Performance analysis of a keyword search system

تعدين البيانات هي عميلة لاكتشاف انماط في مجموعة من البيانات وفقاً للكلمة الرئيسية. البحث عن الكلمة الرئيسية هي الطريق الاكثر فاعلية لاكتشاف المعلومات في الوثائق. ولكن في مكان ما، في بعض الأحيان فقط البحث عن الكلمة الرئيسية ليست كافية، مع البحث في تقييد تلك الكلمة الرئيسية أصبح ضرورة. كما هو الحال في إساءة استخدام وسائل الاعلام الاجتماعية من كلمة آخذت في الازدياد. عملت العديد من الأنظمة على الكشف عن كلمة غير ملائمة فقط؛ وليس على تقييد تلك الكلمة. حتى هنا في ورقة البحث الكلمة المقترحة في طريقة وسائل الاعلام الاجتماعية التي لا يجد فقط الكلمات غير المناسبة، ولكن أيضا تقييد تلك الكلمة من النشر على وسائل الإعلام.Data mining is the process of discovering patterns in a data set by keyword. Keyword search is the most effective way to discover information in documents. But somewhere, sometimes just searching for a keyword is not enough; with research restricting that keyword has become a necessity. Like in social media abuse of word is increasing. Many systems worked on only detecting an inappropriate word; not on restriction of that word. So here in this paper keyword search method is proposed for social media which not only finds the inappropriate words, but also restrict that word from publishing on the media

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Using Document Indexers for Faceted Search in Dataspaces

Efficient information retrieval is essential to enrich user experience when searching for documents in dataspaces. With the continued growth in the volume and complexity of documents, the efficient information retrieval for searches has become increasingly challenging. To improve users’ search experience, faceted search combines direct keyword search methods with faceted browsing using a predefined set of categories (facets). This paper studies a faceted search approach that integrates dynamic facets generation with search. To further enhance the faceted search, alternative indexers based on pre-defined ontology for data repositories within dataspaces are evaluated in terms of execution time and data size. Experimental results suggest that combining the proposed faceted search with appropriate indexers improves search performance enhancing user experience

Review Paper Title: Research & Evaluation of Keyword Search Techniques over Relational Data

Currently the relational keyword based searches techniques consider the large number of data’s to provide efficient result while the user searching. There is an issue of limited memory hence there is a need of the implementation of the novel techniques/ algorithm. To improve the search technique process by optimizing the query from that has to contain the memory optimization with the help of the genetic algorithm. The process is executed in the dynamic manner which is considered as the real time scenario in that have to execute the whole process as the dynamic based on the user given query. The proposed system is Research and Evaluation of Keyword Search Techniques over Relational Data. Results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. Keyword Search with ranking so that our execution time consumption is less, file length and execution time can be seen, ranking can be seen by using chart

Piggy Bank: Experience the Semantic Web Inside Your Web Browser

The original publication is available at www.springerlink.com http://dx.doi.org/10.1007/11574620_31The Semantic Web Initiative envisions a Web wherein information is offered free of presentation, allowing more effective exchange and mixing across web sites and across web pages. But without substantial Semantic Web content, few tools will be written to consume it; without many such tools, there is little appeal to publish Semantic Web content. To break this chicken-and-egg problem, thus enabling more flexible information access, we have created a web browser extension called Piggy Bankthat lets users make use of Semantic Web content within Web content as users browse the Web. Wherever Semantic Web content is not available, Piggy Bank can invoke screenscrapers to restructure information within web pages into Semantic Web format. Through the use of Semantic Web technologies, Piggy Bank provides direct, immediate benefits to users in their use of the existing Web. Thus, the existence of even just a few Semantic Web-enabled sites or a few scrapers already benefits users. Piggy Bank thereby offers an easy, incremental upgrade path to users without requiring a wholesale adoption of the Semantic Web’s vision. To further improve this Semantic Web experience, we have created Semantic Bank, a web server application that lets Piggy Bank users share the Semantic Web information they have collected, enabling collaborative efforts to build sophisticated Semantic Web information repositories through simple, everyday’s use of Piggy Bank

SODA: Generating SQL for Business Users

The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.Comment: VLDB201

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