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

A Review: Data Mining Technique Used for Searching the Keywords

Convential Spatial queries contains range search, nearest neighbor retrival involve only conditions on object geometric properties. Today, many modern applications call for innovative kind of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “Dosa, Idli, Wadapav” all at the same time. Currently the best solution to such queries is based on the IR2-tree, which, such type of queries can be efficiently handled by IR2tree. In the proposed work, we are developing a system searching is done on the basis of methods like nearest neighbor search with keywords is done by IR2 tree and spatial inverted index.We could first fetch all the restaurants whose menus contain the set of keywords {Dosa, Idli, Wadapav}, and then from the retrieved restaurants, find the nearest one The IR2-tree combines the R tree with signature files. Inverted indexes (I-index) have proved to be an effective access method for keyword based document retrieval

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

Fast Nearest Neighbor Search with Keywords in Spatial Databases

In these days, many modern purposes name for novel varieties of queries that purpose to find objects pleasing both a spatial predicate, and a predicate on their related texts. Present answer for such queries has a couple of deficiencies that critically influence its effectivity. Prompted by way of this, in this venture, development of a new entry process called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and is derived with algorithms that may reply nearest neighbor queries with key words in actual time. As tested via experiments, the proposed approaches outperform the IR2-tree in question response time tremendously, more commonly through a factor of orders of magnitude. DOI: 10.17762/ijritcc2321-8169.15080

Querying cohesive subgraphs by keywords

© 2018 IEEE. Keyword search problem has been widely studied to retrieve related substructures from graphs for a keyword set. However, existing well-studied approaches aim at finding compact trees/subgraphs containing the keywords, and ignore a critical measure, density, to reflect how strongly and stablely the keyword nodes are connected in the substructure. In this paper, we study the problem of finding a cohesive subgraph containing the query keywords based on the k-Truss model, and formulate it as minimal dense truss search problem, i.e., finding minimal subgraph with maximum trussness covering the keywords. We first propose an efficient algorithm to find the dense truss with the maximum trussness containing keywords based on a novel hybrid KT-Index (Keyword-Truss Index). Then, we develop a novel refinement approach to extract the minimal dense truss based on the anti-monotonicity property of k-Truss. Experimental studies on real datasets show the outperformance of our method

Практический подход к реализации приложений семантического ВЕБ

Одной из ключевых характеристик приложений семантического Веб является доступность машинно-читаемых метаданных. В данной работе описывается практический подход к семантическому Веб, который охватывает данные и метаданные на предприятии. Эти данные могут быть структурированными, слабоструктурированными или неструктурированными; хранящиеся в любых базах данных и созданные любым приложением. В работе представлена архитектура, которая новым способом интегрирует возможности семантического Веб в существующие технологии. Ключевая цель архитектуры состоит в том, чтобы позволить организациям видеть сервисы в Интернете с унифицированным семантическим описанием. Приведен также прототип практической реализации подхода.It is well understood that the key for successful Semantic Web applications depends the availability of machine understandable metadata. In this paper, we describe a practical approach to the Semantic Web called Information Grid. Information Grid resources span all the data in the organization and all the metadata required to make it meaningful. This data maybe structured, semistructured, or unstructured; stored anywhere; and created by any application. We present an architecture that integrates Semantic Web techniques into existing technologies in a novel way. We show the design and implementation of a prototype that demonstrates the ideas presented in the paper

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

Fast Nearest Neighbor Search with Keywords Using IR2-Tree

Conventional abstraction queries, like vary search and nearest neighbor retrieval, involve alone conditions on objects geometric properties. Today, many trendy applications concern novel varieties of queries that aim to go looking out objects satisfying every a abstraction predicate, and a predicate on their associated texts. As associate example, instead of considering all the restaurants, a nearest neighbor question would instead provoke the eating place that is the nearest among those whose menus contain asteak, ˆ spaghetti, brandyaˆ all at identical time. Currently, the best answer to such queries depends on the IR2-tree, which, as shown throughout this paper, contains many deficiencies that seriously impact its efficiency. motivated by this, It tend to develop a latest access methodology called the abstraction inverted index that extends the traditional inverted index to subsume f-dimensional info, and comes with algorithms that will answer nearest neighbor queries with keywords in real time. As verified by experiments, the projected techniques trounce the IR2-tree in question latent amount considerably, generally by a part of orders of magnitude