Paper Digest: CIKM 2014 Highlights

November 1, 2014June 26, 2020 admin

The ACM Conference on Information and Knowledge Management (CIKM) is an annual computer science research conference dedicated to information management and knowledge management.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest service to get updates on new papers published in your area every day. You are also welcome to follow us on Twitter and Linkedin to get updated with new conference digests.

Paper Digest Team
team@paperdigest.org

TABLE 1: CIKM 2014 Papers

	Title	Authors	Highlight
1	Rubato DB: A Highly Scalable Staged Grid Database System for OLTP and Big Data Applications	Li-Yan Yuan, Lengdong Wu, Jia-Huai You, Yan Chi	This paper proposes a new formula protocol for distributed concurrency control, and specifies a staged grid architecture for highly scalable database management systems.
2	MaC: A Probabilistic Framework for Query Answering with Machine-Crowd Collaboration	Chen Jason Zhang, Lei Chen, Yongxin Tong	In this paper, we propose a framework, called MaC, to combine the powers of both CPUs and HPUs.
3	Templated Search over Relational Databases	Anastasios Zouzias, Michail Vlachos, Vagelis Hristidis	As a solution to the above, we propose a TEmplated Search paradigm (TES) for exploring relational data that combines the advantages of keyword search interfaces with the expressive power of question-answering systems.
4	ExpressQ: Identifying Keyword Context and Search Target in Relational Keyword Queries	Zhong Zeng, Zhifeng Bao, Thuy Ngoc Le, Mong Li Lee, Wang Tok Ling	In this work, we extend keyword queries to enhance their expressive power and describe an semantic approach to process these queries. Then, we construct a set of minimal connected graphs called query patterns, to represent user’s possible search intentions.
5	Pulling Conjunctive Query Equivalence out of the Bag	Stefan Böttcher, Sebastian Link, Lin Zhang	We present LECQTER, a tool for generating a ‘perfect example’ database, called exemplar, for a given conjunctive query.
6	Machine-Assisted Search Preference Evaluation	Ahmed Hassan Awadallah, Imed Zitouni	In this work, we investigate how machine learned models can assist human judges in order to collect reliable result list preference judgments at large scale with lower judgment-cost.
7	Designing Test Collections for Comparing Many Systems	Tetsuya Sakai	We provide practical solutions to researchers like her using power analysis and sample size design techniques, and demonstrate its usefulness for several IR tasks and evaluation measures.
8	Multileaved Comparisons for Fast Online Evaluation	Anne Schuth, Floor Sietsma, Shimon Whiteson, Damien Lefortier, Maarten de Rijke	We propose a new approach to online evaluation called multileaved comparisons that is useful in the prevalent case where designers are interested in the relative performance of more than two rankers.
9	A Retrievability Analysis: Exploring the Relationship Between Retrieval Bias and Retrieval Performance	Colin Wilkie, Leif Azzopardi	In this paper, we perform a comprehensive empirical evaluation analysing the relationship between retrieval bias and retrieval performance using several well known retrieval models, five large TREC test collections and ten performance measures (including the recently proposed PRES, Time Biased Gain (TBG) and U-Measure).
10	Relevance and Effort: An Analysis of Document Utility	Emine Yilmaz, Manisha Verma, Nick Craswell, Filip Radlinski, Peter Bailey	In this paper, we study one important source of the mis-match between user data and relevance judgments, those due to the high degree of effort required by users to identify and consume the information in a document.
11	A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval	Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, Grégoire Mesnil	In this paper, we propose a new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents.
12	A Comparison of Retrieval Models using Term Dependencies	Samuel Huston, W. Bruce Croft	In this paper, we compare the effectiveness of recent bi-term dependency models over a range of TREC collections, for both short (title) and long (description) queries.
13	Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation	Xiaozhong Liu, Yingying Yu, Chun Guo, Yizhou Sun	By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers.
14	A Fixed-Point Method for Weighting Terms in Verbose Informational Queries	Jiaul H. Paik, Douglas W. Oard	This paper proposes a novel unsupervised method for weighting terms in verbose informational queries that relies instead on iteratively estimating which terms are most central to the query.
15	Term Selection and Result Reranking for Question Retrieval by Exploiting Hierarchical Classification	Wen Chan, Jintao Du, Weidong Yang, Jinhui Tang, Xiangdong Zhou	In this paper, we present a novel method for improving the question retrieval performance by investigating the question term selection and weighting as well as reranking results.
16	Analysis on Community Variational Trend in Dynamic Networks	Xiaowei Jia, Nan Du, Jing Gao, Aidong Zhang	In this paper we investigate the problem of detecting and tracking the variational communities within a given time period.
17	Learning Interactions for Social Prediction in Large-scale Networks	Xiaofeng Yu, Junqing Xie	We propose a unified coherent framework, namely mutual latent random graphs (MLRGs), to exploit mutual interactions and benefits for predicting social actions (e.g., users’ behaviors, opinions, preferences or interests) and discovering social ties (e.g., multiple labeled relationships between users) simultaneously in large-scale social networks.
18	Influence Maximization over Large-Scale Social Networks: A Bounded Linear Approach	Qi Liu, Biao Xiang, Enhong Chen, Hui Xiong, Fangshuang Tang, Jeffrey Xu Yu	To this end, in this paper, we provide a bounded linear approach for influence computation and influence maximization.
19	Predictability of Distrust with Interaction Data	Jiliang Tang, Xia Hu, Yi Chang, Huan Liu	In this work, we investigate whether we can obtain distrust information via learning when it is not directly available, and propose to study a novel problem – predicting distrust using pervasively available interaction data in an online world.
20	Optimizing Multi-Relational Factorization Models for Multiple Target Relations	Lucas Rego Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Wolfgang Nejdl	In this paper, we argue that a model optimized for each target relation individually has better predictive performance than models optimized for a compromise on the performance on all target relations.
21	Learning to Propagate Rare Labels	Rakesh Pimplikar, Dinesh Garg, Deepesh Bharani, Gyana Parija	In this paper, we have proposed and justified the use of an alternative formulation for graph label propagation under such extreme behavior of the datasets.
22	A Mixtures-of-Trees Framework for Multi-Label Classification	Charmgil Hong, Iyad Batal, Milos Hauskrecht	We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y\|X).
23	Solving Linear SVMs with Multiple 1D Projections	Johannes Schneider, Jasmina Bogojeska, Michail Vlachos	We present a new methodology for solving linear Support Vector Machines (SVMs) that capitalizes on multiple 1D projections.
24	Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering	Ibrahim M. Alabdulmohsin, Xin Gao, Xiangliang Zhang	In this paper, we discuss why reverse engineering attacks can be carried out quite efficiently against fixed classifiers, and investigate the use of randomization as a suitable strategy for mitigating their risk.
25	Active Learning based Survival Regression for Censored Data	Bhanukiran Vinzamuri, Yan Li, Chandan K. Reddy	With this motivation, we address this problem by providing an active learning based survival model which uses a novel model discriminative gradient based sampling scheme.
26	Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups	Yinqing Xu, Wai Lam, Tianyi Lin	We propose a new generative model to predict user’s ratings on previously unrated items by considering review texts as well as hidden user communities and item groups relationship.
27	Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering	Tong Zhao, Julian McAuley, Irwin King	Using social connections to better estimate users’ rankings of products is the task we consider in this paper.
28	Deviation-Based Contextual SLIM Recommenders	Yong Zheng, Bamshad Mobasher, Robin Burke	In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM).
29	User Interests Imbalance Exploration in Social Recommendation: A Fitness Adaptation	Tianchun Wang, Xiaoming Jin, Xuetao Ding, Xiaojun Ye	Based on this finding, we proposed the social regulatory factor regression model (SRFRM) which could connect different interest spaces in different contexts together in an unified latent factor model.
30	CARS2: Learning Context-aware Representations for Context-aware Recommendations	Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic	In this work we propose CARS2, a novel approach for learning context-aware representations for context-aware recommendations.
31	Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty	Richard McCreadie, Craig Macdonald, Iadh Ounis	In this paper, we propose a novel IUS approach that adaptively alters the volume of content issued as updates over time with respect to the prevalence and novelty of discussions about the event.
32	A Dynamic Reconstruction Approach to Topic Summarization of User-Generated-Content	Zhao Yan Ming, Jintao Ye, Tat Seng Chua	In this paper, we introduce a timely task of dynamic structural and textual summarization.
33	Using Crowdsourcing to Investigate Perception of Narrative Similarity	Dong Nguyen, Dolf Trieschnigg, Mariët Theune	This paper presents the first large-scale empirical study that investigates perception of narrative similarity using crowdsourcing.
34	Correct Me If I’m Wrong: Fixing Grammatical Errors by Preposition Ranking	Roman Prokofyev, Ruslan Mavlyutov, Martin Grund, Gianluca Demartini, Philippe Cudré-Mauroux	In this paper, we propose and extensively evaluate a series of approaches for correcting prepositions, analyzing a large body of high-quality textual content to capture language usage.
35	Mining Semi-Structured Online Knowledge Bases to Answer Natural Language Questions on Community QA Websites	Parikshit Sondhi, ChengXiang Zhai	In this paper, we show that the answers to some of these questions are available in online domain-specific knowledge bases and propose an approach to automatically discover those answers.
36	Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis	David Carmel, Avihai Mejer, Yuval Pinter, Idan Szpektor	In this work we study how term weighting may benefit from syntactic analysis of the corpus.
37	Social Book Search Reranking with Generalized Content-Based Filtering	Bo-Wen Zhang, Xu-Cheng Yin, Xiao-Ping Cui, Jiao Qu, Bin Geng, Fang Zhou, Li Song, Hong-Wei Hao	In this paper, taking Social Book Search as an example, we propose a general search-recommendation hybrid system for this topic.
38	Question Retrieval with High Quality Answers in Community Question Answering	Kai Zhang, Wei Wu, Haocheng Wu, Zhoujun Li, Ming Zhou	To address these problems, we propose a supervised question-answer topic modeling approach.
39	Controllable Information Sharing for User Accounts Linkage across Multiple Online Social Networks	Yilin Shen, Hongxia Jin	Controllable Information Sharing for User Accounts Linkage across Multiple Online Social Networks
40	Identifying Your Customers in Social Networks	Chun-Ta Lu, Hong-Han Shuai, Philip S. Yu	In this paper, we study the problem of customer identification in social networks, i.e., connecting customer accounts at e-commerce sites to the corresponding user accounts in online social networks such as Twitter.
41	Learning a Linear Influence Model from Transient Opinion Dynamics	Abir De, Sourangshu Bhattacharya, Parantapa Bhattacharya, Niloy Ganguly, Soumen Chakrabarti	We present novel algorithms to estimate edge influence strengths while tackling these aggressively realistic assumptions.
42	Modeling Paying Behavior in Game Social Networks	Zhanpeng Fang, Xinyu Zhou, Jie Tang, Wei Shao, A.C.M. Fong, Longjun Sun, Ying Ding, Ling Zhou, Jarder Luo	In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games.
43	Enabling Precision/Recall Preferences for Semi-supervised SVM Training	Zeyi Wen, Rui Zhang, Kotagiri Ramamohanarao	In this paper, we propose a method that allows to specify a precision/recall preference while maximising the F₁ score.
44	A Cross-modal Multi-task Learning Framework for Image Annotation	Liang Xie, Peng Pan, Yansheng Lu, Shixun Wang	In this paper, we propose the cross-modal multi-task learning (CMMTL) framework for image annotation.
45	Multi-task Multi-view Learning for Heterogeneous Tasks	Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo, Qing He	This new learning scenario is called Multi-task Multi-view Learning for Heterogeneous Tasks in this study.
46	Multi-task Sparse Structure Learning	Andre R. Goncalves, Puja Das, Soumyadeep Chatterjee, Vidyashankar Sivakumar, Fernando J. Von Zuben, Arindam Banerjee	In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships.
47	Truth Discovery in Crowdsourced Detection of Spatial Events	Robin Wentao Ouyang, Mani Srivastava, Alice Toniolo, Timothy J. Norman	In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling.
48	Maximizing Multi-scale Spatial Statistical Discrepancy	Weishan Dong, Renjie Yao, Chunyang Ma, Changsheng Li, Lei Shi, Lu Wang, Yu Wang, Peng Gao, Junchi Yan	To solve the problem, in this paper we propose a novel discrepancy maximization algorithm, RefineScan.
49	Mining and Planning Time-aware Routes from Check-in Data	Hsun-Ping Hsieh, Cheng-Te Li	In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data.
50	High Impact Academic Paper Prediction Using Temporal and Topological Features	Feruz Davletov, Ali Selman Aydin, Ali Cakmak	This paper proposes a novel technique to predict a paper’s future impact (i.e., number of citations) by using temporal and topological features derived from citation networks.
51	Robust Entity Linking via Random Walks	Zhaochen Guo, Denilson Barbosa	In this work, we present a novel approach that is guided by a natural notion of semantic similarity which is less amenable to such bias.
52	SemStore: A Semantic-Preserving Distributed RDF Triple Store	Buwen Wu, Yongluan Zhou, Pingpeng Yuan, Hai Jin, Ling Liu	In this paper, we address the challenging problems of data partitioning and query optimization in a scale-out RDF engine.
53	Pattern Match Query in a Large Uncertain Graph	Ye Yuan, Guoren Wang, Lei Chen	Therefore, in this paper, we study pattern matching in a large uncertain graph.
54	Semantic Approximate Keyword Query Based on Keyword and Query Coupling Relationship Analysis	Xiangfu Meng, longbing Cao, Jingyu Shao	By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query approach which generates semantic approximate answers by identifying a set of keyword queries from the query history whose semantics are related to the given keyword query.
55	The Effects of Vertical Rank and Border on Aggregated Search Coherence and Search Behavior	Jaime Arguello, Robert Capra	Prior work investigated the "spill-over" effect between a set of blended vertical results and the web results.
56	An Eye-tracking Study of User Interactions with Query Auto Completion	Kajta Hofmann, Bhaskar Mitra, Filip Radlinski, Milad Shokouhi	We present the results of an in-depth user study of user interactions with QAC in web search.
57	Improving Tail Query Performance by Fusion Model	Shuai Huo, Min Zhang, Yiqun Liu, Shaoping Ma	In this study, we improve the tail query performance by fusing the results from original query and the query reformulation candidates.
58	Predicting Search Task Difficulty at Different Search Stages	Chang Liu, Jingjing Liu, Nicholas J. Belkin	We compared how the behavioral features calculated at these three points were different between difficult and easy search tasks, and identified behavioral features during search sessions that can be used in real-time to predict perceived task difficulty.
59	Re-call and Re-cognition in Episode Re-retrieval: A User Study on News Re-finding a Fortnight Later	Shuya Ochiai, Makoto P. Kato, Katsumi Tanaka	Our four main contributions can be summarized as follows: (i) we developed a method to investigate the effects of memory loss on episode refinding tasks on a large scale; (ii) our user study revealed a big drop on search performances in the refinding task after a fortnight and several differences between search queries input immediately after news browsing and ones at a later time; (iii) we found that asking questions and expanding input queries on the basis of the answers significantly improved the search performance in the news refinding task; and (iv) the users’ recognition abilities were different than their recall abilities, e.g. object names in a news story could be correctly recognized even though they were rarely recalled.
60	Online Exploration for Detecting Shifts in Fresh Intent	Damien Lefortier, Pavel Serdyukov, Maarten de Rijke	We propose a method for solving this problem.
61	Effect of Intent Descriptions on Retrieval Evaluation	Emine Yilmaz, Evangelos Kanoulas, Nick Craswell	We show that intent descriptions have a significant impact in adhoc evaluation and that special care should be given as to how the intent descriptions are selected.
62	Search Result Diversification via Filling Up Multiple Knapsacks	Hai-Tao Yu, Fuji Ren	Solving the 0-1 MSKP is NP-hard, we treat the optimization of 0-1 MSKP using a graphical model over latent binary variables as a maximum posterior inference problem, and tackle it with the max-sum belief propagation algorithm.
63	Query Augmentation based Intent Matching in Retail Vertical Ads	Huasha Zhao, Ye Chen, John Canny, Tak Yan	In this paper, we propose an ad retrieval framework for retail vertical ads, based on query rewrite using personal history data to improve ad recall rate.
64	Sketch-based Influence Maximization and Computation: Scaling up with Guarantees	Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck	Basic computational problems in the study of diffusion are influence queries (determining the potency of a specified seed set of nodes) and Influence Maximization (identifying the most influential seed set of a given size).
65	Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference	Joseph John Pfeiffer, Jennifer Neville, Paul N. Bennett	In particular, we demonstrate that these partial networks can exhibit extreme label correlation bias, which makes it difficult for conventional relational learning methods to accurately estimate relational parameters.
66	Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks	Huan Gui, Yizhou Sun, Jiawei Han, George Brova	In this paper, we propose to model information diffusion in such multi-relational networks, by distinguishing the power in passing information around for different types of relationships.
67	Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences	Quan Yuan, Gao Cong, Aixin Sun	In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time.
68	On Building Decision Trees from Large-scale Data in Applications of On-line Advertising	Shivaram Kalyanakrishnan, Deepthi Singh, Ravi Kant	Evaluated on three distinct probability-estimation tasks in on-line advertising, our method, "CCDT", shows significant improvements in the accuracy of prediction.
69	Improving Co-Cluster Quality with Application to Product Recommendations	Michail Vlachos, Francesco Fusco, Charalambos Mavroforakis, Anastasios Kyrillidis, Vassilios G. Vassiliadis	In this work, we introduce a new algorithm for co-clustering that is both scalable and highly resilient to noise.
70	Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD)	Xinsheng Li, Shengyu Huang, Kasim Selçuk Candan, Maria Luisa Sapino	In this paper, we first recognize that in many applications, the user may have a focus of interest — i.e., part of the data for which the user needs high accuracy — and beyond this area focus, accuracy may not be as critical.
71	Ranking-based Clustering on General Heterogeneous Information Networks by Network Projection	Chuan Shi, Ran Wang, Yitong Li, Philip S. Yu, Bin Wu	In this paper, we study the ranking-based clustering problem in a general heterogeneous information network and propose a novel solution HeProjI.
72	NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites	Jingyuan Zhang, Xiangnan Kong, Roger Jie Luo, Yi Chang, Philip S. Yu	In this paper, we specifically focus on the ranking problem of co-ranking questions, answers and users in a Q&A website.
73	Similarity Search using Concept Graphs	Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi	In this paper, we develop an efficient retrieval system, only assuming an oracle access to a traditional search engine that admits ‘succinct’ keyword queries for retrieving objects of a desired media type.
74	"Strength Lies in Differences": Diversifying Friends for Recommendations through Subspace Clustering	Eirini Ntoutsi, Kostas Stefanidis, Katharina Rausch, Hans-Peter Kriegel	We extend the concept of fault tolerance to density-based subspace clustering, and to speed up our algorithms, we introduce the significance threshold for considering only promising dimensions for subspace extension.
75	Exploiting Geographical Neighborhood Characteristics for Location Recommendation	Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao	In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location.
76	Increasing the Responsiveness of Recommended Expert Collaborators for Online Open Projects	Mohammad Y. Allaho, Wang-Chien Lee	In this paper, we consider the Degree of Knowledge (DoK) which imposes the knowledge of the skills factor, and the Social Relative Importance (SRI) which imposes the social distance factor to tackle the aforementioned challenges.
77	Dual-Regularized One-Class Collaborative Filtering	Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw K. Szymanski, Jian Lu	In this paper, we propose a dual-regularized model for one-class collaborative filtering.
78	HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation	Xin Wang, Weike Pan, Congfu Xu	As a response, we design a novel algorithm, i.e., hierarchical group matrix factorization (HGMF), in order to explore and model the structure correlations among users and items in a principled way.
79	SocialTransfer: Transferring Social Knowledge for Cold-Start Cowdsourcing	Zhou Zhao, James Cheng, Furu Wei, Ming Zhou, Wilfred Ng, Yingjun Wu	We propose a new approach to address this issue.
80	CAST: A Context-Aware Story-Teller for Streaming Social Content	Pei Lee, Laks V.S. Lakshmanan, Evangelos Milios	In this paper, we propose CAST, which is a context-aware story-teller that discovers new stories from social streams and tracks their structural context on the fly to build a vein of stories.
81	Distributed Graph Summarization	Xingjie Liu, Yuanyuan Tian, Qi He, Wang-Chien Lee, John McPherson	In this paper, we introduce three distributed graph summarization algorithms to address this problem.
82	Efficient Probabilistic Supergraph Search Over Large Uncertain Graphs	Yongxin Tong, Xiaofei Zhang, Caleb Chen Cao, Lei Chen	In this paper, we study a new type of uncertain graph query, probabilistic supergraph containment query over large uncertain graphs.
83	Narrow or Broad?: Estimating Subjective Specificity in Exploratory Search	Kumaripaba Athukorala, Antti Oulasvirta, Dorota Głowacka, Jilles Vreeken, Giulio Jacucci	We propose a formal model – motivated by Information Foraging Theory – for predicting the subjective specificity of search results based on simple observables such as result-clicks.
84	Supporting Complex Search Tasks	Ahmed Hassan Awadallah, Ryen W. White, Patrick Pantel, Susan T. Dumais, Yi-Min Wang	We present methods to automatically identify and recommend sub-tasks to help people explore and accomplish complex search tasks.
85	Extending Faceted Search to the General Web	Weize Kong, James Allan	In this paper, we explore this potential by extending faceted search into the open-domain web setting, which we call Faceted Web Search.
86	From Skimming to Reading: A Two-stage Examination Model for Web Search	Yiqun Liu, Chao Wang, Ke Zhou, Jianyun Nie, Min Zhang, Shaoping Ma	In this study, we design an experimental search engine to collect both the user’s feedback on their examinations and the eye-tracking/click-through data.
87	Generative Modeling of Entity Comparisons in Text	Maksim Tkachenko, Hady W. Lauw	We therefore propose a generative model for comparative text, which jointly models comparative directions at the sentence level, and ranking at the entity level.
88	How Many Folders Do You Really Need?: Classifying Email into a Handful of Categories	Mihajlo Grbovic, Guy Halawi, Zohar Karnin, Yoelle Maarek	We propose here a novel approach for (1) automatically distinguishing between personal and machine-generated email and (2) classifying messages into latent categories, without requiring users to have defined any folder.
89	Latent Aspect Mining via Exploring Sparsity and Intrinsic Information	Yinqing Xu, Tianyi Lin, Wai Lam, Zirui Zhou, Hong Cheng, Anthony Man-Cho So	We propose a new generative model to tackle the latent aspect mining problem in an unsupervised manner.
90	Recognizing Humor on Twitter	Renxian Zhang, Naishi Liu	In this paper, we present our work of humor recognition on Twitter, which will facilitate affect and sentimental analysis in the social network.
91	Towards Consistency Checking over Evolving Ontologies	Jiewen Wu, Freddy Lecue	In this paper, we present an approach to check the consistency over an evolving ontology resulting from data insertions and deletions, given by some expressive underlying Description Logic dialect.
92	A Practical Fine-grained Approach to Resolving Incoherent OWL 2 DL Terminologies	Jianfeng Du, Guilin Qi, Xuefeng Fu	Based on this modification function and the method for computing fine-grained repairs, we develop an automatic approach to resolving incoherent OWL 2 DL terminologies.
93	Domain Cartridge: Unsupervised Framework for Shallow Domain Ontology Construction from Corpus	Subhabrata Mukherjee, Jitendra Ajmera, Sachindra Joshi	In this work we propose an unsupervised framework to construct a shallow domain ontology from corpus.
94	Faceted Search over Ontology-Enhanced RDF Data	Marcelo Arenas, Bernardo Cuenca Grau, Evgeny Kharlamov, Sarunas Marciuska, Dmitriy Zheleznyakov	In this paper, we provide such solid foundations.
95	DFD: Efficient Functional Dependency Discovery	Ziawasch Abedjan, Patrick Schulze, Felix Naumann	We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification.
96	Estimating the Number and Sizes of Fuzzy-Duplicate Clusters	Arvid Heise, Gjergji Kasneci, Felix Naumann	This paper investigates the problem of estimating the number and sizes of duplicate record clusters in advance and describes a sampling-based method for solving this problem.
97	Efficient Static and Dynamic In-Database Tensor Decompositions on Chunk-Based Array Stores	Mijung Kim, K. Selçuk Candan	In this paper, we present techniques for efficient implementations of in-database tensor decompositions on chunk-based array data stores.
98	Efficient Filter Approximation Using the Earth Mover’s Distance in Very Large Multimedia Databases with Feature Signatures	Merih Seran Uysal, Christian Beecks, Jochen Schmücking, Thomas Seidl	In this paper, we propose an efficient filter approximation technique to lower bound the Earth Mover’s Distance on feature signatures by restricting the number of earth flows locally.
99	Time-Aware Rank Aggregation for Microblog Search	Shangsong Liang, Zhaochun Ren, Wouter Weerkamp, Edgar Meij, Maarten de Rijke	We propose a rank aggregation method, TimeRA, that is able to infer the rank scores of documents via latent factor modeling.
100	Tagging Your Tweets: A Probabilistic Modeling of Hashtag Annotation in Twitter	Zongyang Ma, Aixin Sun, Quan Yuan, Gao Cong	To understand the factors (e.g., user interest, posting time and tweet content) that may affect hashtag annotation in Twitter and to capture the implicit relations between latent topics in tweets and their corresponding hashtags, we propose two PLSA-style topic models to model the hashtag annotation behavior in Twitter.
101	People Search within an Online Social Network: Large Scale Analysis of Facebook Graph Search Query Logs	Nikita V. Spirin, Junfeng He, Mike Develin, Karrie G. Karahalios, Maxime Boucher	In this paper we explore people search on Facebook by analyzing an anonymized social graph, anonymized user profiles, and large scale anonymized query logs generated by users of Facebook Graph Search. Based on these insights, we present a set of design implications to guide the research and development of the OSN search in the future.
102	Automatic Social Circle Detection Using Multi-View Clustering	Yuhao Yang, Chao Lan, Xiaoli Li, Bo Luo, Jun Huan	In this paper, we introduce a social circle discovery approach using multi-view clustering.
103	Semantic Compositionality in Tree Kernels	Paolo Annesi, Danilo Croce, Roberto Basili	In this paper, a novel kernel called Compositionally Smoothed Partial Tree Kernel is proposed to integrate DCS operators into the tree kernel evaluation, by acting both over lexical leaves and non-terminal, i.e. complex compositional, nodes.
104	Focused Crawling for Structured Data	Robert Meusel, Peter Mika, Roi Blanco	We propose new methods of focused crawling specifically designed for collecting data-rich pages with greater efficiency.
105	Ranking Optimization with Constraints	Fangzhao Wu, Jun Xu, Hang Li, Xin Jiang	This paper addresses the problem of post-processing of ranking in search, referred to as post ranking.
106	Supervised Nested PageRank	Maxim Zhukovskiy, Gleb Gusev, Pavel Serdyukov	This paper addresses the problem of weighting nodes and edges according to this intuition by realizing it in a general ranking model and an efficient algorithm of tuning the parameters of that model.
107	Concept-based Short Text Classification and Ranking	Fang Wang, Zhongyuan Wang, Zhoujun Li, Ji-Rong Wen	In this paper, we propose using “Bag-of-Concepts” in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem.
108	EgoCentric: Ego Networks for Knowledge-based Short Text Classification	William Lucia, Elena Ferrari	In this paper, we propose a new unsupervised knowledge-based classifier for short text messages, where each category is represented by an ego-network.
109	A Cross-Lingual Joint Aspect/Sentiment Model for Sentiment Analysis	Zheng Lin, Xiaolong Jin, Xueke Xu, Weiping Wang, Xueqi Cheng, Yuanzhuo Wang	For instance, in the reviews on a cell phone, long is positive for the lifespan of its battery, but negative for the response time of its operating system.
110	Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring	Victor W. Chu, Raymond K. K. Wong, Fang Chen, Chi-Hung Chi	A recent study on collective attention in Twitter shows that an epidemic spreading of hashtags is predominantly driven by external factors.
111	Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams	Yongsub Lim, Jihoon Choi, U. Kang	In this paper, we propose TwMinSwap, a fast, accurate, and space-efficient method for tracking recent frequent items.
112	GI-NMF: Group Incremental Non-Negative Matrix Factorization on Data Streams	Xilun Chen, K. Selçuk Candan	In this paper, we recognize that many applications involve redundancies and we argue that these redundancies can and should be leveraged for reducing the computational cost of the NMF process: Firstly, online applications involving data streams often include temporal redundancies.
113	Active Learning for Streaming Networked Data	Zhilin Yang, Jie Tang, Yutao Zhang	In this paper, we study the problem of active learning for streaming networked data.
114	Online User Location Inference Exploiting Spatiotemporal Correlations in Social Streams	Yuto Yamaguchi, Toshiyuki Amagasa, Hiroyuki Kitagawa, Yohei Ikawa	The main idea of this paper is that we can infer the locations of users who simultaneously post about a local event (e.g., earthquakes).
115	Robust Principal Component Analysis with Missing Data	Fanhua Shang, Yuanyuan Liu, James Cheng, Hong Cheng	In this paper, we propose a robust principal component analysis (RPCA) plus matrix completion framework to recover low-rank and sparse matrices from missing and grossly corrupted observations.
116	Model Selection with the Covering Number of the Ball of RKHS	Lizhong Ding, Shizhong Liao	In this paper, we take balls of reproducing kernel Hilbert spaces (RKHSs) as candidate hypothesis spaces and propose a novel model selection criterion via minimizing the empirical optimal error in the ball of RKHS and the covering number of the ball.
117	A Flexible Framework for Projecting Heterogeneous Data	Aubrey Gress, Ian Davidson	We present a framework for projecting heterogeneous data from multiple data sets into a common lower dimensional space using a rich range of guidance which does not assume any overlap between the instances or features in different data sets.
118	Fair Allocation in Online Markets	Sreenivas Gollapudi, Debmalya Panigrahi	We give two generic online allocation algorithms to address this problem.
119	Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables	Yongfeng Zhang, Min Zhang, Yi Zhang, Yiqun Liu, Shaoping Ma	In this work, we clarify data sparsity by bounding the solution space of MF algorithms.
120	Structure Learning via Parameter Learning	William Yang Wang, Kathryn Mazaitis, William W. Cohen	This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
121	Scalable Distributed Belief Propagation with Prioritized Block Updates	Jiangtao Yin, Lixin Gao	In this paper, we propose a new scheduling scheme that selects a set of messages to update at a time and leverages a novel priority to determine which messages are selected.
122	RC-NET: A General Framework for Incorporating Knowledge into Word Representations	Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, Gang Wang, Xiaoguang Liu, Tie-Yan Liu	Hence, in this paper, we introduce a novel framework called RC-NET to leverage both the relational and categorical knowledge to produce word representations of higher quality.
123	On Independence Atoms and Keys	Miika Hannula, Juha Kontinen, Sebastian Link	The applications can be effectively unlocked by providing efficient solutions to the underlying implication problems of keys and independence atoms.
124	Rebuilding the Tower of Babel: Towards Cross-System Malware Information Sharing	Ting Wang, Shicong Meng, Wei Gao, Xin Hu	In this paper we explore a new, more pragmatic alternative.
125	Computing Multi-Relational Sufficient Statistics for Large Databases	Zhensong Qian, Oliver Schulte, Yan Sun	We solve this problem with a new dynamic programming algorithm that performs a virtual join, where the requisite counts are computed without materializing join tables.
126	Distributed Stochastic ADMM for Matrix Factorization	Zhi-Qin Yu, Xing-Jian Shi, Ling Yan, Wu-Jun Li	In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems.
127	Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression	Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, U. Kang	In this paper we propose DF-DSCD (Data/Feature Distributed Stochastic Coordinate Descent), an efficient distributed algorithm for logistic regression, or L1 regularized loss minimization in general.
128	Exploring Ensemble of Models in Taxonomy-based Cross-Domain Sentiment Classification	Cong-Kai Lin, Yang-Yin Lee, Chi-Hsin Yu, Hsin-Hsi Chen	With multiple domains (or, nodes) organized in a tree-structured representation, we propose a general ensemble algorithm which takes into account: 1) the model application, 2) the model weight and 3) the strategies for selecting the most related models with respect to a target node.
129	Verifiable UML Artifact-Centric Business Process Models	Diego Calvanese, Marco Montali, Montserrat Estañol, Ernest Teniente	In this paper, we merge these two lines of research, by showing how recent theoretical decidability results for verification can be fruitfully transferred to a concrete UML-based modeling methodology.
130	Transfer Understanding from Head Queries to Tail Queries	Yangqiu Song, Haixun Wang, Weizhu Chen, Shusen Wang	In this paper, we leverage knowledge from two resources to fill the gap.
131	What a Nasty Day: Exploring Mood-Weather Relationship from Twitter	Jiwei Li, Xun Wang, Eduard Hovy	In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter.
132	Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon	Kar Wai Lim, Wray Buntine	In this paper, we propose an LDA-based opinion model named Twitter Opinion Topic Model (TOTM) for opinion mining and sentiment analysis.
133	Analysis of Physical Activity Propagation in a Health Social Network	NhatHai Phan, Dejing Dou, Xiao Xiao, Brigitte Piniewski, David Kil	In this work we introduce a Community-level Physical Activity Propagation (CPP) model to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level).
134	Predicting the Popularity of Online Serials with Autoregressive Models	Biao Chang, Hengshu Zhu, Yong Ge, Enhong Chen, Hui Xiong, Chang Tan	To this end, in this paper we present a comprehensive study for predicting the popularity of online serials with autoregressive models.
135	Sequential Action Patterns in Collaborative Ontology-Engineering Projects: A Case-Study in the Biomedical Domain	Simon Walk, Philipp Singer, Markus Strohmaier	In this paper we approach this task by (i) exploring whether regularities and common patterns in user action sequences, derived from change-logs of five different collaborative ontology-engineering projects from the biomedical domain, exist.
136	Towards Pathway Variation Identification: Aligning Patient Records with a Care Pathway	Haifeng Liu, Yang Liu, Xiang Li, Guotong Xie, Geetika T. Lakshmanan	This paper proposes to solve this problem by developing a Hierarchical Markov Random Field (HMRF) method so that a set of patient records can best fit a given care pathway.
137	PatentDom: Analyzing Patent Relationships on Multi-View Patent Graphs	Longhui Zhang, Lei Li, Tao Li, Dingding Wang	In this paper, we propose a unified framework, named PatentDom, to identify important patents related to key techniques from a large number of patent documents.
138	Exploring Legal Patent Citations for Patent Valuation	Shuting Wang, Zhen Lei, Wang-Chien Lee	We argue that patent citations can either be technological citations that indicate knowledge transfer or be legal citations that delimit the legal scope of citing patents.
139	Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-Rescaling Model	Hideaki Kim, Noriko Takaya, Hiroshi Sawada	In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting.
140	Robust and Skew-resistant Parallel Joins in Shared-Nothing Systems	Long Cheng, Spyros Kotoulas, Tomas E. Ward, Georgios Theodoropoulos	In this paper, we propose PRPQ (partial redistribution & partial query), an efficient and robust join algorithm for processing large-scale joins over distributed systems.
141	SharkDB: An In-Memory Column-Oriented Trajectory Storage	Haozhou Wang, Kai Zheng, Jiajie Xu, Bolong Zheng, Xiaofang Zhou, Shazia Sadiq	We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss.
142	Deal or deceit: detecting cheating in distribution channels	Kai Shu, Ping Luo, Wan Li, Peifeng Yin, Linpeng Tang	Thus, in this study we propose the method to rank all partners by the degree of cheating, either as seller or buyer.
143	An Appliance-Driven Approach to Detection of Corrupted Load Curve Data	Guoming Tang, Kui Wu, Jian Pei, Jiuyang Tang, Jingsheng Lei	In this paper, we propose to seek aid from the demand side (i.e., electricity service users).
144	Understanding Within-Content Engagement through Pattern Analysis of Mouse Gestures	Ioannis Arapakis, Mounia Lalmas, George Valkanas	To address this gap, we perform a controlled user study where we observe how users respond to online news in the presence or lack of interest. We collect mouse tracking data, which are known to correlate with visual attention, and examine how cursor behaviour can inform user engagement measures.
145	Modelling and Detecting Changes in User Satisfaction	Julia Kiseleva, Eric Crestan, Riccardo Brigo, Roland Dittel	In this paper, we examine how to detect changes in user satisfaction if some events affect user information goals but search results remained the same.
146	"Picture the scene…";: Visually Summarising Social Media Events	Philip J. McParlane, Andrew James McMinn, Joemon M. Jose	In this paper, we investigate how images can be used as a source for summarising events.
147	Competitive Game Designs for Improving the Cost Effectiveness of Crowdsourcing	Markus Rokicki, Sergiu Chelaru, Sergej Zerr, Stefan Siersdorfer	Crowd based online work is leveraged in a variety of applications such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models.
148	Cross-Modality Submodular Dictionary Learning for Information Retrieval	Fan Zhu, Ling Shao, Mengyang Yu	A greedy dictionary construction approach is introduced for learning an isomorphic feature space, to which cross-modality data can be adapted while data smoothness is guaranteed.
149	A Word-Scale Probabilistic Latent Variable Model for Detecting Human Values	Yasuhiro Takayama, Yoichi Tomiura, Emi Ishita, Douglas W. Oard, Kenneth R. Fleischmann, An-Shou Cheng	This paper describes a probabilistic latent variable model that is designed to detect human values such as justice or freedom that a writer has sought to reflect or appeal to when participating in a public debate.
150	Searching Locally-Defined Entities	Zhaohui Wu, Yuanhua Lv, Ariel Fuxman	To tackle this problem, we present algorithms for semantic matching and relevance ranking that enable users to effectively search and understand entities that have been defined in the content that they are consuming, which we call locally-defined entities.
151	Customized Organization of Social Media Contents using Focused Topic Hierarchy	Xingwei Zhu, Zhao-Yan Ming, Yu Hao, Xiaoyan Zhu, Tat-Seng Chua	This research aims at the customized organization of a social media corpus using focused topic hierarchy.
152	Sampling Triples from Restricted Networks using MCMC Strategy	Mahmudur Rahman, Mohammad Al Hasan	In this work we present two indirect triple sampling methods that are based on Markov Chain Monte Carlo (MCMC) sampling strategy.
153	Efficient Subgraph Skyline Search Over Large Graphs	Weiguo Zheng, Lei Zou, Xiang Lian, Liang Hong, Dongyan Zhao	In this paper, we propose subgraph skyline search problem, denoted as S³, to support more complicated analysis over graph data.
154	Within-Network Classification Using Radius-Constrained Neighborhood Patterns	Jialong Han, Ji-Rong Wen, Jian Pei	In this paper, we demonstrate that frequent neighborhood patterns, originally studied in the pattern mining literature, serve as a strong class of structure-aware features and provide satisfactory effectiveness in WNC.
155	Pushing the Envelope in Graph Compression	Panagiotis Liakos, Katia Papakonstantinopoulou, Michael Sioutis	We improve the state-of-the-art method for the compression of web and other similar graphs by introducing an elegant technique which further exploits the clustering properties observed in these graphs.
156	PraDa: Privacy-preserving Data-Deduplication-as-a-Service	Boxiang Dong, Ruilin Liu, Wendy Hui Wang	In this paper, we focus on data deduplication as the main data cleaning task, and design two efficient privacy-preserving data-deduplication methods for the DCaS paradigm.
157	Aroma: A New Data Protection Method with Differential Privacy and Accurate Query Answering	Chunyao Song, Tingjian Ge	We propose a new local data perturbation method called Aroma.
158	Fast Heuristics for Near-Optimal Task Allocation in Data Stream Processing over Clusters	Andreas Chatzistergiou, Stratis D. Viglas	We aim to minimize the transfer latency while keeping the nodes below some computational load threshold.
159	Truth Discovery in Data Streams: A Single-Pass Probabilistic Approach	Zhou Zhao, James Cheng, Wilfred Ng	In this paper, we propose a probabilistic model that transforms the problem of truth discovery over data streams into a probabilistic inference problem.
160	Time-sensitive Personalized Query Auto-Completion	Fei Cai, Shangsong Liang, Maarten de Rijke	We propose a hybrid QAC model that considers both of these aspects: time-sensitivity and personalization.
161	Document Prioritization for Scalable Query Processing	Hao Wu, Hui Fang	In this paper, we propose a novel query evaluation method that aims to achieve a better balance between the efficiency and effectiveness of top-K query processing.
162	Analytical Performance Modeling for Top-K Query Processing	Hao Wu, Hui Fang	In this paper, we propose a novel analytical performance modeling framework for top-K query processing.
163	Compact Auxiliary Dictionaries for Incremental Compression of Large Repositories	Jiancong Tong, Anthony Wirth, Justin Zobel	In this paper, we describe effective techniques for extending the original dictionary to manage new data.
164	Modelling Relevance towards Multiple Inclusion Criteria when Ranking Patients.	Nut Limsopatham, Craig Macdonald, Iadh Ounis	We propose a novel approach for modelling the coverage of the query inclusion criteria within the records of a particular patient, and thereby rank highly those patients whose medical records are likely to cover all of the specified criteria.
165	Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining	Yang Liu, Songhua Xu, Lian Duan	To overcome the first limitation, we propose a new algorithm that can systematically and comprehensively detect relevant relationships useful for the prediction of an arbitrarily given target relationship through a disciplined graph searching process.
166	Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation	Parvaz Mahdabi, Fabio Crestani	In this paper, we propose a method based on a time-aware random walk on a weighted network of patent citations, the weights of which are characterized by contextual similarity relations between two nodes on the network.
167	Cross-Device Search	George D. Montanez, Ryen W. White, Xiao Huang	In this paper, we study search across devices and propose models to predict aspects of cross-device search transitions.
168	Canonicalizing Open Knowledge Bases	Luis Galárraga, Geremy Heitz, Kevin Murphy, Fabian M. Suchanek	In this paper, we present an approach based on machine learning methods that can canonicalize such Open IE triples, by clustering synonymous names and phrases.
169	A Fresh Look on Knowledge Bases: Distilling Named Events from News	Erdal Kuzey, Jilles Vreeken, Gerhard Weikum	This paper presents a method for extracting named events from news articles, reconciling them into canonicalized representation, and organizing them into fine-grained semantic classes to populate a knowledge base.
170	Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning	Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang	In this paper, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL.
171	On Efficient Meta-Level Features for Effective Text Classification	Sergio Canuto, Thiago Salles, Marcos André Gonçalves, Leonardo Rocha, Gabriel Ramos, Luiz Gonçalves, Thierson Rosa, Wellington Martins	We propose new meta-level features derived from the class distribution, the entropy and the within-class cohesion observed in the k nearest neighbors of a given test document x, as well as from the distribution of distances of x to these neighbors.
172	Scalable Vaccine Distribution in Large Graphs given Uncertain Data	Yao Zhang, B. Aditya Prakash	In this paper, we study the problem of designing vaccine-distribution algorithms under an uncertain environment, with known information consisting of confirmed cases as well as a probability distribution of unknown cases.
173	Component Detection in Directed Networks	Yu-Keng Shih, Sungmin Kim, Yiye Ruan, Jinxing Cheng, Abhishek Gattani, Tao Shi, Srinivasan Parthasarathy	In this paper, we adopt a novel concept of communities, directional community, and propose a new algorithm based on Markov Clustering to detect directional communities.
174	MapReduce Triangle Enumeration With Guarantees	Ha-Myung Park, Francesco Silvestri, U. Kang, Rasmus Pagh	We describe an optimal randomized MapReduce algorithm for the problem of triangle enumeration that requires O(E^3/2/(M√m) rounds, where m denotes the expected memory size of a reducer and M the total available space.
175	Hotspot Detection in a Service-Oriented Architecture	Pranay Anchuri, Roshan Sumbaly, Sam Shah	In this paper, we present a framework to detect hotspots in a service-oriented architecture.
176	Hashcube: A Data Structure for Space- and Query-Efficient Skycube Compression	Kenneth S. Bøgh, Sean Chester, Darius šidlauskas, Ira Assent	Existing methods for skycube compression sacrifice too much query performance; so, we present a novel hashing- and bitstring-based compressed data structure that supports orders of magnitude faster query performance.
177	Distance or Coverage?: Retrieving Knowledge-Rich Documents From Enterprise Text Collections	Vinay Deolalikar	We formulate a problem that arises in unstructured enterprise information management, and has high commercial impact: retrieve knowledge-rich documents in a large textual collection of technical documents.
178	Indexing Linked Data in a Wireless Broadcast System with 3D Hilbert Space-Filling Curves	Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Wei Emma Zhang, Hua Wang	In this paper, we consider large-scale information sharing scenarios among mobile objects in IoT by leveraging semantic techniques.
179	Towards Efficient Dissemination of Linked Data in the Internet of Things	Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Ali Shemshadi, Edward Curry	In this paper, we leverage semantic technologies which can facilitate machine-to-machine communications, such as Linked Data, to build an efficient information dissemination system for semantic IoT.
180	Tell Me What You Want and I Will Tell Others Where You Have Been	Anthony Quattrone, Elham Naghizade, Lars Kulik, Egemen Tanin	We develop an inference algorithm and show that it can effectively approximate original trajectories using solely the POI query results.
181	Forest-Based Dynamic Sorted Neighborhood Indexing for Real-Time Entity Resolution	Banda Ramadan, Peter Christen	In this paper, we propose a forest-based sorted neighborhood index that uses multiple index trees with different sorting keys to facilitate real-time ER for read-most databases.
182	Travel distance versus navigation complexity: a study on different spatial queries on road networks	Jie Shao, Lars Kulik, Egemen Tanin, Long Guo	This paper presents an evaluation to compare the effectiveness of easiest-to-reach neighbor query against a classic nearest neighbor query in a real-world setting.
183	Scalable Privacy-Preserving Record Linkage for Multiple Databases	Dinusha Vatsalan, Peter Christen	In this paper we consider the problem of linking data from three or more sources in an efficient and secure way.
184	Exploring Tag-Free RFID-Based Passive Localization and Tracking via Learning-Based Probabilistic Approaches	Lina Yao, Wenjie Ruan, Quan Z. Sheng, Xue Li, Nicholas J.G. Falkner	In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment.
185	Simple Arabic Stemmer	Mohammed Algarni, Brent Martin, Tim Bell, Kourosh Neshatian	We propose a root stemmer for the Modern Standard Arabic (MSA) language in an attempt to enhance the performance of Arabic Information Retrieval (AIR).
186	Phrase Query Optimization on Inverted Indexes	Avishek Anand, Ida Mele, Srikanta Bedathur, Klaus Berberich	We show that the underlying optimization problem is NP-hard in the general case and describe an exact exponential algorithm and an approximation algorithm to its solution.
187	CLIR for Informal Content in Arabic Forum Posts	Mossaab Bagdouri, Douglas W. Oard, Vittorio Castelli	This paper describes the development of a small test collection for this task, with questions posed in formal English and the documents consisting of intermixed formal and informal Arabic.
188	Head First: Living Labs for Ad-hoc Search Evaluation	Krisztian Balog, Liadh Kelly, Anne Schuth	This paper presents the first living labs for the IR community benchmarking campaign initiative, taking as test two use-cases: local domain search on a university website and product search on an e-commerce site.
189	Medical Semantic Similarity with a Neural Language Model	Lance De Vine, Guido Zuccon, Bevan Koopman, Laurianne Sitbon, Peter Bruza	In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text.
190	Parameter Tuning with User Models: Influencing Aggregate User Behavior in Cluster Based Retrieval Systems	Vinay Deolalikar	To address this question, we propose an approach based on three components: user model, criterion metric, and sensitivity analysis.
191	On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions	Romain Deveaud, M-Dyaa Albakour, Craig Macdonald, Iadh Ounis	In this paper, we explore a variety of user-dependent and venue-dependent features and apply state-of-the-art learning to rank approaches to the problem of contextual suggestion in order to find what makes a venue relevant for a given context.
192	Modelling Complex Relevance Spaces with Copulas	Carsten Eickhoff, Arjen P. de Vries	In this paper, we investigate the use of copulas, a model family from the domain of robust statistics, for the formal estimation of the probability of relevance in high-dimensional spaces.
193	Identifying Time Intervals of Interest to Queries	Dhruv Gupta, Klaus Berberich	We evaluate our approach on twenty years’ worth of newspaper articles from The New York Times using two novel testbeds consisting of temporally unambiguous and temporally ambiguous queries, respectively.
194	Identification of Answer-Seeking Questions in Arabic Microblogs	Maram Hasanain, Tamer Elsayed, Walid Magdy	In this paper, we tackle the problem of identifying answer-seeking questions in different dialects over a large collection of Arabic tweets.
195	Size and Source Matter: Understanding Inconsistencies in Test Collection-Based Evaluation	Timothy Jones, Andrew Turpin, Stefano Mizzaro, Falk Scholer, Mark Sanderson	Past work showed that significant inconsistencies between retrieval results occurred on different test collections, even when one of the test collections contained only a subset of the documents in the other.
196	Exploiting Knowledge Structure for Proximity-aware Movie Retrieval Model	Sansung Kim, Keejun Han, Mun Y. Yi, Sinhee Cho, Seongchan Kim	As a solution to this problem, our movie title retrieval model proposes a new way of elaborately utilizing associative relations between multiple key terms that exist in the movie plot, in order to improve search performance when users enter more than one keyword.
197	Supervised Hashing with Soft Constraints	Cong Leng, Jian Cheng, Jiaxiang Wu, Xi Zhang, Hanqing Lu	We present a general framework for supervised hashing to address the above two limitations.
198	Probabilistic Classifier Chain Inference via Gibbs Sampling	Li Li, Longkai Zhang, Guangyi Li, Houfeng Wang	To address this problem, we propose a novel inference method with gibbs sampling.
199	GPQ: Directly Optimizing Q-measure based on Genetic Programming	Yuan Lin, Hongfei Lin, Ping Zhang, Bo Xu	Inspired by this, we proposed a novel learning to rank algorithm named GPQ in this paper, in which genetic programming was employed to directly optimize Q-measure evaluation metric.
200	Revisiting the Divergence Minimization Feedback Model	Yuanhua Lv, ChengXiang Zhai	In this paper, we revisit a PRF method based on statistical language models, namely the divergence minimization model (DMM).
201	Vertical-Aware Click Model-Based Effectiveness Metrics	Ilya Markov, Eugene Kharitonov, Vadim Nikulin, Pavel Serdyukov, Maarten de Rijke, Fabio Crestani	In this paper we examine the hypothesis that the use of models that capture user search behavior on heterogeneous result pages helps to improve the quality of offline metrics.
202	Query Performance Prediction for Aspect Weighting in Search Result Diversification	Ahmet Murat Ozdemiray, Ismail Sengor Altingovde	For the first time in the literature, we propose using post-retrieval query performance predictors (QPPs) to estimate, for each aspect, the retrieval effectiveness on the candidate document set, and leverage these estimations to set the aspect weights.
203	Axiomatic Analysis of Cross-Language Information Retrieval	Razieh Rahimi, Azadeh Shakery, Irwin King	In this paper, we present an analytical study of using translation knowledge in CLIR.
204	How People Use the Web in Large Indoor Spaces	Yongli Ren, Martin Tomko, Kevin Ong, Mark Sanderson	The work described in this paper underpins applications such as the prediction of users’ information needs, retail recommendation systems, and improving the mobile Web search experience.
205	Succinct Queries for Linking and Tracking News in Social Media	Luchen Tan, Charles L.A. Clarke	We present an experimental study of this method based on a collection of news articles taken from March-April 2014, with the resulting succinct queries used to re-query social media one week later.
206	Exploring Shared Subspace and Joint Sparsity for Canonical Correlation Analysis	Liang Tao, Horace Ip, Yinglin Wang, Xin Shu	In this paper, we propose a novel framework that integrates joint sparsity and low-rank shared subspace into the least-squares formulation of CCA.
207	Query Performance Prediction By Considering Score Magnitude and Variance Together	Yongquan Tao, Shengli Wu	In this paper, we propose a method that considers both magnitude and variance of scores of the ranked list of results to measure the performance of a query.
208	Log-Bilinear Document Language Model for Ad-hoc Information Retrieval	Xinhui Tu, Jing Luo, Bo Li, Tingting He	In this paper, we study how to efficiently use LBL to improve as-hoc retrieval.
209	Sparse Semantic Hashing for Efficient Large Scale Similarity Search	Qifan Wang, Bin Shen, Zhiwei Zhang, Luo Si	This paper proposes a novel sparse semantic hashing (SpSH) approach that explores the hidden semantic representation of documents in learning their corresponding hashing codes.
210	Spatial Verification for Scalable Mobile Image Retrieval	Xiyu Yang, Xueming Qian	Taking the limited bandwidth and instability into account, we propose an effective scalable mobile image retrieval approach in this paper.
211	A Generative Model for Generating Relevance Labels from Human Judgments and Click-Logs	Xugang Ye, Jingjing Li, Zijie Qi, Bingyue Peng, Dan Massey	In this paper, we present a novel method of generating the relevance labels for media search.
212	Generalized Bias-Variance Evaluation of TREC Participated Systems	Peng Zhang, Linxue Hao, Dawei Song, Jun Wang, Yuexian Hou, Bin Hu	In this paper, motivated by a recently proposed bias-variance based evaluation, we adopt a strong and unbiased "baseline", which is a virtual target model constructed by the best performance (for each query) among all the participated systems in a retrieval task.
213	Aligning Vertical Collection Relevance with User Intent	Ke Zhou, Thomas Demeester, Dong Nguyen, Djoerd Hiemstra, Dolf Trieschnigg	In this work we propose different approaches to define the set of relevant verticals based on document judgments.
214	Multi-document Hyperedge-based Ranking for Text Summarization	Abdelghani Bellaachia, Mohammed Al-Dhelaan	In this paper, we propose to model sentences as hyperedges and words as vertices using a hypergraph and combine it with topic signatures to differentiate between descriptive sentences and non-descriptive sentences.
215	Non-independent Cascade Formation: Temporal and Spatial Effects	Biru Cui, Shanchieh Jay Yang, Christopher Homan	The GPC is defined as a maximally connected component, such that, by applying the independent cascade model, once any node of the component is infected, most of the remaining nodes in the component will eventually become infected with a high probability.
216	What is the Shape of a Cluster?: Structural Comparisons of Document Clusters	Vinay Deolalikar	To address this shortcoming, we propose a rich representation of document clusters that surfaces the concept interactions within a cluster into the representation.
217	Ranking Sentiment Explanations for Review Summarization Using Dual Decomposition	Lei Fang, Qiao Qian, Minlie Huang, Xiaoyan Zhu	In this paper, we address the problem of ranking sentiment explanations by formulating the process as two subproblems: sentence informativeness ranking and structural sentiment analysis.
218	A Meta-reasoner to Rule Them All: Automated Selection of OWL Reasoners Based on Efficiency	Yong-Bin Kang, Shonali Krishnaswamy, Yuan-Fang Li	Based on recently-developed prediction models for various reasoners for reasoning performance, we present our work in developing a meta-reasoner that automatically selects from a number of state-of-the-art OWL reasoners to achieve optimal efficiency.
219	Semantic Topology	Jussi Karlgren, Martin Bohman, Ariel Ekgren, Gabriel Isheden, Emelie Kullmann, David Nilsson	We have found that topological methods are useful for exploring the makeup of a semantic space.
220	CONR: A Novel Method for Sentiment Word Identification	Jiguang Liang, Xiaofei Zhou, Yue Hu, Li Guo, Shuo Bai	In this paper, based on matrix factorization with co-occurrence neighbor regularization which is derived from context, we propose a novel non-seed model called CONR for SWI.
221	Using Local Information to Significantly Improve Classification Performance	Wei Liu, Dong Lee, Kotagiri Rao	In this research we propose to derive new features based on data samples’ local information with the aim of improving the performance of general supervised learning algorithms.
222	Improving Recommendation Accuracy by Combining Trust Communities and Collaborative Filtering	Xiao Ma, Hongwei Lu, Zaobin Gan	In this paper, considering both the trust and distrust relationships, a SVD signs based community mining method is proposed to process the trust relationship matrix in order to discover the trust communities.
223	Nonlinear Classification via Linear SVMs and Multi-Task Learning	Xue Mao, Ou Wu, Weiming Hu, Peter O’Donovan	We propose an efficient classifier for nonlinear data using a new iterative learning algorithm, which partitions the data into clusters, and then trains a linear SVM for each cluster.
224	Dynamic Clustering of Contextual Multi-Armed Bandits	Trong T. Nguyen, Hady W. Lauw	We propose an algorithm to divide the population of users into multiple clusters, and to customize the bandits to each cluster.
225	Unsupervised Feature Selection for Multi-View Clustering on Text-Image Web News Data	Mingjie Qian, Chengxiang Zhai	We propose a new multi-view unsupervised feature selection method in which image local learning regularized orthogonal nonnegative matrix factorization is used to learn pseudo labels and simultaneously robust joint $l_{2,1}$-norm minimization is performed to select discriminative features.
226	Enterprise Discussion Analysis	Sara Rosenthal, Ashish Jagmohan	In this paper we present an enterprise discussion analysis system which seeks to enable rapid interactive inference of insights from virtual online enterprise discussions.
227	A Problem-Action Relation Extraction Based on Causality Patterns of Clinical Events in Discharge Summaries	Jae-Wook Seol, Seung-Hyeon Jo, Wangjin Yi, Jinwook Choi, Kyung-Soon Lee	In this paper, we propose a clinical problem-action relation extraction method.
228	Entity Oriented Task Extraction from Query Logs	Manisha Verma, Emine Yilmaz	In this work, we explore entity specific task extraction from search logs.
229	Modeling Retail Transaction Data for Personalized Shopping Recommendation	Pengfei Wang, Jiafeng Guo, Yanyan Lan	Inspired by association rule mining, we introduce association pattern as a basic unit to capture the correlation between products from both intra- and intertransactions.
230	Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses	Miaomiao Wen, Carolyn Penstein Rose	We propose a novel method to characterize types of sessions in MOOCs by mining the habitual behaviors of students within individual sessions.
231	Constrained Question Recommendation in MOOCs via Submodularity	Diyi Yang, Jingbo Shang, Carolyn Penstein Rosé	In this work, we propose such a constrained question recommendation problem with load balance constraints in discussion forums and use flow based model to generate the optimal solution.
232	Exploit Latent Dirichlet Allocation for One-Class Collaborative Filtering	Haijun Zhang, Zhoujun Li, Yan Chen, Xiaoming Zhang, Senzhang Wang	In this paper, we exploit latent Dirichlet allocation (LDA) model on OCCF problem.
233	A Bootstrapping Based Refinement Framework for Mining Opinion Words and Targets	Qiyun Zhao, Hao Wang, Pin Lv, Chen Zhang	This paper proposes a novel bootstrapping based framework jointed with automatic refinement to extract opinion words and targets.
234	Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks	Hao Zhong, Weike Pan, Congfu Xu, Zhi Yin, Zhong Ming	In this paper, we study on how to learn users’ preferences from abundant online activities, e.g., browsing and examination, which are usually called implicit feedbacks since they cannot be interpreted as users’ likes or dislikes on the corresponding products directly.
235	INK: A Cloud-Based System for Efficient Top-k Interval Keyword Search	Rui Li, Xiao Zhang, Xin Zhou, Shan Wang	In this paper, we presents a cloud-based system named INK that supports efficient execution of TIKQs with appropriate effectiveness on Hadoop and HBase.
236	CoDEM: An Ingenious Tool of Insight into Community Detection in Social Networks	Meng Wang, Chaokun Wang, Jun Chen	In this paper, we build a tool called CoDEM to make both quality evaluations of community detection and an in-depth mining for pivotal nodes inside communities.
237	Faceted Exploring for Domain Knowledge over Linked Open Data	Meng Wang, Jun Liu, Wenqiang Liu, Qinghua Zheng, Wei Zhang, Lingyun Song, Siyu Yao	In this paper, we demonstrate a novel system called KFM, which can aggregate the distributed RDF data of a topic according to the facets of this topic.
238	Building and Exploring Dynamic Topic Models on the Web	Michael Derntl, Nikou Günnemann, Alexander Tillmann, Ralf Klamma, Matthias Jarke	In this paper, we present a visual analytics system for dynamic topic models that goes beyond the existing breed of tools.
239	A Demonstration of SearchonTS: An Efficient Pattern Search Framework for Time Series Data	Xiaomin Xu, Sheng Huang, Yaoliang Chen, Chen Wang, Inge Halilovic, Kevin Brown, Mark Ashworth	This paper presents SearchonTS, an extendable framework for in-database pattern search on time series data.
240	AESTHETICS: Analytics with Strings, Things, and Cats	Johannes Hoffart, Dragan Milchevski, Gerhard Weikum	This paper describes an advanced news analytics and exploration system that allows users to visualize trends of entities like politicians, countries, and organizations in continuously updated news articles.
241	Accelerometer-based Activity Recognition on Smartphone	Xing Su, Hanghang Tong, Ping Ji	We present AcRe, a human activity recognition application on smartphone.
242	Cleanix: A Big Data Cleaning Parfait	Hongzhi Wang, Mingda Li, Yingyi Bu, Jianzhong Li, Hong Gao, Jiacheng Zhang	In this demo, we present Cleanix, a prototype system for cleaning relational Big Data.
243	Keeping You in the Loop: Enabling Web-based Things Management in the Internet of Things	Lina Yao, Quan Z. Sheng, Anne H.H. Ngu, Byron Gao	In this paper, we showcase an IoT prototype system that enables seamless integration of the virtual and the physical worlds and efficient management of things of interest (TOIs), where services and resources offered by things can be easily monitored, visualized, and aggregated for value-added services by users.
244	Anything You Can Do, I Can Do Better: Finding Expert Teams by CrewScout	Naeemul Hassan, Huadong Feng, Ramesh Venkataraman, Gautam Das, Chengkai Li, Nan Zhang	The new contributions of this paper include an end-to-end system with an interactive user interface that assists users in choosing teams and an demonstration of its application domains.
245	WiiCluster: a Platform for Wikipedia Infobox Generation	Kezun Zhang, Yanghua Xiao, Hanghang Tong, Haixun Wang, Wei Wang	We present WiiCluster, a scalable platform for automatically generating infobox for articles in Wikipedia.
246	Negative FaceBlurring: A Privacy-by-Design Approach to Visual Lifelogging with Google Glass	Tengqi Ye, Brian Moynagh, Rami Albatal, Cathal Gurrin	In this paper, we describe a visual lifelogging solution for Google Glass that is designed to capture life experience in rich visual detail, yet maintain the privacy of unknown bystanders.
247	TensorDB: In-Database Tensor Manipulation with Tensor-Relational Query Plans	Mijung Kim, K. Selçuk Candan	We introduce an in-database analytic system for efficient implementations of in-database tensor decompositions on chunk-based array data stores, so called, TensorDB.
248	TweetMogaz v2: Identifying News Stories in Social Media	Eslam Elsawy, Moamen Mokhtar, Walid Magdy	In this demonstration, we present a technique for identifying stories within a stream of microblogs on a given topic.
249	TwinChat: A Twitter and Web User Interactive Chat System	Yuanyuan Wang, Gouki Yasui, Yuji Hosokawa, Yukiko Kawai, Toyokazu Akiyama, Kazutoshi Sumiya	This paper presents TWinChat, a Twitter and Web user interactive chat system to support simultaneous communication between microbloggers and Web users in real-time through both the contents of microblogs and Web pages.
250	VFDS: An Application to Generate Fast Sample Databases	Teodora Sandra Buda, Thomas Cerqueus, John Murphy, Morten Kristiansen	In this paper, we demonstrate \vfds, a novel fast database sampling system that maintains the referential integrity of the data.
251	Knowledge Management for Keyword Search over Data Graphs	Yosi Mass, Yehoshua Sagiv	Knowledge Management for Keyword Search over Data Graphs
252	Clairvoyant: An Early Prediction System For Video Hits	Hao Chen, Qinmin Hu, Liang He	Clairvoyant: An Early Prediction System For Video Hits
253	iMiner: Mining Inventory Data for Intelligent Management	Lei Li, Chao Shen, Long Wang, Li Zheng, Yexi Jiang, Liang Tang, Hongtai Li, Longhui Zhang, Chunqiu Zeng, Tao Li, Jun Tang, Dong Liu	In this demo, we present an intelligent system, called iMiner, to ease the management of enormous inventory data.
254	RApID: A System for Real-time Analysis of Information Diffusion in Twitter	Io Taxidou, Peter M. Fischer	In this demo paper, we present a system for real-time analysis of information diffusion on Twitter; it constructs the so-called information cascades that capture how information is being propagated from user to user. We face the challenge of managing and presenting large and fast-evolving graph data.
255	RecLand: A Recommender System for Social Networks	Ryadh Dahimene, Camelia Constantin, Cédric du Mouza	We present RecLand, a recommender system that takes advantage of the social graph topology and of the existing contextual information to recommend users.
256	MeowsReader: Real-Time Ranking and Filtering of News with Generalized Continuous Top-k Queries	Nelly Vouzoukidou, Bernd Amann, Vassilis Christophides	MeowsReader: Real-Time Ranking and Filtering of News with Generalized Continuous Top-k Queries
257	AMiner-mini: A People Search Engine for University	Jingyuan Liu, Debing Liu, Xingyu Yan, Li Dong, Ting Zeng, Yutao Zhang, Jie Tang	We present a distributed academic search and mining system?
258	DEESSE: entity-Driven Exploratory and sErendipitous Search SystEm	Olivier Van Laere, Ilaria Bordino, Yelena Mejova, Mounia Lalmas	We present DEESSE [1], a tool that enables an exploratory and serendipitous exploration – at entity level, of the content of two different social media: Wikipedia, a user-curated online encyclopedia, and Yahoo Answers, a more unconstrained question/answering forum.
259	Manual Annotation of Semi-Structured Documents for Entity-Linking	Salvatore Trani, Diego Ceccarelli, Claudio Lucchese, Salvatore Orlando, Raffaele Perego	In this demo paper we propose a Web-deployed tool that allows to crowdsource the creation of these datasets, by supporting the collaborative human annotation of semi-structured documents.
260	SmartVenues: Recommending Popular and Personalised Venues in a City	Romain Deveaud, M-Dyaa Albakour, Jarana Manotumruksa, Craig Macdonald, Iadh Ounis	We present SmartVenues, a system that recommends nearby venues to a user who visits or lives in a city.
261	GTE-Rank: Searching for Implicit Temporal Query Results	Ricardo Campos, Gaël Dias, Alípio Mário Jorge, Célia Nunes	In this paper we present GTE-Rank, an online searching tool that takes time into account when ranking time-sensitive query web search results.
262	Exploring Document Collections with Topic Frames	Alexander Hinneburg, Frank Rosner, Stefan Pessler, Christian Oberländer	We demonstrate how part-of-speech (POS) tagging and co-location analysis of terms can be used to derive linguistic frames that yield more interpretable topic representations.
263	CONDOR: A System for CONstraint DiscOvery and Repair	Joshua Segeren, Dhruv Gairola, Fei Chiang	We present CONDOR, a tool for managing constraints towards improved data quality.
264	DTMBIO 2014: International Workshop on Data and Text Mining in Biomedical Informatics	Luonan Chen, Doheon Lee, Hua Xu, Min Song	DTMBIO 2014: International Workshop on Data and Text Mining in Biomedical Informatics
265	DUBMOD14 – International Workshop on Data-driven User Behavioral Modeling and Mining from Social Media	Jalal Mahmud, Jeffrey Nichols, Michelle Zhou, James Caverlee, Yi Zeng, Liang Chen, John O’Donovan	Since mining and understanding user behavior from social media often requires interdisciplinary effort, including machine learning, text mining, human-computer interaction, and social science, our workshop aims to bring together researchers and practitioners from multiple fields to discuss the creation of deeper models of individual users by mining the content that they publish and the social networking behavior that they exhibit.
266	Seventh Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR’14): CIKM 2014 Workshop	Omar Alonso, Jaap Kamps, Jussi Karlgren	The goal of the ESAIR’14 workshop remains to advance the general research agenda on this core problem, with an explicit focus on one of the most challenging aspects to address in the coming years.
267	LocWeb’14 – 4th International Workshop on Location and the Web: CIKM 2014 Workshop Summary	Dirk Ahlers, Erik Wilde, Bruno Martins	LocWeb’14 – 4th International Workshop on Location and the Web: CIKM 2014 Workshop Summary
268	PIKM 2014: The 7th ACM Workshop for Ph.D. Students in Information and Knowledge Management	Gerard de Melo, Mouna Kacimi, Aparna S. Varde	Similarly to the CIKM, PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management.
269	PSBD 2014: Overview of the 1st International Workshop on Privacy and Security of Big Data	Alfredo Cuzzocrea	The ACM 1st International Workshop on Privacy and Security of Big Data (PSBD 2014), held in Shanghai, China on November 7, 2014, in conjunction with the ACM 23rd International Conference on Information and Knowledge Management (CIKM 2014), presents research on privacy and security of big data, an emerging challenge in actual database and data mining research.
270	Web-KR 2014: The 5th International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning	Yi Zeng, Spyros Kotoulas, Zhisheng Huang	This summary introduces the major contributions of accepted papers in the Web-KR 2014 workshop.