Changxuan Wan

Returning Clustered Results for Keyword Search on XML Documents

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. In this paper, we address the problem of returning clustered results for keyword search on XML documents. We first propose a novel semantics for answers to an XML keyword query. The core of the semantics is the conceptually related relationship between keyword matches, which is based on the conceptual relationship between nodes in XML trees. Then, we propose a new clustering methodology for XML search results, which clusters results according to the way they match the given query. Two approaches to implement the methodology are discussed. The first approach is a conventional one which does clustering after search results are retrieved; the second one clusters search results actively, which has characteristics of clustering on the fly. The generated clusters are then organized into a cluster hierarchy with different granularities to enable users locate the results of interest easily and precisely. Experimental results demonstrate the meaningfulness of the proposed semantics as well as the efficiency of the proposed methods.

Exploiting structures in keyword queries for effective XML search

Keyword search on XML documents has received considerable research interests recently. Most existing methods put their emphases on the document side, and focus on how to utilize structural properties of XML documents to produce better search results, more effective ranking methods, or more efficient algorithms. However, effective XML search requires a full understanding of not only XML documents but also XML keyword queries, whereas little attention has been paid to the latter. In this paper, we focus on the query side of XML keyword search instead of the document side. We show that keyword queries have structures, and define a concept called keyword query with structure (QWS) to capture query structure. As query structure provides hints about the intent of the query, it can be used to improve the quality of the search results. We exploit some key observations to characterize the structure in a keyword query and show how to refine search results with the assistance of query structure. In order to take the benefits of query structure, we design a query processing approach to produce results given a keyword query. It first derives some QWSs based on heuristics, and computes results of these queries, then expands the results if needed. We implement the proposed methods and conduct comprehensive experiments. Experimental results verify the effectiveness of our methods.

Effective XML content and structure retrieval with relevance ranking

XML documents can be retrieved by means of not only content-only (CO) queries, but also content-and-structure (CAS) queries. Though promising better retrieval precision, CAS queries introduce several new challenges. To address these challenges, we propose a novel approach for XML CAS retrieval. The distinctive feature of the approach is that it adopts a content-oriented point of view. Specifically, the approach first decomposes a CAS query into several fragments, then retrieves results for each query fragment in a content-centric way, and finally scores each answer node. The approach is adaptive to versatile homogeneous and heterogeneous data environments. To assess the relevance of retrieval results to a query fragment, we present a scoring strategy that measures relevance from both content and structure perspectives. In addition, an effective approach is proposed to infer answer nodes based on the CAS query and document structure. An efficient algorithm is also presented for CAS retrieval. Finally, we demonstrate the effectiveness of the proposed methods through comprehensive experimental studies.

Automatically weighting tags in XML collection

In XML retrieval, nodes with different tags play different roles in XML documents and then tags should be reflected in the relevance ranking. An automatic method is proposed in this paper to infer the weights of tags. We first investigate 15 features about tags, and then select five of them based on the correlations between these features and manual tag weights. Using these features, a tag weight assignment model, ATG, is designed. We evaluate the performance of ATG on two real data sets, IEEECS and Wikipedia from two different perspectives. One is to evaluate the quality of the model by measuring the correlation between weights generated by our model and those given by experts. The other is to test the effectiveness of the model in improving retrieval performance. Experimental results show that the tag weights generated by ATG are highly correlated with the manually assigned weights and the ATG model improves retrieval effectiveness significantly.

KLEAP: an efficient cleaning method to remove cross-reads in RFID streams

Recently, the RFID technology has been widely used in many kinds of applications. However, because of the interference from environmental factors and limitations of the radio frequency technology, the data streams collected by the RFID readers are usually contain a lot of cross-reads. To address this issue, we propose a KerneL dEnsity-bAsed Probability cleaning method (KLEAP) to remove cross-reads within a sliding window. The method estimates the density of each tag using a kernel-based function. The reader corresponding to the micro-cluster with the largest density will be regarded as the position that the tagged object should locate in current window, and the readings derived from other readers will be treated as the cross-reads. Experiments verify the effectiveness and efficiency of the proposed method.

LINQ: A Framework for Location-Aware Indexing and Query Processing

This paper studies the generic location-aware rank query (GLRQ) over a set of location-aware objects. A GLRQ is composed of a spatial location, a set of keywords, a query predicate, and a ranking function formulated on location, text and other attributes. The result consists of k objects satisfying the predicate ranked according to the ranking function. An example is a query searching for the restaurants that 1) are nearby, 2) offer “American” food, and 3) have high ratings (rating > 4.0). Such queries can not be processed efficiently using existing techniques. In this work, we propose a novel framework called LINQ for efficient processing of GLRQs. To handle the predicate and the attribute-based scoring, we devise a new index structure called synopses tree, which contains the synopses of different subsets of the dataset. The synopses tree enables pruning of search space according to the satisfiability of the predicate. To process the query constraints over the location and keywords, the framework integrates the synopses tree with the spatio-textual index such as IR-tree. The framework therefore is capable of processing the GLRQs efficiently and holistically. We conduct extensive experiments to demonstrate that our solution provides excellent query performance.

Top-k entities query processing on uncertainly fused multi-sensory data

Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertainty exists in these data because sensors are not precise enough. Hence, the intuitive method to store this kind of data is using uncertain database. Finding the top-k entities according to one or more attributes is a powerful technique when the uncertain database contains large quantity of data. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. We propose a method to process entity–based global top-k aggregate queries in uncertain database, which returns the top-k entities that have the highest aggregate value. Our method has two levels, entity state generation and G-topk-E query processing. In the former level, entity states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming–based global top-k entity query processing is employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.

A practice probability frequent pattern mining method over transactional uncertain data streams

In recent years, large amounts of uncertain data are emerged with the widespread employment of the new technologies, such as wireless sensor networks, RFID and privacy protection. According to the features of the uncertain data streams such as incomplete, full of noisy, non-uniform and mutable, this paper presents a probability frequent pattern tree called PFP-tree and a method called PFP-growth, to mine probability frequent patterns based on probability damped windows. The main characteristics of the suggested method include: (1) adopting time-based probability damped window model to enhance the accuracy of mined frequent patterns; (2) setting an item index table and a transaction index table to speed up retrieval on the PFP-tree; and (3) pruning the tree to remove the items that cannot become frequent patterns;. The experimental results demonstrate that PFP-growth method has better performance than the main existing schemes in terms of accuracy, processing time and storage space.

JUFE at INEX 2011 Snippet Retrieval Track

Jiangxi University of Finance and Economics (JUFE) submitted 8 runs to the Snippet Retrieval Track at INEX 2011.This report describes an XML snippet retrieval method based on Average Topic Generalization (ATG) model used by JUFE. The basic idea of the ATG is that different element in an XML document plays different role and hence should has distinguishing importance. The ATG model sets a weight automatically to each element according to its tag or path in the XML document. Then, the BM25EW model based on the ATG is proposed to retrieve and rank the relevant elements in an XML document collection. All windows in the most relevant elements are scored and those windows with higher scores are extracted as snippets. By comparing with the runs under different strategies, the performance are discussed and analyzed in detail.

Global Top-k Aggregate Queries Based on X-tuple in Uncertain Database

A Top-k aggregate query, which is a powerful technique when dealing with large quantity of data, ranks groups of tuples by their aggregate values and returns k groups with the highest aggregate values. However, compared to Top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. As a powerful semantic of Top-k in uncertain database, Global Top-k return k highest-ranked tuples according to their probabilities of being in the Top-k answers in possible worlds. We propose a x-tuple based method to process Global Top-k aggregate queries in uncertain database. Our method has two levels, group state generation and G-x-Top-k query processing. In the former level, group states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming based Global x-tuple Top-k query processing are employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.