Memory-Efficient Query Processing over XML Fragment Stream with Fragment Labeling

The portable/hand-held devices deployed in mobile computing environment are mostly limited in memory. To make it possible for them to locally process queries over a large volume of XML data, the data needs to be streamed in fragments of manageable size and the queries need to be processed over the stream with as little memory as possible. In this paper, we report a considerable improvement of the state-of-the-art techniques of query processing over XML fragment stream in memory efficiency. We use XML fragment labeling (XFL) as a method of representing XML fragmentation, and show that XFL is much more effective than the popular hole-filler (HF) model employed in the state-of-the-art in reducing the amount of memory required for query processing. The state-of-the-art with the HF model requires more memory as the stream size increases. With XFL, we overcome this fundamental limitation, proposing the techniques to make query processing scalable in the sense that memory requirement is not affected by the size of the stream as long as the stream is bounded. The improvement is verified through implementation and a detailed set of experiments

A Method of XML Document Fragmentation for Reducing Time of XML Fragment Stream Query Processing

As XML has been established as the standard for data exchange not just on the Web but among heterogeneous devices, systems, and applications, effective processing of XML queries is one of core components of ubiquitous computing. Most of the mobile/hand-held devices deployed in ubiquitous computing environment are still limited in memory and processing power. An effective query processing is required when the source XML document is of large volume. The framework of fragmenting an XML document and streaming the XML fragments for query processing at the mobile devices has received much attention. However, the main focus was on the memory efficiency to cope with the memory constraint in the mobile devices. Query processing time might be compromised in those techniques. Since the processing power is also limited in the mobile devices, the time optimization deserves attention. We have found out that the query processing time is significantly affected by how the source XML document is fragmented. In this paper, we propose a method of XML document fragmentation whereby query processing gets efficient in time while the size constraint for each resulting fragment is satisfied. Through implementation and a set of detailed experiments, we show that our proposed method considerably outperforms other methods

XQuery Streaming by Forest Transducers

Streaming of XML transformations is a challenging task and only very few systems support streaming. Research approaches generally define custom fragments of XQuery and XPath that are amenable to streaming, and then design custom algorithms for each fragment. These languages have several shortcomings. Here we take a more principles approach to the problem of streaming XQuery-based transformations. We start with an elegant transducer model for which many static analysis problems are well-understood: the Macro Forest Transducer (MFT). We show that a large fragment of XQuery can be translated into MFTs --- indeed, a fragment of XQuery, that can express important features that are missing from other XQuery stream engines, such as GCX: our fragment of XQuery supports XPath predicates and let-statements. We then rely on a streaming execution engine for MFTs, one which uses a well-founded set of optimizations from functional programming, such as strictness analysis and deforestation. Our prototype achieves time and memory efficiency comparable to the fastest known engine for XQuery streaming, GCX. This is surprising because our engine relies on the OCaml built in garbage collector and does not use any specialized buffer management, while GCX's efficiency is due to clever and explicit buffer management.Comment: Full version of the paper in the Proceedings of the 30th IEEE International Conference on Data Engineering (ICDE 2014

XPath: Looking Forward

The location path language XPath is of particular importance for XML applications since it is a core component of many XML processing standards such as XSLT or XQuery. In this paper, based on axis symmetry of XPath, equivalences of XPath 1.0 location paths involving reverse axes, such as anc and prec, are established. These equivalences are used as rewriting rules in an algorithm for transforming location paths with reverse axes into equivalent reverse-axis-free ones. Location paths without reverse axes, as generated by the presented rewriting algorithm, enable efficient SAX-like streamed data processing of XPath

Logics for Unranked Trees: An Overview

Labeled unranked trees are used as a model of XML documents, and logical languages for them have been studied actively over the past several years. Such logics have different purposes: some are better suited for extracting data, some for expressing navigational properties, and some make it easy to relate complex properties of trees to the existence of tree automata for those properties. Furthermore, logics differ significantly in their model-checking properties, their automata models, and their behavior on ordered and unordered trees. In this paper we present a survey of logics for unranked trees

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

AT-GIS: highly parallel spatial query processing with associative transducers

Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3× the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10× for aggregation queries