Author
Jianhua Feng
Bio: Jianhua Feng is an academic researcher from Tsinghua University. The author has contributed to research in topics: XML & String metric. The author has an hindex of 44, co-authored 169 publications receiving 7242 citations.
Papers published on a yearly basis
Papers
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01 Jul 2012
TL;DR: This work proposes a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are use to verify only the most likely matching pairs, and develops a novel two-tiered heuristic approach for creating batched tasks.
Abstract: Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a more accurate but expensive (and slow) way to bring human insight into the process. Previous work has proposed batching verification tasks for presentation to human workers but even with batching, a human-only approach is infeasible for data sets of even moderate size, due to the large numbers of matches to be tested. Instead, we propose a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are used to verify only the most likely matching pairs. We show that for such a hybrid system, generating the minimum number of verification tasks of a given size is NP-Hard, but we develop a novel two-tiered heuristic approach for creating batched tasks. We describe this method, and present the results of extensive experiments on real data sets using a popular crowdsourcing platform. The experiments show that our hybrid approach achieves both good efficiency and high accuracy compared to machine-only or human-only alternatives.
499 citations
Posted Content•
TL;DR: In this paper, a hybrid human-machine approach is proposed, in which machines are used to do an initial, coarse pass over all the data, and people were used to verify only the most likely matching pairs.
Abstract: Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a more accurate but expensive (and slow) way to bring human insight into the process. Previous work has proposed batching verification tasks for presentation to human workers but even with batching, a human-only approach is infeasible for data sets of even moderate size, due to the large numbers of matches to be tested. Instead, we propose a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are used to verify only the most likely matching pairs. We show that for such a hybrid system, generating the minimum number of verification tasks of a given size is NP-Hard, but we develop a novel two-tiered heuristic approach for creating batched tasks. We describe this method, and present the results of extensive experiments on real data sets using a popular crowdsourcing platform. The experiments show that our hybrid approach achieves both good efficiency and high accuracy compared to machine-only or human-only alternatives.
450 citations
09 Jun 2008
TL;DR: An extended inverted index is proposed to facilitate keyword-based search, and a novel ranking mechanism for enhancing search effectiveness is presented, which achieves both high search efficiency and high accuracy.
Abstract: Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogenous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.
422 citations
01 Aug 2009
TL;DR: Three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time are introduced and result shows that these methods achieve good scalability in terms of both the number of graphs and the size of graphs.
Abstract: Graph data have become ubiquitous and manipulating them based on similarity is essential for many applications. Graph edit distance is one of the most widely accepted measures to determine similarities between graphs and has extensive applications in the fields of pattern recognition, computer vision etc. Unfortunately, the problem of graph edit distance computation is NP-Hard in general. Accordingly, in this paper we introduce three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time. Applying these methods, two algorithms AppFull and AppSub are introduced to perform different kinds of graph search on graph databases. Comprehensive experimental studies are conducted on both real and synthetic datasets to examine various aspects of the methods for bounding graph edit distance. Result shows that these methods achieve good scalability in terms of both the number of graphs and the size of graphs. The effectiveness of these algorithms also confirms the usefulness of using our bounds in filtering and searching of graphs.
413 citations
18 May 2009
TL;DR: This paper proposes the novel notion of individual life pattern, which captures individual's general life style and regularity, and proposes the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data.
Abstract: The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual's general life style and regularity. Concretely, we propose the life pattern normal form (the LP-normal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework.
292 citations
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Book•
[...]
05 Jun 2007
TL;DR: The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content.
Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.
2,579 citations
TL;DR: A systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics, and introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors.
Abstract: The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
1,289 citations
Book•
[...]
11 Jan 2013
TL;DR: Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit.
Abstract: With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysisis a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.
1,278 citations