Comparative Analysis of Relational Keyword search Systems

Today with the growth of the Internet, there has been a big growth in the number of users who want to access information without having a detailed knowledge of the query languages; even simple query languages are designed for them that are too complicated for people who dont have sufficient knowledge of language. A large number of methods and prototypes also proposed and implemented, but, there remains a several limitations. So that in this paper, we are overcoming the limitations of previous methods. In literature review indicating that existing systems are using document order so that they are not providing better ranking of keywords. In this paper we are using Top-K based algorithm, ranking function and presenting evaluation of performance of relational keyword search systems. top-k query processing provides highest ranked search results

Blogs Search Engine Adopting RSS Syndication Using Fuzzy Logic

The rapid development of Internet increases the writers of blog sites. Sometimes these blog sites focused on solving some important problems. To find specific blogs are hard problem for the users because a lot of these blogs contain unuseful information such as online advertisements, notice and noise which minimize the rank of blog site. Furthermore to retrieve more relevant blogs is another problem which lowering the search performance. This study proposes blogs search engine adopting RSS syndication using Fuzzy logic. The blogs search engine consists of three main phases which are crawling using RSS feeds algorithm, indexing weblogs algorithm and searching technique with Fuzzy logic. In RSS crawling process RSS feeds need to be gathered to extract useful information such as title, links, publish time and description. Indexing weblogs use the links to retrieve the blogs sites for text processing and construct indexing database. In order to retrieve such information needed by any user, there is user interface to search for keyword with importance degree and compute the density of keyword from the indexing database. The rank of the pages is computed based on fuzzy weighted average value. A prototype is built using visual basic 2008 to validate the proposed blogs search engine. It is a windows application with http connection protocol. In system evaluation used two measurement performances which are precision and mean average precision. The parameters of precision determine based on respondents whom determine the total retrieved links and the total relevant links for the keyword search result. The number of keywords that used in testing system is five pairs keywords. The experimental results show that the mean average precision is 81.7% of the whole system performance. The percent of respondents is 80% who knows and uses the blogs and 20% don’t have knowledge. The execution time of the system based on respondents is 70% between 3-5 minute and 30% less than 3 minute. This percentage is good considering the rate of satisfaction for system is 80% satisfied and 20% strongly satisfied

Interactive Fuzzy based Search over XML Data for Optimized Performance

Abstract -In a traditional keyword-search system over XML data, a user composes a keyword query, submits it to the system, and retrieves relevant answers. In the case where the user has limited knowledge about the data, often the user feels "left in the dark" when issuing queries, and has to use a try-and-see approach for finding information. In this paper we study, TASX -Type-Ahead Search in XML data, a new information-access paradigm in which the system searches XML data on the fly as the user types in query keywords. It allows users to explore data as they type, even in the presence of minor errors of their keywords. TASX provides friendly interface for users to explore XML data and can save users typing effort

Value of Parsimonious Nutritional Information, Consumer-oriented Foods Cluster, and Predicting Food Price

Abstract: This dissertation focuses on three topics that relate to consumer behavior and the food industry. The first chapter investigates consumers’ beliefs about the tastiness and healthfulness of 173 food items in a framed field experiment. Using data collected from 129 food shoppers in Grenoble France, demand models are estimated to determine how choices change with the provision of objective health information. We elicit and convey health information using simple nutritional indices meant to lower search and cognitive processing costs. The results indicate that consumers are willing to pay for tastier foods and for healthier foods, particularly if the consumers have objective information on nutrient content. The estimates suggest that the value of the type of nutritional information provided in the experiment is €0.98 per day. The second chapter investigates USA, China, and Korea consumers’ perceptions about the health, taste, and price of 60 different food items to determine country-specific food clusters before and after the provision of objective health information. Subsequent analysis relates cluster characteristics to purchase intentions. For Hedonic and Taste-oriented cluster products, Koreans’ purchase intentions rise if the products are perceived as expensive before the provision of information; however the purchase intention of Americans and Chinese is not affected by beliefs about affordability. These results could help retailers in each country identify appropriate food groupings, from the consumers’ perspective, to improve category management, marketing, and pricing. The last chapter explores whether unconventional consumer-oriented variables might be useful in predicting the Bureau of Labor Statistics (BLS) Food and Beverages Consumer Price Index (CPI). We determine the ability of an Internet search-based index related to food prices (the Google trends index) and a survey-based consumer sentiment index to predict changes in food-related BLS prices from January 2004 to July 2015. A vector autoregression (VAR) model has the best predictive performance with the moving window structure and a vector error correction model (VECM) performs best with the expanding window structure. Encompassing tests reveal that our model out-predicts USDA Economic Research Service food-related CPI forecasts.Agricultural Economic

Unlocking innovation potential : A typology of family business innovation postures and the critical role of the family system

How can family firms unlock their innovation potential? Despite the recent growth in research on family business innovation, existing literature has yielded controversial findings. Family firms are recognized as more conservative and steadfast to their tradition, however many of the most innovative firms worldwide are family businesses. This points to an apparent willingness-ability paradox in family business innovation. Drawing on family business innovation and family systems literature, we argue that family characteristics are an important yet overlooked driver of this paradoxical tension. We develop the construct of family business innovation posture, and identify a typology of four ideal types: Seasoner, Re-enactor, Digger, and Adventurer. Furthermore, we explore and illustrate with empirical data the necessary fit between the family business innovation posture and family-related dimensions to resolve the willingness-ability paradox. The article examines the implications of the typology for family business innovation research by exploring the effects of intra-family succession, outlining important directions for future research aimed at advancing current understanding of the role of the family in family business innovation, and providing practical insights for family business owners, managers, and consultants

Volume 30, Number 1

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