OpenRaiser/Intern-Atlas huggingface dataset:
GTE-Rank: Searching for Implicit Temporal Query Results
Authors: Ricardo Campos, Gaël Dias, Alípio Mário Jorge, Célia Nunes
Venue: CIKM
Year: 2014

Temporal information retrieval has been a topic of great interest in recent years. Despite the efforts that have been conducted so far, most popular search engines remain underdeveloped when it comes to explicitly considering the use of temporal information in their search process. In this paper we present GTE-Rank, an online searching tool that takes time into account when ranking time-sensitive query web search results. GTE-Rank is defined as a linear combination of topical and temporal scores to reflect the relevance of any web page both in topical and temporal dimensions. The resulting system can be explored graphically through a search interface made available for research purposes.
On Efficient Meta-Level Features for Effective Text Classification
Authors: Sérgio D. Canuto, Thiago Salles, Marcos André Gonçalves, Leonardo Rocha, Gabriel Spada Ramos, Luiz Gonçalves, Thierson Couto Rosa, Wellington Santos Martins
Venue: CIKM
Year: 2014

This paper addresses the problem of automatically learning to classify texts by exploiting information derived from meta-level features (i.e., features derived from the original bag-of-words representation). 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. The set of proposed features is capable of transforming the original feature space into a new one, potentially smaller and more informed. Experiments performed with several standard datasets demonstrate that the effectiveness of the proposed meta-level features is not only much superior than the traditional bag-of-word representation but also superior to other state-of-art meta-level features previously proposed in the literature. Moreover, the proposed meta-features can be computed about three times faster than the existing meta-level ones, making our proposal much more scalable. We also demonstrate that the combination of our meta features and the original set of features produce significant improvements when compared to each feature set used in isolation.
Term Selection and Result Reranking for Question Retrieval by Exploiting Hierarchical Classification
Authors: Wen Chan, Jintao Du, Weidong Yang, Jinhui Tang, Xiangdong Zhou
Venue: CIKM
Year: 2014

Question retrieval aims to increase the accessibility of the community Question Answer (cQA) archives and has attracted increasing research interests recently. 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. Different from previous work, we propose a hierarchical question classification method with a sparse regularization to mimc user's question labeling in cQAs. Based on the hierarchical classification, we explore the local context of the question for term selection and reranking results and then integrating them into our proposed general question retrieval framework. The experimental results on a Yahoo! Answers dataset show the effectiveness of our method as compared to existing general question retrieval models and some state-of-the-art methods of utilizing category information for question retrieval.
Fast Heuristics for Near-Optimal Task Allocation in Data Stream Processing over Clusters
Authors: Andreas Chatzistergiou, Stratis D. Viglas
Venue: CIKM
Year: 2014

We study provisioning and job reconfiguration techniques for adapting to execution environment changes when processing data streams on cluster-based deployments. By monitoring the performance of an executing job, we identify computation and communication bottlenecks. In such cases we reconfigure the job by reallocating its tasks to minimize the communication cost. Our work targets data-intensive applications where the inter-node transfer latency is significant. We aim to minimize the transfer latency while keeping the nodes below some computational load threshold. We propose a scalable centralized scheme that employs fast allocation heuristics. Our techniques are based on a general group-based job representation that is commonly found in many distributed data stream processing frameworks. Using this representation we devise linear-time task allocation algorithms that improve existing quadratic-time solutions in practical cases. We have implemented and evaluated our proposals using both synthetic and real-world scenarios. Our results show that our algorithms: (a) exhibit significant allocation throughput while producing near-optimal allocations, and (b) significantly improve existing task-level approaches.
GI-NMF: Group Incremental Non-Negative Matrix Factorization on Data Streams
Authors: Xilun Chen, K. Selçuk Candan
Venue: CIKM
Year: 2014

Non-negative matrix factorization (NMF) is a well known method for obtaining low rank approximations of data sets, which can then be used for efficient indexing, classification, and retrieval. The non-negativity constraints enable probabilistic interpretation of the results and discovery of generative models. One key disadvantage of the NMF, however, is that it is costly to obtain and this makes it difficult to apply NMF in applications where data is dynamic. 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. Secondly, and perhaps less obviously, many applications include integration of multiple data streams (with potential overlaps) and/or involves tracking of multiple similar (but different) queries; this leads to significant data and query redundancies, which if leveraged properly can help alleviate computational cost of NMF. Based on these observations, we introduce Group Incremental Non-Negative Matrix Factorization (GI-NMF) which leverages redundancies across multiple NMF tasks over data streams. The proposed algorithm relies on a novel group multiplicative update rules (G-MUR) method to significantly reduce the cost of NMF. GMUR is further complemented to support incremental update of the factors where data evolves continuously. Experiments show that GI-NMF significantly reduces the processing time, with minimal error overhead.
Clairvoyant: An Early Prediction System For Video Hits
Authors: Hao Chen, Qinmin Hu, Liang He
Venue: CIKM
Year: 2014

Our slogan for the proposed Clairvoyant system is "with several clicks, the future is in your hand, the plan comes into your mind". Clairvoyant is to predict the future of new videos with only few data. The core function in the system is the novel shifted shape match prediction algorithm, based on a K-Nearest Neighbor model. Tons of experiments have been conducted on the open data sets. The experimental results confirms that the proposed SSMP algorithm is promising and outperforms the baselines with significant improvements on various evaluation methods. A demonstration video has been published at http://1drv.ms/1nyH3hD.
DTMBIO 2014: International Workshop on Data and Text Mining in Biomedical Informatics
Authors: Luonan Chen, Doheon Lee, Hua Xu, Min Song
Venue: CIKM
Year: 2014

Held each year in conjunction with one of the largest data management conferences, CIKM, the Eighth ACM International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 14) is organized to bring together researchers interested in development and application of cutting-edge biomedical and healthcare technology. The purpose of DTMBIO is to foster discussions regarding the state-of-the-art applications of data and text mining on biomedical research problems. DTMBIO 14 will help scientists navigate emerging trends and opportunities in the evolving area of informatics related techniques and problems in the context of biomedical research.
Robust and Skew-resistant Parallel Joins in Shared-Nothing Systems
Authors: Long Cheng, Spyros Kotoulas, Tomas E. Ward, Georgios Theodoropoulos
Venue: CIKM
Year: 2014

The performance of joins in parallel database management systems is critical for data intensive operations such as querying. Since data skew is common in many applications, poorly engineered join operations result in load imbalance and performance bottlenecks. State-of-the-art methods designed to handle this problem offer significant improvements over naive implementations. However, performance could be further improved by removing the dependency on global skew knowledge and broadcasting. In this paper, we propose PRPQ (partial redistribution & partial query), an efficient and robust join algorithm for processing large-scale joins over distributed systems. We present the detailed implementation and a quantitative evaluation of our method. The experimental results demonstrate that the proposed PRPQ algorithm is indeed robust and scalable under a wide range of skew conditions. Specifically, compared to the state-of-art PRPD method, we achieve 16% - 167% performance improvement and 24% - 54% less network communication under different join workloads.
Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring
Authors: Victor W. Chu, Raymond K. Wong, Fang Chen, Chi-Hung Chi
Venue: CIKM
Year: 2014

A recent study on collective attention in Twitter shows that an epidemic spreading of hashtags is predominantly driven by external factors. We extend a time-series form of susceptible-infectious-recovered (SIR) model to monitor microblog emerging outbreaks by considering both endogenous and exogenous drivers. In addition, we adopt partially labeled Dirichlet allocation (PLDA) model to generate both background latent topics and hashtag topics. It overcomes the problem of small available samples in hashtag analysis by including related but unlabeled tweets through inference. We standardize hashtag topic contagiousness measure as the estimated effective-reproduction-number R derived from epidemiology. It is obtained by Bayesian parameter estimation. Guided by R, one can profile and categorize emerging topics, and generate alerts on potential outbreaks. Experiment results confirm the effectiveness of this approach.
Documents Search Using Semantics Criteria
Authors: Santiago Cotelo, Alejandro Makowski, Luis Chiruzzo, Dina Wonsever
Venue: CIKM
Year: 2014

Current Information Retrieval systems generally search documents using a keywords model, which is often not expressive enough for the user. In this paper we describe some directions for improving an Information Retrieval system by letting the user specify different semantics constraints in her query, using a language based on a simplified version of first-order logic. The user can write queries that express the association between objects and attributes, temporal constraints and negation of attributes, and also perform synonyms expansion of queries. In order to evaluate the relevance of a candidate document with respect to the query, the dependency parse tree of the document is used, as well as other linguistic resources. The system was evaluated using a set of queries and a corpus extracted from the British newspaper The Times. The results are compared against the newspaper's own search engine and they look promising, showing an important improvement in precision in the first documents returned by the query.
Linking to Web Knowledge Bases and Applications to Web Search
Authors: Silviu-Petru Cucerzan
Venue: CIKM
Year: 2014

The development and availability of Web knowledge repositories, in particular Wikipedia, as the largest, inter-linked, and up-to-the-minute encyclopedic collection, have changed remarkably not only the way in which people fulfill their informational needs on the Web but also the way in which information can be organized and provided by Web search engines. The talk will focus on the task of entity extraction and linking to Wikipedia and other Web knowledge repositories. It will also cover work that employs such entity repositories in conjunction with query logs of commercial Web search engines to address Web information retrieval tasks such as context-aware search, query suggestion, question answering, retrieval of support for factual statements, and automatic aggregation of topic pages as an alternative to the ten blue links.
Non-independent Cascade Formation: Temporal and Spatial Effects
Authors: Biru Cui, Shanchieh Jay Yang, Christopher Homan
Venue: CIKM
Year: 2014

Determining cascade size and the factors affecting cascade size are two fundamental research problems in social network analysis. The commonly considered independent cascade model, when applied to social networks such as Digg, produces a phase-transition phenomenon where the cascade is either very small or very large. This phenomenon can be explained based on the concept of Giant Propagation Component (GPC). 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. While GPC exists in social networks, the phase-transition phenomenon, is not observed in the actual cascade size distribution when the information propagation is due to actions such as ``like'' or ``dig''.
PSBD 2014: Overview of the 1st International Workshop on Privacy and Security of Big Data
Authors: Alfredo Cuzzocrea
Venue: CIKM
Year: 2014

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. PSBD 2014 program has two interesting s/essions on (i) scalable privacy-preserving and security-control methods for big data processing, and (ii) user-oriented and data-oriented privacy methods for big data processing, plus a panel discussing current challenges and future research perspectives of privacy and security of big data.
Distance or Coverage?: Retrieving Knowledge-Rich Documents From Enterprise Text Collections
Authors: Vinay Deolalikar
Venue: CIKM
Year: 2014

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. We call such documents principal documents.
Parameter Tuning with User Models: Influencing Aggregate User Behavior in Cluster Based Retrieval Systems
Authors: Vinay Deolalikar
Venue: CIKM
Year: 2014

Can we effectively influence aggregate user behavior in a cluster based retrieval (CBR) system by tuning its parameters? This question combines parameter tuning with models of user behavior. To address this question, we propose an approach based on three components: user model, criterion metric, and sensitivity analysis. We then demonstrate this approach on one of the most frequently asked questions to designers and operators of CBR systems in enterprises: namely, "suggest a value for k." Both the users and the system desire a value that is likely to maximize user satisfaction, and sway them towards a cluster based examination of their retrieved result set (rather than prefer the original ranked retrieved list). Based on observed user behavior in CBR systems, we posit a two-stage user model. We isolate its core element, which is a "query coverage metric." We then perform an empirical sensitivity analysis of this metric. Our analysis reveals that this metric is, surprisingly, robust to changes in k (i.e., insensitive to k) in a wide range around its de-facto value. We conclude that in cases where our model approximates user behavior, the system cannot substantially increase the chances of the user resorting to CBR by tuning k. This has practical implications on the design and day-to-day operation of CBR systems. Similar analyses can be carried out for other parameters.
What is the Shape of a Cluster?: Structural Comparisons of Document Clusters
Authors: Vinay Deolalikar
Venue: CIKM
Year: 2014

Even today, most interfaces to document clustering present clusters to users and applications as a "bag of descriptive terms"? a technique that was proposed two decades ago. Consequently, users and applications are not able to obtain sophisticated structural knowledge that is indeed lying hidden in document clusters. In particular, the structural information about interaction of concepts that the cluster speaks about is completely missing from the bag of terms presentation. As the needs of unstructured information management increase, this shortcoming is coming into sharper focus. To address this shortcoming, we propose a rich representation of document clusters that surfaces the concept interactions within a cluster into the representation. We show that these interactions give a "shape" to the cluster. This "shape" is conveniently captured using a directed, colored, vertex-weighted graph, called the shape graph or, simply, the shape of the cluster. We show that shapes convey important structural information about document clusters, and can be computed efficiently.
Can Corpus Similarity-Based Self-Annotation Assist Information Retrieval?
Authors: Vinay Deolalikar
Venue: CIKM
Year: 2014

The use of external means to annotate corpora to assist in retrieval is gaining research attention. These external means include user provided annotations, use of linkages between documents (eg. the web), use of knowledge bases such as wikipedia, etc. However, for corpora that are internally generated within an enterprise, most of these methods are not readily applicable.
Building and Exploring Dynamic Topic Models on the Web
Authors: Michael Derntl, Nikou Günnemann, Alexander Tillmann, Ralf Klamma, Matthias Jarke
Venue: CIKM
Year: 2014

Topic modeling is a machine learning technique that identifies latent topics in a text corpus. There are several existing tools that allow end-users to create and explore topic models using graphical user interfaces. In this paper, we present a visual analytics system for dynamic topic models that goes beyond the existing breed of tools. First, it decouples the Web-based user interface from the underlying data sets, enabling exploration of arbitrary text data sets in the Web browser. Second, it allows users to explore dynamic topic models, while existing tools are often limited to static topic models. Finally, it comes with a tool server in the backend that allows the design and execution of scientific workflows to build topic models from any data source. The system is demonstrated by building and exploring a dynamic topic model of CIKM proceedings published since 2001.
SmartVenues: Recommending Popular and Personalised Venues in a City
Authors: Romain Deveaud, M-Dyaa Albakour, Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
Venue: CIKM
Year: 2014

We present SmartVenues, a system that recommends nearby venues to a user who visits or lives in a city. SmartVenues models the variation over time of each venue's level of attendance, and uses state-of-the-art time series forecasting algorithms to predict the future attendance of these venues. We use the predicted levels of attendance to infer the popularity of a venue at future points in time, and to provide the user with recommendations at different times of the day. If the users log in with their Facebook account, the recommendations are personalised using the pages they "like". In this demonstrator, we detail the architecture of the system and the data that we collect in real-time to be able to perform the predictions. We also present two different interfaces that build upon our system to display the recommendations: a web-based application and a mobile application.
On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions
Authors: Romain Deveaud, M-Dyaa Albakour, Craig Macdonald, Iadh Ounis
Venue: CIKM
Year: 2014

Suggesting venues to a user in a given geographic context is an emerging task that is currently attracting a lot of attention. Existing studies in the literature consist of approaches that rank candidate venues based on different features of the venues and the user, which either focus on modeling the preferences of the user or the quality of the venue. However, while providing insightful results and conclusions, none of these studies have explored the relative effectiveness of these different features. 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. Using the test collection of the TREC 2013 Contextual Suggestion track, we perform a number of experiments to evaluate our approach. Our results suggest that a learning to rank technique can significantly outperform a Language Modelling baseline that models the positive and negative preferences of the user. Moreover, despite the fact that the contextual suggestion task is a personalisation task (i.e. providing the user with personalised suggestions of venues), we surprisingly find that user-dependent features are less effective than venue-dependent features for estimating the relevance of a suggestion.
PraDa: Privacy-preserving Data-Deduplication-as-a-Service
Authors: Boxiang Dong, Ruilin Liu, Wendy Hui Wang
Venue: CIKM
Year: 2014

The data-cleaning-as-a-service (DCaS) paradigm enables users to outsource their data and data cleaning needs to computationally powerful third-party service providers. It raises several security issues. One of the issues is how the client can protect the private information in the outsourced data. 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. We analyze the robustness of our two methods against the attacks that exploit the auxiliary frequency distribution and the knowledge of the encoding algorithms. Our empirical study demonstrates the efficiency and effectiveness of our privacy preserving approaches.
Maximizing Multi-scale Spatial Statistical Discrepancy
Authors: Weishan Dong, Renjie Yao, Chunyang Ma, Changsheng Li, Lei Shi, Lu Wang, Yu Wang, Peng Gao, Junchi Yan
Venue: CIKM
Year: 2014

Detecting anomalous events from spatial data has important applications in real world. The spatial scan statistic methods are popular in this area. With maximizing the spatial statistical discrepancy by comparing observed data with a given baseline data distribution, significant spatial overdensity and underdensity can be detected. In reality, the spatial discrepancy is often irregularly shaped and has a structure of multiple spatial scales. However, a large-scale discrepancy pattern may not be significant when conducting fine granularity analysis. Meanwhile, local irregular boundaries of a maximized discrepancy cannot be well approximated with a coarse granularity analysis. Existing methods mostly work either on a fixed granularity, or with a regularly shaped scanning window. Thus, they have difficulties in characterizing such flexible spatial discrepancies. To solve the problem, in this paper we propose a novel discrepancy maximization algorithm, RefineScan. A grid hierarchy encoding multi-scale information is employed, making the algorithm capable of maximizing spatial discrepancies with multi-scale structures and irregular shapes. Experiments on a wide range of datasets demonstrate the advantages of RefineScan over the state-of-the-art algorithms: It always finds the largest discrepancy scores and remarkably better characterizes multi-scale discrepancy boundaries. Theoretical and empirical analyses also show that RefineScan has a moderate computational complexity and a good scalability.