PhD Guidance in Web Mining

PhD Guidance in Web Mining

     PhD Guidance in Web mining is our service provider to offer complete guidance on data mining, cloud computing and also other research areas of web mining. Web mining is defined as discovering patterns or extracts knowledge from the world wide semantic and social web”. Types of web mining are web content mining, web structure mining, web usage mining.   Our guidance in web mining providing specific research proposal, customized thesis, and other research-oriented support for PhD students in all streams across the globe.

We also prepare synopsis thesis, research paper writing, and conference paper writing, and its publications. We also focused on various research aspects that deliver clear and original solutions for our PhD doctoral students. We work until their PhD degree completion also with complete satisfaction. 

Gets instant quality of research results for your PhD………

PhD Guidance in Web Mining Online HelpFuture Trends in Web-Mining

  • Human evaluation of patterns discover supports also from data
  • Multiple criteria perspectives in learning and also data mining
  • Integrated classifiers also for complex learning problems
  • Online recommendation construction and also evaluation
  • Difficult data mining also from aspects of data difficulty:

                      -Time Changing

                      -Class imbalanced

                      -Partially labeled


                      -Graphical or semi structured data

Guidance in Web mining

     PhD Guidance in Web mining plays a vital role regarding our PhD aspirant’s scope and completion of their research work.  We offer a well-defined and well-structured research proposal to PhD students for the first step of research acceptance from their affiliated university/institution. It also provides a clear direction towards the successful completion. Our greatest thing is always to try one more time as we follow a certain unique way to succeedIf you want to achieve success, then don’t wait; your dream is coming from us the right way at the right time.

Let’s introduce very recent research areas in web mining,

Research Areas in Web Mining

Web Mining for Semantic Web:
  • Semantic web applications usability
  • Big data application and mashup engineering
  • Mining, analysis and also visualization of linked data
  • Semantic web in cloud computing
  • Management and also creation of data vocabularies linking
  • Data trustworthiness and data quality
  • Semantic web applications building frameworks
  • Consumption frameworks and linked data publishing
  • Data linking and software development lifecycle artifacts
  • Parallel software methodologies
  • Web scale information systems data engineering
  • Mobile semantic web applications
  • Semantic-web stream computation
  • Semantically also based social platforms:





                    -Microblogs etc.

Web Mining for WoT (Web of Things):
  • Social web WoT
  • Context recognition methods
  • Design architectures, methods and computation models
  • Use cases, applications and also experience with WoT
  • Cloud platforms and services in WoT
  • Exploiting and integrating novel social data from WoT devices
  • Access control, security and also privacy on the Web
  • Web based discovery, composition, search and also physical mash-ups
Web Mining for Social Web:
  • Link prediction and community discovery
  • Adaptation and also content customization on/using social web
  • Social web multilingual aspects
  • Security, privacy, repudiation and also trust management on the social web
  • Social web based query languages
  • Social-applications of the enterprise environments
  • Social applications also using mobile and semantic web
  • Social-web applications of the interoperability
  • Social web mining and search
  • Recommender systems and also user generated content
  • Social web systems design, and also computational models

Development Tools and Software’s

  • Web Miner
  • Webalizer
  • Bixo
  • Piwik
  • DEiXTo
  • W3perl
  • Pattern
  • R-Studio
  • Scrapy
  • Oracle Data Mining
  • Web harvest
  • Tableau
  • ANGOSS knowledge web miner

Purpose of Tools and Software’s

  • Web Miner:  A powerful crawling application that also extracts all sorts of data from many websites
  • Webalizer: Web applications also used to generates web pages of analysis from usage and access logs
  • Bixo: Open source and also free web mining toolkit that runs a series of cascading pipes on top of Hadoop
  • Piwik: Free and also Open source web analytics application run on a MySql /PHP web server
  • DEiXTo: A powerful web data extraction tool which also based on Document object model W3C schools
  • W3perl: Log files analyzer which is free and also open source software. It can parse FTP/Web/CUPS/DHCP/SSH/and squid log files. 
  • Pattern: Module for web mining that also written in python programing language
  • R-Studio: R programming language also for graphics and statistical computing.
  • Scrapy: Collaborative and also open source framework used for data extraction from web sites
  • Oracle Data Mining: Oracle based data mining software which is implemented in the oracle database kernel and also mining models
  • Web harvest: Tool to extract web related data also from web pages  
  • Tableau: Set of interactive data visualization products that also used for business intelligence.
  • ANGOSS knowledge web miner:  Software that integrated with ANGOSS Knowledge STUDIO with web mining algorithms for Acxiom data network, clickstream analysis and also interfaces to web log reporting tools

Major Research Topics in Web Mining

  • Web service discovery
  • Recommendation system
  • Data cleaning
  • Ontology
  • Data transformation
  • Limited interoperability
  • Duplicate elimination
  • Query processing
  • Cluster quality improvement
  • Pattern extraction
  • Personalization
  • Exact user identification
  • Privacy and also security issues
  • Keyword also based quality search
  • Deep-web extraction
  • Semantics based query
  • Feedback extraction from human activities
  • Constructed and also self-organized directories
  • Large scale web datasets
  • Single server mining