OpenRaiser/Intern-Atlas huggingface dataset:
VizQ: A System for Scalable Processing of Visibility Queries in 3D Spatial Databases
Authors: Arif Arman, Mohammed Eunus Ali, Farhana Murtaza Choudhury, Kaysar Abdullah
Venue: CIKM
Year: 2017

In this demonstration, we present VizQ, an efficient, scalable, and interactive system to process and visualize a comprehensive collection of novel visibility queries in the presence of obstacles in 3D space. Specifically, we demonstrate four types of query processing: (i) k Maximum Visibility Query (kMVQ), that finds k locations with the maximum visibility of a target object (ii) Visibility Color Map (VCM), where each point in the space is assigned a color value denoting the visibility measure of the target (iii) Continuous Maximum Visibility (CMV) that continuously finds the location that provides the best view of a moving target, and (iv) Text Visibility Color Map (TVCM), where VCM is generated considering readability of text data displayed on a target. We are the first to propose efficient algorithms to run all of the above four types of visibility queries in the context of a large number of 3D obstacle database. We exploit human visibility metrics to design our data structures and algorithms to efficiently process queries, and our approaches outperform baseline approaches in several order of magnitude both in terms of I/Os and processing time. The link of our demonstration video is https://youtu.be/rcizJtFvQfU.
A Neural Collaborative Filtering Model with Interaction-based Neighborhood
Authors: Ting Bai, Ji-Rong Wen, Jun Zhang, Wayne Xin Zhao
Venue: CIKM
Year: 2017

Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, they tend to be ineffective to capture localized information. Localized information, such as neighborhood, is important to recommender systems in complementing the user-item interaction data. Based on this consideration, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF). To the best of our knowledge, it is the first time that the neighborhood information is integrated into the neural collaborative filtering methods. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model for the implicit recommendation task.
QLever: A Query Engine for Efficient SPARQL+Text Search
Authors: Hannah Bast, Björn Buchhold
Venue: CIKM
Year: 2017

We present QLever, a query engine for efficient combined search on a knowledge base and a text corpus, in which named entities from the knowledge base have been identified (that is, recognized and disambiguated). The query language is SPARQL extended by two QLever-specific predicates ql:contains-entity and ql:contains-word, which can express the occurrence of an entity or word (the object of the predicate) in a text record (the subject of the predicate). We evaluate QLever on two large datasets, including FACC (the ClueWeb12 corpus linked to Freebase). We compare against three state-of-the-art query engines for knowledge bases with varying support for text search: RDF-3X, Virtuoso, Broccoli. Query times are competitive and often faster on the pure SPARQL queries, and several orders of magnitude faster on the SPARQL+Text queries. Index size is larger for pure SPARQL queries, but smaller for SPARQL+Text queries.
CoreDB: a Data Lake Service
Authors: Amin Beheshti, Boualem Benatallah, Reza Nouri, Van Munin Chhieng, HuangTao Xiong, Xu Zhao
Venue: CIKM
Year: 2017

The continuous improvement in connectivity, storage and data processing capabilities allow access to a data deluge from sensors, social-media, news, user-generated, government and private data sources. Accordingly, in a modern data-oriented landscape, with the advent of various data capture and management technologies, organizations are rapidly shifting to datafication of their processes. In such an environment, analysts may need to deal with a collection of datasets, from relational to NoSQL, that holds a vast amount of data gathered from various private/open data islands, i.e. Data Lake. Organizing, indexing and querying the growing volume of internal data and metadata, in a data lake, is challenging and requires various skills and experiences to deal with dozens of new databases and indexing technologies: How to store information items? What technology to use for persisting the data? How to deal with the large volume of streaming data? How to trace and persist information about data? What technology to use for indexing the data? How to query the data lake? To address the above mentioned challenges, we present CoreDB - an open source data lake service - which offers researchers and developers a single REST API to organize, index and query their data and metadata. CoreDB manages multiple database technologies and offers a built-in design for security and tracing.
Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities
Authors: Asia J. Biega, Azin Ghazimatin, Hakan Ferhatosmanoglu, Krishna P. Gummadi, Gerhard Weikum
Venue: CIKM
Year: 2017

Search engines in online communities such as Twitter or Facebook not only return matching posts, but also provide links to the profiles of the authors. Thus, when a user appears in the top-k results for a sensitive keyword query, she becomes widely exposed in a sensitive context. The effects of such exposure can result in a serious privacy violation, ranging from embarrassment all the way to becoming a victim of organizational discrimination. In this paper, we propose the first model for quantifying search exposure on the service provider side, casting it into a reverse k-nearest-neighbor problem. Moreover, since a single user can be exposed by a large number of queries, we also devise a learning-to-rank method for identifying the most critical queries and thus making the warnings user-friendly. We develop efficient algorithms, and present experiments with a large number of user profiles from Twitter that demonstrate the practical viability and effectiveness of our framework.
Learning Biological Sequence Types Using the Literature
Authors: Mohamed Reda Bouadjenek, Karin Verspoor, Justin Zobel
Venue: CIKM
Year: 2017

We explore in this paper automatic biological sequence type classification for records in biological sequence databases. The sequence type attribute provides important information about the nature of a sequence represented in a record, and is often used in search to filter out irrelevant sequences. However, the sequence type attribute is generally a non-mandatory free-text field, and thus it is subject to many errors including typos, mis-assignment, and non-assignment. In GenBank, this problem concerns roughly 18% of records, an alarming number that should worry the biocuration community. To address this problem of automatic sequence type classification, we propose the use of literature associated to sequence records as an external source of knowledge that can be leveraged for the classification task. We define a set of literature-based features and train a machine learning algorithm to classify a record into one of six primary sequence types. The main intuition behind using the literature for this task is that sequences appear to be discussed differently in scientific articles, depending on their type. The experiments we have conducted on the PubMed Central collection show that the literature is indeed an effective way to address this problem of sequence type classification. Our classification method reached an accuracy of 92.7%, and substantially outperformed two baseline approaches used for comparison.
Multi-Label Feature Selection using Correlation Information
Authors: Ali Braytee, Wei Liu, Daniel R. Catchpoole, Paul J. Kennedy
Venue: CIKM
Year: 2017

High-dimensional multi-labeled data contain instances, where each instance is associated with a set of class labels and has a large number of noisy and irrelevant features. Feature selection has been shown to have great benefits in improving the classification performance in machine learning. In multi-label learning, to select the discriminative features among multiple labels, several challenges should be considered: interdependent labels, different instances may share different label correlations, correlated features, and missing and flawed labels. This work is part of a project at The Children's Hospital at Westmead (TB-CHW), Australia to explore the genomics of childhood leukaemia. In this paper, we propose a CMFS (Correlated- and Multi-label Feature Selection method), based on non-negative matrix factorization (NMF) for simultaneously performing feature selection and addressing the aforementioned challenges. Significantly, a major advantage of our research is to exploit the correlation information contained in features, labels and instances to select the relevant features among multiple labels. Furthermore, l2,1 -norm regularization is incorporated in the objective function to undertake feature selection by imposing sparsity on the feature matrix rows. We employ CMFS to decompose the data and multi-label matrices into a low-dimensional space. To solve the objective function, an efficient iterative optimization algorithm is proposed with guaranteed convergence. Finally, extensive experiments are conducted on high-dimensional multi-labeled datasets. The experimental results demonstrate that our method significantly outperforms state-of-the-art multi-label feature selection methods.
Covering the Optimal Time Window Over Temporal Data
Authors: Bin Cao, Chenyu Hou, Jing Fan
Venue: CIKM
Year: 2017

In this paper, we propose a new problem: covering the optimal time window over temporal data. Given a duration constraint d and a set of users where each user has multiple time intervals, the goal is to find all time windows which (1) are greater than or equal to the duration d, and (2) can be covered by the intervals from as many as possible users. This problem can be applied to real scenarios where people need to determine the best time for maximizing the number of people to be involved in an activity, e.g., the meeting organization and the online live video broadcasting. As far as we know, there is no existing algorithm that can solve the problem directly. In this paper, we propose two algorithms to solve the problem, the first one is considered as a baseline algorithm called sliding time window (STW), where we utilize the start and end points of all users - intervals to construct time windows satisfying duration d. And then we calculate the number of users whose intervals can cover the current time window. The second method, named TLI, is designed based on the the data structures from the Timeline Index in SAP HANA. In TLI algorithm, we conduct three consecutive phases to achieve the purpose of efficiency improvement, namely construction of Timeline Index, calculation of valid user set and calculation of time windows. Within the third phase, we prune the number of time windows by keeping track of the number of users in current optimal time window, which can help shrink the search space. Through extensive experimental evaluations, we find TLI algorithm outperforms STW two orders of magnitude in terms of querying time.
PMS: an Effective Approximation Approach for Distributed Large-scale Graph Data Processing and Mining
Authors: Yingjie Cao, Yangyang Zhang, Jianxin Li
Venue: CIKM
Year: 2017

Recently, large-scale graph data processing and mining has drawn great attention, and many distributed graph processing systems have been proposed. However, large-scale graph processing remains a challenging problem. Because the computation time in some cases is still unacceptable especially when the time is limited. As illustrated in Table 1, nearly three hours are needed when running Single-Source Shortest Path algorithm on the USA-road dataset using performant open-source distributed graph processing systems.
Compact Multiple-Instance Learning
Authors: Jing Chai, Weiwei Liu, Ivor W. Tsang, Xiao-Bo Shen
Venue: CIKM
Year: 2017

The weakly supervised Multiple-Instance Learning (MIL) problem has been successfully applied in information retrieval tasks. Two related issues might affect the performance of MIL algorithms: how to cope with label ambiguities and how to deal with non-discriminative components, and we propose COmpact MultiPle-Instance LEarning (COMPILE) to consider them simultaneously. To treat label ambiguities, COMPILE seeks ground-truth positive instances in positive bags. By using weakly supervised information to learn data's short binary representations, COMPILE enhances discrimination via strengthening discriminative components and suppressing non-discriminative ones. We adapt block coordinate descent to optimize COMPILE efficiently. Experiments on text categorization empirically show: 1) COMPILE unifies disambiguation and data preprocessing successfully; 2) it generates short binary representations efficiently to enhance discrimination at significantly reduced storage cost.
Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining
Authors: Elaheh Alipour Chavary, Sarah M. Erfani, Christopher Leckie
Venue: CIKM
Year: 2017

Extracting knowledge from the massive volumes of network traffic is an important challenge in network and security management. In particular, network managers require concise reports about significant changes in their network traffic. While most existing techniques focus on summarizing a single traffic dataset, the problem of finding significant differences between multiple datasets is an open challenge. In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers. We propose the use of contrast pattern mining, which finds patterns whose support differs significantly from one dataset to another. We show that contrast patterns are highly effective at extracting meaningful changes in traffic data. We also propose several evaluation metrics that reflect the interpretability of patterns for security managers. Our experimental results show that with the proposed unsupervised approach, the vast majority of extracted patterns are pure, i.e., most changes are either attack traffic or normal traffic, but not a mixture of both.
Scaling Probabilistic Temporal Query Evaluation
Authors: Melisachew Wudage Chekol
Venue: CIKM
Year: 2017

Open information extraction has driven automatic construction of (temporal) knowledge graphs (e.g. YAGO) that maintain probabilistic (temporal) facts and inference rules. One of the most important tasks in these knowledge graphs is query evaluation. This task is well known to be #P-hard. One of the bottlenecks of probabilistic (temporal) query evaluation is finding efficient ways of grounding the query and inference rules, to generate a factor graph that can be used for approximate query evaluation or to retrieve lineages of queries for exact evaluation. In this work, we propose the PRATiQUE (PRobAbilistic Temporal QUery Evaluation) framework for scalable temporal query evaluation. It harnesses the structure of temporal inference rules for efficient in-database grounding, i.e., it uses partitions to store structurally equivalent rules. Besides,PRATiQUE leverages a state-of-the-art Gibbs sampler to compute marginal probabilities of query answers. We report on an extensive experimental evaluation, which confirms the efficiency of our proposal.
Spreadsheet Property Detection With Rule-assisted Active Learning
Authors: Zhe Chen, Sasha Dadiomov, Richard Wesley, Gang Xiao, Daniel Cory, Michael J. Cafarella, Jock D. Mackinlay
Venue: CIKM
Year: 2017

Spreadsheets are a critical and widely-used data management tool. Converting spreadsheet data into relational tables would bring benefits to a number of fields, including public policy, public health, and economics. Research to date has focused on designing domain-specific languages to describe transformation processes or automatically converting a specific type of spreadsheets. To handle a larger variety of spreadsheets, we have to identify various spreadsheet properties, which correspond to a series of transformation programs that contribute towards a general framework that converts spreadsheets to relational tables.
Movie Fill in the Blank with Adaptive Temporal Attention and Description Update
Authors: Jie Chen, Jie Shao, Fumin Shen, Chengkun He, Lianli Gao, Heng Tao Shen
Venue: CIKM
Year: 2017

Recently, a new type of video understanding task called Movie-Fill-in-the-Blank (MovieFIB) has attracted many research attentions. Given a pair of movie clip and description with one blank word as input, MovieFIB aims to automatically predict the blank word. Because of the advantage in processing sequence data, Long-Short Term Memory (LSTM) has been used as a key component in existing MovieFIB methods to generate representations of videos and descriptions. However, most of these methods fail to emphasize the salient parts of videos. To address this problem, in this paper we propose to use a novel LSTM network called LSTM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB. Specifically, we first use LSTM to produce video features, which are then used to update the text representation. Finally, we put the updated text into two opposite directional LSTMwL layers to infer the blank word. Experimental results demonstrate that our approach outperforms state-of-the-art models for MovieFIB.
Modeling Opinion Influence with User Dual Identity
Authors: Chengyao Chen, Zhitao Wang, Wenjie Li
Venue: CIKM
Year: 2017

Exploring the mechanism that explains how a user's opinion changes under the influence of his/her neighbors is of practical importance (e.g., for predicting the sentiment of his/her future opinion) and has attracted wide attention from both enterprises and academics.Though various opinion influence models have been proposed for opinion prediction, they only consider users' personal identities, but ignore their social identities with which people behave to fit the expectations of the others in the same group. In this work, we explore users' dual identities, including both personal identities and social identities to build a more comprehensive opinion influence model for a better understanding of opinion behaviors. A novel joint learning framework is proposed to simultaneously model opinion dynamics and detect social identity in a unified model. The effectiveness of the proposed approach is demonstrated through the experiments conducted on Twitter datasets
HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks
Authors: Liangzhe Chen, Xinfeng Xu, Sangkeun Lee, Sisi Duan, Alfonso G. Tarditi, Supriya Chinthavali, B. Aditya Prakash
Venue: CIKM
Year: 2017

Critical Infrastructure Systems such as transportation, water and power grid systems are vital to our national security, economy, and public safety. Recent events, like the 2012 hurricane Sandy, show how the interdependencies among different CI networks lead to catastrophic failures among the whole system. Hence, analyzing these CI networks, and modeling failure cascades on them becomes a very important problem. However, traditional models either do not take multiple CIs or the dynamics of the system into account, or model it simplistically. In this paper, we study this problem using a heterogeneous network viewpoint. We first construct heterogeneous CI networks with multiple components using national-level datasets. Then we study novel failure maximization problems on these networks, to compute critical nodes in such systems. We then provide HotSpots, a scalable and effective algorithm for these problems, based on careful transformations. Finally, we conduct extensive experiments on real CIS data from multiple US states, and show that our method HotSpots outperforms non-trivial baselines, gives meaningful results and that our approach gives immediate benefits in providing situational-awareness during large-scale failures.
Community-Based Network Alignment for Large Attributed Network
Authors: Zheng Chen, Xinli Yu, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang
Venue: CIKM
Year: 2017

Network alignment is becoming an active topic in network data analysis. Despite extensive research, we realize that efficient use of topological and attribute information for large attributed network alignment has not been sufficiently addressed in previous studies. In this paper, based on Stochastic Block Model (SBM) and Dirichlet-multinomial, we propose "divide-and-conquer" models CAlign that jointly consider network alignment, community discovery and community alignment in one framework for large networks with node attributes, in an effort to reduce both the computation time and memory usage while achieving better or competitive performance. It is provable that the algorithms derived from our model have sub-quadratic time complexity and linear space complexity on a network with small densification power, which is true for most real-world networks. Experiments show CAlign is superior to two recent state-of-art models in terms of accuracy, time and memory on large networks, and CAlign is capable of handling millions of nodes on a modern desktop machine.
A Study of Main-Memory Hash Joins on Many-core Processor: A Case with Intel Knights Landing Architecture
Authors: Xuntao Cheng, Bingsheng He, Xiaoli Du, Chiew Tong Lau
Venue: CIKM
Year: 2017

Advanced processor architectures have been driving new designs, implementations and optimizations of main-memory hash join algorithms recently. The newly released Intel Xeon Phi many-core processor of the Knights Landing architecture (KNL) embraces interesting hardware features such as many low-frequency out-of-order cores connected on a 2D mesh, and high-bandwidth multi-channel memory (MCDRAM). In this paper, we experimentally revisit the state-of-the-art main-memory hash join algorithms to study how the new hardware features of KNL affect the algorithmic design and tuning as well as to identify the opportunities for further performance improvement on KNL. Our experiments show that, although many existing optimizations are still valid on KNL with proper tuning, even the state-of-the-art algorithms have severely underutilized the memory bandwidth and other hardware resources.
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
Authors: Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang
Venue: CIKM
Year: 2017

Aspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment words for the given aspect.Recent work exploits attention neural networks to allocate sentiment words and achieves the state-of-the-art performance. However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect. To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification. The HEAT network contains a hierarchical attention module, consisting of aspect attention and sentiment attention. The aspect attention extracts the aspect-related information to guide the sentiment attention to better allocate aspect-specific sentiment words of the text. Moreover, the HEAT network supports to extract the aspect terms together with aspect-level sentiment classification by introducing the Bernoulli attention mechanism. To verify the proposed method, we conduct experiments on restaurant and laptop review data sets from SemEval at both the sentence level and the review level. The experimental results show that our model better allocates appropriate sentiment expressions for a given aspect benefiting from the guidance of aspect terms. Moreover, our method achieves better performance on aspect-level sentiment classification than state-of-the-art models.