2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第8页

Robust Discovery of Positive and Negative Rules in Knowledge Bases 知识库中正反规则的鲁棒发现

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00108

Stefano Ortona, Venkata Vamsikrishna Meduri, Paolo Papotti

{"title":"Robust Discovery of Positive and Negative Rules in Knowledge Bases","authors":"Stefano Ortona, Venkata Vamsikrishna Meduri, Paolo Papotti","doi":"10.1109/ICDE.2018.00108","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00108","url":null,"abstract":"We present RUDIK, a system for the discovery of declarative rules over knowledge-bases (KBs). RUDIK discovers rules that express positive relationships between entities, such as \"if two persons have the same parent, they are siblings\", and negative rules, i.e., patterns that identify contradictions in the data, such as \"if two persons are married, one cannot be the child of the other\". While the former class infers new facts in the KB, the latter class is crucial for other tasks, such as detecting erroneous triples in data cleaning, or the creation of negative examples to bootstrap learning algorithms. The system is designed to: (i) enlarge the expressive power of the rule language to obtain complex rules and wide coverage of the facts in the KB, (ii) discover approximate rules (soft constraints) to be robust to errors and incompleteness in the KB, (iii) use disk-based algorithms, effectively enabling rule mining in commodity machines. In contrast with traditional ranking of all rules based on a measure of support, we propose an approach to identify the subset of useful rules to be exposed to the user. We model the mining process as an incremental graph exploration problem and prove that our search strategy has guarantees on the optimality of the results. We have conducted extensive experiments using real-world KBs to show that RUDIK outperforms previous proposals in terms of efficiency and that it discovers more effective rules for the application at hand.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"20 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":"114487963","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: 61

Ensemble Direct Density Ratio Estimation for Multistream Classification 多流分类的集合直接密度比估计

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00151

Swarup Chandra, Ahsanul Haque, Hemeng Tao, Jie Liu, L. Khan, C. Aggarwal

{"title":"Ensemble Direct Density Ratio Estimation for Multistream Classification","authors":"Swarup Chandra, Ahsanul Haque, Hemeng Tao, Jie Liu, L. Khan, C. Aggarwal","doi":"10.1109/ICDE.2018.00151","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00151","url":null,"abstract":"In traditional machine learning, it is assumed that training data conforming to the stationary distribution of test data is readily available. Yet, such an assumption is not valid in practice due to a high cost of obtaining the truth value of data instances. This is particularly true when computing over non-stationary data streams. Recent studies in the multistream setting aim to address this issue by leveraging a stream of data with biased labeled instances (called the source stream) to train a suitable model for prediction over unlabeled instances (called the target stream). They use sampling bias correction techniques as a preprocessing step for estimating source instance weights for training a bias-corrected classifier useful to predict label of target data instances. In this regard, a recent framework proposes to utilize a Gaussian kernel model to estimate source instance weights. Unfortunately, it suffers from large computational time complexity and consequently deteriorates the rate at which streaming data is processed. In this paper, we address this issue by proposing a divide-and-conquer method suitable for simultaneously evaluating source instance weights and detecting changes in distribution over time. Our empirical results demonstrate a significant gain in execution time, compared to the previous approach, while achieving similar or better classification accuracy on real-world datasets.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"298 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":"132368024","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: 6

Continuous Maintenance of Range Sum Heat Maps 持续维护范围和热图

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00192

Jianzhong Qi, Vivek Kumar, Rui Zhang, E. Tanin, Goce Trajcevski, P. Scheuermann

{"title":"Continuous Maintenance of Range Sum Heat Maps","authors":"Jianzhong Qi, Vivek Kumar, Rui Zhang, E. Tanin, Goce Trajcevski, P. Scheuermann","doi":"10.1109/ICDE.2018.00192","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00192","url":null,"abstract":"We study the problem of continuous maintenance of range sum heat maps over dynamically updating data objects. The range sum (RS) here refers to the sum of the weights of the data objects enclosed by a given range (rectangle) R. Range sum problems are useful in spatio-temporal data analytics and decision making processes. Recent studies on range sum problems focus on computing the MaxRS query, which finds a location to place a rectangle R such that its RS is maximized. In real applications, knowing only the location with the maximum RS may be insufficient, because decision making is a multi-factor process where maximizing the RS may just be one of the factors. It is also important to gain an overview of the RS distribution at different locations, so that decisions can be made based on global knowledge. We therefore propose to compute a range-sum heat map that visualizes the RS value for every location in a data space. Considering that data objects may be inserted into or removed from the data space dynamically, we further study the continuous maintenance of range-sum heat maps over dynamically updating data objects. We adapt algorithms to compute range-sum heat maps and to perform heat map updates. We build a demo system to showcase the usefulness of range sum heat maps and the effectiveness of the adapted algorithms.","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":"134449830","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: 3

Easy Lock-Free Indexing in Non-Volatile Memory 在非易失性存储器中容易无锁索引

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00049

Tianzheng Wang, Justin J. Levandoski, P. Larson

{"title":"Easy Lock-Free Indexing in Non-Volatile Memory","authors":"Tianzheng Wang, Justin J. Levandoski, P. Larson","doi":"10.1109/ICDE.2018.00049","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00049","url":null,"abstract":"Large non-volatile memories (NVRAM) will change the durability and recovery mechanisms of main-memory database systems. Today, these systems make operations durable through logging and checkpointing to secondary storage, and recover by rebuilding the in-memory database (records and indexes) from on-disk state. A main-memory database stored in NVRAM, however, can potentially recover instantly after a power failure. Modern main-memory databases typically use lock-free index structures to enable a high degree of concurrency. Thus NVRAM-resident databases need indexes that are both lock-free, persistent, and able to recover (almost) instantly after a crash. In this paper, we show how to easily build such index structures. A key enabling component of our scheme is a multi-word compare-and-swap operation, PMwCAS, that is lock-free, persistent, and efficient. PMwCAS significantly reduces the complexity of building lock-free indexes, which we illustrate by implementing both doubly-linked skip lists and the Bw-tree lock-free B+-tree for NVRAM. Experimental results show that PMwCAS's runtime overhead is very low (~4–6% under realistic workloads). This overhead is sufficiently low that the same implementation can be used for both DRAM and NVRAM resident indexes.","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":"134104006","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: 72

Performance of OLTP via Intelligent Scheduling 基于智能调度的OLTP性能研究

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00132

Tieying Zhang, A. Tomasic, Yangjun Sheng, Andrew Pavlo

引用次数: 12

Incremental Frequent Subgraph Mining on Large Evolving Graphs 大型演化图的增量频繁子图挖掘

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00241

Ehab Abdelhamid, Mustafa Canim, Mohammad Sadoghi, Bishwaranjan Bhattacharjee, Yuan-Chi Chang, Panos Kalnis

引用次数: 4

LeanStore: In-Memory Data Management beyond Main Memory LeanStore:内存数据管理超越主内存

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00026

Viktor Leis, Michael Haubenschild, A. Kemper, Thomas Neumann

引用次数: 68

Why-Not Questions on Top-k Geo-Social Keyword Queries in Road Networks 道路网络中Top-k地理社交关键字查询的Why-Not问题

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00091

Jingwen Zhao, Yunjun Gao, Gang Chen, R. Chen

引用次数: 18

Skyline Diagram: Finding the Voronoi Counterpart for Skyline Queries 天际线图:为天际线查询寻找Voronoi对应

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00065

Jinfei Liu, Juncheng Yang, Li Xiong, J. Pei, Jun Luo

引用次数: 14

Persistent Community Search in Temporal Networks 时态网络中的持久社区搜索

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00077

Ronghua Li, Jiao Su, Lu Qin, J. Yu, Qiangqiang Dai

引用次数: 70