Top PDF A Search Engine for Finding and Reusing Architecturally Significant Code

A Search Engine for Finding and Reusing Architecturally Significant Code

A Search Engine for Finding and Reusing Architecturally Significant Code

In this work, we use one of the most high-performance, scalable, full-featured, open- source inverted index libraries named Apache Lucene [8]. Apache Lucene is a search library written in Java. Its popular in both academic and commercial settings due to its performance, configurability, and generous licensing terms. Lucene index, as shown in Figure 2.1 is a set of documents that are to be searched. The index may be composed of multiple sub-indexes, or segments. Each segment is a fully independent index, which could be searched separately. A document is essentially a collection of fields. A field consists of a field name that is a string, and one or more field values. Fields are constrained to store only one kind of data, binary, numeric, or text data. There are two ways to store text data: string fields store the entire item as one string; text fields store the data as a series of tokens,the text is broken up into terms at index time. Lucene provides many ways to break a piece of text into tokens, as well as hooks that allow developers to write custom tokenizers. We built our search engine on top of Lucene index that contains all source files in our repository. In section 3.3 we discuss in detail how this index was built.
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Index Terms: Search Engine, Meta Search Engine, Directories search, Index Search, Crawler, World Wide Web, Precision.

Index Terms: Search Engine, Meta Search Engine, Directories search, Index Search, Crawler, World Wide Web, Precision.

In Present day internet are most power full network and it given a space for storing all type of importance information. If anybody, person or organization can be wanted to any type of information, data, news or other things he searches to WWW through search engine. World Wide Web is a storing to all type of data and Search engine given the searched item related list of link and person select the one link at a time. Search engine given the some importance link they related to search item and some link is not related to search item. In present day basically three type of search engine are used first is Index base search engines search second is Directories search engines and third is Meta search engine. Main aim of this research paper is finding on which search engine are given to best result and how many link are related to searched item. This paper help to they person or organization which are searching on data or information to all time because we try to calculation on performance of Index base search engines, Directories search engines and Meta search engine.
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Cloud based search engine

Cloud based search engine

Given that Cloudle needs to satisfy three types of requirements - functional, technical, and budgetary - sometimes, it may be difficult to find services that will exactly match these three types of requirements. Similarity reasoning is designed so that finding relevant alternatives for any service is made easier. For instance, if some exact matching service found by the user is beyond a consumer’s price ranging, other services that are similar to the user requirement within the limited price range may be suggested. An intuitive way to decide the degree of similarity between two concepts x and y is to determine how much x and y share in common. In similarity reasoning, the Agents determines the similarity between x and y by counting their common reachable nodes. A. Similarity reasoning
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The Role of Search Engine Optimization in Search Rankings

The Role of Search Engine Optimization in Search Rankings

It is not clear how the search engine should resolve this potential conflict between the two lists. Even though organic links bring in visitors, if the results are too satisfactory they do not click on the sponsored links. Furthermore, if advertisers receive enough visits through organic links, they potentially have lower willingness to pay for sponsored visits. Thus, one may argue that advertisers who spend resources on SEO, will spend less on sponsored links. We investigate this problem in Section 5 where we study the interaction between the SEO contest for organic links and the auction for sponsored links. We find that, surprisingly, SEO not only leads to higher traffic in many cases, but also increases search engine revenues under certain conditions. This follows from a more general finding that sponsored revenues are not necessarily hurt by better quality organic link. Indeed, when the best quality site acquires the top organic link, the second best site might pay a higher price for the sponsored link than in the reverse case. As part of the SEO profitability analysis we identify the exact conditions under which providing high quality organic links is profitable for the search engine.
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Search Engine Optimization

Search Engine Optimization

Sitemap displays the structure of the website in a hierarchical way. It should always make two sitemaps, one for users and other for search engines and make the sites easier to navigate. Sitemaps designed for visitors help visitors if they have problems finding the pages on a site

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Search Engine Strategies

Search Engine Strategies

simplicity led to a period where web masters looked for ways to “spam” key words into their sites to get ranked higher while search engines looked for ways to identify pages that were “spamming” key words and demoted their rank, de-listed them, or black balled them permanently. Common old-style techniques were based around finding places to stuff key words that would improve your ranking, but not make the page look terrible to the visitors. People began cramming key words into:

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A Bandwidth Friendly Search Engine

A Bandwidth Friendly Search Engine

The Internet has become a significant publication medium, it plays host to many millions of documents and images and is increasing in size all the time [1]. The massive size of the internet has created a market need for impressive navigational tools. The largest search engines currently cover less than half the cacheable contents of the Web [2], and are unable to accurately interpret the simple search terms preferred by users. Should search engine developers create bigger indexes to give better internet coverage, thereby increasing the numbers of spiders accessing and querying new sites ? We believe this would simply add to network congestion without benefit. It would merely increase the number of irrelevant search results the user is required to sort through, thereby increasing not only the spider traffic but the user traffic too. On the other hand, in order to provide better chances of finding material the user would find useful, the source database must clearly become more comprehensive.
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SpotWeb: Characterizing framework API usages through a code search engine

SpotWeb: Characterizing framework API usages through a code search engine

Abstract. The essentials of modern software development (such as low cost and high efficiency) demand software developers to make intensive reuse of the existing open source frameworks or libraries (generally re- ferred as frameworks) available on the web. However, developers often face challenges in reusing these frameworks due to several factors such as the complexity and lack of proper documentation. In this paper, we pro- pose a code-search-engine-based approach that tries to detect hotspots in a given framework; these hotspots are the APIs that are frequently reused. Hotspots can serve as starting points for developers in under- standing and reusing the given framework. Our approach also detects deadspots, which are the APIs that are rarely used. Deadspots serve as caveats for developers as there can be difficulties in finding related code examples and are generally less exercised compared to hotspots. We de- veloped a tool, called SpotWeb, for frameworks or libraries written in the Java programming language and used our tool to detect hotspots and deadspots of eight open source frameworks including JUnit, Log4j, Grappa, JGraphT, OpenJGraph, JUNG, BCEL, and Javassit.
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Boozy Categorizing Search Engine for Best Search

Boozy Categorizing Search Engine for Best Search

II. PROBLEM DEFINITION AND DESCRIPTION: The project developed to do search better.Spider can work only on particular port and indexing also not good.Reindexing is not possible.People in other languages cannot use this search engine since there is no multilingual support.

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A Search Engine Modification for Medical Content Search

A Search Engine Modification for Medical Content Search

After implementation and profiling if same contents are searched as shown in Figure 6, it is found that it provides much nearer results for the same page. This is due to profiling and modification of user query according to the selected profile. Additionally, result optimizer performs the pruning process of results where the duplicate links (results) and other contents are removed from the search results before it is mapped on page. After processing only 9 results out of 10 are found. Here one link is removed by the result optimizer and real nearly matched results are listed using a web page.
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Location Search Engine for Smartphones

Location Search Engine for Smartphones

SpyNB is prediction algorithm used for checking the query weather it is content or location concept.In PMSE’s client-server construction, PMSE clients are dependable for storing the user clickthroughs and ontologies copied from the PMSE server. Easy tasks, like updating clickthoughs and ontologies, making feature vectors, and showing reranked search results are controled by the PMSE clients with less computational power.

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Search Engine Optimization (Seo)

Search Engine Optimization (Seo)

We crawl any information through the site via internet. The main aim of digital marketing, i.e. to rank your site under guidance in search engine algorithms and more ranking factors followed. For gain profit and increment on the business, it is necessary to analytics the site performance. This is the main object of it, how to defense if manual team banned your site and virus attacked on the site.

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Finding Significant Correlates of Conscious Activity in Rhythmic EEG

Finding Significant Correlates of Conscious Activity in Rhythmic EEG

Presented procedure gives high-resolution and free-of-cross- terms estimates of the average time-frequency energy den- sity of event-related EEG, revealing the microstructure of rhythms. Time-frequency area of significant changes are as- sessed via objective statistical procedures. This allows for ex- ample to investigate the minimum number of repetitions re- quired to delineate the reactive rhythms. Application of this methodology may bring a significant improvement in basic research on the event-related changes of EEG rhythms, as well as “per subject” customization of the ERD/ERS-based BCI.
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An Efficient Video Search Engine

An Efficient Video Search Engine

In this context having an efficient content based video retrieval system is a must. In a content based video retrieval system some key frames from the video is to be extracted and within the video or within any database of other videos that key frame should be searched based on some robust feature extraction and feature matching techniques. Work has been done before for content based video retrieval . Yarmohammadi, H and others [1] uses the information theory based technique for content based video retrieval . The authors uses Shot Boundery Detection , Key Frame Extraction , and Video Indexing. Dyana, A and others [2] show that combining features for shape and motion trajectory of video objects works well for content based video retrieval . The authors also use CSS based shape representation and trajectory based motion representation. Previously Asha, S. and others [3] proposes SURF based content based video retrieval technique .B.V Patel [4] video retrieval of Near-Duplicates using K-NN retrieval of Spatio- Temporal descriptors describes a novel methodology for implementing video search functions such as retrieval of near-duplicate videos and recognition of actions in surveillance video.
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The Role of Search Engine Optimization in Search Marketing

The Role of Search Engine Optimization in Search Marketing

To approach the issue of diminished welfare from SEO, we first focus on the case where spon- sored links are not available to advertisers and consumers. This base model serves as a benchmark and gives us a deeper understanding of the nature of the competition for organic links when using SEO activities. Our first result reveals that SEO can be advantageous by improving the organic ranking. In the absence of sponsored links, this only happens when advertiser quality and valua- tion are positively correlated. That is, if sites’ valuations for consumers are correlated with their qualities then consumers are better off with some positive level of SEO than without. By contrast, if there are sites that extract high value from visitors yet provide them with low quality then SEO is generally detrimental to consumer welfare. The SEO process essentially allows sites with a high value for consumers to correct the search engine’s imperfect ranking through a contest.
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Review on Search Engine Optimization

Review on Search Engine Optimization

and link popularity are critical to optimizing your website. Crawler-based search engines consider many factors when indexing websites. Search engines are designed to try to find and rank the best websites (i.e. most usable, most informative, most credible). One of the most important ways for a search engine to rank the site is to consider the number and quality of websites that link to the website. In addition, a search engine might also consider the credibility of the website that is linked to the website. The exact methods used by crawler-based search engines on how they rate content for rankings is not public knowledge. In addition, these rules change over time. However, There are some common techniques that can help to optimize a website to increase traffic.
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On development of search engine for geodata

On development of search engine for geodata

Velké množství firem a institucí v současné době při své práci využívá geodata a značná část z nich geo data přímo vytváří. Aktuální geodata jsou také zásadním zdrojem informací pro řadu aplikací z oblasti managementu a obchodu – systémy pro podporu rozhodování, dolování dat atp. Jedním z klíčových problémů současné geoinformatiky je efektivní správa a sdílení těchto geodat. Převažující metodou jejich publikování jsou webové mapové služby. Na jedné straně je nevyhovující, že značná část mapových služeb obsahuje velmi podobná nebo zcela duplicitní data. Na druhé straně je často problém nalézt jakoukoliv službu, která požadovaná data obsahuje. Jedná se o obdobnou situaci jako v případě webových stránek před nástupem vyhledávacích nástrojů. Nicméně v oblasti webových mapových služeb žádný podobný vyhledávací engine neexistuje. Tento článek navrhuje novou po- dobu vyhledávacího nástroje pro geodata (nazvaného Moebius) a na základě analýzy popisuje jeho strukturu a kostru elementárního vyhledávacího jazyka odvozeného z používaných standardů. Prin- cip funkce je ilustrován na příkladech a podložen experimentální imple mentací.
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Lucene Search Engine: An Overview

Lucene Search Engine: An Overview

DSpace is a digital document object management system used to store, archive, search and retrieve “digitally-born” documents. DSpace uses the Jakarta search engine Lucene. Lucene is a simple, but high-performance and powerful search engine. It gives the capabilities of fielded searching, stop word removal, stemming, and the ability to incrementally add new indexed content without regenerating the entire index.

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Search Engine For Ebook Portal

Search Engine For Ebook Portal

Indexing involves parsing, storing and analyzing data for the purpose of quick retrieval. We have used elasticsearch engine for indexing. As mentioned earlier documents have been represented using the Vector Space Model. Mathematically, this model is represented as a TF-IDF matrix. Elasticsearch [6] Engine uses the TF-IDF representation and hence giving a better understanding of the working of Elasticsearch. This engine facilitates fast searching, provides multi language support, allows full text search, can be used to store big data and scales to Terabytes. Elasticsearch is based on Lucene [4] . Elasticsearch is essentially a document store. The documents looks like json files and are accessed over HTTP. The conceptual model of elasticsearch includes indices, doctype and contents in a format similar to a hash table. The document store can have multiple doctypes and does not have any particular schema but doctype parameter allows us to specify a user defined doctype. The data is ingested into the indexer. A dictionary consisting of document id, book name, author name, cluster id, score and contents of the document is formed by parsing through the dataset. Full text search requires every document to be assigned a unique Document ID. The index has to be finely grained. The document store will contain a record of the occurrence of terms in the document along with the position of occurrence of the term in the document. This facilitates full text search.
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Optimized Mobile Search Engine

Optimized Mobile Search Engine

The given example content ontology created for the query “hotel,” where content concepts linked with a one sided arrow ( ! ) are parent-child concepts, and concepts linked with a double-sided arrow ( $ ) are similar concepts. Fig. 2 shows the possible concept space determined for the query “hotel,” while the click through data determine the user preferences on the concept space. In general, the ontology covers more than what the user actually wants. The concept space for the query “hotel” consists of “map,” “reservation,” “room rate,”..., etc. If the user is indeed interested in information about hotel rates and clicks on pages containing “room rate” and “special discount rate” concepts, the captured click through favors the two clicked concepts. Feature vectors containing the concepts “room rate” and “special discount rate” as positive preferences will be created corresponding to the query “hotel.” As indicated in Fig. 2, when the query is issued again later, these feature vectors will be transmitted to the Optimized Personal Search Engine server and transformed into a content weight vector to rank the search results according to the user’s content preferences.
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