Jianyong Wang

EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data

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.

Frequent pattern mining with uncertain data

This paper studies the problem of frequent pattern mining with uncertain data. We will show how broad classes of algorithms can be extended to the uncertain data setting. In particular, we will study candidate generate-and-test algorithms, hyper-structure algorithms and pattern growth based algorithms. One of our insightful observations is that the experimental behavior of different classes of algorithms is very different in the uncertain case as compared to the deterministic case. In particular, the hyper-structure and the candidate generate-and-test algorithms perform much better than tree-based algorithms. This counter-intuitive behavior is an important observation from the perspective of algorithm design of the uncertain variation of the problem. We will test the approach on a number of real and synthetic data sets, and show the effectiveness of two of our approaches over competitive techniques.

TFP: an efficient algorithm for mining top-k frequent closed itemsets

Frequent itemset mining has been studied extensively in literature. Most previous studies require the specification of a min/spl I.bar/support threshold and aim at mining a complete set of frequent itemsets satisfying min/spl I.bar/support. However, in practice, it is difficult for users to provide an appropriate min/spl I.bar/support threshold. In addition, a complete set of frequent itemsets is much less compact than a set of frequent closed itemsets. In this paper, we propose an alternative mining task: mining top-k frequent closed itemsets of length no less than min/spl I.bar/l, where k is the desired number of frequent closed itemsets to be mined, and min/spl I.bar/l is the minimal length of each itemset. An efficient algorithm, called TFP, is developed for mining such itemsets without mins/spl I.bar/support. Starting at min/spl I.bar/support = 0 and by making use of the length constraint and the properties of top-k frequent closed itemsets, min/spl I.bar/support can be raised effectively and FP-Tree can be pruned dynamically both during and after the construction of the tree using our two proposed methods: the closed node count and descendant/spl I.bar/sum. Moreover, mining is further speeded up by employing a top-down and bottom-up combined FP-Tree traversing strategy, a set of search space pruning methods, a fast 2-level hash-indexed result tree, and a novel closed itemset verification scheme. Our extensive performance study shows that TFP has high performance and linear scalability in terms of the database size.

Effective keyword search for valuable lcas over xml documents

In this paper, we study the problem of effective keyword search over XML documents. We begin by introducing the notion of Valuable Lowest Common Ancestor (VLCA) to accurately and effectively answer keyword queries over XML documents. We then propose the concept of Compact VLCA (CVLCA) and compute the meaningful compact connected trees rooted as CVLCAs as the answers of keyword queries. To efficiently compute CVLCAs, we devise an effective optimization strategy for speeding up the computation, and exploit the key properties of CVLCA in the design of the stack-based algorithm for answering keyword queries. We have conducted an extensive experimental study and the experimental results show that our proposed approach achieves both high efficiency and effectiveness when compared with existing proposals.

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.

Comparing stars: on approximating graph edit distance

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.

Frequent Closed Sequence Mining without Candidate Maintenance

Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only a more compact yet complete result set but also better efficiency. However, most of the previously developed closed pattern mining algorithms work under the candidate maintenance-and- test paradigm, which is inherently costly in both runtime and space usage when the support threshold is low or the patterns become long. In this paper, we present BIDE, an efficient algorithm for mining frequent closed sequences without candidate maintenance. It adopts a novel sequence closure checking scheme called Bl-Directional Extension and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method. A thorough performance study with both sparse and dense, real, and synthetic data sets has demonstrated that BIDE significantly outperforms the previous algorithm: It consumes an order(s) of magnitude less memory and can be more than an order of magnitude faster. It is also linearly scalable in terms of database size.Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only a m...

Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams

Real-time surveillance systems, telecommunication systems, and other dynamic environments often generate tremendous (potentially infinite) volume of stream data: the volume is too huge to be scanned multiple times. Much of such data resides at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes (such as trends and outliers). To discover such high-level characteristics, one may need to perform on-line multi-level, multi-dimensional analytical processing of stream data. In this paper, we propose an architecture, called stream_cube, to facilitate on-line, multi-dimensional, multi-level analysis of stream data.For fast online multi-dimensional analysis of stream data, three important techniques are proposed for efficient and effective computation of stream cubes. First, a tilted time frame model is proposed as a multi-resolution model to register time-related data: the more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage of time-related data and adapts nicely to the data analysis tasks commonly encountered in practice. Second, instead of materializing cuboids at all levels, we propose to maintain a small number of critical layers. Flexible analysis can be efficiently performed based on the concept of observation layer and minimal interesting layer. Third, an efficient stream data cubing algorithm is developed which computes only the layers (cuboids) along a popular path and leaves the other cuboids for query-driven, on-line computation. Based on this design methodology, stream data cube can be constructed and maintained incrementally with a reasonable amount of memory, computation cost, and query response time. This is verified by our substantial performance study.

LINDEN: linking named entities with knowledge base via semantic knowledge

Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking. Specifically, entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base. However, this task is challenging due to name ambiguity, textual inconsistency, and lack of world knowledge in the knowledge base. Several methods have been proposed to tackle this problem, but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity. In this paper, we propose LINDEN, a novel framework to link named entities in text with a knowledge base unifying Wikipedia and WordNet, by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base. We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy.

Coherent closed quasi-clique discovery from large dense graph databases

Frequent coherent subgraphs can provide valuable knowledge about the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large dense graph databases has been witnessed several applications and received considerable attention in the graph mining community recently. In this paper, we study how to efficiently mine the complete set of coherent closed quasi-cliques from large dense graph databases, which is an especially challenging task due to the downward-closure property no longer holds. By fully exploring some properties of quasi-cliques, we propose several novel optimization techniques, which can prune the unpromising and redundant sub-search spaces effectively. Meanwhile, we devise an efficient closure checking scheme to facilitate the discovery of only closed quasi-cliques. We also develop a coherent closed quasi-clique mining algorithm, <B>Cocain</B>1 Thorough performance study shows that Cocain is very efficient and scalable for large dense graph databases.