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共 105 条记录，以下是91-100 订阅

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Matrix Factorization with Interval-Valued Data

Matrix Factorization with Interval-Valued Data

引用

international conference on Data Engineering

作者： Mao-Lin Li Francesco Di Mauro K Selçuk Candan Maria Luisa Sapino School of Computing Informatics and Decision System Engineering Arizona State University Tempe United States Department of Computer Science University of Torino Turin Italy

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.

关键词： Matrix decomposition Semantics Face Probabilistic logic Collaboration conferences Data engineering

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Sya: Enabling Spatial Awareness inside Probabilistic Knowledge Base Construction

Sya: Enabling Spatial Awareness inside Probabilistic Knowled...

引用

international conference on Data Engineering

作者： Ibrahim Sabek Mohamed F. Mokbel Department of Computer Science and Engineering University of Minnesota Minnesota USA Qatar Computing Research Institute Hamad bin Khalifa University Doha Qatar

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

this paper presents Sya; the first spatial probabilistic knowledge base construction system, based on Markov logic Networks (MLN). Sya injects the awareness of spatial relationships inside the MLN grounding and inference phases, which are the pillars of the knowledge base construction process, and hence results in a better knowledge base output. In particular, Sya generates a probabilistic model that captures both logical and spatial correlations among knowledge base relations. Sya provides a simple spatial high-level language, a spatial variation of factor graph, a spatial rules-query translator, and a spatially-equipped statistical inference technique to infer the factual scores of relations. In addition, Sya provides an optimization that ensures scalable grounding and inference for large-scale knowledge bases. Experimental evidence, based on building two real knowledge bases with spatial nature, shows that Sya can achieve 70% higher F1-score on average over the state-of-the-art DeepDive system, while achieving at least 20% reduction in the execution times.

关键词： Knowledge based systems Grounding Correlation Probabilistic logic Spatial databases Standards Data mining

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Online Indices for Predictive Top-k Entity and Aggregate Queries on Knowledge Graphs

Online Indices for Predictive Top-k Entity and Aggregate Que...

引用

international conference on Data Engineering

作者： Yan Li Tingjian Ge Cindy Chen University of Massachusetts Lowell

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Knowledge graphs have seen increasingly broad applications. However, they are known to be incomplete. We define the notion of a virtual knowledge graph which extends a knowledge graph with predicted edges and their probabilities. We focus on two important types of queries: top-k entity queries and aggregate queries. To improve query processing efficiency, we propose an incremental index on top of low dimensional entity vectors transformed from network embedding vectors. We also devise query processing algorithms with the index. Moreover, we provide theoretical guarantees of accuracy, and conduct a systematic experimental evaluation. the experiments show that our approach is very efficient and effective. In particular, with the same or better accuracy guarantees, it is one to two orders of magnitude faster in query processing than the closest previous work which can only handle one relationship type.

关键词： Aggregates Transforms Query processing Prediction algorithms Probabilistic logic Indexing

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Efficient Structural Clustering in Large Uncertain Graphs

Efficient Structural Clustering in Large Uncertain Graphs

引用

international conference on Data Engineering

作者： Yongjiang Liang Tingting Hu Peixiang Zhao Department of Computer Science Florida State University Tallahassee USA Department of Mathematics Embry-Riddle Aeronautical University Daytona Beach USA

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Clustering uncertain graphs based on the probabilistic graph model has sparked extensive research and widely varying applications. Existing structural clustering methods rely heavily on the computation of pairwise reliable structural similarity between vertices, which has proven to be extremely costly, especially in large uncertain graphs. In this paper, we develop a new, decomposition-based method, ProbSCAN, for efficient reliable structural similarity computation with theoretically improved complexity. We further design a cost-effective index structure UCNO-Index, and a series of powerful pruning strategies to expedite reliable structural similarity computation in uncertain graphs. Experimental studies on eight real-world uncertain graphs demonstrate the effectiveness of our proposed solutions, which achieves orders of magnitude improvement of clustering efficiency, compared with the state-of-the-art structural clustering methods in large uncertain graphs.

关键词： Reliability theory Indexes Probabilistic logic Time complexity Computer science

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User-driven Error Detection for Time Series with Events

User-driven Error Detection for Time Series with Events

引用

international conference on Data Engineering

作者： Kim-Hung Le Paolo Papotti EURECOM France

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Anomalies are pervasive in time series data, such as sensor readings. Existing methods for anomaly detection cannot distinguish between anomalies that represent data errors, such as incorrect sensor readings, and notable events, such as the watering action in soil monitoring. In addition, the quality performance of such detection methods highly depends on the configuration parameters, which are dataset specific. In this work, we exploit active learning to detect both errors and events in a single solution that aims at minimizing user interaction. For this joint detection, we introduce an algorithm that accurately detects and labels anomalies with a non-parametric concept of neighborhood and probabilistic classification. Given a desired quality, the confidence of the classification is then used as termination condition for the active learning algorithm. Experiments on real and synthetic datasets demonstrate that our approach achieves F-score above 80% in detecting errors by labeling 2 to 5 points in one data series. We also show the superiority of our solution compared to the state-of-the-art approaches for anomaly detection. Finally, we demonstrate the positive impact of our error detection methods in downstream data repairing algorithms.

关键词： Time series analysis Anomaly detection Monitoring Soil Labeling Computational modeling Probabilistic logic

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Indoor Top-k Keyword-aware Routing Query

Indoor Top-k Keyword-aware Routing Query

引用

international conference on Data Engineering

作者： Zijin Feng Tiantian Liu Huan Li Hua Lu Lidan Shou Jianliang Xu Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computer Science Aalborg University Denmark Department of Computer Science Zhejiang University China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

People have many activities indoors and there is an increasing demand of keyword-aware route planning for indoor venues. In this paper, we study the indoor top-k keyword-aware routing query (IKRQ). Given two indoor points s and t, an IKRQ returns k s-to-t routes that do not exceed a given distance constraint but have optimal ranking scores integrating keyword relevance and spatial distance. It is challenging to efficiently compute the ranking scores and find the best yet diverse routes in a large indoor space with complex topology. We propose prime routes to diversify top-k routes, devise mapping structures to organize indoor keywords and compute route keyword relevances, and derive pruning rules to reduce search space in routing. With these techniques, we design two search algorithms with different routing expansions. Experiments on synthetic and real data demonstrate the efficiency of our proposals.

关键词： Routing Planning Computer science Topology Proposals Airports logic gates

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Crowdsourced Collective Entity Resolution with Relational Match Propagation

Crowdsourced Collective Entity Resolution with Relational Ma...

引用

international conference on Data Engineering

作者： Jiacheng Huang Wei Hu Zhifeng Bao Yuzhong Qu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China RMIT University Melbourne Australia

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the same real-world object. Recent studies have shown significant benefits of involving humans in the loop of ER. they often resolve entities with pairwise similarity measures over attribute values and resort to the crowds to label uncertain ones. However, existing methods still suffer from high labor costs and insufficient labeling to some extent. In this paper, we propose a novel approach called crowdsourced collective ER, which leverages the relationships between entities to infer matches jointly rather than independently. Specifically, it iteratively asks human workers to label picked entity pairs and propagates the labeling information to their neighbors in distance. During this process, we address the problems of candidate entity pruning, probabilistic propagation, optimal question selection and error-tolerant truth inference. Our experiments on real-world datasets demonstrate that, compared with state-of-the-art methods, our approach achieves superior accuracy with much less labeling.

关键词： Erbium Labeling Probabilistic logic Inference algorithms Task analysis Object recognition Forestry

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HowSim: A General and Effective Similarity Measure on Heterogeneous Information Networks

HowSim: A General and Effective Similarity Measure on Hetero...

引用

international conference on Data Engineering

作者： Yue Wang Zhe Wang Ziyuan Zhao Zijian Li Xun Jian Lei Chen Jianchun Song Shenzhen Institute of Computing Sciences Shenzhen Univ CSE Dept HKUST Wechat Group Tencent

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. Measuring the similarity among objects is an important task in data mining applications. Currently, several similarity measures are defined for HIN. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of the similarity scores. this makes the existing similarity measures difficult to use in real applications. To address this problem, we extend SimRank, a well-known similarity measure for homogeneous graphs, to HINs, by introducing the concept of decay graph. the newly proposed relevance measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. the generality and effectiveness of HowSim, are demonstrated by extensive experiments.

关键词： Semantics Current measurement Data mining Aggregates Probabilistic logic Task analysis Writing

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Continuously Tracking Core Items in Data Streams with Probabilistic Decays

Continuously Tracking Core Items in Data Streams with Probab...

引用

international conference on Data Engineering

作者： Junzhou Zhao Pinghui Wang Jing Tao Shuo Zhang John C.S. Lui MOEKLINNS Lab School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an P. R. China Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

the sheer scale of big data causes the information overload issue and there is an urgent need for tools that can draw valuable insights from massive data. this paper investigates the core items tracking (CIT) problem where the goal is to continuously track representative items, called core items, in a data stream so to best represent/summarize the stream. In order to simultaneously satisfy the recency and continuity requirements, we consider CIT over probabilistic-decaying streams where items in the stream are forgotten gradually in a probabilistic manner. We first introduce an algorithm, called PNDCIT, to find core items in a special kind of probabilistic non-decaying streams. Furthermore, using PNDCIT as a building block, we design two novel algorithms, namely PDCIT and PDCIT+, to maintain core items over probabilistic-decaying streams with constant approximation ratios. Finally, extensive experiments on real data demonstrate that PDCIT+ achieves a speedup of up to one order of magnitude over a batch algorithm while providing solutions with comparable quality.

关键词： Probabilistic logic Approximation algorithms Analytical models History Windows Data models Big Data

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Mining Verb-Oriented Commonsense Knowledge

Mining Verb-Oriented Commonsense Knowledge

引用

international conference on Data Engineering

作者： Jingping Liu Yuanfu Zhou Dan Wu Chao Wang Haiyun Jiang Sheng Zhang Bo Xu Yanghua Xiao Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Ping An Health Technology China School of Computer Science and Technology Donghua University China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Commonsense knowledge acquisition is one of the fundamental issues in the implementation of human-level AI. However, commonsense is difficult to obtain, because it is a human consensus and rarely explicitly appears in texts or other data. In this paper, we focus on the automatic acquisition of a typical kind of implicit verb-oriented commonsense knowledge (e.g., "person eats food"), which is the concept level knowledge of verb phrases. For this purpose, we propose a knowledge-driven approach to mine verb-oriented commonsense knowledge from verb phrases with the help of taxonomy. First, we design an entropy-based filter to cope with noisy input verb phrases. then, we propose a joint model based on minimum description length and a neural language model to generate verb-oriented common-sense knowledge. We conduct extensive experiments to show that our solution is more effective to mine verb-oriented commonsense knowledge than competitors, and finally, we harvest 18K verb-oriented commonsense knowledge.

关键词： Taxonomy Noise measurement Generators Probabilistic logic Indexes Encoding

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