OpenRaiser/Intern-Atlas huggingface dataset:
Data Driven Science: SIGKDD Panel
Authors: Katharina Morik, Hugh F. Durrant-Whyte, Gary C. Hill, Dietmar Müller, Tanya Y. Berger-Wolf
Venue: KDD
Year: 2015

The panel session 'Data Driven Science' discusses application and use of knowledge discovery, machine learning and data analytics in science disciplines; in natural, physical, medical and social science; from physics to geology, and from neuroscience to population health. Knowledge discovery methods are finding broad application in all areas of scientific endeavor, to explore experimental data, to discover new models, to propose new scientific theories and ideas. In addition, the availability of ever larger scientific data sets is driving a new data-driven paradigm for modeling of complex phenomena in physical, natural and social sciences.
Graph Query Reformulation with Diversity
Authors: Davide Mottin, Francesco Bonchi, Francesco Gullo
Venue: KDD
Year: 2015

We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Given a query issued by the user, the system, apart from returning the result patterns, also proposes a number of specializations (i.e., supergraphs) of the original query to facilitate the exploration of the results.
Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
Authors: Meenakshi Nagarajan, Angela D. Wilkins, Benjamin J. Bachman, Ilya B. Novikov, Shenghua Bao, Peter J. Haas, María E. Terrón-Díaz, Sumit Bhatia, Anbu K. Adikesavan, Jacques J. Labrie, Sam Regenbogen, Christie M. Buchovecky, Curtis R. Pickering, Linda Kato, Andreas Martin Lisewski, Ana Lelescu, Houyin Zhang, Stephen Boyer, Griff Weber, Ying Chen, Lawrence A. Donehower, W. Scott Spangler, Olivier Lichtarge
Venue: KDD
Year: 2015

We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the understanding of disease. Unlike a prior study, KnIT is now fully automated and demonstrably scalable. It extracts information from the scientific literature, automatically identifying direct and indirect references to protein interactions, which is knowledge that can be represented in network form. It then reasons over this network with techniques such as matrix factorization and graph diffusion to predict new, previously unknown interactions. The accuracy and scope of KnIT's knowledge extractions are validated using comparisons to structured, manually curated data sources as well as by performing retrospective studies that predict subsequent literature discoveries using literature available prior to a given date. The KnIT methodology is a step towards automated hypothesis generation from text, with potential application to other scientific domains.
Learning a Hierarchical Monitoring System for Detecting and Diagnosing Service Issues
Authors: Vinod Nair, Ameya Raul, Shwetabh Khanduja, Vikas Bahirwani, Sundararajan Sellamanickam, S. Sathiya Keerthi, Steve Herbert, Sudheer Dhulipalla
Venue: KDD
Year: 2015

We propose a machine learning based framework for building a hierarchical monitoring system to detect and diagnose service issues. We demonstrate its use for building a monitoring system for a distributed data storage and computing service consisting of tens of thousands of machines. Our solution has been deployed in production as an end-to-end system, starting from telemetry data collection from individual machines, to a visualization tool for service operators to examine the detection outputs. Evaluation results are presented on detecting 19 customer impacting issues in the past three months.
Flexible and Robust Multi-Network Clustering
Authors: Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang
Venue: KDD
Year: 2015

Integrating multiple graphs (or networks) has been shown to be a promising approach to improve the graph clustering accuracy. Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In these methods, a network is treated as a view or domain.The key assumption is that there is a common clustering structure shared across all views (domains), and different views (domains) provide compatible and complementary information on this underlying clustering structure. However, in many emerging real-life applications, different networks have different data distributions, where the assumption that all networks share a single common clustering structure does not hold. In this paper, we propose a flexible and robust framework that allows multiple underlying clustering structures across different networks. Our method models the domain similarity as a network, which can be utilized to regularize the clustering structures in different networks. We refer to such a data model as a network of networks (NoN). We develop NoNClus, a novel method based on non-negative matrix factorization (NMF), to cluster an NoN. We provide rigorous theoretical analysis of NoNClus in terms of its correctness, convergence and complexity. Extensive experimental results on synthetic and real-life datasets show the effectiveness of our method.
Extreme States Distribution Decomposition Method for Search Engine Online Evaluation
Authors: Kirill Nikolaev, Alexey Drutsa, Ekaterina Gladkikh, Alexander Ulianov, Gleb Gusev, Pavel Serdyukov
Venue: KDD
Year: 2015

Nowadays, the development of most leading web services is controlled by online experiments that qualify and quantify the steady stream of their updates. The challenging problem is to define an appropriate online metric of user behavior, so-called Overall Evaluation Criterion (OEC), which is both interpretable and sensitive. The state-of-the-art approach is to choose a type of entities to observe in the behavior data, to define a key metric for these observations, and to estimate the average value of this metric over the observations in each of the system versions. A significant disadvantage of the OEC obtained in this way is that the average value of the key metric does not necessarily change, even if its distribution changes significantly. The reason is that the difference between the mean values of the key metric over the two variants of the system does not necessarily reflect the character of the change in the distribution.
Fast and Robust Parallel SGD Matrix Factorization
Authors: Jinoh Oh, Wook-Shin Han, Hwanjo Yu, Xiaoqian Jiang
Venue: KDD
Year: 2015

Matrix factorization is one of the fundamental techniques for analyzing latent relationship between two entities. Especially, it is used for recommendation for its high accuracy. Efficient parallel SGD matrix factorization algorithms have been developed for large matrices to speed up the convergence of factorization. However, most of them are designed for a shared-memory environment thus fail to factorize a large matrix that is too big to fit in memory, and their performances are also unreliable when the matrix is skewed. This paper proposes a fast and robust parallel SGD matrix factorization algorithm, called MLGF-MF, which is robust to skewed matrices and runs efficiently on block-storage devices (e.g., SSD disks) as well as shared-memory. MLGF-MF uses Multi-Level Grid File (MLGF) for partitioning the matrix and minimizes the cost for scheduling parallel SGD updates on the partitioned regions by exploiting partial match queries processing. Thereby, MLGF-MF produces reliable results efficiently even on skewed matrices. MLGF-MF is designed with asynchronous I/O permeated in the algorithm such that CPU keeps executing without waiting for I/O to complete. Thereby, MLGF-MF overlaps the CPU and I/O processing, which eventually offsets the I/O cost and maximizes the CPU utility. Recent flash SSD disks support high performance parallel I/O, thus are appropriate for executing the asynchronous I/O. From our extensive evaluations, MLGF-MF significantly outperforms (or converges faster than) the state-of-the-art algorithms in both shared-memory and block-storage environments. In addition, the outputs of MLGF-MF is significantly more robust to skewed matrices. Our implementation of MLGF-MF is available at http ://dm.postech.ac.kr/MLGF-MF as executable files.
Efficient PageRank Tracking in Evolving Networks
Authors: Naoto Ohsaka, Takanori Maehara, Ken-ichi Kawarabayashi
Venue: KDD
Year: 2015

Real-world networks, such as the World Wide Web and online social networks, are very large and are evolving rapidly. Thus tracking personalized PageRank in such evolving networks is an important challenge in network analysis and graph mining.
Powering Real-time Decision Engines in Finance and Healthcare using Open Source Software
Authors: Bassel Ojjeh
Venue: KDD
Year: 2015

Financial services and healthcare companies could be the biggest beneficiaries of big data. Their real-time decision engines can be vastly improved by leveraging the latest advances in big data analytics. However, these companies are challenged in leveraging Open Software Systems (OSS). This presentation covers how, in collaboration with financial services and healthcare institutions, we built an OSS project to deliver a real-time decisioning engine for their respective applications. I will address two key issues. First, I will describe the strategy behind our hiring process to attract millennial big data developers and the results of this endeavor. Second, I will recount the collaboration effort that we had with our large clients and the various milestones we achieved during that process. I will explain the goals regarding big data analysis that our large clients presented to us and how we accomplished those goals. In particular, I will discuss how we leveraged open source to deliver a real-time decisioning software product called Fatafat to these institutions. An advantage of developing applications in Fatafat is that it is already integrated with Hadoop, Kafka for real-time data streaming, HBase and Cassandra for NoSQL data storage. I will talk about how these companies benefited from Fatafat and some of challenges we had in the design of this software. I will provide quantifiable improvements in key metrics driven by Fatafat and interesting, unsolved problems/challenges that need to be addressed for faster and wider adoption of OSS by these companies.
Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning
Authors: Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos
Venue: KDD
Year: 2015

This work presents a probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailment: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented as a deterministic Bayesian network. Marginal probabilities are entered as soft evidence in the network and through probabilistic inference become consistent with the discovered knowledge. Our approach offers robust improvements in mean average precision compared to the standard binary relevance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.
Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts
Authors: Kevin B. Pratt
Venue: KDD
Year: 2015

We demonstrate a protocol for proving strongly that a black-box machine learning technique robustly predicts the future in dynamic, indefinite contexts. We propose necessary components of the proof protocol and demonstrate results visualizations to support evaluation of the proof components. Components include contemporaneously verifiable discrete predictions, deterministic computability of longitudinal predictions, imposition of realistic costs and domain constraints, exposure to diverse contexts, statistically significant excess benefits relative to a priori benchmarks and Monte Carlo trials, insignificant decay of excess benefits, pathology detection and an extended real-time trial "in the wild." We apply the protocol to a big data machine learning technique deployed since 2011 that finds persistent, exploitable opportunities in many of 41 segments of US financial markets, the existence of which opportunities substantially contradict the Efficient Market Hypothesis.
A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams
Authors: Abdulhakim Ali Qahtan, Basma Alharbi, Suojin Wang, Xiangliang Zhang
Venue: KDD
Year: 2015

Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations.
State-Driven Dynamic Sensor Selection and Prediction with State-Stacked Sparseness
Authors: Guo-Jun Qi, Charu C. Aggarwal, Deepak S. Turaga, Daby M. Sow, Phil Anno
Venue: KDD
Year: 2015

An important problem in large-scale sensor mining is that of selecting relevant sensors for prediction purposes. Selecting small subsets of sensors, also referred to as active sensors, often leads to lower operational costs, and it reduces the noise and information overload for prediction. Existing sensor selection and prediction models either select a set of sensors a priori, or they use adaptive algorithms to determine the most relevant sensors for prediction. Sensor data sets often show dynamically varying patterns, because of which it is suboptimal to select a fixed subset of active sensors. To address this problem, we develop a novel dynamic prediction model that uses the notion of hidden system states to dynamically select a varying subset of sensors. These hidden system states are automatically learned by our model in a data-driven manner. The proposed algorithm can rapidly switch between different sets of active sensors when the model detects the (periodic or intermittent) change in the system state. We derive the dynamic sensor selection strategy by minimizing the error rates in tracking and predicting sensor readings over time. We introduce the notion of state-stacked sparseness to select a subset of the most critical sensors as a function of evolving system state. We present experimental results on two real sensor datasets, corresponding to oil drilling rig sensors and intensive care unit (ICU) sensors, and demonstrate the superiority of our approach with respect to other models.
Collective Opinion Spam Detection: Bridging Review Networks and Metadata
Authors: Shebuti Rayana, Leman Akoglu
Venue: KDD
Year: 2015

Online reviews capture the testimonials of "real" people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem, with often paid spam reviewers writing fake reviews to unjustly promote or demote certain products or businesses. Existing approaches to opinion spam have successfully but separately utilized linguistic clues of deception, behavioral footprints, or relational ties between agents in a review system.
Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach
Authors: Xiang Ren, Ahmed El-Kishky, Chi Wang, Jiawei Han
Venue: KDD
Year: 2015

In today's computerized and information-based society, we are soaked with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To unlock the value of these unstructured text data from various domains, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their types (<i>e.g.</i>, people, product, food) in a scalable way. We demonstrate on real datasets including news articles and tweets how these typed entities aid in knowledge discovery and management.
Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages
Authors: Matteo Riondato, Eli Upfal
Venue: KDD
Year: 2015

We present an algorithm to extract an high-quality approximation of the (top-k) Frequent itemsets (FIs) from random samples of a transactional dataset. With high probability the approximation is a superset of the FIs, and no itemset with frequency much lower than the threshold is included in it. The algorithm employs progressive sampling, with a stopping condition based on bounds to the empirical Rademacher average, a key concept from statistical learning theory. The computation of the bounds uses characteristic quantities that can be obtained efficiently with a single scan of the sample. Therefore, evaluating the stopping condition is fast, and does not require an expensive mining of each sample. Our experimental evaluation confirms the practicality of our approach on real datasets, outperforming approaches based on one-shot static sampling.
VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms
Authors: Matteo Riondato, Eli Upfal
Venue: KDD
Year: 2015

Rademacher Averages and the Vapnik-Chervonenkis dimension are fundamental concepts from statistical learning theory. They allow to study simultaneous deviation bounds of empirical averages from their expectations for classes of functions, by considering properties of the functions, of their domain (the dataset), and of the sampling process. In this tutorial, we survey the use of Rademacher Averages and the VC-dimension in sampling-based algorithms for graph analysis and pattern mining. We start from their theoretical foundations at the core of machine learning, then show a generic recipe for formulating data mining problems in a way that allows to use these concepts in efficient randomized algorithms for those problems. Finally, we show examples of the application of the recipe to graph problems (connectivity, shortest paths, betweenness centrality) and pattern mining. Our goal is to expose the usefulness of these techniques for the data mining researcher, and to encourage research in the area.
Dynamic Hierarchical Classification for Patient Risk-of-Readmission
Authors: Senjuti Basu Roy, Ankur Teredesai, Kiyana Zolfaghar, Rui Liu, David Hazel, Stacey Newman, Albert Marinez
Venue: KDD
Year: 2015

Congestive Heart Failure (CHF) is a serious chronic condition often leading to 50% mortality within 5 years. Improper treatment and post-discharge care of CHF patients leads to repeat frequent hospitalizations (i.e., readmissions). Accurately predicting patient's risk-of-readmission enables care-providers to plan resources, perform factor analysis, and improve patient quality of life. In this paper, we describe a supervised learning framework, Dynamic Hierarchical Classification (DHC) for patient's risk-of-readmission prediction. Learning the hierarchy of classifiers is often the most challenging component of such classification schemes. The novelty of our approach is to algorithmically generate various layers and combine them to predict overall 30-day risk-of-readmission. While the components of DHC are generic, in this work, we focus on congestive heart failure (CHF), a pressing chronic condition. Since healthcare data is diverse and rich and each source and feature-subset provides different insights into a complex problem, our DHC based prediction approach intelligently leverages each source and feature-subset to optimize different objectives (such as, Recall or AUC) for CHF risk-of-readmission. DHC's algorithmic layering capability is trained and tested over two real world datasets and is currently integrated into the clinical decision support tools at MultiCare Health System (MHS), a major provider of healthcare services in the northwestern US. It is integrated into a QlikView App (with EMR integration planned for Q2) and currently scores patients everyday, helping to mitigate readmissions and improve quality of care, leading to healthier outcomes and cost savings.
Stochastic Divergence Minimization for Online Collapsed Variational Bayes Zero Inference of Latent Dirichlet Allocation
Authors: Issei Sato, Hiroshi Nakagawa
Venue: KDD
Year: 2015

The collapsed variational Bayes zero (CVB0) inference is a variational inference improved by marginalizing out parameters, the same as with the collapsed Gibbs sampler. A drawback of the CVB0 inference is the memory requirements. A probability vector must be maintained for latent topics for every token in a corpus. When the total number of tokens is N and the number of topics is K, the CVB0 inference requires Ο(NK) memory. A stochastic approximation of the CVB0 (SCVB0) inference can reduce Ο(NK) to Ο(VK), where V denotes the vocabulary size. We reformulate the existing SCVB0 inference by using the stochastic divergence minimization algorithm, with which convergence can be analyzed in terms of Martingale convergence theory. We also reveal the property of the CVB0 inference in terms of the leave-one-out perplexity, which leads to the estimation algorithm of the Dirichlet distribution parameters. The predictive performance of the propose SCVB0 inference is better than that of the original SCVB0 inference in four datasets.
An Architecture for Agile Machine Learning in Real-Time Applications
Authors: Johann Schleier-Smith
Venue: KDD
Year: 2015

Machine learning techniques have proved effective in recommender systems and other applications, yet teams working to deploy them lack many of the advantages that those in more established software disciplines today take for granted. The well-known Agile methodology advances projects in a chain of rapid development cycles, with subsequent steps often informed by production experiments. Support for such workflow in machine learning applications remains primitive.
Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery
Authors: Manu Sethi, Yupeng Yan, Anand Rangarajan, Ranga Raju Vatsavai, Sanjay Ranka
Venue: KDD
Year: 2015

Urban neighborhood classification using very high resolution (VHR) remote sensing imagery is a challenging and {\em emerging} application. A semi-supervised learning approach for identifying neighborhoods is presented which employs superpixel tessellation representations of VHR imagery. The image representation utilizes homogeneous and irregularly shaped regions termed superpixels and derives novel features based on intensity histograms, geometry, corner and superpixel density and scale of tessellation. The semi-supervised learning approach uses a support vector machine (SVM) to obtain a preliminary classification which is then subsequently refined using graph Laplacian propagation. Several intermediate stages in the pipeline are presented to showcase the important features of this approach. We evaluated this approach on four different geographic settings with varying neighborhood types and compared it with the recent Gaussian Multiple Learning algorithm. This evaluation shows several advantages, including model building, accuracy, and efficiency which makes it a great choice for deployment in large scale applications like global human settlement mapping and population distribution (e.g., LandScan), and change detection.