OpenRaiser/Intern-Atlas huggingface dataset:
Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach
Authors: Sheng Li, Yaliang Li, Yun Fu
Venue: CIKM
Year: 2016

By virtue of the increasingly large amount of various sensors, information about the same object can be collected from multiple views. These mutually enriched information can help many real-world applications, such as daily activity recognition in which both video cameras and on-body sensors are continuously collecting information. Such multivariate time series (m.t.s.) data from multiple views can lead to a significant improvement of classification tasks. However, the existing methods for time series data classification only focus on single-view data, and the benefits of mutual-support multiple views are not taken into account. In light of this challenge, we propose a novel approach, named Multi-view Discriminative Bilinear Projections (MDBP), for extracting discriminative features from multi-view m.t.s. data. First, MDBP keeps the original temporal structure of m.t.s. data, and projects m.t.s. from different views onto a shared latent subspace. Second, MDBP incorporates discriminative information by minimizing the within-class separability and maximizing the between-class separability of m.t.s. in the shared latent subspace. Moreover, a Laplacian regularization term is designed to preserve the temporal smoothness within m.t.s.. Extensive experiments on two real-world datasets demonstrate the effectiveness of our approach. Compared to the state-of-the-art multi-view learning and m.t.s. classification methods, our approach greatly improves the classification accuracy due to the full exploration of multi-view streaming data. Moreover, by using a feature fusion strategy, our approach further improves the classification accuracy by at least 10%.
OptMark: A Toolkit for Benchmarking Query Optimizers
Authors: Zhan Li, Olga Papaemmanouil, Mitch Cherniack
Venue: CIKM
Year: 2016

Query optimizers have long been considered as among the most complex components of a database engine, while the assessment of an optimizer's quality remains a challenging task. Indeed, existing performance benchmarks for database engines (like TPC benchmarks) produce a performance assessment of the query runtime system rather than its query optimizer. To address this challenge, this paper introduces OptMark, a toolkit for evaluating the quality of a query optimizer. OptMark is designed to offer a number of desirable properties. First, it decouples the quality of an optimizer from the quality of its underlying execution engine. Second it evaluates independently both the effectiveness of an optimizer (i.e., quality of the chosen plans) and its efficiency (i.e., optimization time). OptMark includes also a generic benchmarking toolkit that is minimum invasive to the DBMS that wishes to use it. Any DBMS can provide a system-specific implementation of a simple API that allows OptMark to run and generate benchmark scores for the specific system. This paper discusses the metrics we propose for evaluating an optimizer's quality, the benchmark's design and the toolkit's API and functionality. We have implemented OptMark on the open-source MySQL engine as well as two commercial database systems. Using these implementations we are able to assess the quality of the optimizers on these three systems based on the TPC-DS benchmark queries.
TweetSift: Tweet Topic Classification Based on Entity Knowledge Base and Topic Enhanced Word Embedding
Authors: Quanzhi Li, Sameena Shah, Xiaomo Liu, Armineh Nourbakhsh, Rui Fang
Venue: CIKM
Year: 2016

Classifying tweets into topic categories is necessary and important for many applications, since tweets are about a variety of topics and users are only interested in certain topical areas. Many tweet classification approaches fail to achieve high accuracy due to data sparseness issue. Tweet, as a special type of short text, in additional to its text, also has other metadata that can be used to enrich its context, such as user name, mention, hashtag and embedded link. In this demonstration, we present TweetSift, an efficient and effective real time tweet topic classifier. TweetSift exploits external tweet-specific entity knowledge to provide more topical context for a tweet, and integrates them with topic enhanced word embeddings for topic classification. The demonstration will show how TweetSift works and how it is incorporated with our social media event detection system.
Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank
Authors: Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang
Venue: CIKM
Year: 2016

In this paper, we present a new approach of recommending hashtags for tweets. It uses Learning to Rank algorithm to incorporate features built from topic enhanced word embeddings, tweet entity data, hashtag frequency, hashtag temporal data and tweet URL domain information. The experiments using millions of tweets and hashtags show that the proposed approach outperforms the three baseline methods -- the LDA topic, the tf.idf based and the general word embedding approaches.
TEAMOPT: Interactive Team Optimization in Big Networks
Authors: Liangyue Li, Hanghang Tong, Nan Cao, Kate Ehrlich, Yu-Ru Lin, Norbou Buchler
Venue: CIKM
Year: 2016

The science of team science is a rapidly emerging research field that studies strategies to understand and enhance the process and outcomes of collaborative, team-based research. An interesting research question we address in this work is how to maintain and optimize the team performance should certain changes happen to the team. In particular, we take the network approach to understanding the teams and consider optimizing the teams with several operations (e.g., replacement, expansion, shrinkage). We develop TEAMOPT, a system to assist users in optimizing the team performance interactively to support the changes to a team. TEAMOPT takes as input a large network of individuals (e.g., co-author network of researchers) and is able to assist users in assembling a team with specific requirements and optimizing the team in response to the changes made to the team. It is effective in finding the best candidates, and interactive with users' feedback in the loop. The system is developed using HTML5, JavaScript, D3.js (front-end) and Python CGI (back-end). A prototype system is already deployed. We will invite the audience to experiment with our TEAMOPT in terms of its effectiveness, efficiency and applicability to various scenarios.
DI-DAP: An Efficient Disaster Information Delivery and Analysis Platform in Disaster Management
Authors: Tao Li, Wubai Zhou, Chunqiu Zeng, Qing Wang, Qifeng Zhou, Dingding Wang, Jia Xu, Yue Huang, Wentao Wang, Minjing Zhang, Steven Luis, Shu-Ching Chen, Naphtali Rishe
Venue: CIKM
Year: 2016

In disaster management, people are interested in the development and the evolution of the disasters. If they intend to track the information of the disaster, they will be overwhelmed by the large number of disaster-related documents, microblogs, and news, etc. To support disaster management and minimize the loss during the disaster, it is necessary to efficiently and effectively collect, deliver, summarize, and analyze the disaster information, letting people in affected area quickly gain an overview of the disaster situation and improve their situational awareness.
Memory-Optimized Distributed Graph Processing through Novel Compression Techniques
Authors: Panagiotis Liakos, Katia Papakonstantinopoulou, Alex Delis
Venue: CIKM
Year: 2016

A multitude of contemporary applications now involve graph data whose size continuously grows and this trend shows no signs of subsiding. This has caused the emergence of many distributed graph processing systems including Pregel and Apache Giraph. However, the unprecedented scale now reached by real-world graphs hardens the task of graph processing even in distributed environments and the current memory usage patterns rapidly become a primary concern for such contemporary graph processing systems. We seek to address this challenge by exploiting empirically-observed properties demonstrated by graphs that are generated by human activity. In this paper, we propose three space-efficient adjacency list representations that can be applied to any distributed graph processing system. Our suggested compact representations reduce respective memory requirements for accommodating the graph elements up to 5 times if compared with state-of-the-art methods. At the same time, our memory-optimized methods retain the efficiency of uncompressed structures and enable the execution of algorithms for large scale graphs in settings where contemporary alternative structures fail due to memory errors.
Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference
Authors: Tianyi Lin, Siyuan Zhang, Hong Cheng
Venue: CIKM
Year: 2016

With the soaring popularity of online social media like Twitter, analyzing short text has emerged as an increasingly important task which is challenging to classical topic models, as topic sparsity exists in short text. Topic sparsity refers to the observation that individual document usually concentrates on several salient topics, which may be rare in entire corpus. Understanding this sparse topical structure of short text has been recognized as the key ingredient for mining user-generated Web content and social medium, which are featured in the form of extremely short posts and discussions. However, the existing sparsity-enhanced topic models all assume over-complicated generative process, which severely limits their scalability and makes them unable to automatically infer the number of topics from data.
Relational Database Schema Design for Uncertain Data
Authors: Sebastian Link, Henri Prade
Venue: CIKM
Year: 2016

We investigate the impact of uncertainty on relational data\-base schema design. Uncertainty is modeled qualitatively by assigning to tuples a degree of possibility with which they occur, and assigning to functional dependencies a degree of certainty which says to which tuples they apply. A design theory is developed for possibilistic functional dependencies, including efficient axiomatic and algorithmic characterizations of their implication problem. Naturally, the possibility degrees of tuples result in a scale of different degrees of data redundancy. Scaled versions of the classical syntactic Boyce-Codd and Third Normal Forms are established and semantically justified in terms of avoiding data redundancy of different degrees. Classical decomposition and synthesis techniques are scaled as well. Therefore, possibilistic functional dependencies do not just enable designers to control the levels of data integrity and losslessness targeted but also to balance the classical trade-off between query and update efficiency. Extensive experiments confirm the efficiency of our framework and provide original insight into relational schema design.
The Solitude of Relevant Documents in the Pool
Authors: Aldo Lipani, Mihai Lupu, Evangelos Kanoulas, Allan Hanbury
Venue: CIKM
Year: 2016

Pool bias is a well understood problem of test-collection based benchmarking in information retrieval. The pooling method itself is designed to identify all relevant documents. In practice, 'all' translates to `as many as possible given some budgetary constraints' and the problem persists, albeit mitigated. Recently, methods to address this pool bias for previously created test collections have been proposed, for the evaluation measure precision at cut-off ([email protected]). Analyzing previous methods, we make the empirical observation that the distribution of the probability of providing new relevant documents to the pool, over the runs, is log-normal (when the pooling strategy is fixed depth at cut-off). We use this observation to calculate a prior probability of providing new relevant documents, which we then use in a pool bias estimator that improves upon previous estimates of precision at cut-off. Through extensive experimental results, covering 15 test collections, we show that the proposed bias correction method is the new state of the art, providing the closest estimates yet when compared to the original pool.
Where are You Tweeting?: A Context and User Movement Based Approach
Authors: Zhi Liu, Yan Huang
Venue: CIKM
Year: 2016

Geotagged tweets allow one to extract geo-information-trend, search local events, and identify natural disasters. In this paper, we propose a Hidden-Markov-based model to integrate tweet contents and user movements for geotagging. A language model is obtained for different locations from training datasets and movements of users among cities are analyzed. Home cities of users are considered in modeling the patterns of user movements. Evaluation on a large Twitter dataset shows that our method can significantly improve geotagging accuracy by 55% for home cities and 2% for other non-home cities as well as reduce error distances by orders of magnitude compared with pure text-based methods.
Online Adaptive Passive-Aggressive Methods for Non-Negative Matrix Factorization and Its Applications
Authors: Chenghao Liu, Steven C. H. Hoi, Peilin Zhao, Jianling Sun, Ee-Peng Lim
Venue: CIKM
Year: 2016

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key parameters which are often difficult to be tuned manually, particularly in a practical online learning system. In this work, we present a novel family of online Adaptive Passive-Aggressive (APA) learning algorithms for NMF, named "NN-APA", which overcomes two critical limitations of NN-PA by (i) exploiting second-order information to enhance PA in making more informative updates at each iteration; and (ii) achieving the parameter auto-selection by exploring the idea of online learning with expert advice in deciding the optimal combination of the key parameters in NMF. We theoretically analyze the regret bounds of the proposed method and show its advantage over the state-of-the-art NN-PA method, and further validate the efficacy and scalability of the proposed technique through an extensive set of experiments on a variety of large-scale real recommender systems datasets.
Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media
Authors: Xinyue Liu, Xiangnan Kong, Yanhua Li
Venue: CIKM
Year: 2016

Traffic prediction has become an important and active research topic in the last decade. Existing solutions mainly focus on exploiting the past and current traffic data, collected from various kinds of sensors, such as loop detectors, GPS devices, etc. In real-world road systems, only a small fraction of the road segments are deployed with sensors. For all the other road segments without sensors or historical traffic data, previous methods may no longer work. In this paper, we propose to use location-based social media, which captures a much larger area of the road systems than deployed sensors, to predict the traffic conditions. A simple but effective method called CTP is proposed to incorporate location-based social media semantics into the learning process. CTP also exploits complex dependencies among different regions to improve the prediction performances through collective inference. Empirical studies using traffic data and tweets collected in Los Angeles area demonstrate the effectiveness of CTP.
Reuters Tracer: A Large Scale System of Detecting & Verifying Real-Time News Events from Twitter
Authors: Xiaomo Liu, Quanzhi Li, Armineh Nourbakhsh, Rui Fang, Merine Thomas, Kajsa Anderson, Russ Kociuba, Mark Vedder, Steven Pomerville, Ramdev Wudali, Robert Martin, John Duprey, Arun Vachher, William Keenan, Sameena Shah
Venue: CIKM
Year: 2016

News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public.
FIN10K: A Web-based Information System for Financial Report Analysis and Visualization
Authors: Yu-Wen Liu, Liang-Chih Liu, Chuan-Ju Wang, Ming-Feng Tsai
Venue: CIKM
Year: 2016

In this demonstration, we present FIN10K, a web-based information system that facilitates the analysis of textual information in financial reports. The proposed system has three main components: (1) a 10-K Corpus, including an inverted index of financial reports on Form 10-K, several numerical finance measures, and pre-trained word embeddings; (2) an information retrieval system; and (3) two data visualizations of the analyzed results. The system can be of great help in revealing valuable insights within large amounts of textual information. The system is now online available at http: //clip.csie.org/10K/.
Detecting Promotion Campaigns in Query Auto Completion
Authors: Yuli Liu, Yiqun Liu, Ke Zhou, Min Zhang, Shaoping Ma, Yue Yin, Hengliang Luo
Venue: CIKM
Year: 2016

Query Auto Completion (QAC) aims to provide possible suggestions to Web search users from the moment they start entering a query, which is thought to reduce their physical and cognitive efforts in query formulation. However, the QAC has been misused by malicious users, being transformed into a new form of promotion campaign. These malicious users attack the search engines to replace legitimate auto-completion candidate suggestions with manipulated contents. Through this way, they provide a new malicious advertising service to promote their customers' products or services in QAC. To our best knowledge, we are among the first to investigate this new type of Promotion Campaign in QAC (PCQ). Firstly, we look into the causes of PCQ based on practical commercial search query logs. We found that various queries containing certain promotion intents are submitted multiple times to search engines to promote their rankings in QAC. Secondly, an effective promotion query detection framework is proposed by promotion intent propagation on query-user bipartite graph, which takes into account the behavioral characteristics of promotion campaigns. Finally, we extend the query detection framework to promotion target detection to identify the consistent promotion target which is the inherent goal of the promotion campaign. Large-scale manual annotations on practical data set convey both the effectiveness of our proposed algorithm, and an in-depth understanding of PCQ.
Deola: A System for Linking Author Entities in Web Document with DBLP
Authors: Yinan Liu, Wei Shen, Xiaojie Yuan
Venue: CIKM
Year: 2016

In this paper, we present Deola, an Online system for Author Entity Linking with DBLP. Unlike most existing entity linking systems which focus on linking entities with Wikipedia and depend largely on the special features associated with Wikipedia (e.g., Wikipedia articles), Deola links author names appearing in the web document which belongs to the domain of computer science with their corresponding entities existing in the DBLP network. This task is helpful for the enrichment of the DBLP network and the understanding of the domain-specific document. This task is challenging due to name ambiguity and limited knowledge existing in DBLP. Given a fragment of domain-specific web document belonging to the domain of computer science, Deola can return the mapping entity in DBLP for each author name appearing in the input document.
GiraphAsync: Supporting Online and Offline Graph Processing via Adaptive Asynchronous Message Processing
Authors: Yuqiong Liu, Chang Zhou, Jun Gao, Zhiguo Fan
Venue: CIKM
Year: 2016

It is highly desired for existing distributed graph processing systems to support both offline analytics and online queries adaptively. Existing offline graph analytics systems are mostly based on synchronous model. Although achieving high throughput, they suffer relatively high latency in answering simple queries due to synchronization overhead and slow convergence. On the other hand, online graph query systems adopting asynchronous model can response at any time, while incur overwhelmed messages and network packets, making them unable to meet the high throughput demand of offline analytics. In this work, we propose an adaptive asynchronous message processing (AAMP) method, which improves the efficiency of network communication while maintains low latency, to efficiently support offline analytics and online queries in one graph processing framework. We then design GiraphAsync, an implementation of AAMP on top of Apache Giraph, and evaluate it using several representative offline analytics and online queries on large graph datasets. Experimental results show that GiraphAsync gains an up to 10X improvement over synchronous model systems for graph analytics, while performs as well as specialized systems for online graph queries.
Scarce Feature Topic Mining for Video Recommendation
Authors: Wei Lu, Korris Fu-Lai Chung, Kunfeng Lai
Venue: CIKM
Year: 2016

Recommendation for user generated content sites has gained significant attention nowadays. To satisfy the niche tastes of users, product recommendation poses more challenges due to the data sparsity issue. This work is motivated by a real world online video recommendation problem, where the click records database suffers from sparseness of video inventory and video tags. Targeting the long tail phenomena of user behavior and sparsity of item features, we propose a personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS). Assuming that each record is generated from a representation of user preferences, DPIS is a probit classifier utilizing record topical clustering on the user part for recommendation. As demonstrated by the real-world application, the proposed DPIS achieves better performance than traditional methods.
Location-aware Friend Recommendation in Event-based Social Networks: A Bayesian Latent Factor Approach
Authors: Yao Lu, Zhi Qiao, Chuan Zhou, Yue Hu, Li Guo
Venue: CIKM
Year: 2016

In this paper we study the friend recommendation problem in event-based social networks (EBSNs). Effective friend recommendation is of benefit to EBSNs, since it can promote user interaction and accelerate information diffusion for promoted events. Different from usual friend recommendations, the aim of making friends in EBSNs is to better participate offline events and enhance user experience. Meanwhile friend recommendation in EBSNs encounters three types of data, i.e. geographical information, implicate user rating, and user behavior. These differences imply that existing friend recommendation approaches are not adequate any more for EBSNs. Under this background, in this paper we propose a Bayesian latent factor model, which can jointly formulate above three types of data, for friend recommendation with better event promotion and user experience. Results on real-world datasets show the efficacy of our approach.