' has now become a very popular social function by allowing users to express their like of certain objects. It provides an accurate way of estimating user interests and an effective way of sharing/promoting information in social media. In this demo, we propose a system called LikeMiner to mine the power of 'like' in social media networks. We introduce a heterogeneous network model for social media with 'likes', and propose 'like' mining algorithms to estimate representativeness and influence of objects. The implemented prototype system demonstrates the effectiveness of the proposed approach using the large scale Facebook data.
Matching unstructured product offers to structured product specifications
Authors: Anitha Kannan, Inmar E. Givoni, Rakesh Agrawal, Ariel Fuxman
Venue: KDD
Year: 2011

An e-commerce catalog typically comprises of specifications for millions of products. The search engine receives millions of sales offers from thousands of independent merchants that must be matched to the right products. We describe the challenges that a system for matching unstructured offers to structured product descriptions must address, drawing upon our experience from building such a system for Bing Shopping. The heart of our system is a data-driven component that learns the matching function off-line, which is then applied at run-time for matching offers to products. We provide the design of this and other critical components of the system as well as the details of the extensive experiments we performed to assess the readiness of the system. This system is currently deployed in an experimental Commerce Search Engine and is used to match all the offers received by Bing Shopping to the Bing product catalog.
Leakage in data mining: formulation, detection, and avoidance
Authors: Shachar Kaufman, Saharon Rosset, Claudia Perlich
Venue: KDD
Year: 2011

Deemed "one of the top ten data mining mistakes", leakage is essentially the introduction of information about the data mining target, which should not be legitimately available to mine from. In addition to our own industry experience with real-life projects, controversies around several major public data mining competitions held recently such as the INFORMS 2010 Data Mining Challenge and the IJCNN 2011 Social Network Challenge are evidence that this issue is as relevant today as it has ever been. While acknowledging the importance and prevalence of leakage in both synthetic competitions and real-life data mining projects, existing literature has largely left this idea unexplored. What little has been said turns out not to be broad enough to cover more complex cases of leakage, such as those where the classical i.i.d. assumption is violated, that have been recently documented. In our new approach, these cases and others are explained by explicitly defining modeling goals and analyzing the broader framework of the data mining problem. The resulting definition enables us to derive general methodology for dealing with the issue. We show that it is possible to avoid leakage with a simple specific approach to data management followed by what we call a learn-predict separation, and present several ways of detecting leakage when the modeler has no control over how the data have been collected.
Dual active feature and sample selection for graph classification
Authors: Xiangnan Kong, Wei Fan, Philip S. Yu
Venue: KDD
Year: 2011

Graph classification has become an important and active research topic in the last decade. Current research on graph classification focuses on mining discriminative subgraph features under supervised settings. The basic assumption is that a large number of labeled graphs are available. However, labeling graph data is quite expensive and time consuming for many real-world applications. In order to reduce the labeling cost for graph data, we address the problem of how to select the most important graph to query for the label. This problem is challenging and different from conventional active learning problems because there is no predefined feature vector. Moreover, the subgraph enumeration problem is NP-hard. The active sample selection problem and the feature selection problem are correlated for graph data. Before we can solve the active sample selection problem, we need to find a set of optimal subgraph features. To address this challenge, we demonstrate how one can simultaneously estimate the usefulness of a query graph and a set of subgraph features. The idea is to maximize the dependency between subgraph features and graph labels using an active learning framework. We propose a branch-and-bound algorithm to search for the optimal query graph and optimal features simultaneously. Empirical studies on nine real-world tasks demonstrate that the proposed method can obtain better accuracy on graph data than alternative approaches.
Temporal multi-hierarchy smoothing for estimating rates of rare events
Authors: Nagaraj Kota, Deepak Agarwal
Venue: KDD
Year: 2011

We consider the problem of estimating rates of rare events obtained through interactions among several categorical variables that are heavy-tailed and hierarchical. In our previous work, we proposed a scalable log-linear model called LMMH (Log-Linear Models for Multiple Hierarchies) that combats data sparsity at granular levels through small sample size corrections that borrow strength from rate estimates at coarser resolutions. This paper extends our previous work in two directions. First, we model excess heterogeneity by fitting local LMMH models to relatively homogeneous subsets of the data. To ensure scalable computation, these subsets are induced through a decision tree, we call this Treed-LMMH. Second, the Treed-LMMH method is coupled with temporal smoothing procedure based on a fast Kalman filter style algorithm. We show that simultaneously performing hierarchical and temporal smoothing leads to significant improvement in predictive accuracy. Our methods are illustrated on a large scale computational advertising dataset consisting of billions of observations and hundreds of millions of attribute combinations(cells).
Mining mobility data to minimise travellers' spending on public transport
Authors: Neal Lathia, Licia Capra
Venue: KDD
Year: 2011

As the public transport infrastructure of large cities expands, transport operators are diversifying the range and prices of tickets that can be purchased for travel. However, selecting the best fare for each individual traveller's needs is a complex process that is left almost completely unaided. By examining the relation between urban mobility and fare purchasing habits in large datasets from London, England's public transport network, we estimate that travellers in the city cumulatively spend, per year, up to approximately GBP 200 million more than they need to, as a result of purchasing the incorrect fares. We propose to address these incorrect purchases by leveraging the huge volumes of data that travellers create as they move about the city, by providing, to each of them, personalised ticket recommendations based on their estimated future travel patterns. In this work, we explore the viability of building a fare-recommendation system for public transport networks by (a) formalising the problem as two separate prediction problems and (b) evaluating a number of algorithms that aim to match travellers to the best fare. We find that applying data mining techniques to public transport data has the potential to provide travellers with substantial savings.
Protecting location privacy using location semantics
Authors: Byoungyoung Lee, Jinoh Oh, Hwanjo Yu, Jong Kim
Venue: KDD
Year: 2011

As the use of mobile devices increases, a location-based service (LBS) becomes increasingly popular because it provides more convenient context-aware services. However, LBS introduces problematic issues for location privacy due to the nature of the service. Location privacy protection methods based on k-anonymity and l-diversity have been proposed to provide anonymized use of LBS. However, the k-anonymity and l-diversity methods still can endanger the user's privacy because location semantic information could easily be breached while using LBS. This paper presents a novel location privacy protection technique, which protects the location semantics from an adversary. In our scheme, location semantics are first learned from location data. Then, the trusted-anonymization server performs the anonymization using the location semantic information by cloaking with semantically heterogeneous locations. Thus, the location semantic information is kept secure as the cloaking is done with semantically heterogeneous locations and the true location information is not delivered to the LBS applications. This paper proposes algorithms for learning location semantics and achieving semantically secure cloaking.
Social flocks: a crowd simulation framework for social network generation, community detection, and collective behavior modeling
Authors: Cheng-Te Li, Shou-De Lin
Venue: KDD
Year: 2011

This work combines the central ideas from two different areas, crowd simulation and social network analysis, to tackle some existing problems in both areas from a new angle. We present a novel spatio-temporal social crowd simulation framework, Social Flocks, to revisit three essential research problems, (a) generation of social networks, (b) community detection in social networks, (c) modeling collective social behaviors in crowd simulation. Our framework produces social networks that satisfy the properties of high clustering coefficient, low average path length, and power-law degree distribution. It can also be exploited as a novel dynamic model for community detection. Finally our framework can be used to produce real-life collective social behaviors over crowds, including community-guided flocking, leader following, and spatio-social information propagation. Social Flocks can serve as visualization of simulated crowds for domain experts to explore the dynamic effects of the spatial, temporal, and social factors on social networks. In addition, it provides an experimental platform of collective social behaviors for social gaming and movie animations. Social Flocks demo is at http://mslab.csie.ntu.edu.tw/socialflocks/ .
ThermoCast: a cyber-physical forecasting model for datacenters
Authors: Lei Li, Chieh-Jan Mike Liang, Jie Liu, Suman Nath, Andreas Terzis, Christos Faloutsos
Venue: KDD
Year: 2011

Efficient thermal management is important in modern data centers as cooling consumes up to 50% of the total energy. Unlike previous work, we consider proactive thermal management, whereby servers can predict potential overheating events due to dynamics in data center configuration and workload, giving operators enough time to react. However, such forecasting is very challenging due to data center scales and complexity. Moreover, such a physical system is influenced by cyber effects, including workload scheduling in servers. We propose ThermoCast, a novel thermal forecasting model to predict the temperatures surrounding the servers in a data center, based on continuous streams of temperature and airflow measurements. Our approach is (a) capable of capturing cyberphysical interactions and automatically learning them from data; (b) computationally and physically scalable to data center scales; (c) able to provide online prediction with real-time sensor measurements. The paper's main contributions are: (i) We provide a systematic approach to integrate physical laws and sensor observations in a data center; (ii) We provide an algorithm that uses sensor data to learn the parameters of a data center's cyber-physical system. In turn, this ability enables us to reduce model complexity compared to full-fledged fluid dynamics models, while maintaining forecast accuracy; (iii) Unlike previous simulation-based studies, we perform experiments in a production data center. Using real data traces, we show that ThermoCast forecasts temperature better than a machine learning approach solely driven by data, and can successfully predict thermal alarms 4.2 minutes ahead of time.