Energy-Efficient XML Stream Processing through Element-Skipping Parsing

2013 ◽  
Author(s):  
Toshiyuki Amagasa ◽  
Mana Seino ◽  
Hiroyuki Kitagawa
Keyword(s):  
Energy Efficient ◽  
Stream Processing ◽  
Xml Stream

Foundation of XML Stream Processing Techniques

2013 ◽  
pp. 48-59
Author(s):  
Weidong Yang ◽  
Hao Zhu
Keyword(s):  
Optimization Technique ◽  
Stream Processing ◽  
Research Community ◽  
Processing Methods ◽  
Xml Data ◽  
Research Status ◽  
Basic Concepts ◽  
Xml Stream ◽  
Processing Techniques ◽  
Area Section

The problem of processing streaming XML data is gaining widespread attention from the research community, and various XML stream processing methods are put forward, including automaton-based methods, index-based methods, and so forth. In this chapter, the basic concepts and several existing typical approaches of XML stream processing are discussed. Section 1 introduces the background and current research status of this area. Section 2 focuses on the discussion of automaton-based methods, for example, X/YFilter, XPush, et cetera. In section 3, the index-based methods are given. In section 4, other methods such us Fist and XTrie are discussed briefly. Section 4 discusses some optimization technique of XML stream processing. Section 5 summarizes this chapter.

XML Stream Query Processing

2010 ◽  
pp. 89-107
Author(s):  
Mingzhu Wei ◽  
Ming Li ◽  
Elke A. Rundensteiner ◽  
Murali Mani ◽  
Hong Su
Keyword(s):  
Query Processing ◽  
Input Data ◽  
Stream Processing ◽  
Query Execution ◽  
Data Semantics ◽  
Stream Query Processing ◽  
Xml Stream ◽  
And Algebra ◽  
Xml Streams ◽  
Compare And Contrast

Stream applications bring the challenge of efficiently processing queries on sequentially accessible XML data streams. In this chapter, the authors study the current techniques and open challenges of XML stream processing. Firstly, they examine the input data semantics in XML streams and introduce the state-of-the-art of XML stream processing. Secondly, they compare and contrast the automatonbased and algebra-based techniques used in XML stream query execution. Thirdly, they study different optimization strategies that have been investigated for XML stream processing – in particular, they discuss cost-based optimization as well as schema-based optimization strategies. Lastly but not least, the authors list several key open challenges in XML stream processing.

Automaton meets algebra: A hybrid paradigm for XML stream processing

2006 ◽  
Vol 59 (3) ◽  
pp. 576-602 ◽  
Author(s):  
Hong Su ◽  
Elke A. Rundensteiner ◽  
Murali Mani
Keyword(s):  
Stream Processing ◽  
Xml Stream

scholarly journals XKFitler

2011 ◽  
Vol 1 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Weidong Yang ◽  
Fei Fang ◽  
Nan Li ◽  
Zhongyu (Joan) Lu
Keyword(s):  
Common Ancestor ◽  
Keyword Search ◽  
Stream Processing ◽  
Query Languages ◽  
Information Discovery ◽  
Text Documents ◽  
Filter System ◽  
Lowest Common Ancestor ◽  
Xml Stream ◽  
User Friendly

Most existing XML stream processing systems adopt full structured query languages, such as XPath or XQuery, but they are difficult for ordinary users to learn and use. Keyword search is a user-friendly information discovery technique that has been extensively studied for text documents. This paper presents an XML stream filter system called XKFitler, which is the first system for supporting keyword search over XML stream. In XKFitler, the concepts of XLCA (eXclusive Lowest Common Ancestor) and XLCA Connecting Tree (XLCACT) are used to define the search semantic and results of keywords, and present an approach to filter XML stream according to keywords. The prototype XKFilter is implemented in the experiments.

XML Stream Processing

2010 ◽  
pp. 184-206
Author(s):  
Junichi Tatemura
Keyword(s):  
Query Processing ◽  
Time Complexity ◽  
Stream Processing ◽  
Query Evaluation ◽  
Streaming Algorithms ◽  
Buffer Memory ◽  
Recent Advances ◽  
Xml Stream ◽  
Multiple Query ◽  
Partial Query

This chapter reviews recent advances on stream XML query evaluation algorithms with stack-based encoding of intermediary data. Originally proposed for disk-resident XML, the stack-based architecture has been extended for streaming algorithms for both single and multiple query processing, ranging from XPath filtering to more complex XQuery. The key benefit of the stack-based architecture is its succinct encoding of partial query results, which can cause exponential enumeration if encoded naively. In addition, the chapter discusses opportunities to integrate benefits demonstrated in the reviewed work. For single-query processing, a sketch is given for an integrated algorithm, StreamTwig2Stack, that achieves all the benefits of existing algorithms in terms of functionality, time complexity, and buffer memory optimality.

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