OpenRaiser/Intern-Atlas huggingface dataset:
Latent graphical models for quantifying and predicting patent quality
Authors: Yan Liu, Pei-yun Hseuh, Rick Lawrence, Steve Meliksetian, Claudia Perlich, Alejandro Veen
Venue: KDD
Year: 2011

The number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents.
Benefits of bias: towards better characterization of network sampling
Authors: Arun S. Maiya, Tanya Y. Berger-Wolf
Venue: KDD
Year: 2011

From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural representativeness. We show that certain biases are, in fact, beneficial for many applications, as they "push" the sampling process towards inclusion of desired properties. Finally, we describe how these sampling biases can be exploited in several, real-world applications including disease outbreak detection and market research.
Spatially regularized logistic regression for disease mapping on large moving populations
Authors: Vuk Malbasa, Slobodan Vucetic
Venue: KDD
Year: 2011

Spatial analysis of disease risk, or disease mapping, typically relies on information about the residence and health status of individuals from population under study. However, residence information has its limitations because people are exposed to numerous disease risks as they spend time outside of their residences. Thanks to the wide-spread use of mobile phones and GPS-enabled devices, it is becoming possible to obtain a detailed record about the movement of human populations. Availability of movement information opens up an opportunity to improve the accuracy of disease mapping. Starting with an assumption that an individual's disease risk is a weighted average of risks at the locations which were visited, we show that disease mapping can be accomplished by spatially regularized logistic regression. Due to the inherent sparsity of movement data, the proposed approach can be applied to large populations and over large spatial grids. In our experiments, we were able to map disease for a simulated population with 1.6 million people and a spatial grid with 65 thousand locations in several minutes. The results indicate that movement information can improve the accuracy of disease mapping as compared to residential data only. We also studied a privacy-preserving scenario in which only the aggregate statistics are available about the movement of the overall population, while detailed movement information is available only for individuals with disease. The results indicate that the accuracy of disease mapping remains satisfactory when learning from movement data sanitized in this way.
From market baskets to mole rats: using data mining techniques to analyze RFID data describing laboratory animal behavior
Authors: Daniel P. McCloskey, Michael E. Kress, Susan P. Imberman, Igor Kushnir, Susan Briffa-Mirabella
Venue: KDD
Year: 2011

The use of new technologies, such as RFID sensors, provides scientists with novel ways of doing experimental research. As scientists become more technologically savvy and use these techniques, the traditional approaches to data analysis fail given the huge amounts of data produced by these methods. In this paper we describe an experiment in which colonies of naked mole rats were tagged with RFID transponders. RFID sensors were strategically placed in the mole rat caging system. The goal of this experiment was to document and analyze the interactions between animals. The huge amount of data produced by the sensors was not analyzable using the traditional methods employed by behavioral neuroscience researchers. Computational methods used by data miners, such as cluster analysis, association rule mining, and graphical models, were able to scale to the data and produce knowledge and insight that was previously unknown. This paper describes in detail the experimental setup and the computational methods used.
Operational security analytics: doing more with less
Authors: Colleen McCue
Venue: KDD
Year: 2011

Why just count crime when you can anticipate, prevent and respond more effectively? Companies in the commercial sector have long understood the importance of being able to anticipate or predict future behavior and demand in order to respond efficiently and effectively. Embracing the promise of predictive analytics, the public safety community is moving from a focus on "what happened," to a system that enables the ability to anticipate future events and effectively deploy resources in front of crime; thereby, changing outcomes. While we have become familiar with the use of advanced analytics in support of fraud detection and prevention, techniques similar to those used to support customer loyalty programs and supply chain management have been used to prevent and solve violent crimes, enhance investigative pace and efficacy, support information-based risk and threat assessment, and deploy public safety resources more efficiently. As public safety agencies increasingly are asked to do more with less, the ability to anticipate crime represents a game changing paradigm shift; enabling information-based tactics, strategy and policy in support of prevention and response. Reporting, collecting and compiling data are necessary but not sufficient to increasing public safety. Ultimately, the ability to anticipate, prevent and respond more effectively will enable us to do more with less and change public safety outcomes.
Response prediction using collaborative filtering with hierarchies and side-information
Authors: Aditya Krishna Menon, Krishna Prasad Chitrapura, Sachin Garg, Deepak Agarwal, Nagaraj Kota
Venue: KDD
Year: 2011

In online advertising, response prediction is the problem of estimating the probability that an advertisement is clicked when displayed on a content publisher's webpage. In this paper, we show how response prediction can be viewed as a problem of matrix completion, and propose to solve it using matrix factorization techniques from collaborative filtering (CF). We point out the two crucial differences between standard CF problems and response prediction, namely the requirement of predicting probabilities rather than scores, and the issue of confidence in matrix entries. We address these issues using a matrix factorization analogue of logistic regression, and by applying a principled confidence-weighting scheme to its objective. We show how this factorization can be seamlessly combined with explicit features or side-information for pages and ads, which let us combine the benefits of both approaches. Finally, we combat the extreme sparsity of response prediction data by incorporating hierarchical information about the pages and ads into our factorization model. Experiments on three very large real-world datasets show that our model outperforms current state-of-the-art methods for response prediction.
Active learning using on-line algorithms
Authors: Chris Mesterharm, Michael J. Pazzani
Venue: KDD
Year: 2011

This paper describes a new technique and analysis for using on-line learning algorithms to solve active learning problems. Our algorithm is called Active Vote, and it works by actively selecting instances that force several perturbed copies of an on-line algorithm to make mistakes. The main intuition for our result is based on the fact that the number of mistakes made by the optimal on-line algorithm is a lower bound on the number of labels needed for active learning. We provide performance bounds for Active Vote in both a batch and on-line model of active learning. These performance bounds depend on the algorithm having a set of unlabeled instances in which the various perturbed on-line algorithms disagree. The motivating application for Active Vote is an Internet advertisement rating program. We conduct experiments using data collected for this advertisement problem along with experiments using standard datasets. We show Active Vote can achieve an order of magnitude decrease in the number of labeled instances over various passive learning algorithms such as Support Vector Machines.
Estimating the number of users behind ip addresses for combating abusive traffic
Authors: Ahmed Metwally, Matt Paduano
Venue: KDD
Year: 2011

This paper addresses estimating the number of the users of a specific application behind IP addresses (IPs). This problem is central to combating abusive traffic, such as DDoS attacks, ad click fraud and email spam. We share our experience building a general framework at Google for estimating the number of users behind IPs, called hereinafter the sizes of the IPs. The primary goal of this framework is combating abusive traffic without violating the user privacy. The estimation techniques produce statistically sound estimates of sizes relying solely on passively mining aggregated application log data, without probing machines or deploying active content like Java applets. This paper also explores using the estimated sizes to detect and filter abusive traffic. The proposed framework was used to build and deploy an ad click fraud filter at Google. The first 50M clicks tagged by the filter had a significant recall of all tagged clicks, and their false positive rate was below 1.4%. For the sake of comparison, we simulated a naive IP-based filter that does not consider the sizes of the IPs. To reach a comparable recall, the naive filter's false positive rate was 37% due to aggressive tagging.
Linear scale semantic mining algorithms in microsoft SQL server's semantic platform
Authors: Kunal Mukerjee, Todd Porter, Sorin Gherman
Venue: KDD
Year: 2011

This paper describes three linear scale, incremental, and fully automatic semantic mining algorithms that are at the foundation of the new Semantic Platform being released in the next version of SQL Server. The target workload is large (10 -- 100 million) Enterprise document corpuses. At these scales, anything short of linear scale and incremental is costly to deploy. These three algorithms give rise to three weighted physical indexes: Tag Index (top keywords in each document); Document Similarity Index (top closely related documents given any document); and Semantic Phrase Similarity Index (top semantically related phrases, given any phrase), which are then query-able through the SQL interface. The need for specifically creating these three indexes was motivated by observing typical stages of document research, and gap analysis, given current tools and technology at the Enterprise. We describe the mining algorithms and architecture, and also outline some compelling user experiences that are enabled by the indexes.
Collective graph identification
Authors: Galileo Namata, Stanley Kok, Lise Getoor
Venue: KDD
Year: 2011

Data describing networks (communication networks, transaction networks, disease transmission networks, collaboration networks, etc.) is becoming increasingly ubiquitous. While this observational data is useful, it often only hints at the actual underlying social or technological structures which give rise to the interactions. For example, an email communication network provides useful insight but is not the same as the "real" social network among individuals. In this paper, we introduce the problem of graph identification, i.e., the discovery of the true graph structure underlying an observed network. We cast the problem as a probabilistic inference task, in which we must infer the nodes, edges, and node labels of a hidden graph, based on evidence provided by the observed network. This in turn corresponds to the problems of performing entity resolution, link prediction, and node labeling to infer the hidden graph. While each of these problems have been studied separately, they have never been considered together as a coherent task. We present a simple yet novel approach to address all three problems simultaneously. Our approach, called C3, consists of Coupled Collective Classifiers that are iteratively applied to propagate information among solutions to the problems. We empirically demonstrate that C3 is superior, in terms of both predictive accuracy and runtime, to state-of-the-art probabilistic approaches on three real-world problems.
The power of analysis and data
Authors: David Norton
Venue: KDD
Year: 2011

Caesars Entertainment, the largest provider of branded casino entertainment, captures a wealth of data for 40 million+ customers through its Total Rewards program. In-depth data analysis has helped Caesars weather the economic downturn by prioritizing marketing spend, expense savings targets and identifying new revenue opportunities. This talk will describe how closed-loop marketing, state-of-the-art user segmentation, and ongoing experimentation via test and control groups have enabled Caesars Entertainment to achieve all-time high customer satisfaction scores and outperform the competition in a challenging economic climate. The lessons learned are generic and apply across multiple industries. Insights will also be provided on the next wave of challenges to be answered analytically.
Internet scale data analysis
Authors: Peter Norvig
Venue: KDD
Year: 2011

This talk covers techniques for analyzing data sets with up to trillions of examples with billions of features, using thousands of computers. To operate at this scale requires an understanding of an increasing complex hardware hierarchy (e.g. cache, memory, SSD, another machine in the rack, disk, a machine in another data center, ...); a model for recovering from inevitable hardware and software failures; a machine learning model that allows for efficient computation over large, continuously updated data sets; and a way to visualize and share the results.
Display advertising impact: search lift and social influence
Authors: Panagiotis Papadimitriou, Hector Garcia-Molina, Prabhakar Krishnamurthy, Randall A. Lewis, David H. Reiley
Venue: KDD
Year: 2011

We study the impact of display advertising on user search behavior using a field experiment. In such an experiment, the treatment group users are exposed to some display advertising campaign, while the control group users are not. During the campaign and the post-campaign period we monitor the user search queries and we label them as relevant or irrelevant to the campaign using techniques that leverage the bipartite query-URL click graph. Our results indicate that users who are exposed to the advertising campaign submit 5% to 25% more queries that are relevant to it compared to the unexposed users.
Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges
Authors: Debprakash Patnaik, Patrick Butler, Naren Ramakrishnan, Laxmi Parida, Benjamin J. Keller, David A. Hanauer
Venue: KDD
Year: 2011

The standardization and wider use of electronic medical records (EMR) creates opportunities for better understanding patterns of illness and care within and across medical systems. Our interest is in the temporal history of event codes embedded in patients' records, specifically investigating frequently occurring sequences of event codes across patients. In studying data from more than 1.6 million patient histories at the University of Michigan Health system we quickly realized that frequent sequences, while providing one level of data reduction, still constitute a serious analytical challenge as many involve alternate serializations of the same sets of codes. To further analyze these sequences, we designed an approach where a partial order is mined from frequent sequences of codes. We demonstrate an EMR mining system called EMRView that enables exploration of the precedence relationships to quickly identify and visualize partial order information encoded in key classes of patients. We demonstrate some important nuggets learned through our approach and also outline key challenges for future research based on our experiences.
Democrats, republicans and starbucks afficionados: user classification in twitter
Authors: Marco Pennacchiotti, Ana-Maria Popescu
Venue: KDD
Year: 2011

More and more technologies are taking advantage of the explosion of social media (Web search, content recommendation services, marketing, ad targeting, etc.). This paper focuses on the problem of automatically constructing user profiles, which can significantly benefit such technologies. We describe a general and robust machine learning framework for large-scale classification of social media users according to dimensions of interest. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business.
Classification of functional magnetic resonance imaging data using informative pattern features
Authors: Francisco Pereira, Matthew M. Botvinick
Venue: KDD
Year: 2011

The canonical technique for analyzing functional magnetic resonance imaging (fMRI) data, statistical parametric mapping, produces maps of brain locations that are more active during performance of a task than during a control condition. In recent years, there has been increasing awareness of the fact that there is information in the entire pattern of brain activation and not just in saliently active locations. Classifiers have been the tool of choice for capturing this information and used to make predictions ranging from what kind of object a subject is thinking about to what decision they will make. Such classifiers are usually trained on a selection of voxels from the 3D grid that makes up the activation pattern; often this means the best accuracy is obtained using few voxels, from all across the brain, and that different voxels will be chosen in different cross-validation folds, making the classifiers hard to interpret. The increasing commonality of datasets with tens to hundreds of classes makes this problem even more acute. In this paper we introduce a method for identifying informative subsets of adjacent voxels, corresponding to brain patches that distinguish subsets of classes. These patches can then be used to train classifiers for the distinctions they support and used as "pattern features" for a meta-classifier. We show that this method permits classification at a higher accuracy than that obtained with traditional voxel selection, and that the sets of voxels used are more reproducible across cross-validation folds than those identified with voxel selection, and lie in plausible brain locations.
Frontex real-time news event extraction framework
Authors: Jakub Piskorski, Martin Atkinson
Venue: KDD
Year: 2011

An ever-growing amount of information relevant for early detection of certain threats can be extracted from on-line news. This led to an emergence of news mining tools to help analysts to digest the overflow of information and to extract valuable knowledge from on line news sources. This paper gives an overview of the fully operational Real-time News Event Extraction Framework developed for Frontex, the EU Border Agency, to facilitate the process of extracting structured information on border security-related events from on-line news. In particular, a hybrid event extraction system has been constructed, which is applied to the stream of news articles continuously gathered and pre-processed by the Europe Media Monitor - a large-scale multilingual news aggregation engine. The framework consists also of an earth browser, in which events are visualized and an event moderation tool, which allows to access the database of automatically extracted event descriptions and to clean, validate, group, enhance and export them into other knowledge repositories.
Mining partially annotated images
Authors: Zhongang Qi, Ming Yang, Zhongfei (Mark) Zhang, Zhengyou Zhang
Venue: KDD
Year: 2011

In this paper, we study the problem of mining partially annotated images. We first define what the problem of mining partially annotated images is, and argue that in many real-world applications annotated images are typically partially annotated and thus that the problem of mining partially annotated images exists in many situations. We then propose an effective solution to this problem based on a statistical model we have developed called the Semi-Supervised Correspondence Hierarchical Dirichlet Process (SSCHDP). The main idea of this model lies in exploiting the information pertaining to partially annotated images or even unannotated images to achieve semi-supervised learning under the HDP structure. We apply this model to completing the annotations appropriately for partially annotated images in the training data and then to predicting the annotations appropriately and completely for all the unannotated images either in the training data or in any unseen data beyond the training process. Experiments show that SSC-HDP is superior to the peer models from the recent literature when they are applied to solving the problem of mining partially annotated images.
Video analytics solution for tracking customer locations in retail shopping malls
Authors: Harikrishna G. N. Rai, Kishore Jonna, P. Radha Krishna
Venue: KDD
Year: 2011

Due to increased adoption of digital inclusion in various businesses, location based services are gaining importance to provide value-added services for their customers. In this work, we present a computer vision based system for tracking customer locations by recognizing individual shopping carts inside shopping malls in order to facilitate location based services. We provide an efficient approach for cart recognition that consists of two stages: cart detection and then cart recognition. A binary pattern is placed between two pre-defined color markers and attached to each cart for recognition. The system takes live video feed as input from the cameras mounted on the aisles of the shopping mall and processes frames in real-time. In the cart detection stage, color segmentation, feature extraction and classification are used for detection of binary pattern along with color markers. In recognition stage, segmented binary strip is processed using spatial image processing techniques to decode the cart identification number.
Ask me better questions: active learning queries based on rule induction
Authors: Parisa Rashidi, Diane J. Cook
Venue: KDD
Year: 2011

Active learning methods are used to improve the classification accuracy when little labeled data is available. Most traditional active learning methods pose a very specific query to the oracle, i.e. they ask for the label of an unlabeled example. This paper proposes a novel active learning method called RIQY (Rule Induced active learning QuerY). It can construct generic active learning queries based on rule induction from multiple unlabeled instances. These queries are shorter and more readable for the oracle and encompass many similar cases. Also the learning algorithm can achieve higher accuracy rates by asking fewer queries. We evaluate our algorithm on 12 different real datasets. Our results show that we can achieve higher accuracy rates using fewer queries compared to the traditional active learning methods.