2,960 research outputs found
XML Reconstruction View Selection in XML Databases: Complexity Analysis and Approximation Scheme
Query evaluation in an XML database requires reconstructing XML subtrees
rooted at nodes found by an XML query. Since XML subtree reconstruction can be
expensive, one approach to improve query response time is to use reconstruction
views - materialized XML subtrees of an XML document, whose nodes are
frequently accessed by XML queries. For this approach to be efficient, the
principal requirement is a framework for view selection. In this work, we are
the first to formalize and study the problem of XML reconstruction view
selection. The input is a tree , in which every node has a size
and profit , and the size limitation . The target is to find a subset
of subtrees rooted at nodes respectively such that
, and is maximal.
Furthermore, there is no overlap between any two subtrees selected in the
solution. We prove that this problem is NP-hard and present a fully
polynomial-time approximation scheme (FPTAS) as a solution
Database Learning: Toward a Database that Becomes Smarter Every Time
In today's databases, previous query answers rarely benefit answering future
queries. For the first time, to the best of our knowledge, we change this
paradigm in an approximate query processing (AQP) context. We make the
following observation: the answer to each query reveals some degree of
knowledge about the answer to another query because their answers stem from the
same underlying distribution that has produced the entire dataset. Exploiting
and refining this knowledge should allow us to answer queries more
analytically, rather than by reading enormous amounts of raw data. Also,
processing more queries should continuously enhance our knowledge of the
underlying distribution, and hence lead to increasingly faster response times
for future queries.
We call this novel idea---learning from past query answers---Database
Learning. We exploit the principle of maximum entropy to produce answers, which
are in expectation guaranteed to be more accurate than existing sample-based
approximations. Empowered by this idea, we build a query engine on top of Spark
SQL, called Verdict. We conduct extensive experiments on real-world query
traces from a large customer of a major database vendor. Our results
demonstrate that Verdict supports 73.7% of these queries, speeding them up by
up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM
SIGMOD conference 201
Optimizing Sample Design for Approximate Query Processing
The rapid increase of data volumes makes sampling a crucial component of modern data management systems. Although there is a large body of work on database sampling, the problem of automatically determine the optimal sample for a given query remained (almost) unaddressed. To tackle this problem the authors propose a sample advisor based on a novel cost model. Primarily designed for advising samples of a few queries specified by an expert, the authors additionally propose two extensions of the sample advisor. The first extension enhances the applicability by utilizing recorded workload information and taking memory bounds into account. The second extension increases the effectiveness by merging samples in case of overlapping pieces of sample advice. For both extensions, the authors present exact and heuristic solutions. Within their evaluation, the authors analyze the properties of the cost model and demonstrate the effectiveness and the efficiency of the heuristic solutions with a variety of experiments
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