986 research outputs found
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
Sensitivity of Semantic Signatures in Text Mining
The rapid development of the Internet and the ability to store data relatively inexpensively has contributed to an information explosion that did not exist a few years ago. Just a few keystrokes on search engines on any given subject will provide more web pages than any time before. As the amount of data available to us is so overwhelming, the ability to extract relevant information from it remains a challenge.;Since 80% of the available data stored world wide is text, we need advanced techniques to process this textual data and extract useful in formation. Text mining is one such process to address the information explosion problem that employs techniques such as natural language processing, information retrieval, machine learning algorithms and knowledge management. In text mining, the subjected text undergoes a transformation where essential attributes of the text are derived. The attributes that form interesting patterns are chosen and machine learning algorithms are used to find similar patterns in desired corpora. At the end, the resulting texts are evaluated and interpreted.;In this thesis we develop a new framework for the text mining process. An investigator chooses target content from training files, which is captured in semantic signatures. Semantic signatures characterize the target content derived from training files that we are looking for in testing files (whose content is unknown). The semantic signatures work as attributes to fetch and/or categorize the target content from a test corpus. A proof of concept software package, consisting of tools that aid an investigator in mining text data, is developed using Visual studio, C# and .NET framework.;Choosing keywords plays a major role in designing semantic signatures; careful selection of keywords leads to a more accurate analysis, especially in English, which is sensitive to semantics. It is interesting to note that when words appear in different contexts they carry a different meaning. We have incorporated stemming within the framework and its effectiveness is demonstrated using a large corpus. We have conducted experiments to demonstrate the sensitivity of semantic signatures to subtle content differences between closely related documents. These experiments show that the newly developed framework can identify subtle semantic differences substantially
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
The Tree Inclusion Problem: In Linear Space and Faster
Given two rooted, ordered, and labeled trees and the tree inclusion
problem is to determine if can be obtained from by deleting nodes in
. This problem has recently been recognized as an important query primitive
in XML databases. Kilpel\"ainen and Mannila [\emph{SIAM J. Comput. 1995}]
presented the first polynomial time algorithm using quadratic time and space.
Since then several improved results have been obtained for special cases when
and have a small number of leaves or small depth. However, in the worst
case these algorithms still use quadratic time and space. Let , , and
denote the number of nodes, the number of leaves, and the %maximum depth
of a tree . In this paper we show that the tree inclusion
problem can be solved in space and time: O(\min(l_Pn_T, l_Pl_T\log
\log n_T + n_T, \frac{n_Pn_T}{\log n_T} + n_{T}\log n_{T})). This improves or
matches the best known time complexities while using only linear space instead
of quadratic. This is particularly important in practical applications, such as
XML databases, where the space is likely to be a bottleneck.Comment: Minor updates from last tim
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