OpenRaiser/Intern-Atlas huggingface dataset:
Explaining Sentiment Spikes in Twitter
Authors: Anastasia Giachanou, Ida Mele, Fabio Crestani
Venue: CIKM
Year: 2016

Tracking public opinion in social media provides important information to enterprises or governments during a decision making process. In addition, identifying and extracting the causes of sentiment spikes allows interested parties to redesign and adjust strategies with the aim to attract more positive sentiments. In this paper, we focus on the problem of tracking sentiment towards different entities, detecting sentiment spikes and on the problem of extracting and ranking the causes of a sentiment spike. Our approach combines LDA topic model with Relative Entropy. The former is used for extracting the topics discussed in the time window before the sentiment spike. The latter allows to rank the detected topics based on their contribution to the sentiment spike.
CyberRank: Knowledge Elicitation for Risk Assessment of Database Security
Authors: Hagit Grushka-Cohen, Oded Sofer, Ofer Biller, Bracha Shapira, Lior Rokach
Venue: CIKM
Year: 2016

Security systems for databases produce numerous alerts about anomalous activities and policy rule violations. Prioritizing these alerts will help security personnel focus their efforts on the most urgent alerts. Currently, this is done manually by security experts that rank the alerts or define static risk scoring rules. Existing solutions are expensive, consume valuable expert time, and do not dynamically adapt to changes in policy. Adopting a learning approach for ranking alerts is complex due to the efforts required by security experts to initially train such a model. The more features used, the more accurate the model is likely to be, but this will require the collection of a greater amount of user feedback and prolong the calibration process. In this paper, we propose CyberRank, a novel algorithm for automatic preference elicitation that is effective for situations with limited experts' time and outperforms other algorithms for initial training of the system. We generate synthetic examples and annotate them using a model produced by Analytic Hierarchical Processing (AHP) to bootstrap a preference learning algorithm. We evaluate different approaches with a new dataset of expert ranked pairs of database transactions, in terms of their risk to the organization. We evaluated using manual risk assessments of transaction pairs, CyberRank outperforms all other methods for cold start scenario with error reduction of 20%.
A Nonparametric Model for Event Discovery in the Geospatial-Temporal Space
Authors: Jinjin Guo, Zhiguo Gong
Venue: CIKM
Year: 2016

The availability of geographical and temporal tagged documents enables many location and time based mining tasks. Event discovery is one of such tasks, which is to identify interesting happenings in the geographical and temporal space. In recent years, several techniques have been proposed. However, no existing work has provided a nonparametric algorithm for detecting events in the joint space crossing geographical and temporal dimensions. Furthermore, though some prior works proposed to capture the periodicities of topics in their solutions, some restrictions on the temporal patterns are often placed and they usually ignore the spatial patterns of the topics. To break through such limitations, in this paper we propose a novel nonparametric model to identify events in the geographical and temporal space, where any recurrent patterns of events can be automatically captured. In our approach, parameters are automatically determined by exploiting a Dirichlet Process. To reduce the influence from noisy terms in the detection, we distinguish its event role from its background role using a Bernoulli model in the solution. Experimental results on three real world datasets show the proposed algorithm outperforms previous state-of-the-art approaches.
Large-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with Mobile Proactive Systems
Authors: Qi Guo, Yang Song
Venue: CIKM
Year: 2016

Recently, proactive systems such as Google Now and Microsoft Cortana have become increasingly popular in reforming the way users access information on mobile devices. In these systems, relevant content is presented to users based on their context without a query in the form of information cards that do not require a click to satisfy the users. As a result, prior approaches based on clicks cannot provide reliable measurements of user satisfaction with such systems. It is also unclear how much of the previous findings regarding good abandonment with reactive Web searches can be applied to these proactive systems due to the intrinsic difference in user intent, the greater variety of content types and their presentations. In this paper, we present the first large-scale analysis of viewing behavior based on the viewport (the visible fraction of a Web page) of the mobile devices, towards measuring user satisfaction with the information cards of the mobile proactive systems. In particular, we identified and analyzed a variety of factors that may influence the viewing behavior, including biases from ranking positions, the types and attributes of the information cards, and the touch interactions with the mobile devices. We show that by modeling the various factors we can better measure user satisfaction with the mobile proactive systems, enabling stronger statistical power in large-scale online A/B testing.
Ease the Process of Machine Learning with Dataflow
Authors: Tianyou Guo, Jun Xu, Xiaohui Yan, Jianpeng Hou, Ping Li, Zhaohui Li, Jiafeng Guo, Xueqi Cheng
Venue: CIKM
Year: 2016

Machine learning algorithms have become the key components in many big data applications. However, the full potential of machine learning is still far from been realized because using machine learning algorithms is hard, especially on distributed platforms such as Hadoop and Spark. The key barriers come from not only the implementation of the algorithms themselves, but also the processing for applying them to real applications which often involve multiple steps and different algorithms. In this demo we present a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real world tasks. In the system, a learning task is formulated as a directed acyclic graph (DAG) in which each node represents an operation (e.g., a machine learning algorithm), and each edge represents the flow of the data from one node to its descendants. Graphical user interface is implemented for making users to create, configure, submit, and monitor a task in a drag-and-drop manner. Advantages of the system include 1) lowering the barriers of defining and executing machine learning tasks; 2) sharing and re-using the implementations of the algorithms, the task dataflow DAGs, and the (intermediate) experimental results; 3) seamlessly integrating the stand-alone algorithms as well as the distributed algorithms in one task. The system has been deployed as a machine learning service and can be access from the Internet.
Framing Mobile Information Needs: An Investigation of Hierarchical Query Sequence Structure
Authors: Shuguang Han, Xing Yi, Zhen Yue, Zhigeng Geng, Alyssa Glass
Venue: CIKM
Year: 2016

When using search engines, people often issue multiple related queries to accomplish a complex search task. A simple query-task structure may not fully capture the complexity of query relations since people may divide a task into multiple subtasks. As a result, this paper applies a three-level hierarchical structure with query, goal and mission - a mission includes several goals, and a goal consists of multiple queries. Particularly, we focus on analyzing query-goal-mission structure for mobile web search because of its increasing popularity and lack of investigation in the literature. This study has three main contributions: (1) we study the query-goal-mission structure for mobile web search, which was not studied before. (2) We identify several differences between mobile and desktop search patterns in terms of goal/mission length, duration and interleaving. (3) We demonstrate that the query-goal-mission structure can be applied to design better user satisfaction metrics. Specifically, goal-based search success rate and mission-based abandonment rate are better aligned with users' long-term engagement than query and session based metrics.
Effective Spelling Correction for Eye-based Typing using domain-specific Information about Error Distribution
Authors: Raíza Hanada, Maria da Graça Campos Pimentel, Marco Cristo, Fernando Anglada Lores
Venue: CIKM
Year: 2016

Spelling correction methods, widely used and researched, usually assume a low error probability and a small number of errors per word. These assumptions do not hold in very noisy input scenarios such as eye-based typing systems. In particular for eye typing, insertion errors are much more common than in traditional input systems, due to specific sources of noise such as the eye tracker device, particular user behaviors, and intrinsic characteristics of eye movements. The large number of common errors in such a scenario makes the use of traditional approaches unfeasible. Moreover, the lack of a large corpus of errors makes it hard to adopt probabilistic approaches based on information extracted from real world data. We address these problems by combining estimates extracted from general error corpora with domain-specific knowledge about eye-based input. Further, by relaxing restrictions on edit distance specifically related to insertion errors, we propose an algorithm that is able to find dictionary word candidates in an attainable time. We show that our method achieves good results to rank the correct word, given the input stream and similar space and time restrictions, when compared to the state-of-the-art baselines.
Computing and Summarizing the Negative Skycube
Authors: Nicolas Hanusse, Patrick Kamnang Wanko, Sofian Maabout
Venue: CIKM
Year: 2016

Given a table T with a set of dimensions D, the skycube of T is the union of all skylines obtained by considering each of the subsets of D (subspaces). The number of these skylines is exponential w.r.t D. To make the skycube practically useful, two lines of research have been pursued so far: the first one aims to propose efficient algorithms for computing it and the second one considers either that the skycube is too large to be computed in a reasonable time or it requires too much memory space to be stored. They therefore propose skycube summarization techniques to reduce time and space consumption. Intuitively, previous efforts have been devoted to compute or summarize the following information: ``for every tuple t, list the skylines where t belongs to". In this paper, we consider the complementary statement, i.e., ``for every tuple t, list the skylines where t does not belong to". This is what we call the negative skycube. Despite the apparent equivalence between these two statements, our analysis and extensive experiments show that these two points of views do not lead to the same behavior of the related algorithms. More specifically, our proposal shows that (i) the negative summary can be obtained much faster than state of the art techniques for positive summaries, (ii) in general, it consumes less space, (iii) skyline queries evaluation using this summary are much faster, (iv) the positive skycube can be obtained much more rapidly than state of the art algorithms, and (v) it can be used for a larger class of queries, namely k-domination skylines.
Vandalism Detection in Wikidata
Authors: Stefan Heindorf, Martin Potthast, Benno Stein, Gregor Engels
Venue: CIKM
Year: 2016

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity. Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata. We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).
A Unified Index for Spatio-Temporal Keyword Queries
Authors: Tuan-Anh Hoang-Vu, Huy T. Vo, Juliana Freire
Venue: CIKM
Year: 2016

From tweets to urban data sets, there has been an explosion in the volume of textual data that is associated with both temporal and spatial components. Efficiently evaluating queries over these data is challenging. Previous approaches have focused on the spatial aspect. Some used separate indices for space and text, thus incurring the overhead of storing separate indices and joining their results. Others proposed a combined index that either inserts terms into a spatial structure or adds a spatial structure to an inverted index. These benefit queries with highly-selective constraints that match the primary index structure but have limited effectiveness and pruning power otherwise. We propose a new indexing strategy that uniformly handles text, space and time in a single structure, and is thus able to efficiently evaluate queries that combine keywords with spatial and temporal constraints. We present a detailed experimental evaluation using real data sets which shows that not only our index attains substantially lower query processing times, but it can also be constructed in a fraction of the time required by state-of-the-art approaches.
Tracking Virality and Susceptibility in Social Media
Authors: Tuan-Anh Hoang, Ee-Peng Lim
Venue: CIKM
Year: 2016

In social media, the magnitude of information propagation hinges on the virality and susceptibility of users spreading and receiving the information respectively, as well as the virality of information items. These users' and items' behavioral factors evolve dynamically at the same time interacting with one another. Previous works however measure the factors statically and independently in a restricted case: each user has only a single adoption on each item, and/or users' exposure to items are observable. In this work, we investigate the inter-relationship among the factors and users' multiple adoptions on items to propose both new static and temporal models for measuring the factors without requiring user - item exposure. These models are designed to cope with even more realistic propagation scenarios where an item may be propagated many times from the same user(s) to the same other user(s). We further propose an incremental model for measuring the factors in large data streams. We evaluated the proposed models and existing models through extensive experiments on a large Twitter dataset covering information propagation in one month. The experiments show that our proposed models can effectively mine the behavioral factors and outperform the existing ones in a propagation prediction task. The incremental model is shown more than 10 times faster than the temporal model, while still obtains very similar results.
PISA: An Index for Aggregating Big Time Series Data
Authors: Xiangdong Huang, Jianmin Wang, Raymond K. Wong, Jinrui Zhang, Chen Wang
Venue: CIKM
Year: 2016

Aggregation operation plays an important role in time series database management. As the amount of data increases, current solutions such as summary table and MapReduce-based methods struggle to respond to such queries with low latency. Other approaches such as segment tree based methods have a poor insertion performance when the data size exceeds the available memory. This paper proposes a new segment tree based index called PISA, which has fast insertion performance and low latency for aggregation queries. PISA uses a forest to overcome the performance disadvantages of insertions in traditional segment trees. By defining two kinds of tags, namely code number and serial number, we propose an algorithm to accelerate queries by avoiding reading unnecessary data on disk. The index is stored on disk and only takes a few hundred bytes of memory for billions of data points. PISA can be easily implemented on both traditional databases and NoSQL systems, examples including MySQL and Cassandra. It handles aggregation queries within milliseconds on a commodity server for a time range that may contain tens of billions of data points.
BICP: Block-Incremental CP Decomposition with Update Sensitive Refinement
Authors: Shengyu Huang, K. Selçuk Candan, Maria Luisa Sapino
Venue: CIKM
Year: 2016

With many applications relying on multi-dimensional datasets for decision making, tensors (or multi-dimensional arrays) are emerging as a popular data representation to support diverse types of data, such as sensor streams and social networks. Consequently, tensor decomposition forms the basis for many data analysis and knowledge discovery tasks, from clustering, trend detection, anomaly detection, to correlation analysis. In applications where data evolves over time and the tensor-based analysis results need to be continuously maintained, re-computation of the whole tensor decomposition with each update will cause high computational costs and incur large memory overheads. In this paper, we propose a two-phase block-incremental CP-based tensor decomposition technique, BICP, that efficiently and effectively maintains tensor decomposition results in the presence of dynamically evolving tensor data. In its first phase, instead of repeatedly conducting ALS on each sub-tensor, BICP only revises the decompositions of the tensors that contain updated data. Moreover, when updates are relatively small with respect to the block size, BICP relies on a incremental factor tracking to avoid re-decomposition the updated sub-tensor. In its second phase, BICP limits the block-centric refinement process to only those blocks that are critical given the update. Experiment results show that the proposed method significantly reduces the execution time while assuring high accuracy.
PARC: Privacy-Aware Data Cleaning
Authors: Dejun Huang, Dhruv Gairola, Yu Huang, Zheng Zheng, Fei Chiang
Venue: CIKM
Year: 2016

Poor data quality has become a persistent challenge for organizations as data continues to grow in complexity and size. Existing data cleaning solutions focus on identifying repairs to the data to minimize either a cost function or the number of updates. These techniques, however, fail to consider underlying data privacy requirements that exist in many real data sets containing sensitive and personal information. In this demonstration, we present PARC, a Privacy-AwaRe data Cleaning system that corrects data inconsistencies w.r.t. a set of FDs, and limits the disclosure of sensitive values during the cleaning process. The system core contains modules that evaluate three key metrics during the repair search, and solves a multi-objective optimization problem to identify repairs that balance the privacy vs. utility tradeoff. This demonstration will enable users to understand: (1) the characteristics of a privacy-preserving data repair; (2) how to customize data cleaning and data privacy requirements using two real datasets; and (3) the distinctions among the repair recommendations via visualization summaries.
TGraph: A Temporal Graph Data Management System
Authors: Haixing Huang, Jinghe Song, Xuelian Lin, Shuai Ma, Jinpeng Huai
Venue: CIKM
Year: 2016

Temporal graphs are a class of graphs whose nodes and edges, together with the associated properties, continuously change over time. Recently, systems have been developed to support snapshot queries over temporal graphs. However, these systems barely support aggregate time range queries. Moreover, these systems cannot guarantee ACID transactions, an important feature for data management systems as long as concurrent processing is involved. To solve these issues, we design and develop TGraph, a temporal graph data management system, that assures the ACID transaction feature, and supports fast temporal graph queries.
Predicting Importance of Historical Persons using Wikipedia
Authors: Adam Jatowt, Daisuke Kawai, Katsumi Tanaka
Venue: CIKM
Year: 2016

Wikipedia contains a lot of contemporary as well as history-related information, and given its vast coverage and richness, it can be used to rank entities in a variety of different ways. In this work, we are interested in utilizing Wikipedia for judging historical person's importance. Based on the two well-known lists of the most important people in the last millennium, we look closely into factors that determine significance of historical persons. We predict person's importance using six classifiers equipped with features derived from link structure, visit logs and article content.
Forecasting Geo-sensor Data with Participatory Sensing Based on Dropout Neural Network
Authors: Jyun-Yu Jiang, Cheng-Te Li
Venue: CIKM
Year: 2016

Nowadays, geosensor data, such as air quality and traffic flow, have become more and more essential in people's daily life. However, installing geosensors or hiring volunteers at every location and every time is so expensive. Some organizations may have only few facilities or limited budget to sense these data. Moreover, people usually tend to know the forecast instead of ongoing observations, but the number of sensors (or volunteers) will be a hurdle to make precise prediction. In this paper, we propose a novel concept to forecast geosensor data with participatory sensing. Given a limited number of sensors or volunteers, participatory sensing assumes each of them can observe and collect data at different locations and at different time. By aggregating these sparse data observations in the past time, we propose a neural network based approach to forecast the future geosensor data in any location of an urban area. The extensive experiments have been conducted with large-scale datasets of the air quality in three cities and the traffic of bike sharing systems in two cities. Experimental results show that our predictive model can precisely forecast the air quality and the bike rentle traffic as geosensor data.
Privacy-Preserving Reachability Query Services for Massive Networks
Authors: Jiaxin Jiang, Peipei Yi, Byron Choi, Zhiwei Zhang, Xiaohui Yu
Venue: CIKM
Year: 2016

This paper studies privacy-preserving reachability query services under the paradigm of data outsourcing. Specifically, graph data have been outsourced to a third-party service provider (SP), query clients submit their queries to the (SP), and the (SP) returns the query answers to the clients. However, the (SP) may not always be trustworthy. Hence, this paper investigates protecting the structural information of the graph data and the query answers from the (SP). Existing techniques are either insecure or not scalable. This paper proposes a privacy-preserving labeling, called ppTopo. To our knowledge, ppTopo is the first work that can produce reachability index on massive networks and is secure against known plaintext attacks (KPA). Specifically, we propose a scalable index construction algorithm by employing the idea of topological folding, recently proposed by Cheng et al. We propose a novel asymmetric scalar product encryption in modulo 3 (ASPE3). It allows us to encrypt the index labels and transforms the queries into scalar products of encrypted labels. We perform an experimental study of the proposed technique on the SNAP networks. Compared with the existing methods, our results show that our technique is capable of producing the encrypted indexes at least 5 times faster for massive networks and the client's decryption time is 2-3 times smaller for most graphs.