Studies on the occurrence of enteric bacteria in the estuarine waters along the Mangalore coast

Occurrence of enteric bacteria in water, sediment and shellfishes of Mulki, Pavanje, Gurpur and Netravathi estuaries of the Mangalore coast is reported. 70 water samples, 71 sediment samples and 37 shellfish samples were analysed in 18 months. Total bacterial load in sediment and shellfishes was found to be more than that in water samples. The total bacterial load was not very high. However, enterococci, particularly coliforms in sediments, water and shellfishes were found to be quite high, indicative of faecal pollution. The incidence of Salmonella spp. was recorded in all the estuaries except the Mulki estuary

Web Caching and Prefetching with Cyclic Model Analysis of Web Object Sequences

Web caching is the process in which web objects are temporarily stored to reduce bandwidth consumption, server load and latency. Web prefetching is the process of fetching web objects from the server before they are actually requested by the client. Integration of caching and prefetching can be very beneficial as the two techniques can support each other. By implementing this integrated scheme in a client-side proxy, the perceived latency can be reduced for not one but many users. In this paper, we propose a new integrated caching and prefetching policy called the WCP-CMA which makes use of a profit-driven caching policy that takes into account the periodicity and cyclic behaviour of the web access sequences for deriving prefetching rules. Our experimental results have shown a 10%-15% increase in the hit ratios of the cached objects and 5%-10% decrease in delay compared to the existing schem

MOP: Predicting Multiple Output in Multi-Sharing System

Cloud computing is relatively advanced field in which we believe resource utilization hasn't yet been optimized to its complete potential and inaccuracy of prediction leads to several minutes of delays in instant resource allocation due to scarcity of resources in Multi-Sharing System. In this paper, we develop Extraction of Transaction Log Files to Predict Multiple Output (MOP) in Multi-Sharing System based on resource utilization for higher accuracy using prediction techniques Random Forest and majority voting algorithms. The goal is to gratify upcoming resource demands and to avoid over or under provisioning of resources. The accuracy results show that the proposed model provides higher accuracy in predicting resource utilization for upcoming resource demands and prediction cost and time are reduced

Personalized Recommendation Systems (PRES): A Comprehensive Study and Research Issues.

The type of information systems used to recommend items to the users are called Recommendation systems. The concept of recommendations was seen among cavemen, ants and other creatures too. Users often rely on opinion of their peers when looking for selecting something, this usual behavior of the humans, led to the development of recommendation systems. There exist various recommender systems for various areas. The existing recommendation systems use different approaches. The applications of recommendation systems are increasing with increased use of web based search for users’ specific requirements. Recommendation techniques are employed by general purpose websites such as google and yahoo based on browsing history and other information like user’s geographical locations, interests, behavior in the web, history of purchase and the way they entered the website. Document recommendation systems recommend documents depending on the similar search done previously by other users. Clickstream data which provides information like user behavior and the path the users take are captured and given as input to document recommendation system. Movie recommendation systems and music recommendation systems are other areas in use and being researched to improve. Social recommendation is gaining the momentum because of huge volume of data generated and diverse requirements of the users. Current web usage trends are forcing companies to continuously research for best ways to provide the users with the suitable information as per the need depending on the search and preferences. This paper

Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard

Similarity based dynamic web data extraction and integration system from search engine result pages for web content mining

There is an explosive growth of information in the World Wide Web thus posing a challenge to Web users to extract essential knowledge from the Web. Search engines help us to narrow down the search in the form of Search Engine Result Pages (SERP). Web Content Mining is one of the techniques that help users to extract useful information from these SERPs. In this paper, we propose two similarity based mechanisms; WDES, to extract desired SERPs and store them in the local depository for offline browsing and WDICS, to integrate the requested contents and enable the user to perform the intended analysis and extract the desired information. Our experimental results show that WDES and WDICS outperform DEPTA [1] in terms of Precision and Recall

Construction of Topic Directories Using Levenshtein Similarity Weight

Topic directories are search engines consisting of categories in hierarchical manner. Mapping of a new Web page to an appropriate category of a topic directory is one of the major challenges faced by human-based topic directories due to the rapid pace of growth of the WWW and also the presence of a large number of categories. So, the mapping of new pages onto categories by human experts is an expensive process. Hence, the automation of this process is needed and can be performed using standard similarity measures. In this chapter, we propose an algorithm called Mapping of Web Pages to Categories using Levenshtein Similarity Weight (MPC-LSW algorithm) that performs this mapping of Web pages to categories by comparing the similarity of the pages, ie a page-based comparison instead of the traditional term-based comparison. The time complexity of MPC-LSW is observed to be O (mk) as the terms are eliminated and processing is faster because pages are compressed into strings. Hence, it is an efficient method of mapping

Peer to Peer Communication Mechanism Across Web Browsers

Concerns over data ownership and misuse of personal data over the Web have become increasingly widespread in recent years; especially, as most web service providers are moving towards closed silo-based platforms, making the web more and more centralized. This is concerning, because, as service providers move towards centralized data storage and management, end-users become more susceptible to loss of data ownership and misuse of personal data. While in recent years, quite a few solutions have been proposed to solve these issues, the issues themselves still prevail, primarily due to lack of acceptance. That said, in this paper, we build on our previously proposed browser-based Peer-to-Peer Data Sharing Framework. We first explain the requirements and design choices which we had to keep in mind while designing the framework. And then, we provide insights into how we evaluated the functionalities and security features of the framework, through lab experiments. Finally, we elucidate the direction in which we would like to develop the framework in the near future

Carry Forward and Access Control for Unused Resources in Multi Sharing System of Hybrid Cloud

The cloud is a collection of resources like hardwares and softwares etc., and these resources are shared between public and private cloud for better resource utilization. The entire idea behind our model is to concentrate on how to magnify the resource usage on Multi sharing system with reservation policy plans in hybrid cloud environment and how to provide access control to Carry Forward of Unused Resources. In existing system, the cloud offers various types of reservation plans and on-demand plans with restrictions. Scope for either under or over provisioning of reserved resources was not taken care of. It was very much restricted to sharing of resources with multiple users. This was leading to an over and under provisioning of resources and no option was provided to carry forward the unused resources and there was no access control for their contribution of resources. So, to overcome with this problem “Multi

K Shreekrishna Kumar, P Deepa Shenoy, Venugopal KR, SS Iyengar and LM Patnaik. APST: Approximation and Prediction of Stock Time-Series Data using Pattern Sequence

Time series data is a sequence database which consists of sequence of values obtained over repeated measurements of time. Processing and forecasting huge time series data is a challenging task. This work is concerned with predicting the direction of change of stock price indices, using a new method called Approximation and Prediction of Stock Time-series data (APST) using similarity of Pattern Sequence. APST algorithm first performs data approximation by using the technique called Multilevel… Expan