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XOO7: applying OO7 benchmark to XML query processing tool

If XML is to play the critical role of the lingua franca for Internet data interchange that many predict, it is necessary to start designing and adopting benchmarks allowing the comparative performance analysis of the tools being developed and proposed. The effectiveness of existing XML query languages has been studied by many, with a focus on the comparison of linguistic features, implicitly reflecting the fact that most XML tools exist only on paper. In this paper, with a focus on efficiency and concreteness, we propose a pragmatic first step toward the systematic benchmarking of XML query processing platforms with an initial focus on the data (versus document) point of view. We propose XOO7, an XML version of the OO7 benchmark. We discuss the applicability of XOO7, its strengths, limitations and the extensions we are considering. We illustrate its use by presenting and discussing the performance comparison against XOO7 of three different query processing platforms for XML.

Answering Imprecise Queries over Autonomous Web Databases

Current approaches for answering queries with imprecise constraints require user-specific distance metrics and importance measures for attributes of interest - metrics that are hard to elicit from lay users. We present AIMQ, a domain and user independent approach for answering imprecise queries over autonomous Web databases. We developed methods for query relaxation that use approximate functional dependencies. We also present an approach to automatically estimate the similarity between values of categorical attributes. Experimental results demonstrating the robustness, efficiency and effectiveness of AIMQ are presented. Results of a preliminary user study demonstrating the high precision of the AIMQ system is also provided.

Mining approximate functional dependencies and concept similarities to answer imprecise queries

Current approaches for answering queries with imprecise constraints require users to provide distance metrics and importance measures for attributes of interest. In this paper we focus on providing a domain and end-user independent solution for supporting imprecise queries over Web databases without affecting the underlying database. We propose a query processing framework that integrates techniques from IR and database research to efficiently determine answers for imprecise queries. We mine and use approximate functional dependencies between attributes to create precise queries having tuples relevant to the given imprecise query. An approach to automatically estimate the semantic distances between values of categorical attributes is also proposed. We provide preliminary results showing the utility of our approach.

Current approaches to XML management

The Extensible Markup Language has become the standard for information interchange on the Web. We study the data- and document-centric uses of XML management systems (XMLMS). We want to provide XML data users with a guideline for choosing the data management system that best meets their needs. Because the systems we test are first-generation approaches, we suggest a hypothetical design for a useful XML database that could use all the expressive power of XML and XML query languages.

The XOO7 Benchmark

As XML becomes the standard for electronic data interchange, benchmarks are needed to provide a comparative performance analysis of XML Management Systems (XMLMS). Typically a benchmark should adhere to four criteria: relevance, portability, scalability and simplicity [1]. The data structure of a benchmark for XML must be complex enough to capture the characteristics of XML data representation. Data sets should be in various sizes. Benchmark queries should only be defined with the primitives of the language.

Optimizing Recursive Information Gathering Plans in EMERAC

In this paper we describe two optimization techniques that are specially tailored for information gathering. The first is a greedy minimization algorithm that minimizes an information gathering plan by removing redundant and overlapping information sources without loss of completeness. We then discuss a set of heuristics that guide the greedy minimization algorithm so as to remove costlier information sources first. In contrast to previous work, our approach can handle recursive query plans that arise commonly in the presence of constrained sources. Second, we present a method for ordering the access to sources to reduce the execution cost. This problem differs significantly from the traditional database query optimization problem as sources on the Internet have a variety of access limitations and the execution cost in information gathering is affected both by network traffic and by the connection setup costs. Furthermore, because of the autonomous and decentralized nature of the Web, very little cost statistics about the sources may be available. In this paper, we propose a heuristic algorithm for ordering source calls that takes these constraints into account. Specifically, our algorithm takes both access costs and traffic costs into account, and is able to operate with very coarse statistics about sources (i.e., without depending on full source statistics). Finally, we will discuss implementation and empirical evaluation of these methods in Emerac, our prototype information gathering system.

Answering imprecise database queries: a novel approach

A growing number of databases especially those published on the Web are becoming available to external users. Users of these databases are provided simple form-based query interfaces that hide the underlying schematic details. Constrained by the expressiveness of the query interface users often have difficulty in articulating a precise query over the database. Supporting imprecise queries over such systems would allow users to quickly find relevant answers without iteratively refining their queries. For databases to support imprecise queries they must provide answers that closely match the query constraints. In this paper we focus on answering imprecise user queries without changing the existing database system. We propose to support imprecise queries over a database by identifying a set of related precise queries that provide answers that are relevant to the user given query. We present a domain independent approach based on information retrieval techniques to estimate the distance between queries. To demonstrate the utility and usefulness of our approach we perform usability tests and provide results.

Mining coverage statistics for websource selection in a mediator

Recent work in data integration has shown the importance of statistical information about the coverage and overlap of sources for efficient query processing. Despite this recognition there are no effective approaches for learning the needed statistics. The key challenge in learning such statistics is keeping the number of needed statistics low enough to have the storage and learning costs manageable. Naive approaches can become infeasible very quickly. In this paper we present a set of connected techniques that estimate the coverage and overlap statistics while keeping the needed statistics tightly under control. Our approach uses a hierarchical classification of the queries, and threshold based variants of familiar data mining techniques to dynamically decide the level of resolution at which to learn the statistics. We describe the details of our method, and present experimental results demonstrating the efficiency of the learning algorithms and the effectiveness of the learned statistics.

Providing ranked relevant results for web database queries

Often Web database users experience difficulty in articulating their needs using a precise query. Providing ranked set of possible answers would benefit such users. We propose to provide ranked answers to user queries by identifying a set of queries from the query log whose answers are relevant to the given user query. The relevance detection is done using a domain and end-user independent content similarity estimation technique.

Efficient XML Data Management: An Analysis

With XML rapidly gaining popularity as the standard for data exchange on the World Wide Web, a variety of XML management systems (XMLMS) are becoming available. The choice of an XMLMS is made difficult by the significant difference in the expressive power of the queries and the performance shown by these XMLMS. Most XMLMS are legacy systems (mostly relational) extended to load, query, and publish data in XML format. A few are native XMLMS and capture all the characteristics of XML data representation. This paper looks at expressive power and efficiency of various XMLMS. The performance analysis relies on the testbed provided by XOO7, a benchmark derived from OO7 to capture both data and document characteristics of XML. We present efficiency results for two native XMLMS, an XML-enabled semistructured data management system and an XML-enabled RDBMS, which emphasize the need for a delicate balance between the data-centric and document-centric aspects of XML query processing.