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With the growth of information technology and computer networks, there is a vital need for optimal design of distributed databases with the aim of performance improvement in terms of minimizing the round-trip response time and query transmission and processing costs. To address this issue, new fragmentation, data allocation, and replication techniques are required. In this paper, we propose enhanced vertical fragmentation, allocation, and replication schemes to improve the performance of distributed database systems. The proposed fragmentation scheme clusters highly-bonded attributes (i.e., normally accessed together) into a single fragment in order to minimize the query processing cost. The allocation scheme is proposed to find an optimized allocation to minimize the round-trip response time. The replication scheme partially replicates the fragments to increase the local execution of queries in a way that minimizes the cost of transmitting replicas to the sites. Experimental results show that, on average, the proposed schemes reduce the round-trip response time of queries by 23% and query processing cost by 15%, as compared to the related work.

Today’s digital world is filled with a vast multitude of content such as text and multimedia. Various attempts are being made to develop modern and powerful search engines in order to support diverse queries on this large collection of textual and multimedia data. For supporting intelligent search, particularly for multimedia data such as images, additional metadata plays a crucial role in helping a search engine handpick the most relevant information for a query. A common technique that is used to generate pertinent metadata for visual multimedia content is by the process of annotation. Automating the annotation process given the large volume of visual content available on the Web is highly advantageous. In this paper, we propose an automated image annotation system that employs a content-based visual information retrieval technique using certain features of the image. Experimental evaluation and analysis of the proposed work have shown promising results.

In recent years, there has been a fast growth in the year-to-year acceleration of technology and information. With the convenience of creating a website, it is one of the key causes in the appearance of new websites every day. Another item that is growing because of the advancement of existing communication technology is the increase in the number of internet users throughout the world. Supported by the advancement of website and internet technology, as well as ground-breaking innovations, the website has become the most frequented and sought-after on the internet. White Hat SEO is a search engine optimization method that adheres to search engine rules and conditions. It is believed that by implementing the White Hat SEO strategy to the Website Management System, visitor traffic would improve, and promotional media can enhance the number of website visits. A good SERP is essential to at least be in the top place in the search engines in order to acquire the desired amount of visits. Search engines are used by about 95% of users in online activities such as browsing and surfing. In its implementation, the White Hat SEO method in the Website Management System employs SEO techniques based on on-site optimization methods that include four critical components: keyword optimization, website page directory structure optimization, website content optimization, and link optimization on website pages (backlinks).

Objectives: To find a better indexing method of search engine for better information retrieval. Methods/Statistical Analysis: Indexing technique is very important part of any search engine or information retrieval system. There are many indexing techniques proposed earlier but they are not accurately retrieving information from the database. In this paper we are trying to propose a new indexing technique for maximizing attempt to get proper information according to search query by the information retrieval system. There are many different parts to a search engine index, such as design factors and data structures. When a search engine index is being built, there are also many different types of data structures to choose from. Some well known data structures are suffix tree, tree, inverted index, Citation index, Ngram index, Term document matrix, which are all used for different type of index designing for search engine. In this paper combination of different existing indexing methods has been used to form a new indexing technique. Findings: The experimental results described in the paper show that the accuracy of search results using S-N Indexing methods is 5% better than existing search engine indexing techniques. Application/Improvements: In this paper we are trying to design an indexing technique which is combination of Ngram index and suffix tree index for better information retrieval process by search engines. So, proposed method of indexing technique can improve searching results with more accurate and more related way. Keywords: Concept based, Concept Based Index, Improved Search Index, New Index, Search Engine, Search Engine Index

Now days, the Word Wide Web (WWW) covers most of the information channels across the world and is the cheapest resource to publish the corporate as well as individual information. The key advantage of WWW is that the published information instantly becomes available around the globe. Since the information load on the Internet is increasing day by day, therefore, it is causing serious troubles for the researchers and scientists to retrieve the targeted and relevant information from the huge bulk of data. While surfing the Internet, precious time of researchers and scientists is wasted due to browsing of the irrelevant and unnecessary material. Such an approach of information retrieval results in overlooking the important contents. In this paper, we look into the elementary components of a customized search engine in order to develop an agent-based search engine tool that may help researchers and scientists to find their desired information in an efficient manner and with minimal clicks of pointer. Through this tool, the researchers and scientists will be able to find a summarized report against their search phrase along with the search details, which will provide them a solid background to their research subject based on history available on the WWW. The important aspect of this search engine is that it retrieves the subject-specific material from the designated websites in accordance with the user-defined criteria.

Mobile devices have become very common in our daily life. Recent years have witnessed a continual growth in the use of mobile handsets to gather information, in a way that is much easier than the way personal computers (PC) do. This paper describes a prototype design and proposes an infrastructure framework,from which people can inquire and organize web resources based on Chinese semantics to do some reasoning. We use Ontology to address the knowledge reasoning that is utilized in search engines in order to get the appropriate results that can meet the users' expectations and intentions. First of all, to accommodate complex Chinese semantics, this paper presents the semantic Ontology-based tree algorithm for the Chinese word splitter. Second of all, the paper further analyzes the fundamental problems and gives a detailed solutions technology. Third of all, a search engine prototype is proposed to allow mobile terminal to make queries by making inference and automatically integrate some distributed search services. Finally, the advantages of our approach proved by experiments show that it is an improved retrieval method.

For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing.

Efficient storage and handling of data stored in a peer-to-peer (P2P) network, proves vital for various applications such as query processing and data mining. This paper presents a distributed, scalable and robust layered overlay (L-overlay) to index and manage multidimensional data in a dynamic P2P network. The proposed method distinguishes between the data and peer layers, with efficient mapping between the two. The data is organized such that semantically similar data objects are accessed hastily. Grid and tree structures are proposed for the peer layer. As application examples of L-overlay in query processing and data mining, k-nearest neighbors query processing and distributed Naive Bayes classification algorithms, are proposed. We show the effectiveness of our scheme in static and dynamic environments using simulation. L-overlay is shown to be more efficient than SSW, an available semantic overly, in terms of maintenance and query processing costs.

The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.

Preprocessing is a process to avail a clean and ready data for data mining analysis. Four stages are carried out in this process i.e. data selecting, data cleaning, data integrating and data transforming. The numerous semi structured pattern of web server log data accumulation has made the data cleaning-up to select the really needed data difficult. Hence, in this research a technique to carry out data cleaning using parser algorithm and query processing is proposed. The parser algorithm is written using PHP programming as web based programming, while the query processing is implemented using relationship database management system (RDBMS) MySQL. The computation system is tested using twenty six different sized trial data, originated from six web server. The final result concluded that in general the data being tested have 80 percent decreasing average with average processing velocity of 9.28 mbps Keywords: query processing, parser algorithm, semi structured data, web log data

Skyline queries help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. A challenging problem is to support skyline queries in distributed environments, where data is scattered over independent sources. The query response time of skyline processing over distributed data depends on the amount of transferred data and the query processing cost at each server. In this paper, we propose AGiDS, a framework for efficient skyline processing over distributed data. Our approach reduces significantly the amount of transferred data, by using a grid-based data summary that captures the data distribution on each server. AGiDS consists of two phases to compute the result: in the first phase the querying server gathers the grid-based summary, whereas in the second phase a skyline request is sent only to the servers that may contribute to the skyline result set asking only for the points of non-dominated regions. We provide an experimental evaluation showing that our approach performs efficiently and outperforms existing techniques.

Googling for Health Information

Unstructured peer-to-peer infrastructure has been widely employed to support large-scale distributed applications. Many of these applications, such as location-based services and multimedia content distribution, require the support of range selection queries. Under the widely adopted query shipping protocols, the cost of query processing is affected by the number of result copies or replicas in the system. Since range queries can return results that include poorly-replicated data items, the cost of these queries is usually dominated by the retrieval cost of these data items. In this work, we propose a popularity-aware prefetch-based approach that can effectively facilitate the caching of poorly-replicated data items that are potentially requested in subsequent range queries, resulting in substantial cost savings. We prove that the performance of retrieving poorly-replicated data items is guaranteed to improve under an increasing query load. Extensive experiments show that the overall range query processing cost decreases significantly under various query load settings.

In a multidatabase system, the participating databases are autonomous. The schemas of these databases may be different in various ways, while the same information is represented. A global query issued against the global database needs to be translated to a proper form before it can be executed in a local database. Since data requested by a query (or a part of a query) is sometimes available in multiple sites, the site (database) that processes the query with the least cost is the desired query processing site. The authors study the effect of differences in schemas on the cost of query processing in a multidatabase environment. They first classify schema conflicts to different types. For each type of conflict, they show how much more or less complex a translated query can become in comparison with the originally user-issued global query. Based on this observation, they propose an analytical method that considers the conflicts between local databases and finds the database(s) that renders the least execution cost in processing a global query. This research introduces a new level of query optimization (termed the schema-level optimization) in multidatabase environments. The results provide a new dimension of enhancement for the capability of a query optimizer in multidatabase systems.

In order to facilitate efficient query processing, the information contained in data warehouses is typically stored as a set of materialized views. Deciding which views to materialize represent a challenge in order to minimize view maintenance and query processing costs. Some existing approaches are applicable only for small problems, which are far from reality. In this paper we introduce a new approach for materialized view selection using Parallel Simulated Annealing (PSA) that selects views from an input Multiple View Processing Plan (MVPP). With PSA, we are able to perform view selection on MVPPs having hundreds of queries and thousands of views. Also, in our experimental study we show that our method provides a significant improvement in the quality of the obtained set of materialized views over existing heuristic and sequential simulated annealing algorithms.