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2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献

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Traffic-Aware Routing in Road Networks 道路网络中的交通感知路由
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00172
D. Delling, D. Schieferdecker, Christian Sommer
{"title":"Traffic-Aware Routing in Road Networks","authors":"D. Delling, D. Schieferdecker, Christian Sommer","doi":"10.1109/ICDE.2018.00172","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00172","url":null,"abstract":"We study how to compute routes that avoid traffic in road networks. Imperfections in real-time traffic feeds may yield routes with undesirable detours through parking lots or residential areas. The main challenge we address in this work is that of defining and computing paths that incorporate a volatile secondary cost function. We define the problem, study its complexity, and present algorithms that compute routes without undesirable detours. Experiments on continental-sized road networks demonstrate the feasibility of our approach.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126697176","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}
引用次数: 9
Discovering Mis-Categorized Entities 发现分类错误的实体
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00045
Shuang Hao, N. Tang, Guoliang Li, Jianhua Feng
{"title":"Discovering Mis-Categorized Entities","authors":"Shuang Hao, N. Tang, Guoliang Li, Jianhua Feng","doi":"10.1109/ICDE.2018.00045","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00045","url":null,"abstract":"Entity categorization – the process of grouping entities into categories for some specific purpose – is an important problem with a great many applications, such as Google Scholar and Amazon products. Unfortunately, in practice, many entities are mis-categorized. In this paper, we study the problem of discovering mis-categorized entities from a given group of entities. This problem is inherently hard: all entities within the same group have been \"well\" categorized by state-of-the-art solutions. Apparently, it is nontrivial to differentiate them. We propose a novel rule-based framework to solve this problem. It first uses positive rules to compute disjoint partitions of entities, where the partition with the largest size is taken as the correctly categorized partition, namely the pivot partition. It then uses negative rules to identify mis-categorized entities in other partitions that are dissimilar to the entities in the pivot partition. We describe optimizations on applying these rules, and discuss how to generate positive/negative rules. Extensive experimental results on two real-world datasets show the effectiveness of our solution.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124279341","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}
引用次数: 2
Augmented Access for Querying and Exploring a Polystore 用于查询和探索Polystore的增强访问
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00017
A. Maccioni, Riccardo Torlone
{"title":"Augmented Access for Querying and Exploring a Polystore","authors":"A. Maccioni, Riccardo Torlone","doi":"10.1109/ICDE.2018.00017","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00017","url":null,"abstract":"The huge diversity of database technologies in use inside organizations pose today new challenges of data management and integration. Polystores provide a solution to this scenario based on a loosely coupled integration of data sources and the direct access, with the local language, to each storage engine for exploiting its distinctive features. However, given the absence of a global schema, it is hard to know if a query to one system can be satisfied with data stored elsewhere in the polystore. We address this issue by introducing query augmentation, a data manipulation operator for polystores based on the automatic enrichment of the answer to a local query with related data in the rest of the polystore. Augmentation can be used to implement two effective methods for data access in polystores: augmented search and augmented exploration. We show that they provide effective tools for information discovery in polystores that avoid middleware layers, abstract query languages, and shared data models. We also illustrate the design of QUEPA, a system that fully implements our approach in an efficient way. A comprehensive campaign of experiments done with QUEPA shows that our approach is feasible and, unlike other approaches, scales nicely as the polystore grows in the number of stores and size of databases.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114634636","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}
引用次数: 11
Querying Cohesive Subgraphs by Keywords 按关键字查询内聚子图
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00141
Yuanyuan Zhu, Qian Zhang, Lu Qin, Lijun Chang, J. Yu
{"title":"Querying Cohesive Subgraphs by Keywords","authors":"Yuanyuan Zhu, Qian Zhang, Lu Qin, Lijun Chang, J. Yu","doi":"10.1109/ICDE.2018.00141","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00141","url":null,"abstract":"Keyword search problem has been widely studied to retrieve related substructures from graphs for a keyword set. However, existing well-studied approaches aim at finding compact trees/subgraphs containing the keywords, and ignore a critical measure, density, to reflect how strongly and stablely the keyword nodes are connected in the substructure. In this paper, we study the problem of finding a cohesive subgraph containing the query keywords based on the k-truss model, and formulate it as minimal dense truss search problem, i.e., finding minimal subgraph with maximum trussness covering the keywords. We first propose an efficient algorithm to find the dense truss with the maximum trussness containing keywords based on a novel hybrid KT-Index (Keyword-Truss Index). Then, we develop a novel refinement approach to extract the minimal dense truss based on the anti-monotonicity property of k-truss. Experimental studies on real datasets show the outperformance of our method.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123524185","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}
引用次数: 11
CrowdSheet: Instant Implementation and Out-of-Hand Execution of Complex Crowdsourcing 众包:复杂众包的即时执行和失控执行
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00194
Rikuya Suzuki, T. Sakaguchi, Masaki Matsubara, H. Kitagawa, Atsuyuki Morishima
{"title":"CrowdSheet: Instant Implementation and Out-of-Hand Execution of Complex Crowdsourcing","authors":"Rikuya Suzuki, T. Sakaguchi, Masaki Matsubara, H. Kitagawa, Atsuyuki Morishima","doi":"10.1109/ICDE.2018.00194","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00194","url":null,"abstract":"We demonstrate CrowdSheet, a spreadsheet interface for implementing complex crowdsourcing. Despite its appeal, adoption of the spreadsheet paradigm is associated with two nontrivial problems: (1) how to design the interface, which must be a natural extension of existing spreadsheets, while guaranteeing a reasonable expressive power, and (2) how to incorporate techniques for improving data quality without sacrificing the spreadsheet's easy-to-use feature. In this demo, we show three things. First, CrowdSheet allows non IT experts to easily implement crowdsourcing applications with complex workflows. Second, CrowdSheet adopts only new two spreadsheet functions to implement a fairly wide range of real-world applications. Third, its modular architecture gives CrowdSheet a declarative feature that lets users choose alternative plans for improving data quality, while keeping the CrowdSheet description simple.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115336429","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}
引用次数: 1
Query and Resource Optimization: Bridging the Gap 查询和资源优化:弥合差距
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00156
Lalitha Viswanathan, Alekh Jindal, Konstantinos Karanasos
{"title":"Query and Resource Optimization: Bridging the Gap","authors":"Lalitha Viswanathan, Alekh Jindal, Konstantinos Karanasos","doi":"10.1109/ICDE.2018.00156","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00156","url":null,"abstract":"Modern big data systems run on cloud environments where resources are shared among several users and applications. As a result, declarative user queries need to be optimized and executed over resources that constantly change and are provisioned on demand for each job. This requires us to rethink traditional query optimization designed for systems that run on dedicated resources. In this paper, we show evidence that the choice of query plans depends heavily on the resources that the plan will be executed on. The current practice of determining query plans without accounting for resources could lead to significant performance loss in popular big data systems, such as Hive and SparkSQL. Therefore, we make a case for Query and Resource Optimization (or QROP), i.e., choosing both the query plan and the resource configuration at the same time, and present a research agenda towards this direction.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389323","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}
引用次数: 19
Consistent Snapshot Algorithms for In-Memory Database Systems: Experiments and Analysis 内存数据库系统的一致性快照算法:实验与分析
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00131
Liang Li, Guoren Wang, Gang Wu, Ye Yuan
{"title":"Consistent Snapshot Algorithms for In-Memory Database Systems: Experiments and Analysis","authors":"Liang Li, Guoren Wang, Gang Wu, Ye Yuan","doi":"10.1109/ICDE.2018.00131","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00131","url":null,"abstract":"In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Consistent snapshot is a key step in backup and recovery of IMDBs, thus an important factor for system performance of IMDBs. Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items, and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in the academia to trade off throughput and latency, yet industrial IMDBs such as Redis still stick to the simple fork algorithm. As an understanding of this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from the academia. Finally, we have opensourced the implementation of all the above snapshot algorithms to facilitate practitioners to benchmark the performance of each algorithm and select proper methods for different application scenarios.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129154857","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}
引用次数: 8
Adaptive Execution of Compiled Queries 编译查询的自适应执行
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00027
André Kohn, Viktor Leis, Thomas Neumann
{"title":"Adaptive Execution of Compiled Queries","authors":"André Kohn, Viktor Leis, Thomas Neumann","doi":"10.1109/ICDE.2018.00027","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00027","url":null,"abstract":"Compiling queries to machine code is a very efficient way for executing queries. One often overlooked problem with compilation is the time it takes to generate machine code. Even with fast compilation frameworks like LLVM, generating machine code for complex queries often takes hundreds of milliseconds. Such durations can be a major disadvantage for workloads that execute many complex, but quick queries. To solve this problem, we propose an adaptive execution framework, which dynamically switches from interpretation to compilation. We also propose a fast bytecode interpreter for LLVM, which can execute queries without costly translation to machine code and dramatically reduces the query latency. Adaptive execution is fine-grained, and can execute code paths of the same query using different execution modes. Our evaluation shows that this approach achieves optimal performance in a wide variety of settings—low latency for small data sets and maximum throughput for large data sizes.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124538107","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}
引用次数: 53
Interactive Inference of SPARQL Queries Using Provenance 使用来源的SPARQL查询交互推断
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00059
Efrat Abramovitz, Daniel Deutch, Amir Gilad
{"title":"Interactive Inference of SPARQL Queries Using Provenance","authors":"Efrat Abramovitz, Daniel Deutch, Amir Gilad","doi":"10.1109/ICDE.2018.00059","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00059","url":null,"abstract":"Inference of queries from their output examples has been extensively studied in multiple contexts as means to ease the formulation of queries. In this paper we propose a novel approach for the problem, based on provenance. The idea is to use provenance in two manners: first as an additional information that is associated with the given examples and explains their rationale; and then again as a way to show users a description of the difference between candidate queries, prompting their feedback. We have implemented the framework in the context of simple graph patterns and union thereof, and demonstrate its effectiveness in the context of multiple ontologies.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582002","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}
引用次数: 10
FAHES: Detecting Disguised Missing Values FAHES:检测隐藏的缺失值
2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00188
A. Qahtan, A. Elmagarmid, M. Ouzzani, N. Tang
{"title":"FAHES: Detecting Disguised Missing Values","authors":"A. Qahtan, A. Elmagarmid, M. Ouzzani, N. Tang","doi":"10.1109/ICDE.2018.00188","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00188","url":null,"abstract":"It is well established that missing values, if not dealt with properly, may lead to poor data analytics models, misleading conclusions, and limitation in the generalization of findings. A key challenge in detecting these missing values is when they manifest themselves in a form that is otherwise valid, making it hard to distinguish them from other legitimate values. We propose to demonstrate FAHES, a system for detecting different types of disguised missing values (DMVs) which often occur in real world data. FAHES consists of several components, namely a profiler to generate rules for detecting repeated patterns, an outlier detection module, and a module to detect values that are used repeatedly in random records. Using several real world datasets, we will demonstrate how FAHES can easily catch DMVs.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116739993","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}
引用次数: 7
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