Dynamic replication of web contents

2007 ◽  
Vol 50 (6) ◽  
pp. 811-830 ◽  
Author(s):  
Amjad Mahmood
Keyword(s):  
Dynamic Replication ◽  
Web Contents

Coarse-grain replica management strategies for dynamic replication of Web contents

2004 ◽  
Vol 45 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Norihito Fujita ◽  
Yuichi Ishikawa ◽  
Atsushi Iwata ◽  
Rauf Izmailov
Keyword(s):  
Management Strategies ◽  
Coarse Grain ◽  
Dynamic Replication ◽  
Web Contents ◽  
Replica Management

scholarly journals State Management for Cloud-Native Applications

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 423
Author(s):  
Márk Szalay ◽  
Péter Mátray ◽  
László Toka
Keyword(s):  
Large Scale ◽  
Distributed Databases ◽  
Access Time ◽  
Replica Placement ◽  
State Management ◽  
Placement Decisions ◽  
Dynamic Replication ◽  
Cloud Databases ◽  
Placement Algorithm ◽  
The One

The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.

Dynamic replication algorithms for the multi-tier Data Grid

2005 ◽  
Vol 21 (5) ◽  
pp. 775-790 ◽  
Author(s):  
Ming Tang ◽  
Bu-Sung Lee ◽  
Chai-Kiat Yeo ◽  
Xueyan Tang
Keyword(s):  
Data Grid ◽  
Dynamic Replication

Pundit: augmenting web contents with semantics

2013 ◽  
Vol 28 (4) ◽  
pp. 640-659 ◽  
Author(s):  
M. Grassi ◽  
C. Morbidoni ◽  
M. Nucci ◽  
S. Fonda ◽  
F. Piazza
Keyword(s):  
Web Contents

Research on Efficient Spatial Keyword Queries Supporting Wildcard

2013 ◽  
Vol 427-429 ◽  
pp. 2554-2557
Author(s):  
Jin Kun Pan ◽  
Dong Sheng Li
Keyword(s):  
Bloom Filter ◽  
Location Based Services ◽  
High Time ◽  
Exact Match ◽  
Inverted File ◽  
Space Efficiency ◽  
Integrated Index ◽  
Web Contents ◽  
High Space ◽  
Realistic Scenes

With the popularity of location-based services, Web contents are being geo-tagged and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. The existing spatial keyword queries focus on exact match or prefix match of the keywords cannot satisfy the demand of wildcard based imprecise match in many realistic scenes. Aiming to solve this problem, two methods which are fit for different situation are put forward: the inverted file and R-tree integrated index which fits for the situation that requires high time efficiency and the Prefix Bloom Filter and R-tree integrated index which fits for the situation requiring high space efficiency. The effectiveness of the two indexes is valid through experiments.

Influence of COVID-19 Epidemic on Dark Web Contents

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Bakhytzhan Valiyev ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Saule Amanzholova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Technical Progress ◽  
Confidential Information ◽  
Web Contents ◽  
Global Events ◽  
Dark Web

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

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