Real time business search largely has the involvement of Internet of Things which has now become a huge set of objects with large magnitude of intercommunication links and services. This scalability issue can be resolved by bringing in (SIoT) Social IoT i.e. an object looking for its social partners who will have similar set of rules to positively influence the performance of the service. In this work, we are putting forward the analysis of the impact of deep web on real time search. We also propose a novel approach by analyzing the shortcomings of the existing techniques. The novel idea is the distribution of web search engine to take care of sub parts of entity and sensor pages by distributing the search engine itself along with the index over multiple nodes. Deep websites are needed to be brought in for accurate results. This work offers better accuracy and a significant speed up for multiple query execution in distributed environment.

Analyzing the impact of deep web on real-time business search

User intention and nature of network plays a vital role towards the quality of response received as the result of any user query. Therefore, the need of system understanding the user's intent and network dynamism as well is highly apparent. The proposed query processing and analysing system (QPAS) for social networks is based on extracting user's intent from various social networks using existing NLP techniques. It fetches the information and further employs hybrid ensemble k-means hierarchical agglomerative clustering (HEKHAC) and modified Bitonic sort to improve the responses. The proposed approach offers an edge over other mechanisms as it not only retrieves more user-centric results as compared to traditional way of keyword-based searching but also in timely manner as well. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a noteworthy revolution scoring up to precision and recall respectively.

Design of a Novel Query System for Social Network

An efficient search engine needs to be designed in such a way that is able to provide relevant and accurate information in accordance with user needs and interests. The quality of downloaded records can be guaranteed only when website pages of high pertinence are downloaded by the crawlers in accordance with the current topics or user trends. Earlier Focused Crawlers were used to download topic specific pages but these crawlers were not able to adapt to the changing interest of the users. Therefore, there is a need to design crawlers that are able to naturally track the present pattern points and download site pages that meet client's present need. In this paper, a priority assigner and scheduler method for organizing Uniform Resource Locators (URLs) is being proposed that helps the crawler in tracking user's interest and prioritize downloading documents that are relevant to the user's choice in addition to current trends. The experimental results conforms that the proposed priority assigner and URL scheduler‐based crawling outshines conventional crawling strategies based on Change‐history or Site‐Map‐based methods in terms of quality of downloaded web pages and reducing network traffic over the Internet.

A novel user trend-based priority assigner and URL scheduler for dynamic incremental crawling

Characterising user demographics across social network.

The efforts are made to extract the most relevant results while searching for the specific query in huge web storage available in the WWW. Majority of the Web pages are written in such a way that the crawler finds it difficult to extract any specific domain. The concept of ontology is used to find domain specific results. The purpose of this work is to design a crawler which crawls through the domain specific web pages in the web according to the stated ontologies. To minimize the bias on accessing the highly relevant web links in a deep web, we are proposing an intelligent crawling mechanism. The intelligent crawler makes use of effective page ranking algorithm which leads the user to the most relevant results in a specific domain. The proposed Internet of Things (IoT) service will incorporate this mechanism to an effective extent.

An Approach to Design an IoT Service for Business—Domain Specific Web Search

The TTS technology helps easy learning of the written material as it comes in audio form. TTS technique comprises of getting the particular text as an input then converts the graphemes into the phonemes and finally converting those Phonemes into actual speech. Graphemes are the actual text. Phonemes are the smallest meaningful sound element of the language. There are numerous approaches and algorithms available for TTS conversion. Also there are number of automated tools in the market for the TTS conversion of any written material. This work puts forth the various research papers and the novel approach regarding the TTS technology. The basic idea behind the proposed tool, “MathsSays”, is helping legally blind students, also the normal users; understand mathematics better via audio form. The written mathematics material is scanned as an image. Then that image is processed for the text detection. The detected text is then extracted from that image. The extracted text will be provided to the TTS conversion component and as a result, the audio file will be generated which speaks the text in that image. The challenge in this work is how to tackle the formulae which are not in proper English form. For this obstacle, the table will be maintained which has each formula and its respective text. Whenever the formula is encountered, the same will be searched in the table and the respective text will be extracted and spoken out loud. In this way, this system works for the betterment of understanding the mathematics.

Sachin Kulkarni

Papers

An Approach to Design an IoT Service for Business—Domain Specific Web Search

Analyzing the impact of deep web on real-time business search

Text-to-speech conversion for mathematical data