Large XML Files Research Articles

Towards efficient XML parsing through minimization of JVM parameter space

A significant increase in the usage of Extensible Markup Language (XML) data for various protocols and standards emphasizes the development of efficient XML parsers. For the Java language, the XML DOM parser despite performing in-memory operations is unable to achieve peak execution performance on modern systems, especially for parsing large XML files. The issue of inefficient execution may be mitigated by selecting appropriate runtime parameters for the Java Virtual Machine (JVM). This entails to exploring parameter space in an exhaustive manner that is not practically feasible for rapid application development. This paper aims at performance enhancement of XML parsing through selection of optimal set of JVM runtime parameters. The proposed approach works independent of parser design. It reduces JVM parameter space through machine learning-based models which are trained using profile data. The impact of parameters is determined using linear regression and artificial neural network-based models. The subsequent computation of a location-based weight vector along with a threshold value for filtration of parameters generates a set of optimal parameters for performance enhancement. The XML parsing code using the optimal parameters achieves average speedups of 13.18% and 21.42% over the standard code on Intel Xeon and Intel Core i7-based systems, respectively.

Parallel labeling of massive XML data with MapReduce

The volume of XML data has become enormous and still grows very quickly as many data have been typed in XML by virtue of its simplicity and extensibility. While a tree labeling algorithm has a crucial role in XML query processing, conventional algorithms are all sequential so that they fail to label a large volume of XML data in a timely manner. To address this issue, we devise parallel tree labeling algorithms for massive XML data. Specifically, we focus on how to efficiently label a single large XML file in parallel. We first propose parallel versions of two prominent tree labeling schemes based on the MapReduce framework. We then present techniques for runtime workload balancing and data repartition to solve performance issues caused by data skewness and MapReduce's inherited limitation. Through extensive experiments with synthetic and real-world datasets on 15 nodes, we show that our parallel labeling algorithms are up to 17 times faster than conventional algorithms, providing strong durability against data skewness.

A change detection system for unordered XML data using a relational model

The dramatic increase in the evolution of XML data available on the Internet requires a change detection system to keep track of important changes occurring during their life time. In this paper, we introduce a novel approach of detecting changes between two versions of unordered XML data stored in a traditional relational database using approaches like XRel. Most of the existing work in the area of XML change detection is mainly focused on detecting changes between two versions of XML data by constructing their Document Object Model (DOM) trees and then comparing these two tree structures based on Longest Common Sequence (LCS) using minimum edit distances. The basic tree comparison approach is not efficient in handling large XML files due to the fact that (1) an equivalent XML DOM tree will be twice as large as the original document and (2) the entire trees of both versions have to be memory resident during the comparison process. These two issues are constrained by the available main memory. In addition, existing approaches fail to detect changes among versions of XML data stored in relational databases as reverse mapping is not loss-less. We propose an efficient algorithm (XRel_Change_SQL) for detecting unordered changes between two XML data files stored in XRel as the underlying relational data model, using Structured Query Language (SQL). We compare the efficiency and quality of our change detection algorithm with existing XML change detection tools like X-Diff, DeltaXML and XANDY. We provide an experimental evaluation of the results obtained from the benchmark datasets as well as some synthetic datasets to show that our approach is highly scalable, and results in a much better efficiency and delta quality than the aforementioned approaches and tools.

A distributed placement service for graph-structured and tree-structured data

Effective data placement strategies can enhance the performance of data-intensive applications implemented on high end computing clusters. Such strategies can have a significant impact in localizing the computation, in minimizing synchronization (communication) costs, in enhancing reliability (via strategic replication policies), and in ensuring a balanced workload or enhancing the available bandwidth from massive storage devices (e.g. disk arrays). Existing work has largely targeted the placement of relatively simple data types or entities (e.g. elements, vectors, sets, and arrays). Here we investigate several hash-based distributed data placement methods targeting tree- and graph- structured data, and develop a locality enhancing placement service for large cluster systems. Target applications include the placement of a single large graph (e.g. Web graph), a single large tree (e.g. large XML file), a forest of graphs or trees (e.g. XML database) and other specialized graph data types - bi-partite (query-click graphs), directed acyclic graphs etc. We empirically evaluate our service by demonstrating its use in improving mining executions for pattern discovery, nearest neighbor searching, graph computations, and applications that combine link and content analysis.

Defining Mapping Mashups with BioXMash

Summary We present a novel approach to XML data integration which allows a biologist to select data from a large XML file repository, add it to a genome map, and produce a mapping mashup showing integrated data in map context. This approach can be used to produce contextual views of arbitrary XML data which relates to objects shown on a map. A biologist using BioXMash searches in XML tags, and is guided by XML path data availability, shown as the number of values reachable via a path, in both global, genome-wide, and local, per-gene, context. Then she examines sample values in an area of interest on the map. If required, the resulting data is dumped to files, for subsequent analysis.This is a lightweight integration approach, and differs significantly from other known methods. It assumes that data integration can be performed on a lab computer with limited memory, with no database installation or programming knowledge. It is different from BioMarts which predefine possible data selections, in that arbitrary data sources related to map items can be used. BioXMash offers full textual search in XML paths, shows path statistics, and supports visual verification of data values. Repeated scanning of all XML files at query time is avoided by the use of a high level indexing technique. Our prototype demonstrates this new approach. It efficiently supports data browsing on 2 GB od data from GeneCards with an index size of 40 MB.