2,274 research outputs found
Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies
Business Intelligence plays an important role in decision making. Based on
data warehouses and Online Analytical Processing, a business intelligence tool
can be used to analyze complex data. Still, summarizability issues in data
warehouses cause ineffective analyses that may become critical problems to
businesses. To settle this issue, many researchers have studied and proposed
various solutions, both in relational and XML data warehouses. However, they
find difficulty in evaluating the performance of their proposals since the
available benchmarks lack complex hierarchies. In order to contribute to
summarizability analysis, this paper proposes an extension to the XML warehouse
benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate
XML data warehouses with scalable complex hierarchies as well as
summarizability processing. We experimentally demonstrated that complex
hierarchies can definitely be included into a benchmark dataset, and that our
benchmark is able to compare two alternative approaches dealing with
summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP
2012), Maui : United States (2012
Approximate Query Answering and Result Refinement on XML Data
Today, many economic decisions are based on the fast analysis of XML data. Yet, the time to process analytical XML queries is typically high. Although current XML techniques focus on the optimization of query processing, none of these support early approximate feedback as possible in relational Online Aggregation systems. In this paper, we introduce a system that provides fast estimates to XML aggregation queries. While processing, these estimates and the assigned confidence bounds are constantly improving. In our evaluation, we show that without significantly increasing the overall execution time our system returns accurate guesses of the final answer long before traditional systems are able to produce output
Optimal algorithms for selecting top-k combinations of attributes : theory and applications
Traditional top-k algorithms, e.g., TA and NRA, have been successfully applied in many areas such as information retrieval, data mining and databases. They are designed to discover k objects, e.g., top-k restaurants, with highest overall scores aggregated from different attributes, e.g., price and location. However, new emerging applications like query recommendation require providing the best combinations of attributes, instead of objects. The straightforward extension based on the existing top-k algorithms is prohibitively expensive to answer top-k combinations because they need to enumerate all the possible combinations, which is exponential to the number of attributes. In this article, we formalize a novel type of top-k query, called top-k, m, which aims to find top-k combinations of attributes based on the overall scores of the top-m objects within each combination, where m is the number of objects forming a combination. We propose a family of efficient top-k, m algorithms with different data access methods, i.e., sorted accesses and random accesses and different query certainties, i.e., exact query processing and approximate query processing. Theoretically, we prove that our algorithms are instance optimal and analyze the bound of the depth of accesses. We further develop optimizations for efficient query evaluation to reduce the computational and the memory costs and the number of accesses. We provide a case study on the real applications of top-k, m queries for an online biomedical search engine. Finally, we perform comprehensive experiments to demonstrate the scalability and efficiency of top-k, m algorithms on multiple real-life datasets.Peer reviewe
Content-Aware DataGuides for Indexing Large Collections of XML Documents
XML is well-suited for modelling structured data with
textual content. However, most indexing approaches perform
structure and content matching independently, combining
the retrieved path and keyword occurrences in a third
step. This paper shows that retrieval in XML documents can
be accelerated significantly by processing text and structure
simultaneously during all retrieval phases. To this end,
the Content-Aware DataGuide (CADG) enhances the wellknown
DataGuide with (1) simultaneous keyword and path
matching and (2) a precomputed content/structure join. Extensive
experiments prove the CADG to be 50-90% faster
than the DataGuide for various sorts of query and document,
including difficult cases such as poorly structured
queries and recursive document paths. A new query classification
scheme identifies precise query characteristics with
a predominant influence on the performance of the individual
indices. The experiments show that the CADG is applicable
to many real-world applications, in particular large
collections of heterogeneously structured XML documents
An Inflationary Fixed Point Operator in XQuery
We introduce a controlled form of recursion in XQuery, inflationary fixed
points, familiar in the context of relational databases. This imposes
restrictions on the expressible types of recursion, but we show that
inflationary fixed points nevertheless are sufficiently versatile to capture a
wide range of interesting use cases, including the semantics of Regular XPath
and its core transitive closure construct.
While the optimization of general user-defined recursive functions in XQuery
appears elusive, we will describe how inflationary fixed points can be
efficiently evaluated, provided that the recursive XQuery expressions exhibit a
distributivity property. We show how distributivity can be assessed both,
syntactically and algebraically, and provide experimental evidence that XQuery
processors can substantially benefit during inflationary fixed point
evaluation.Comment: 11 pages, 10 figures, 2 table
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