PIDALION: Implementation Issues of a Java-Based Multimedia Search Engine over the Web

2009 ◽  
Author(s):  
Dimitris E. Charilas ◽  
Ourania I. Markaki
Keyword(s):  
Search Engine ◽  
Multimedia Search ◽  
The Web

scholarly journals Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 259
Author(s):  
Ioannis Drivas ◽  
Dimitrios Kouis ◽  
Daphne Kyriaki-Manessi ◽  
Georgios Giannakopoulos
Keyword(s):  
Search Engine ◽  
Common Knowledge ◽  
Organizational Structures ◽  
Content Management ◽  
Data Driven ◽  
Management Systems ◽  
Search Engine Optimization ◽  
Optimization Methodology ◽  
Content Management Systems ◽  
The Web

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Search Engine Update

2001 ◽  
Vol 1 (3) ◽  
pp. 28-31 ◽  
Author(s):  
Valerie Stevenson
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Search Strategy ◽  
Search Strategies ◽  
Boolean Logic ◽  
Web Searches ◽  
Search Techniques ◽  
Looking Back ◽  
The Web

Looking back to 1999, there were a number of search engines which performed equally well. I recommended defining the search strategy very carefully, using Boolean logic and field search techniques, and always running the search in more than one search engine. Numerous articles and Web columns comparing the performance of different search engines came to different conclusions on the ‘best’ search engines. Over the last year, however, all the speakers at conferences and seminars I have attended have recommended Google as their preferred tool for locating all kinds of information on the Web. I confess that I have now abandoned most of my carefully worked out search strategies and comparison tests, and use Google for most of my own Web searches.

scholarly journals A Low-Cost Library Database Solution

2017 ◽  
Vol 19 (1) ◽  
pp. 46-49 ◽  
Author(s):  
Mark England ◽  
Lura Joseph ◽  
Nem W. Schlect
Keyword(s):  
Search Engine ◽  
Programming Language ◽  
Relational Databases ◽  
Low Cost ◽  
The World ◽  
The Web ◽  
Interface Designs

Two locally created databases are made available to the world via the Web using an inexpensive but highly functional search engine created in-house. The technology consists of a microcomputer running UNIX to serve relational databases. CGI forms created using the programming language Perl offer flexible interface designs for database users and database maintainers.

Creating and using Web corpora

2005 ◽  
Vol 10 (4) ◽  
pp. 517-541 ◽  
Author(s):  
Mike Thelwall
Keyword(s):  
Search Engine ◽  
Web Sites ◽  
Web Crawler ◽  
Commercial Search Engine ◽  
British National Corpus ◽  
The Uk ◽  
The University ◽  
The Web ◽  
National Corpus

The Web has recently been used as a corpus for linguistic investigations, often with the help of a commercial search engine. We discuss some potential problems with collecting data from commercial search engine and with using the Web as a corpus. We outline an alternative strategy for data collection, using a personal Web crawler. As a case study, the university Web sites of three nations (Australia, New Zealand and the UK) were crawled. The most frequent words were broadly consistent with non-Web written English, but with some academic-related words amongst the top 50 most frequent. It was also evident that the university Web sites contained a significant amount of non-English text, and academic Web English seems to be more future-oriented than British National Corpus written English.

The Event Search Engine

2012 ◽  
pp. 70-82
Author(s):  
Takeshi Okadome ◽  
Yasue Kishino ◽  
Takuya Maekawa ◽  
Koji Kamei ◽  
Yutaka Yanagisawa ◽  
...  
Keyword(s):  
Search Engine ◽  
Real World ◽  
Local Environment ◽  
Real Space ◽  
Sensor Data ◽  
The Internet ◽  
Web Browser ◽  
Quantity Representation ◽  
Data Segment ◽  
The Web

In a remote or local environment in which a sensor network always collects data produced by sensors attached to physical objects, the engine presented here saves the data sent through the Internet and searches for data segments that correspond to real-world events by using natural language (NL) words in a query that are input in an web browser. The engine translates each query into a physical quantity representation searches for a sensor data segment that satisfies the representation, and sends back the event occurrence time, place, or related objects as a reply to the query to the remote or local environment in which the web browser displays them. The engine, which we expect to be one of the upcoming Internet services, exemplifies the concept of symbiosis that bridges the gaps between the real space and the digital space.

Seek and Ye Shall Find

2011 ◽  
pp. 740-754 ◽  
Author(s):  
Suely Fragoso
Keyword(s):  
Mass Media ◽  
Search Engine ◽  
Mass Distribution ◽  
Search Engines ◽  
Distribution Model ◽  
The Internet ◽  
Information Distribution ◽  
Distribution Mode ◽  
History Of ◽  
The Web

This chapter proposes that search engines apply a verticalizing pressure on the WWW many-to-many information distribution model, forcing this to revert to a distributive model similar to that of the mass media. The argument for this starts with a critical descriptive examination of the history of search mechanisms for the Internet. Parallel to this there is a discussion of the increasing ties between the search engines and the advertising market. The chapter then presents questions concerning the concentration of traffic on the Web around a small number of search engines which are in the hands of an equally limited number of enterprises. This reality is accentuated by the confidence that users place in the search engine and by the ongoing acquisition of collaborative systems and smaller players by the large search engines. This scenario demonstrates the verticalizing pressure that the search engines apply to the majority of WWW users, that bring it back toward the mass distribution mode.

Search Engine-Based Web Information Extraction

2011 ◽  
pp. 2048-2081
Author(s):  
Gijs Geleijnse ◽  
Jan Korst
Keyword(s):  
Semantic Web ◽  
Information Extraction ◽  
Search Engine ◽  
Community Based ◽  
Web Information Extraction ◽  
Structure Information ◽  
Web Information ◽  
Structured Information ◽  
The Web ◽  
Standard Semantic

In this chapter we discuss approaches to find, extract, and structure information from natural language texts on the Web. Such structured information can be expressed and shared using the standard Semantic Web languages and hence be machine interpreted. In this chapter we focus on two tasks in Web information extraction. The first part focuses on mining facts from the Web, while in the second part, we present an approach to collect community-based meta-data. A search engine is used to retrieve potentially relevant texts. From these texts, instances and relations are extracted. The proposed approaches are illustrated using various case-studies, showing that we can reliably extract information from the Web using simple techniques.

Review of Link Structure Based Ranking Algorithms and Hanging Pages

2016 ◽  
pp. 420-459 ◽  
Author(s):  
Ravi P. Kumar ◽  
Ashutosh K. Singh ◽  
Anand Mohan
Keyword(s):  
Search Engine ◽  
Web Sites ◽  
Cyber Security ◽  
Web Security ◽  
Optimization Techniques ◽  
Web Pages ◽  
Link Structure ◽  
Search Engine Optimization ◽  
Ranking Algorithms ◽  
The Web

In this era of Web computing, Cyber Security is very important as more and more data is moving into the Web. Some data are confidential and important. There are many threats for the data in the Web. Some of the basic threats can be addressed by designing the Web sites properly using Search Engine Optimization techniques. One such threat is the hanging page which gives room for link spamming. This chapter addresses the issues caused by hanging pages in Web computing. This Chapter has four important objectives. They are 1) Compare and review the different types of link structure based ranking algorithms in ranking Web pages. PageRank is used as the base algorithm throughout this Chapter. 2) Study on hanging pages, explore the effects of hanging pages in Web security and compare the existing methods to handle hanging pages. 3) Study on Link spam and explore the effect of hanging pages in link spam contribution and 4) Study on Search Engine Optimization (SEO) / Web Site Optimization (WSO) and explore the effect of hanging pages in Search Engine Optimization (SEO).

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