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propose a """"Hybrid Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions"""" (""""IoT-SVK Search Engine"""" for short). The experimental results
The World Wide Web has immense resources for all kind of people for their specific needs. Searching on the Web using search engines such as Google, Bing, Ask have become an extremely common way of locating information. Searches are factorized by using either term or keyword sequentially or through short sentences. The
The World Wide Web has immense resources for all kind of people for their specific needs. Searching on the Web using search engines such as Google, Bing, Ask have become an extremely common way of locating information. Searches are factorized by using either term or keyword sequentially or through short sentences. The
The success of the search engine may be our Newtonian paradigm for the Web. It enables us to do so much information discovery that it is difficult to imagine what we cannot do with it.
issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has
The total information available on WWW (World Wide Web) is huge and is increasing at lightning speed. Existing web is dominated by Search Engines which are running on keyword based search system which in turn leads to wastage of end user's precious time if he do not know the key terms which are utilized to index
Keyword generation for search engine advertising is an important problem for sponsored search or paid-placement advertising. A recent strategy in this area is bidding on nonobvious yet relevant words, which are economically more viable. Targeting many such nonobvious words lowers the advertising cost, while delivering
A dynamic population model is proposed for the study of keyword auctions run on search engines. In this model bidders decide to join or leave the auction, depending on the results of the previous auction round. Through the use of a simulation scenario, we show that the model converges to a steady state quite fast, and
Result integrating is a key component for keyword querying across heterogeneous databases. Once the results from various search engines are collected, the search engine merges them into a single ranked list. In this paper, firstly, we present a novel model of searching, which the database is an undirected graph and
Keyword auctions were widely used by search engines to sell the advertisement on the result pages. To date, the most widely used auction mechanism is wGSP (weighted and generalized second-price). This paper presents an optimal pricing strategy under wGSP for advertiser to bid keywords for their websites. In brief, the
Keyword auctions are being used to sell the positions along the side of organic results shown by search engine when user types a keyword or a query related to keyword in a search engine. It has been a huge revenue generating arena for search engines since last decade. Irrespective of the great success of these types
error, find out the reason of this error, as well as the corresponding solution. Also, it requires to provide the location, phase, etc. of an error. For a multi-software integrated platform, this paper proposed a database design for error searching system based on keyword priority. This DB made the correspondence among
This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: 1) Identify the user search intention, i.e., identify the XML node types that user wants to
quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword and ”, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is
easy to bring the problem of topic excursion. Hits algorithm requires a number of pages as the basic-set for calculating and cannot be used in plain texts. This paper introduces a new algorithm: PK-TDC which makes use of the iterative idea of Hits. PK-TDC searches the authority pages and keywords on the topology of pages
suggest the ways that make and renew the ontology, which are related with the keywords that users input in the search engine, automatically for the automatic generation of ontology that is not limited by specific domain. Input keyword and relation keywords become OWL, and the relation among the created OWL is expressed by
Analyzing users' Web log data and extracting their interests of Web-watching behaviors are important and challenging research topics of Web usage mining. Users visit their favorite sites and sometimes search new sites by performing keyword search on search engines. Users' Web-watching behaviors can be regarded as a
determining how well the search engines meet the information need of users. In this paper we present our approach of search engine evaluation which is based upon page level keywords. Page level keywords are the keywords found in individual pages of a website. Page level keyword is an important factor to measure the relevancy of
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