Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498230
Liu Zheng, Lei Chen
As one of the three major steps (question design, task assignment, answer aggregation) in crowdsourcing, task assignment directly affects the quality of the crowdsourcing result. A good assignment will not only improve the answers' quality, but also boost the workers' willingness to participate. Although a lot of works have been made to produce better assignment, most of them neglected one of its most important properties: the bipartition, which exists widely in real world scenarios. Such ignorance greatly limits their application under general settings.
{"title":"Mutual benefit aware task assignment in a bipartite labor market","authors":"Liu Zheng, Lei Chen","doi":"10.1109/ICDE.2016.7498230","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498230","url":null,"abstract":"As one of the three major steps (question design, task assignment, answer aggregation) in crowdsourcing, task assignment directly affects the quality of the crowdsourcing result. A good assignment will not only improve the answers' quality, but also boost the workers' willingness to participate. Although a lot of works have been made to produce better assignment, most of them neglected one of its most important properties: the bipartition, which exists widely in real world scenarios. Such ignorance greatly limits their application under general settings.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"331 1","pages":"73-84"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77141120","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.2016.7498329
C. Jonathan, A. Magdy, M. Mokbel, A. Jonathan
The recent wide popularity of microblogs (e.g., tweets, online comments) has empowered various important applications, including, news delivery, event detection, market analysis, and target advertising. A core module in all these applications is a frequent/trending query processor that aims to find out those topics that are highly frequent or trending in the social media through posted microblogs. Unfortunately current attempts for such core module suffer from several drawbacks. Most importantly, their narrow scope, as they focus only on solving trending queries for a very special case of localized and very recent microblogs. This paper presents GARNET; a holistic system equipped with one-stop efficient and scalable solution for supporting a generic form of context-aware frequent and trending queries on microblogs. GARNET supports both frequent and trending queries, any arbitrary time interval either current, recent, or past, of fixed granularity, and having a set of arbitrary filters over contextual attributes. From a system point of view, GARNET is very appealing and industry-friendly, as one needs to realize it once in the system. Then, a myriad of various forms of trending and frequent queries are immediately supported. Experimental evidence based on a real system prototype of GARNET and billions of real Twitter data show the scalability and efficiency of GARNET for various query types.
{"title":"GARNET: A holistic system approach for trending queries in microblogs","authors":"C. Jonathan, A. Magdy, M. Mokbel, A. Jonathan","doi":"10.1109/ICDE.2016.7498329","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498329","url":null,"abstract":"The recent wide popularity of microblogs (e.g., tweets, online comments) has empowered various important applications, including, news delivery, event detection, market analysis, and target advertising. A core module in all these applications is a frequent/trending query processor that aims to find out those topics that are highly frequent or trending in the social media through posted microblogs. Unfortunately current attempts for such core module suffer from several drawbacks. Most importantly, their narrow scope, as they focus only on solving trending queries for a very special case of localized and very recent microblogs. This paper presents GARNET; a holistic system equipped with one-stop efficient and scalable solution for supporting a generic form of context-aware frequent and trending queries on microblogs. GARNET supports both frequent and trending queries, any arbitrary time interval either current, recent, or past, of fixed granularity, and having a set of arbitrary filters over contextual attributes. From a system point of view, GARNET is very appealing and industry-friendly, as one needs to realize it once in the system. Then, a myriad of various forms of trending and frequent queries are immediately supported. Experimental evidence based on a real system prototype of GARNET and billions of real Twitter data show the scalability and efficiency of GARNET for various query types.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"50 1","pages":"1251-1262"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87367829","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.2016.7498371
Zhiwen Yu, Guoqiang Han, Le Li, Jiming Liu, Jun Zhang
Cluster ensemble, as one of the important research directions in the ensemble learning area, is gaining more and more attention, due to its powerful capability to integrate multiple clustering solutions and provide a more accurate, stable and robust result. Cluster ensemble has a lot of useful applications in a large number of areas. Although most of traditional cluster ensemble approaches obtain good results, few of them consider how to achieve good performance for noisy datasets. Some noisy datasets have a number of noisy attributes which may degrade the performance of conventional cluster ensemble approaches. Some noisy datasets which contain noisy samples will affect the final results. Other noisy datasets may be sensitive to distance functions.
{"title":"Adaptive noise immune cluster ensemble using affinity propagation","authors":"Zhiwen Yu, Guoqiang Han, Le Li, Jiming Liu, Jun Zhang","doi":"10.1109/ICDE.2016.7498371","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498371","url":null,"abstract":"Cluster ensemble, as one of the important research directions in the ensemble learning area, is gaining more and more attention, due to its powerful capability to integrate multiple clustering solutions and provide a more accurate, stable and robust result. Cluster ensemble has a lot of useful applications in a large number of areas. Although most of traditional cluster ensemble approaches obtain good results, few of them consider how to achieve good performance for noisy datasets. Some noisy datasets have a number of noisy attributes which may degrade the performance of conventional cluster ensemble approaches. Some noisy datasets which contain noisy samples will affect the final results. Other noisy datasets may be sensitive to distance functions.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"5 1","pages":"1454-1455"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80369256","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.2016.7498257
Tanmoy Chakraborty, Ramasuri Narayanam
Several real-life social systems witness the presence of multiple interaction types (or layers) among the entities, thus establishing a collection of co-evolving networks, known as multiplex networks. More recently, there has been a significant interest in developing certain centrality measures in multiplex networks to understand the influential power of the entities (to be referred as vertices or nodes hereafter). In this paper, we consider the problem of studying how frequently the nodes occur on the shortest paths between other nodes in the multiplex networks. As opposed to simplex networks, the shortest paths between nodes can possibly traverse through multiple layers in multiplex networks. Motivated by this phenomenon, we propose a new metric to address the above problem and we call this new metric cross-layer betweenness centrality (CBC). Our definition of CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks. We propose an efficient algorithm to compute CBC and show that it runs much faster than the naïve computation of this measure. We show the efficacy of the proposed algorithm using thorough experimentation on two real-world multiplex networks. We further demonstrate the practical utility of CBC by applying it in the following three application contexts: discovering non-overlapping community structure in multiplex networks, identifying interdisciplinary researchers from a multiplex co-authorship network, and the initiator selection for message spreading. In all these application scenarios, the respective solution methods based on the proposed CBC are found to be significantly better performing than that of the corresponding benchmark approaches.
{"title":"Cross-layer betweenness centrality in multiplex networks with applications","authors":"Tanmoy Chakraborty, Ramasuri Narayanam","doi":"10.1109/ICDE.2016.7498257","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498257","url":null,"abstract":"Several real-life social systems witness the presence of multiple interaction types (or layers) among the entities, thus establishing a collection of co-evolving networks, known as multiplex networks. More recently, there has been a significant interest in developing certain centrality measures in multiplex networks to understand the influential power of the entities (to be referred as vertices or nodes hereafter). In this paper, we consider the problem of studying how frequently the nodes occur on the shortest paths between other nodes in the multiplex networks. As opposed to simplex networks, the shortest paths between nodes can possibly traverse through multiple layers in multiplex networks. Motivated by this phenomenon, we propose a new metric to address the above problem and we call this new metric cross-layer betweenness centrality (CBC). Our definition of CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks. We propose an efficient algorithm to compute CBC and show that it runs much faster than the naïve computation of this measure. We show the efficacy of the proposed algorithm using thorough experimentation on two real-world multiplex networks. We further demonstrate the practical utility of CBC by applying it in the following three application contexts: discovering non-overlapping community structure in multiplex networks, identifying interdisciplinary researchers from a multiplex co-authorship network, and the initiator selection for message spreading. In all these application scenarios, the respective solution methods based on the proposed CBC are found to be significantly better performing than that of the corresponding benchmark approaches.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"13 1","pages":"397-408"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90810164","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.2016.7498228
Yongxin Tong, Jieying She, Bolin Ding, Libin Wang, Lei Chen
With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Most existing studies focus on offline scenarios, where all the spatiotemporal information of micro-tasks and crowd workers is given. However, they are impractical since micro-tasks and crowd workers in real applications appear dynamically and their spatiotemporal information cannot be known in advance. In this paper, to address the shortcomings of existing offline approaches, we first identify a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem. We first extend the state-of-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in real world. Thus, we consider the average performance of online algorithms, a.k.a online random order model.We propose a two-phase-based framework, based on which we present the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model. To improve its efficiency, we further design the TGOA-Greedy algorithm following the framework, which runs faster than the TGOA algorithm but has lower competitive ratio of 1 over 8. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.
{"title":"Online mobile Micro-Task Allocation in spatial crowdsourcing","authors":"Yongxin Tong, Jieying She, Bolin Ding, Libin Wang, Lei Chen","doi":"10.1109/ICDE.2016.7498228","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498228","url":null,"abstract":"With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Most existing studies focus on offline scenarios, where all the spatiotemporal information of micro-tasks and crowd workers is given. However, they are impractical since micro-tasks and crowd workers in real applications appear dynamically and their spatiotemporal information cannot be known in advance. In this paper, to address the shortcomings of existing offline approaches, we first identify a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem. We first extend the state-of-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in real world. Thus, we consider the average performance of online algorithms, a.k.a online random order model.We propose a two-phase-based framework, based on which we present the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model. To improve its efficiency, we further design the TGOA-Greedy algorithm following the framework, which runs faster than the TGOA algorithm but has lower competitive ratio of 1 over 8. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"32 1","pages":"49-60"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81759829","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.2016.7498254
Dong Xie, Guanru Li, Bin Yao, Xuan Wei, Xiaokui Xiao, Yunjun Gao, M. Guo
As location-based services (LBSs) become popular, location-dependent queries have raised serious privacy concerns since they may disclose sensitive information in query processing. Among typical queries supported by LBSs, shortest path queries may reveal information about not only current locations of the clients, but also their potential destinations and travel plans. Unfortunately, existing methods for private shortest path computation suffer from issues of weak privacy property, low performance or poor scalability. In this paper, we aim at a strong privacy guarantee, where the adversary cannot infer almost any information about the queries, with better performance and scalability. To achieve this goal, we introduce a general system model based on the concept of Oblivious Storage (OS), which can deal with queries requiring strong privacy properties. Furthermore, we propose a new oblivious shuffle algorithm to optimize an existing OS scheme. By making trade-offs between query performance, scalability and privacy properties, we design different schemes for private shortest path computation. Eventually, we comprehensively evaluate our schemes upon real road networks in a practical environment and show their efficiency.
{"title":"Practical private shortest path computation based on Oblivious Storage","authors":"Dong Xie, Guanru Li, Bin Yao, Xuan Wei, Xiaokui Xiao, Yunjun Gao, M. Guo","doi":"10.1109/ICDE.2016.7498254","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498254","url":null,"abstract":"As location-based services (LBSs) become popular, location-dependent queries have raised serious privacy concerns since they may disclose sensitive information in query processing. Among typical queries supported by LBSs, shortest path queries may reveal information about not only current locations of the clients, but also their potential destinations and travel plans. Unfortunately, existing methods for private shortest path computation suffer from issues of weak privacy property, low performance or poor scalability. In this paper, we aim at a strong privacy guarantee, where the adversary cannot infer almost any information about the queries, with better performance and scalability. To achieve this goal, we introduce a general system model based on the concept of Oblivious Storage (OS), which can deal with queries requiring strong privacy properties. Furthermore, we propose a new oblivious shuffle algorithm to optimize an existing OS scheme. By making trade-offs between query performance, scalability and privacy properties, we design different schemes for private shortest path computation. Eventually, we comprehensively evaluate our schemes upon real road networks in a practical environment and show their efficiency.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"361-372"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84384233","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.2016.7498291
Xike Xie, Xingjun Hao, T. Pedersen, Peiquan Jin, Jinchuan Chen
On-Line Analytical Processing (OLAP) enables powerful analytics by quickly computing aggregate values of numerical measures over multiple hierarchical dimensions for massive datasets. However, many types of source data, e.g., from GPS, sensors, and other measurement devices, are intrinsically inaccurate (imprecise and/or uncertain) and thus OLAP cannot be readily applied. In this paper, we address the resulting data veracity problem in OLAP by proposing the concept of probabilistic data cubes. Such a cube is comprised of a set of probabilistic cuboids which summarize the aggregated values in the form of probability mass functions (pmfs in short) and thus offer insights into the underlying data quality and enable confidence-aware query evaluation and analysis. However, the probabilistic nature of data poses computational challenges as even simple operations are #P-hard under the possible world semantics. Even worse, it is hard to share computations among different cuboids, as aggregation functions that are distributive for traditional data cubes, e.g., SUM and COUNT, become holistic in probabilistic settings. In this paper, we propose a complete set of techniques for probabilistic data cubes, from cuboid aggregation, over cube materialization, to query evaluation. For aggregation, we focus on how to maximize the sharing of computation among cells and cuboids. We present two aggregation methods: convolution and sketch-based. The two methods scale down the time complexities of building a probabilistic cuboid to polynomial and linear, respectively. Each of the two supports both full and partial data cube materialization. Then, we devise a cost model which guides the aggregation methods to be deployed and combined during the cube materialization. We further provide algorithms for probabilistic slicing and dicing queries on the data cube. Extensive experiments over real and synthetic datasets are conducted to show that the techniques are effective and scalable.
{"title":"OLAP over probabilistic data cubes I: Aggregating, materializing, and querying","authors":"Xike Xie, Xingjun Hao, T. Pedersen, Peiquan Jin, Jinchuan Chen","doi":"10.1109/ICDE.2016.7498291","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498291","url":null,"abstract":"On-Line Analytical Processing (OLAP) enables powerful analytics by quickly computing aggregate values of numerical measures over multiple hierarchical dimensions for massive datasets. However, many types of source data, e.g., from GPS, sensors, and other measurement devices, are intrinsically inaccurate (imprecise and/or uncertain) and thus OLAP cannot be readily applied. In this paper, we address the resulting data veracity problem in OLAP by proposing the concept of probabilistic data cubes. Such a cube is comprised of a set of probabilistic cuboids which summarize the aggregated values in the form of probability mass functions (pmfs in short) and thus offer insights into the underlying data quality and enable confidence-aware query evaluation and analysis. However, the probabilistic nature of data poses computational challenges as even simple operations are #P-hard under the possible world semantics. Even worse, it is hard to share computations among different cuboids, as aggregation functions that are distributive for traditional data cubes, e.g., SUM and COUNT, become holistic in probabilistic settings. In this paper, we propose a complete set of techniques for probabilistic data cubes, from cuboid aggregation, over cube materialization, to query evaluation. For aggregation, we focus on how to maximize the sharing of computation among cells and cuboids. We present two aggregation methods: convolution and sketch-based. The two methods scale down the time complexities of building a probabilistic cuboid to polynomial and linear, respectively. Each of the two supports both full and partial data cube materialization. Then, we devise a cost model which guides the aggregation methods to be deployed and combined during the cube materialization. We further provide algorithms for probabilistic slicing and dicing queries on the data cube. Extensive experiments over real and synthetic datasets are conducted to show that the techniques are effective and scalable.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"51 1","pages":"799-810"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88976090","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.2016.7498318
Maximilian Franzke, Tobias Emrich, Andreas Züfle, M. Renz
The proliferation of the Web 2.0 and the ubiquitousness of social media yield a huge flood of heterogenous data that is voluntarily published and shared by billions of individual users all over the world. As a result, the representation of an entity (such as a real person) in this data may consist of various data types, including location and other numeric attributes, textual descriptions, images, videos, social network information and other types of information. Searching similar entities in this multi-enriched data exploiting the information of multiple representations simultaneously promises to yield more interesting and relevant information than searching among each data type individually. While efficient similarity search on single representations is a well studied problem, existing studies lacks appropriate solutions for multi-enriched data taking into account the combination of all representations as a whole. In this paper, we address the problem of index-supported similarity search on multi-enriched (a.k.a. multi-represented) objects based on a set of metrics, one metric for each representation. We define multimetric similarity search queries by employing user-defined weight function specifying the impact of each metric at query time. Our main contribution is an index structure which combines all metrics into a single multi-dimensional access method that works for arbitrary weights preferences. The experimental evaluation shows that our proposed index structure is more efficient than existing multi-metric access methods considering different cost criteria and tremendously outperforms traditional approaches when querying very large sets of multi-enriched objects.
Web 2.0的扩散和无处不在的社交媒体产生了大量异质数据，这些数据由世界各地数十亿个人用户自愿发布和共享。因此，此数据中实体(例如真人)的表示可能由各种数据类型组成，包括位置和其他数字属性、文本描述、图像、视频、社交网络信息和其他类型的信息。在这种多重丰富的数据中搜索相似的实体，同时利用多种表示的信息，比单独在每种数据类型中搜索更有意义和相关的信息。虽然对单一表示的高效相似性搜索是一个研究得很好的问题，但现有的研究缺乏考虑所有表示整体组合的多丰富数据的适当解决方案。在本文中，我们基于一组度量来解决索引支持的多浓缩(即多表示)对象的相似性搜索问题，每个度量对应一个度量。我们通过在查询时使用用户定义的权重函数指定每个度量的影响来定义多度量相似度搜索查询。我们的主要贡献是一个索引结构，它将所有指标结合到一个单一的多维访问方法中，该方法适用于任意权重偏好。实验结果表明，在考虑不同开销标准的情况下，我们提出的索引结构比现有的多度量访问方法效率更高，并且在查询非常大的多富集对象集时，显著优于传统方法。
{"title":"Indexing multi-metric data","authors":"Maximilian Franzke, Tobias Emrich, Andreas Züfle, M. Renz","doi":"10.1109/ICDE.2016.7498318","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498318","url":null,"abstract":"The proliferation of the Web 2.0 and the ubiquitousness of social media yield a huge flood of heterogenous data that is voluntarily published and shared by billions of individual users all over the world. As a result, the representation of an entity (such as a real person) in this data may consist of various data types, including location and other numeric attributes, textual descriptions, images, videos, social network information and other types of information. Searching similar entities in this multi-enriched data exploiting the information of multiple representations simultaneously promises to yield more interesting and relevant information than searching among each data type individually. While efficient similarity search on single representations is a well studied problem, existing studies lacks appropriate solutions for multi-enriched data taking into account the combination of all representations as a whole. In this paper, we address the problem of index-supported similarity search on multi-enriched (a.k.a. multi-represented) objects based on a set of metrics, one metric for each representation. We define multimetric similarity search queries by employing user-defined weight function specifying the impact of each metric at query time. Our main contribution is an index structure which combines all metrics into a single multi-dimensional access method that works for arbitrary weights preferences. The experimental evaluation shows that our proposed index structure is more efficient than existing multi-metric access methods considering different cost criteria and tremendously outperforms traditional approaches when querying very large sets of multi-enriched objects.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"26 1","pages":"1122-1133"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81064677","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.2016.7498251
S. Karthik, J. Haritsa, Sreyash Kenkre, Vinayaka Pandit
To address the classical selectivity estimation problem in databases, a radically different approach called PlanBouquet was recently proposed in [3], wherein the estimation process is completely abandoned and replaced with a calibrated discovery mechanism. The beneficial outcome of this new construction is that, for the first time, provable guarantees are obtained on worst-case performance, thereby facilitating robust query processing.
{"title":"Platform-independent robust query processing","authors":"S. Karthik, J. Haritsa, Sreyash Kenkre, Vinayaka Pandit","doi":"10.1109/ICDE.2016.7498251","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498251","url":null,"abstract":"To address the classical selectivity estimation problem in databases, a radically different approach called PlanBouquet was recently proposed in [3], wherein the estimation process is completely abandoned and replaced with a calibrated discovery mechanism. The beneficial outcome of this new construction is that, for the first time, provable guarantees are obtained on worst-case performance, thereby facilitating robust query processing.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"4 1","pages":"325-336"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76259139","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.2016.7498292
Byungsoo Jeon, Inah Jeon, Lee Sael, U. Kang
How can we analyze very large real-world tensors where additional information is coupled with certain modes of tensors? Coupled matrix-tensor factorization is a useful tool to simultaneously analyze matrices and a tensor, and has been used for important applications including collaborative filtering, multi-way clustering, and link prediction. However, existing single machine or distributed algorithms for coupled matrix-tensor factorization do not scale for tensors with billions of elements in each mode. In this paper, we propose SCOUT, a large-scale coupled matrix-tensor factorization algorithm running on the distributed MAPREDUCE platform. By carefully reorganizing operations, and reusing intermediate data, SCOUT decomposes up to 100× larger tensors than existing methods, and shows linear scalability for order and machines while other methods are limited in scalability. We also apply SCOUT on real world tensors and discover interesting hidden patterns like seasonal spike, and steady attentions for healthy food on Yelp dataset containing user-business-yearmonth tensor and two coupled matrices.
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