Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498397
Yi-Cheng Chen, Wen-Chih Peng, Suh-Yin Lee
Sequential pattern mining is an important subfield in data mining. Recently, discovering patterns from interval events has attracted considerable efforts due to its widespread applications. However, due to the complex relation between two intervals, mining interval-based sequences efficiently is a challenging issue. In this paper, we develop a novel algorithm, P-TPMiner, to efficiently discover two types of interval-based sequential patterns. Some pruning techniques are proposed to further reduce the search space of the mining process. Experimental studies show that proposed algorithm is efficient and scalable. Furthermore, we apply proposed method to real datasets to demonstrate the practicability of discussed patterns.
{"title":"Mining temporal patterns in interval-based data","authors":"Yi-Cheng Chen, Wen-Chih Peng, Suh-Yin Lee","doi":"10.1109/ICDE.2016.7498397","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498397","url":null,"abstract":"Sequential pattern mining is an important subfield in data mining. Recently, discovering patterns from interval events has attracted considerable efforts due to its widespread applications. However, due to the complex relation between two intervals, mining interval-based sequences efficiently is a challenging issue. In this paper, we develop a novel algorithm, P-TPMiner, to efficiently discover two types of interval-based sequential patterns. Some pruning techniques are proposed to further reduce the search space of the mining process. Experimental studies show that proposed algorithm is efficient and scalable. Furthermore, we apply proposed method to real datasets to demonstrate the practicability of discussed patterns.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"37 1","pages":"1506-1507"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85484923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498302
Lijian Wan, Tingjian Ge
In this paper, we study the problem of learning a regular model from a number of sequences, each of which contains events in a time unit. Assuming some regularity in such sequences, we determine what events should be deemed irregular in their contexts. We perform an in-depth analysis of the model we build, and propose two optimization techniques, one of which is also of independent interest in solving a new problem named the Group Counting problem. Our comprehensive experiments on real and hybrid datasets show that the model we build is very effective in characterizing regularities and identifying irregular events. One of our optimizations improves model building speed by more than an order of magnitude, and the other significantly saves space consumption.
{"title":"Event regularity and irregularity in a time unit","authors":"Lijian Wan, Tingjian Ge","doi":"10.1109/ICDE.2016.7498302","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498302","url":null,"abstract":"In this paper, we study the problem of learning a regular model from a number of sequences, each of which contains events in a time unit. Assuming some regularity in such sequences, we determine what events should be deemed irregular in their contexts. We perform an in-depth analysis of the model we build, and propose two optimization techniques, one of which is also of independent interest in solving a new problem named the Group Counting problem. Our comprehensive experiments on real and hybrid datasets show that the model we build is very effective in characterizing regularities and identifying irregular events. One of our optimizations improves model building speed by more than an order of magnitude, and the other significantly saves space consumption.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"129 1","pages":"930-941"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89493360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498241
Sen Su, Peng Tang, Xiang Cheng, R. Chen, Zequn Wu
In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware search frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
{"title":"Differentially private multi-party high-dimensional data publishing","authors":"Sen Su, Peng Tang, Xiang Cheng, R. Chen, Zequn Wu","doi":"10.1109/ICDE.2016.7498241","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498241","url":null,"abstract":"In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware search frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"65 5 1","pages":"205-216"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89862333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498256
Renjun Hu, C. Aggarwal, Shuai Ma, J. Huai
Network anomaly detection has become very popular in recent years because of the importance of discovering key regions of structural inconsistency in the network. In addition to application-specific information carried by anomalies, the presence of such structural inconsistency is often an impediment to the effective application of data mining algorithms such as community detection and classification. In this paper, we study the problem of detecting structurally inconsistent nodes that connect to a number of diverse influential communities in large social networks. We show that the use of a network embedding approach, together with a novel dimension reduction technique, is an effective tool to discover such structural inconsistencies. We also experimentally show that the detection of such anomalous nodes has significant applications: one is the specific use of detected anomalies, and the other is the improvement of the effectiveness of community detection.
{"title":"An embedding approach to anomaly detection","authors":"Renjun Hu, C. Aggarwal, Shuai Ma, J. Huai","doi":"10.1109/ICDE.2016.7498256","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498256","url":null,"abstract":"Network anomaly detection has become very popular in recent years because of the importance of discovering key regions of structural inconsistency in the network. In addition to application-specific information carried by anomalies, the presence of such structural inconsistency is often an impediment to the effective application of data mining algorithms such as community detection and classification. In this paper, we study the problem of detecting structurally inconsistent nodes that connect to a number of diverse influential communities in large social networks. We show that the use of a network embedding approach, together with a novel dimension reduction technique, is an effective tool to discover such structural inconsistencies. We also experimentally show that the detection of such anomalous nodes has significant applications: one is the specific use of detected anomalies, and the other is the improvement of the effectiveness of community detection.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"385-396"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89906602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498244
A. Boutet, Anne-Marie Kermarrec, Nupur Mittal, François Taïani
K-Nearest-Neighbor (KNN) graphs have emerged as a fundamental building block of many on-line services providing recommendation, similarity search and classification. Constructing a KNN graph rapidly and accurately is, however, a computationally intensive task. As data volumes keep growing, speed and the ability to scale out are becoming critical factors when deploying a KNN algorithm. In this work, we present KIFF, a generic, fast and scalable KNN graph construction algorithm. KIFF directly exploits the bipartite nature of most datasets to which KNN algorithms are applied. This simple but powerful strategy drastically limits the computational cost required to rapidly converge to an accurate KNN solution, especially for sparse datasets. Our evaluation on a representative range of datasets show that KIFF provides, on average, a speed-up factor of 14 against recent state-of-the art solutions while improving the quality of the KNN approximation by 18%.
k -最近邻(KNN)图已经成为许多在线服务的基本构建块，提供推荐、相似性搜索和分类。然而，快速准确地构建KNN图是一项计算密集型任务。随着数据量的不断增长，速度和向外扩展的能力成为部署KNN算法时的关键因素。本文提出了一种通用、快速、可扩展的KNN图构建算法KIFF。KIFF直接利用了KNN算法应用的大多数数据集的二分性。这种简单但功能强大的策略极大地限制了快速收敛到准确的KNN解决方案所需的计算成本，特别是对于稀疏数据集。我们对具有代表性的数据集范围的评估表明，相对于最新的最先进的解决方案，KIFF平均提供了14的加速因子，同时将KNN近似的质量提高了18%。
{"title":"Being prepared in a sparse world: The case of KNN graph construction","authors":"A. Boutet, Anne-Marie Kermarrec, Nupur Mittal, François Taïani","doi":"10.1109/ICDE.2016.7498244","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498244","url":null,"abstract":"K-Nearest-Neighbor (KNN) graphs have emerged as a fundamental building block of many on-line services providing recommendation, similarity search and classification. Constructing a KNN graph rapidly and accurately is, however, a computationally intensive task. As data volumes keep growing, speed and the ability to scale out are becoming critical factors when deploying a KNN algorithm. In this work, we present KIFF, a generic, fast and scalable KNN graph construction algorithm. KIFF directly exploits the bipartite nature of most datasets to which KNN algorithms are applied. This simple but powerful strategy drastically limits the computational cost required to rapidly converge to an accurate KNN solution, especially for sparse datasets. Our evaluation on a representative range of datasets show that KIFF provides, on average, a speed-up factor of 14 against recent state-of-the art solutions while improving the quality of the KNN approximation by 18%.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"20 1","pages":"241-252"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87126657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498338
Guilherme Damasio, Piotr Mierzejewski, Jaroslaw Szlichta, C. Zuzarte
Query performance problem determination is usually performed by analyzing query execution plans (QEPs). Analyzing complex QEPs is excessively time consuming and existing automatic problem determination tools do not provide ability to perform analysis with flexible user-defined problem patterns. We present the novel OptImatch system that allows a relatively naive user to search for patterns in QEPs and get recommendations from an expert and user customizable knowledge base. Our system transforms a QEP into an RDF graph. We provide a web graphical interface for the user to describe a pattern that is transformed with handlers into a SPARQL query. The SPARQL query is matched against the abstracted RDF graph and any matched parts of the graph are relayed back to the user. With the knowledge base the system automatically matches stored patterns to the QEPs by adapting dynamic context through developed tagging language and ranks recommendations using statistical correlation analysis.
{"title":"OptImatch: Semantic web system for query problem determination","authors":"Guilherme Damasio, Piotr Mierzejewski, Jaroslaw Szlichta, C. Zuzarte","doi":"10.1109/ICDE.2016.7498338","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498338","url":null,"abstract":"Query performance problem determination is usually performed by analyzing query execution plans (QEPs). Analyzing complex QEPs is excessively time consuming and existing automatic problem determination tools do not provide ability to perform analysis with flexible user-defined problem patterns. We present the novel OptImatch system that allows a relatively naive user to search for patterns in QEPs and get recommendations from an expert and user customizable knowledge base. Our system transforms a QEP into an RDF graph. We provide a web graphical interface for the user to describe a pattern that is transformed with handlers into a SPARQL query. The SPARQL query is matched against the abstracted RDF graph and any matched parts of the graph are relayed back to the user. With the knowledge base the system automatically matches stored patterns to the QEPs by adapting dynamic context through developed tagging language and ranks recommendations using statistical correlation analysis.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"15 1","pages":"1334-1337"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82056449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498360
Felix Gessert, N. Ritter
The unprecedented scale at which data is consumed and generated today has shown a large demand for scalable data management and given rise to non-relational, distributed “NoSQL” database systems. Two central problems triggered this process: 1) vast amounts of user-generated content in modern applications and the resulting requests loads and data volumes 2) the desire of the developer community to employ problem-specific data models for storage and querying. To address these needs, various data stores have been developed by both industry and research, arguing that the era of one-size-fits-all database systems is over. The heterogeneity and sheer amount of these systems - now commonly referred to as NoSQL data stores - make it increasingly difficult to select the most appropriate system for a given application. Therefore, these systems are frequently combined in polyglot persistence architectures to leverage each system in its respective sweet spot. This tutorial gives an in-depth survey of the most relevant NoSQL databases to provide comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling and querying characteristics. We present how each system's design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges.
{"title":"Scalable data management: NoSQL data stores in research and practice","authors":"Felix Gessert, N. Ritter","doi":"10.1109/ICDE.2016.7498360","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498360","url":null,"abstract":"The unprecedented scale at which data is consumed and generated today has shown a large demand for scalable data management and given rise to non-relational, distributed “NoSQL” database systems. Two central problems triggered this process: 1) vast amounts of user-generated content in modern applications and the resulting requests loads and data volumes 2) the desire of the developer community to employ problem-specific data models for storage and querying. To address these needs, various data stores have been developed by both industry and research, arguing that the era of one-size-fits-all database systems is over. The heterogeneity and sheer amount of these systems - now commonly referred to as NoSQL data stores - make it increasingly difficult to select the most appropriate system for a given application. Therefore, these systems are frequently combined in polyglot persistence architectures to leverage each system in its respective sweet spot. This tutorial gives an in-depth survey of the most relevant NoSQL databases to provide comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling and querying characteristics. We present how each system's design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"15 1","pages":"1420-1423"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84964960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498279
Xiaojun Chen, J. Huang, Jun Luo
Clustering of customer transaction data is usually an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we present to use a “personalized product tree”, called purchase tree, to represent a customer's transaction data. The customer transaction data set can be represented as a set of purchase trees. We propose a PurTreeClust algorithm for clustering of large-scale customers from purchase trees. We define a new distance metric to effectively compute the distance between two purchase trees from the entire levels in the tree. A cover tree is then built for indexing the purchase tree data and we propose a leveled density estimation method for selecting initial cluster centers from a cover tree. PurTreeClust, a fast clustering method for clustering of large-scale purchase trees, is then presented. Last, we propose a gap statistic based method for estimating the number of clusters from the purchase tree clustering results. A series of experiments were conducted on ten large-scale transaction data sets which contain up to four million transaction records, and experimental results have verified the effectiveness and efficiency of the proposed method. We also compared our method with three clustering algorithms, e.g., spectral clustering, hierarchical agglomerative clustering and DBSCAN. The experimental results have demonstrated the superior performance of the proposed method.
{"title":"PurTreeClust: A purchase tree clustering algorithm for large-scale customer transaction data","authors":"Xiaojun Chen, J. Huang, Jun Luo","doi":"10.1109/ICDE.2016.7498279","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498279","url":null,"abstract":"Clustering of customer transaction data is usually an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we present to use a “personalized product tree”, called purchase tree, to represent a customer's transaction data. The customer transaction data set can be represented as a set of purchase trees. We propose a PurTreeClust algorithm for clustering of large-scale customers from purchase trees. We define a new distance metric to effectively compute the distance between two purchase trees from the entire levels in the tree. A cover tree is then built for indexing the purchase tree data and we propose a leveled density estimation method for selecting initial cluster centers from a cover tree. PurTreeClust, a fast clustering method for clustering of large-scale purchase trees, is then presented. Last, we propose a gap statistic based method for estimating the number of clusters from the purchase tree clustering results. A series of experiments were conducted on ten large-scale transaction data sets which contain up to four million transaction records, and experimental results have verified the effectiveness and efficiency of the proposed method. We also compared our method with three clustering algorithms, e.g., spectral clustering, hierarchical agglomerative clustering and DBSCAN. The experimental results have demonstrated the superior performance of the proposed method.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"59 1","pages":"661-672"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83970276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498359
Laure Berti-Équille
The evolution of the Web from a technology platform to a social ecosystem has resulted in unprecedented data volumes being continuously generated, exchanged, and consumed. User-generated content on the Web is massive, highly dynamic, and characterized by a combination of factual data and opinion data. False information, rumors, and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. Truth discovery (also known as fact-checking) has recently gained lot of interest from Data Science communities. This tutorial will attempt to cover recent work on truth-finding and how it can scale Big Data. We will provide a broad overview with new insights, highlighting the progress made on truth discovery from information extraction, data and knowledge fusion, as well as modeling of misinformation dynamics in social networks. We will review in details current models, algorithms, and techniques proposed by various research communities whose contributions converge towards the same goal of estimating the veracity of data in a dynamic world. Our aim is to bridge theory and practice and introduce recent work from diverse disciplines to database people to be better equipped for addressing the challenges of truth discovery in Big Data.
{"title":"Scaling up truth discovery","authors":"Laure Berti-Équille","doi":"10.1109/ICDE.2016.7498359","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498359","url":null,"abstract":"The evolution of the Web from a technology platform to a social ecosystem has resulted in unprecedented data volumes being continuously generated, exchanged, and consumed. User-generated content on the Web is massive, highly dynamic, and characterized by a combination of factual data and opinion data. False information, rumors, and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. Truth discovery (also known as fact-checking) has recently gained lot of interest from Data Science communities. This tutorial will attempt to cover recent work on truth-finding and how it can scale Big Data. We will provide a broad overview with new insights, highlighting the progress made on truth discovery from information extraction, data and knowledge fusion, as well as modeling of misinformation dynamics in social networks. We will review in details current models, algorithms, and techniques proposed by various research communities whose contributions converge towards the same goal of estimating the veracity of data in a dynamic world. Our aim is to bridge theory and practice and introduce recent work from diverse disciplines to database people to be better equipped for addressing the challenges of truth discovery in Big Data.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"30 1","pages":"1418-1419"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89345769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498281
Dong-Wan Choi, J. Pei, Xuemin Lin
The existing works on spatial keyword search focus on finding a group of spatial objects covering all the query keywords and minimizing the diameter of the group. However, we observe that such a formulation may not address what users need in some application scenarios. In this paper, we introduce a novel spatial keyword cover problem (SK-COVER for short), which aims to identify the group of spatio-textual objects covering all keywords in a query and minimizing a distance cost function that leads to fewer proximate objects in the answer set. We prove that SK-COVER is not only NP-hard but also does not allow an approximation better than O(log m) in polynomial time, where m is the number of query keywords. We establish an O(log m)-approximation algorithm, which is asymptotically optimal in terms of the approximability of SK-COVER. Furthermore, we devise effective accessing strategies and pruning rules to improve the overall efficiency and scalability. In addition to our algorithmic results, we empirically show that our approximation algorithm always achieves the best accuracy, and the efficiency of our algorithm is comparable to a state-of-the-art algorithm that is intended for mCK, a problem similar to yet theoretically easier than SK-COVER.
{"title":"Finding the minimum spatial keyword cover","authors":"Dong-Wan Choi, J. Pei, Xuemin Lin","doi":"10.1109/ICDE.2016.7498281","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498281","url":null,"abstract":"The existing works on spatial keyword search focus on finding a group of spatial objects covering all the query keywords and minimizing the diameter of the group. However, we observe that such a formulation may not address what users need in some application scenarios. In this paper, we introduce a novel spatial keyword cover problem (SK-COVER for short), which aims to identify the group of spatio-textual objects covering all keywords in a query and minimizing a distance cost function that leads to fewer proximate objects in the answer set. We prove that SK-COVER is not only NP-hard but also does not allow an approximation better than O(log m) in polynomial time, where m is the number of query keywords. We establish an O(log m)-approximation algorithm, which is asymptotically optimal in terms of the approximability of SK-COVER. Furthermore, we devise effective accessing strategies and pruning rules to improve the overall efficiency and scalability. In addition to our algorithmic results, we empirically show that our approximation algorithm always achieves the best accuracy, and the efficiency of our algorithm is comparable to a state-of-the-art algorithm that is intended for mCK, a problem similar to yet theoretically easier than SK-COVER.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"222 1","pages":"685-696"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77255202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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