157891
ROLEX: relational on-line exchange with XML
1. OVERVIEW ROLEX is a research system for closely coupled XML-relational interoperation [2]. Whereas typical XML-based applications interoperate with existing relational databases via a “shred-and-publish” approach, the ROLEX system seeks to provide direct access to relational data via XML interfaces at the speed of cached XML data. To achieve this, ROLEX is integrated tightly with both the DBMS and the application through a standard interface supported by most XML parsers, the Document Object Model (DOM). Thus, in general, an application need not be modified to be used with ROLEX. With the DBMS providing performance qualitatively similar to cached data, XML applications can rely on it for concurrency control and recovery services. To support our integration model and performance goals, ROLEX is built on the DataBlitz Main-Memory Database System, allowing us to capitalize on low-latency access to data while still providing concurrency control and recovery [1]. The DOM interface supports the expected navigation functions: parent-to-child, child-to-parent, and sibling-to-sibling. A DOM interface to an XML view query supports all the DOM operations and behaves as if the user were navigating the XML document resulting from the query. In ROLEX, we implement a virtual DOM tree that goes a step further by providing the same interface without creating a physical DOM tree. A novelty of ROLEX is that it uses a navigational profile for the user or application when it optimizes view-query plans. While navigational profiles can, in principle, be quite complex, we currently adopt a very simple model. If n is a node in the schema tree with parent p, the navigation profile stores the probability that some node in the DOM tree generated by n will be visited given that its parent, generated by p, has been visited. Query output is produced by a navigable query plan. It provides, for each node n in the schema tree of a view query, two entities: (1) a subplan for evaluating the tag query for n, and (2) a navigation index. The navigation index serves to materialize the output of the tag query and supports efficient lookup based on parameter values,
3427735
RankSQL: Supporting Ranking Queries in Relational Database Management Systems
Ranking queries (or top-k queries) are dominant in many emerging applications, e.g., similarity queries in multimedia databases, searching Web databases, middleware, and data mining. The increasing importance of top-k queries warrants an efficient support of ranking in the relational database management system (RDBMS) and has recently gained the attention of the research community. Top-k queries aim at providing only the top k query results, according to a user-specified ranking function, which in many cases is an aggregate of multiple criteria. The following is an example top-k query.
3458490
Supporting ranking and clustering as generalized order-by and group-by
The Boolean semantics of SQL queries cannot adequately capture the "fuzzy" preferences and "soft" criteria required in non-traditional data retrieval applications. One way to solve this problem is to add a flavor of "information retrieval" into database queries by allowing fuzzy query conditions and flexibly supporting grouping and ranking of the query results within the DBMS engine. While ranking is already supported by all major commercial DBMSs natively, support of flexibly grouping is still very limited (i.e., group-by).
 In this paper, we propose to generalize group-by to enable flexible grouping (clustering specifically) of the query results. Different from clustering in data mining applications, our focus is on supporting efficient clustering of Boolean results generated at query time. Moreover, we propose to integrate ranking and clustering with Boolean conditions, forming a new type of ClusterRank query to allow structured data retrieval. Such an integration is nontrivial in terms of both semantics and query processing. We investigate various semantics of this type of queries. To process such queries, a straightforward approach is to simply glue the techniques developed for ranking-only and clustering-only together. This approach is costly since both ranking and clustering are treated as blocking post-processing tasks upon Boolean query results by existing techniques. We propose a summary-based evaluation method that utilizes bitmap index to seamlessly integrate Boolean conditions, clustering, and ranking. Experimental study shows that our approach significantly outperforms the straightforward one and maintains high clustering quality.
4658514
Structured databases on the web: observations and implications
The Web has been rapidly "deepened" by the prevalence of databases online. With the potentially unlimited information hidden behind their query interfaces, this "deep Web" of searchable databses is clearly an important frontier for data access. This paper surveys this relatively unexplored frontier, measuring characteristics pertinent to both exploring and integrating structured Web sources. On one hand, our "macro" study surveys the deep Web at large, in April 2004, adopting the random IP-sampling approach, with one million samples. (How large is the deep Web? How is it covered by current directory services?) On the other hand, our "micro" study surveys source-specific characteristics over 441 sources in eight representative domains, in December 2002. (How "hidden" are deep-Web sources? How do search engines cover their data? How complex and expressive are query forms?) We report our observations and publish the resulting datasets to the research community. We conclude with several implications (of our own) which, while necessarily subjective, might help shape research directions and solutions.
17572048
Supporting ad-hoc ranking aggregates
This paper presents a principled framework for efficient processing of ad-hoc top-k (ranking) aggregate queries, which provide the k groups with the highest aggregates as results. Essential support of such queries is lacking in current systems, which process the queries in a naïve materialize-group-sort scheme that can be prohibitively inefficient. Our framework is based on three fundamental principles. The Upper-Bound Principle dictates the requirements of early pruning, and the Group-Ranking and Tuple-Ranking Principles dictate group-ordering and tuple-ordering requirements. They together guide the query processor toward a provably optimal tuple schedule for aggregate query processing. We propose a new execution framework to apply the principles and requirements. We address the challenges in realizing the framework and implementing new query operators, enabling efficient group-aware and rank-aware query plans. The experimental study validates our framework by demonstrating orders of magnitude performance improvement in the new query plans, compared with the traditional plans.
8491927
Multiresolution genetic clustering algorithm for texture segmentation
Abstract This work plans to approach the texture segmentation problem by incorporating genetic algorithm and K-means clustering method within a multiresolution structure. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class–position uncertainty and to improve the segmentation accuracy. The procedure is described as follows. In the first step, a quad-tree structure of multiple resolutions is constructed. Sampling windows of different sizes are utilized to partition the underlying image into blocks at different resolution levels and texture features are extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. While the select and mutate operators of the traditional genetic algorithm are adopted in this work, the crossover operator is replaced with K-means clustering method. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.
16331610
Calcification Descriptor and Relevance Feedback Learning Algorithms for Content-Based Mammogram Retrieval
In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed.
2062689
Optimizing Large Join Queries in Mediation Systems
In data integration systems, queries posed to a mediator need to be translated into a sequence of queries to the underlying data sources. In a heterogeneous environment, with sources of diverse and limited query capabilities, not all the translations are feasible. In this paper, we study the problem of finding feasible and efficient query plans for mediator systems. We consider conjunctive queries on mediators and model the source capabilities through attribute-binding adornments. We use a simple cost model that focuses on the major costs in mediation systems, those involved with sending queries to sources and getting answers back. Under this metric, we develop two algorithms for source query sequencing - one based on a simple greedy strategy and another based on a partitioning scheme. The first algorithm produces optimal plans in some scenarios, and we show a linear bound on its worst case performance when it misses optimal plans. The second algorithm generates optimal plans in more scenarios, while having no bound on the margin by which it misses the optimal plans. We also report on the results of the experiments that study the performance of the two algorithms.
8216205
Dissimilarity learning for nominal data
Defining a good distance (dissimilarity) measure between patterns is of crucial importance in many classification and clustering algorithms. While a lot of work has been performed on continuous attributes, nominal attributes are more difficult to handle. A popular approach is to use the value difference metric (VDM) to define a real-valued distance measure on nominal values. However, VDM treats the attributes separately and ignores any possible interactions among attributes. In this paper, we propose the use of adaptive dissimilarity matrices for measuring the dissimilarities between nominal values. These matrices are learned via optimizing an error function on the training samples. Experimental results show that this approach leads to better classification performance. Moreover, it also allows easier interpretation of (dis)similarity between different nominal values.
29028846
Reuse or Never Reuse the Deleted Labels in XML Query Processing Based on Labeling Schemes
To facilitate the XML query processing, several kinds of labeling schemes have been proposed. Based on the labeling schemes, the ancestor-descendant and parent-child relationships in XML queries can be quickly determined without accessing the original XML file. Recently, more researches are focused on how to update the labels when nodes are inserted into the XML. However how to process the deleted labels are not discussed previously. We think that the deleted labels can be processed in two different directions: (1) reuse all the deleted labels to control the label size increasing speed and improve the query performance; (2) never reuse the deleted labels to query different versions of the XML data based on labeling schemes. In this paper, we firstly introduce our previous work, called QED, which can completely avoid the re-labeling in XML updates. Secondly based on QED we propose a new algorithm, called Reuse, which can reuse all the deleted labels to control the label size increasing speed; meanwhile the Reuse algorithm can completely avoid the re-labeling also. Thirdly to query different versions of the XML data, we propose another new algorithm, called NeverReuse, which is the only approach that never reuses any deleted labels. Extensive experimental results show that the algorithms proposed in this paper can control the label size increasing speed when reusing all the deleted labels, and is the only approach to query different versions of the XML data based on labeling schemes.