3430950
Efficient Processing of XML Path Queries Using the Disk-based F&B Index
With the proliferation of XML data and applications on the Internet, efficient XML query processing techniques are in great demand. Answering queries using XML indexes is a natural approach. A number of XML indexes have been proposed in the literature: among them, FB the result is a disk-based F&B Index with good clustering properties. In addition, novel query processing algorithms exploiting the physical organization of the disk-based F&B Indexes are proposed. Experimental results verify that our disk-based F&B Index can scale up for large data size with good query performance compared with state-of-the-art XML query processing algorithms.
6717365
Efficient approximate entity extraction with edit distance constraints
Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance.
 In this paper, we study the problem of approximate dictionary matching with edit distance constraints. Compared to existing studies using token-based similarity constraints, our problem definition enables us to capture typographical or orthographical errors, both of which are common in entity extraction tasks yet may be missed by token-based similarity constraints. Our problem is technically challenging as existing approaches based on q-gram filtering have poor performance due to the existence of many short entities in the dictionary. Our proposed solution is based on an improved neighborhood generation method employing novel partitioning and prefix pruning techniques. We also propose an efficient document processing algorithm that minimizes unnecessary comparisons and enumerations and hence achieves good scalability. We have conducted extensive experiments on several publicly available named entity recognition datasets. The proposed algorithm outperforms alternative approaches by up to an order of magnitude.
15041216
Coding-based Join Algorithms for Structural Queries on Graph-Structured XML Document
In many applications, XML documents need to be modelled as graphs. The query processing of graph-structured XML documents brings new challenges. In this paper, we design a method based on labelling scheme for structural queries processing on graph-structured XML documents. We give each node some labels, the reachability labelling scheme. By extending an interval-based reachability labelling scheme for DAG by Rakesh et al., we design labelling schemes to support the judgements of reachability relationships for general graphs. Based on the labelling schemes, we design graph structural join algorithms to answer the structural queries with only ancestor-descendant relationship efficiently. For the processing of subgraph query, we design a subgraph join algorithm. With efficient data structure, the subgraph join algorithm can process subgraph queries with various structures efficiently. Experimental results show that our algorithms have good performance and scalability.
6867733
Threshold-based probabilistic top-k dominating queries
Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Top-k dominating queries are very important in many applications including decision making in a multidimensional space. In this paper, we study the problem of efficiently computing top-k dominating queries on uncertain data. We first formally define the problem. Then, we develop an efficient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deficiency in an exact computation, we develop an efficient randomized algorithm with an accuracy guarantee. Our extensive experiments demonstrate that both algorithms are quite efficient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice.
10066890
Quantile-based KNN over multi-valued objects
K Nearest Neighbor search has many applications including data mining, multi-media, image processing, and monitoring moving objects. In this paper, we study the problem of KNN over multi-valued objects. We aim to provide effective and efficient techniques to identify KNN sensitive to relative distributions of objects.We propose to use quantiles to summarize relative-distribution-sensitive K nearest neighbors. Given a query Q and a quantile φ ∈ (0, 1], we firstly study the problem of efficiently computing K nearest objects based on a φ-quantile distance (e.g. median distance) from each object to Q. The second problem is to retrieve the K nearest objects to Q based on overall distances in the “best population” (with a given size specified by φ-quantile) for each object. While the first problem can be solved in polynomial time, we show that the 2nd problem is NP-hard. A set of efficient, novel algorithms have been proposed to give an exact solution for the first problem and an approximate solution for the second problem with the approximation ratio 2. Extensive experiment demonstrates that our techniques are very efficient and effective.
9278394
Keyword search on structured and semi-structured data
Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.
2172280
TEAM: efficient two-locus epistasis tests in human genome-wide association study
As a promising tool for identifying genetic markers underlying phenotypic differences, genome-wide association study (GWAS) has been extensively investigated in recent years. In GWAS, detecting epistasis (or gene–gene interaction) is preferable over single locus study since many diseases are known to be complex traits. A brute force search is infeasible for epistasis detection in the genome-wide scale because of the intensive computational burden. Existing epistasis detection algorithms are designed for dataset consisting of homozygous markers and small sample size. In human study, however, the genotype may be heterozygous, and number of individuals can be up to thousands. Thus, existing methods are not readily applicable to human datasets. In this article, we propose an efficient algorithm, TEAM, which significantly speeds up epistasis detection for human GWAS. Our algorithm is exhaustive, i.e. it does not ignore any epistatic interaction. Utilizing the minimum spanning tree structure, the algorithm incrementally updates the contingency tables for epistatic tests without scanning all individuals. Our algorithm has broader applicability and is more efficient than existing methods for large sample study. It supports any statistical test that is based on contingency tables, and enables both family-wise error rate and false discovery rate controlling. Extensive experiments show that our algorithm only needs to examine a small portion of the individuals to update the contingency tables, and it achieves at least an order of magnitude speed up over the brute force approach. Contact: xiang@cs.unc.edu
37290278
cGridex: Efficient Processing of Continuous Range Queries over Moving Objects
In mobile computing environments, the continuous range query is one of the most important types of queries required to support various location-based services. With a large number of queries, the real-time response to query answers and the concurrent execution are two major challenges. In this paper, we propose a novel approach cGridex for efficient processing of continuous range queries over moving objects. cGridex abstracts the dynamic relations between moving objects and concurrent queries, and introduces the concept of coloring on a high level. With colored grids, it forms a direct mapping between moving objects and continuous queries, and can process both of them in a unified way, without redundant evaluation. By precomputing and materializing the color transferring relations, it avoids frequent operations between query sets and spatial join between objects and queries at each time interval, thus leading to fast query evaluation. The experimental results show that cGridex has good performance for the evaluation of continuous range queries.
3247167
Storage and Query over Encrypted Character and Numerical Data in Database
There is some private and sensitive data in database, which need to be protected from attacking. In order to reinforce the security of data, an effective mechanism, cryptographic support has been widely used. However, we must make a tradeoff between the performance and the security because encryption and decryption greatly degrade the query performance. To solve such a problem, a novel approach is proposed in this paper that can quickly execute SQL query on the encrypted data. For character data, it not only encrypts them, but also turns the character data into characteristic values via a characteristic function and stores them as additional fields. For numerical data, it not only encrypts them, but also creates its B+ tree index before the encryption in order to keep the ordering of each record in the index. Furthermore, we give the algorithms of querying the encrypted data based on the storage models. Results of sets of experiments validate the functionality and usability of our approach
14797393
A system for analyzing and indexing human-motion databases
We demonstrate a data-driven approach for representing, compressing, and indexing human-motion databases. Our modeling approach is based on piecewise-linear components that are determined via a divisive clustering method. Selection of the appropriate linear model is determined automatically via a classifier using a subspace of the most significant, or principle features (markers). We show that, after offline training, our model can accurately estimate and classify human motions. We can also construct indexing structures for motion sequences according to their transition trajectories through these linear components. Our method not only provides indices for whole and/or partial motion sequences, but also serves as a compressed representation for the entire motion database. Our method also tends to be immune to temporal variations, and thus avoids the expense of time-warping.
33982421
Positive Sample Enhanced Angle-Diversity Active Learning for SVM Based Image Retrieval
Active learning is a promising tool to improve the performance of content-based image retrieval (CBIR). As a commonly used active learning approach, angle-diversity provides the most informative images to user for feedback. However, it suffers from the problem that the query concept is diverse and the numbers of the positive and the negative images are imbalanced. As a consequence, the positive samples obtained by active learning are inadequate, which degrades the learning efficiency. To deal with this issue, we propose a novel method based on angle-diversity and hyperplane shifting to increase the number of positive images in the active learning results. The experiment is conducted on a test data set with 10,000 images. Compared with the traditional angle-diversity technique, our method can improve the retrieval performance significantly.