Efficient Top-k Keyword Search on XML Streams

2008 ◽  
Author(s):  
Lingli Li ◽  
Hongzhi Wang ◽  
Jianzhong Li ◽  
Jizhou Luo
Keyword(s):  
Keyword Search ◽  
Xml Streams

Efficient Top-K Keyword Search on XML Streams

2012 ◽  
Vol 23 (6) ◽  
pp. 1561-1577
Author(s):  
Ling-Li LI ◽  
Hong-Zhi WANG ◽  
Hong GAO ◽  
Jian-Zhong LI
Keyword(s):  
Keyword Search ◽  
Xml Streams

Path-based keyword search over XML streams

2015 ◽  
Vol 11 (3) ◽  
pp. 347-369 ◽  
Author(s):  
Savong Bou ◽  
Toshiyuki Amagasa ◽  
Hiroyuki Kitagawa
Keyword(s):  
Keyword Search ◽  
Real Data ◽  
Experimental Results ◽  
Data Set ◽  
Content Type ◽  
Finite Automation ◽  
Powerful Means ◽  
Extensible Markup ◽  
Xml Streams ◽  
Better Than

Purpose – In purpose of this paper is to propose a novel scheme to process XPath-based keyword search over Extensible Markup Language (XML) streams, where one can specify query keywords and XPath-based filtering conditions at the same time. Experimental results prove that our proposed scheme can efficiently and practically process XPath-based keyword search over XML streams. Design/methodology/approach – To allow XPath-based keyword search over XML streams, it was attempted to integrate YFilter (Diao et al., 2003) with CKStream (Hummel et al., 2011). More precisely, the nondeterministic finite automation (NFA) of YFilter is extended so that keyword matching at text nodes is supported. Next, the stack data structure is modified by integrating set of NFA states in YFilter with bitmaps generated from set of keyword queries in CKStream. Findings – Extensive experiments were conducted using both synthetic and real data set to show the effectiveness of the proposed method. The experimental results showed that the accuracy of the proposed method was better than the baseline method (CKStream), while it consumed less memory. Moreover, the proposed scheme showed good scalability with respect to the number of queries. Originality/value – Due to the rapid diffusion of XML streams, the demand for querying such information is also growing. In such a situation, the ability to query by combining XPath and keyword search is important, because it is easy to use, but powerful means to query XML streams. However, none of existing works has addressed this issue. This work is to cope with this problem by combining an existing XPath-based YFilter and a keyword-search-based CKStream for XML streams to enable XPath-based keyword search.

Keyword Search in XML Streams

2013 ◽  
pp. 73-89
Author(s):  
Weidong Yang ◽  
Hao Zhu
Keyword(s):  
System Architecture ◽  
Keyword Search ◽  
Query Languages ◽  
Filter System ◽  
Section 8 ◽  
Lowest Common Ancestor ◽  
Xml Stream ◽  
Keyword Searching ◽  
Processing Techniques ◽  
Xml Streams

Most existing XML stream processing techniques adopt full structured query languages such as XPath or XQuery, which are difficult for ordinary users to learn and use. This chapter presents an XML stream filter system called XKFitler, which uses keyword to filter XML streams. In XKFitler, we use the concepts of XLCA (eXclusive Lowest Common Ancestor) and XLCA Connecting Tree (XLCACT) to define the search semantic and results of keywords, and present an approach to filter XML stream according to keywords. In section 1, the background of keyword search in XML streams is introduced. Section 2 explains the searching results. In section 3, a stack-based keyword searching algorithm for XML stream filtering without schemas is presented in-depth. Section 4 presents a keyword search over XML streams by using schema information. The system architecture of XKFilter is described in section 5. Section 6 is the experiments to show the performance. Section 7 discusses the related work. Section 8 is the summaries of this chapter.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

A study of Leakage Ratio for increasing performance of online keyword search advertising

2019 ◽  
Vol 118 (1) ◽  
pp. 36-41
Author(s):  
Jung-Woo Lee ◽  
Seung-Cheon Kim ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim
Keyword(s):  
Online Shopping ◽  
Keyword Search ◽  
Online Advertising ◽  
The Internet ◽  
Shopping Malls ◽  
Portal Site ◽  
Internet Portal ◽  
Search Advertising ◽  
Large Size ◽  
Low Efficiency

Background/Objectives: In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement. Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.

A study of Leakage Ratio for increasing performance of online keyword search advertising

2019 ◽  
Vol 118 (8) ◽  
pp. 308-314
Author(s):  
Jung-Woo Lee ◽  
Seung- Cheon ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim
Keyword(s):  
Online Shopping ◽  
Keyword Search ◽  
Online Advertising ◽  
The Internet ◽  
Shopping Malls ◽  
Portal Site ◽  
Internet Portal ◽  
Search Advertising ◽  
Large Size ◽  
Low Efficiency

In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement. Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.

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