Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources

Author(s):  
Tantan Liu ◽  
Fan Wang ◽  
Gagan Agrawal
Keyword(s):  
2016 ◽  
Vol 1 (1) ◽  
pp. 40-44
Author(s):  
Suchetadevi M. Gaikwad ◽  
Sanjay B. Thakare

As deep web enlarges; there has been increased interest in methods which help efficiently trace deep-web interfaces. However, because of huge volume and varying nature of deep-web, achieving wide coverage and high efficiency is difficult issue. We proposed a three stage framework, an Enhanced Crawler, for efficiently gathering deep web interfaces. In first stage, enhanced crawler performs site based searching of center pages using automated search engines, avoiding visiting an oversized variety of pages and consuming time. In second stage, enhanced crawler achieves quick in site browsing by fetching most relevant links with associate degree of reconciling link ranking. For further enhancement, our system ranks and priorities websites and also uses a link tree data structure to achieve deep coverage. In third stage, our system provides pre-query processing mechanism so as to help users to write their search query easily by providing char by char keyword search with ranked indexing.


1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock

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.


Altamira CUT ◽  
2015 ◽  
Vol 3 (11) ◽  
pp. 34-43
Author(s):  
Jessica A. Arellano Delgado

2017 ◽  
Vol 10 (2) ◽  
Author(s):  
Shipra Jain ◽  
Ekata Gupta
Keyword(s):  

2019 ◽  
Vol 118 (1) ◽  
pp. 36-41
Author(s):  
Jung-Woo Lee ◽  
Seung-Cheon Kim ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim

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.


2019 ◽  
Vol 118 (8) ◽  
pp. 308-314
Author(s):  
Jung-Woo Lee ◽  
Seung- Cheon ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim

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.


2020 ◽  
Author(s):  
Tom Joseph Barry ◽  
David John Hallford ◽  
Keisuke Takano

Decades of research has examined the difficulty that people with psychiatric diagnoses, such as Major Depressive Disorder, Schizophrenia Spectrum Disorders, and Posttraumatic Stress Disorder, have in recalling specific autobiographical memories from events that lasted less than a day. Instead, they seem to retrieve general events that have occurred many times or which occurred over longer periods of time, termed overgeneral memory. We present the first transdiagnostic meta-analysis of memory specificity/overgenerality, and the first meta-regression of proposed causal mechanisms. A keyword search of Embase, PsycARTICLES and PsycINFO databases yielded 74 studies that compared people with and without psychiatric diagnoses on the retrieval of specific (k = 85) or general memories (k = 56). Multi-level meta-analysis confirmed that people with psychiatric diagnoses typically recall fewer specific (g = -0.864, 95% CI[-1.030, -0.698]) and more general (g = .712, 95% CI[0.524, 0.900]) memories than diagnoses-free people. The size of these effects did not differ between diagnostic groups. There were no consistent moderators; effect sizes were not explained by methodological factors such as cue valence, or demographic variables such as participants’ age. There was also no support for the contribution of underlying processes that are thought to be involved in specific/general memory retrieval (e.g., rumination). Our findings confirm that deficits in autobiographical memory retrieval are a transdiagnostic factor associated with a broad range of psychiatric problems, but future research should explore novel causal mechanisms such as encoding deficits and the social processes involved in memory sharing and rehearsal.


2018 ◽  
pp. 48
Author(s):  
Israa Tahseen ◽  
Duaa Salim
Keyword(s):  
Deep Web ◽  

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