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

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

Type-Ahead Search in XML data based on Improved Forward Index Structure: ATASK

The keyword based search system is most widely used in many real time applications for getting the required information from huge amount of dataset in quick time. There are many keyword search based systems and methods presented by various authors already, as the time goes, this methods becomes inefficient in different ways. The all previous methods did not work for search XML data in mode of type-ahead search, and hence it is not trivial to extend existing techniques to support fuzzy type-ahead search in XML data. Previous methods are not purely based on XML data and as XML data is consisting of parent and child nodes, it is complex to understand such format to read for existing methods. Existing methods directly works on single document. Thus to overcome the limitations of existing methods, we need to have efficient XML based type-ahead shear method. Recently we have studied one such method, which is called as TASX (pronounced “task”). This is fuzzy type-ahead search method in XML data. This method searches the XML data during the typing of keyword from user end and it searches XML data even if it’s misspelled. Experimentally this method showing efficient performance as compared to existing methods, but there are still suggestions over this method for improvement. Here, we are presenting extended approach for XML based type-ahead search method ATASX (pronounced “a task”). In this method we are proposing to use improved forward-index structure method with aim of improving the search efficiency it reduces searching time and provides result quality

Expanding Database Keyword Search for Database Exploration

AbstractDatabase keyword search (DB KWS) has received a lot of attention in database research community. Although much of the research has been motivated by improving performance, recent research has also paid increased attention to its role in database contents exploration or data mining. In this paper we explore aspects related to DB KWS in two steps: First, we expand DB KWS by incorporating ontologies to better capture users’ intention. Furthermore, we examine how KWS or ontology-enriched KWS can offer useful hints for better understanding of the data and in-depth analysis of the data contents, or data mining

What Makes Data Possible? A Sociotechnical View on Structured Data Innovations

Drawing from the theory of digital objects, this paper examines the distinction between structured and unstructured data as carriers of facts. We argue that data do not ‘have’ a structure but are made by a structure that confers data their capacity to represent contextual facts. We employ a case vignette involving XBRL (eXtensible Business Reporting Language) and its use in statutory financial reporting to illustrate and explore the sociotechnical nature of data and to describe what we call data innovations: new valuable ways to render phenomena as data. We find that data structure is best viewed as a matter that is relative to a purpose in a context. Theorizing data from a sociotechnical perspective could evolve to provide, in effect, the material science of digital economy

Search and Result Presentation in Scientific Workflow Repositories

We study the problem of searching a repository of complex hierarchical workflows whose component modules, both composite and atomic, have been annotated with keywords. Since keyword search does not use the graph structure of a workflow, we develop a model of workflows using context-free bag grammars. We then give efficient polynomial-time algorithms that, given a workflow and a keyword query, determine whether some execution of the workflow matches the query. Based on these algorithms we develop a search and ranking solution that efficiently retrieves the top-k grammars from a repository. Finally, we propose a novel result presentation method for grammars matching a keyword query, based on representative parse-trees. The effectiveness of our approach is validated through an extensive experimental evaluation