The exponential growth of Web services and Web-based applications has led to an enormous volume of data, providing a rich source for mining valuable insights. Web mining differs from traditional data mining due to the unique nature of the data it handles. Web data exists in diverse forms, including web server logs, news pages, and hyperlinks. As the usage of the internet continues to surge, web mining has become essential to extract meaningful information and patterns from these varied data sources. Traditional data mining methods may not be directly applicable to web data due to its unstructured and heterogeneous nature. Web server logs contain valuable information about user interactions, click-streams, and user preferences, which can be mined to understand user behavior and improve website performance. News pages and other forms of web content are valuable sources for sentiment analysis, topic modeling, and information retrieval, helping businesses and researchers gain insights into public opinions and trends. Additionally, web structure mining deals with the analysis of hyperlinks, enabling the discovery of relationships between web pages and identifying authoritative sources. The continuous growth of web-based data necessitates the use of specialized methods in web mining to effectively extract knowledge and valuable patterns. Researchers and practitioners in this field are constantly exploring innovative techniques to make sense of the vast amount of data available on the World Wide Web. The paper provides web mining techniques on web data and presenting the latest advancements, researchers and practitioners can gain insights into the state of the field and identify potential areas for further exploration. This paper also reports the comparisons and summary of various methods of web data mining with applications, which gives the overview of development in research and some importantresearch issues.
Now a day, the latest digital technologies are involved in agriculture field i.e. Big Data. Big Data plays a crucial role in the advancement of smart farming by boosting the productivity of individual farms and removing the risk of a global food crisis by collection and analysis process of Big Data. With the increasing global population and the growing demand for sustainable food production, the agriculture industry leaders and policymakers faces numerous challenges. Fortunately, advancements in technology, particularly in the field of big data analytics, have paved the way for innovative solutions in agriculture, such as smart farming. Smart farming leverages big data to optimize agriculture farming practices i.e. irrigation, fertilization, pest management and crop selection, helps in making real time decisions, improve efficiency, improve operations, boost productivity and increase yields while minimizing resource consumption and environmental impact (such as weather, soil, diseases). Big Data’s help to farmers is by suggesting pesticides the quantity they could use. Hence there arises the need for advanced practical and systematic strategies to correlate the different factors driving the agriculture to derive valuable information out of it. The Big Data has power to develop technologies to achieve the aim of sustainable and smart agriculture with smart farming to enhanced precision farming, predictive analytics, and real time monitoring in agriculture. Smart farming involves the collection and sharing of sensitive information, ranging from crop yields and livestock health to financial data. Safeguarding this data from unauthorized access and maintaining privacy while still allowing for valuable analytics poses a complex ethical and legal dilemma. This digital revolution in agriculture is very promising and will enable the agriculture sector to move to the next level of farm productivity and profitability. This transformation process is not reversible and poised to revolutionize both agriculture and food sector.
Bacterial endosymbionts are well characterized for plant growth promotion. In this study, the root, nodules, and stem of the Cicer arietinum crop planted in a semi-arid zone were used as a source to isolate potential plant growth bacteria. The ability to grow under salt stress was determined, and the potential isolate was screened for plant growth promotion traits. The selected isolate was identified by the 16S rDNA method. Pot trials were conducted to know the ability of the isolate to promote plant growth in-vivo. Among various isolates obtained, a bacterial isolate obtained from root showed the ability to grow in the presence of 10 % Sodium fluoride (NaF). The isolate produced Indole Acetic acid in an amount of 72 mg per liter in production medium. The bacteria solubilized phosphate and produce exopolysaccharide (2.12 g per liter). The isolate was identified as Pseudomonas sihuiensis. The result of pot trials reveals that the endophyte promotes plant growth under stress conditions and may be used as a bio-fertilizer.
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