2,519 research outputs found

    A Compressive Survey on New Technique Towards Successful Document Research Using Key Phrase Annotations Together with Querying Benefit

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    Generally it can be challenging to find out the particular pertinent data inside unstructured wording paperwork. This kind of information is still suffocated within unstructured wording and terminology. Annotations by means of Characteristic name-value frames tend to be more significant for retrieval of this sort of documents. This system proposes a novel, different, alternative approach for document retrieval which includes annotations identification. This system identifies the values of structured attributes by reading, analyzing and parsing the uploaded documents. This system proposes an approach for efficient document retrieval using effective methods. The main use of this system is that when users of author perform query based search, they could get minimum and distinct accurate results where it could be easy for retr ieval data from the database. By using these techniques two techniques, workload of system can reduce by large amount. And it also, given the fact the effic iency of searching annotation document will be faster because of using the query-based searching technique or content value searching

    Weaving Entities into Relations: From Page Retrieval to Relation Mining on the Web

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    With its sheer amount of information, the Web is clearly an important frontier for data mining. While Web mining must start with content on the Web, there is no effective ``search-based'' mechanism to help sifting through the information on the Web. Our goal is to provide a such online search-based facility for supporting query primitives, upon which Web mining applications can be built. As a first step, this paper aims at entity-relation discovery, or E-R discovery, as a useful function-- to weave scattered entities on the Web into coherent relations. To begin with, as our proposal, we formalize the concept of E-R discovery. Further, to realize E-R discovery, as our main thesis, we abstract tuple ranking-- the essential challenge of E-R discovery-- as pattern-based cooccurrence analysis. Finally, as our key insight, we observe that such relation mining shares the same core functions as traditional page-retrieval systems, which enables us to build the new E-R discovery upon today's search engines, almost for free. We report our system prototype and testbed, WISDM-ER, with real Web corpus. Our case studies have demonstrated a high promise, achieving 83%-91% accuracy for real benchmark queries-- and thus the real possibilities of enabling ad-hoc Web mining tasks with online E-R discovery

    A Review: Data Mining Technique Used for Searching the Keywords

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    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

    Ranking based Dynamic Query forms for Database Queries

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    As the Environment of Internet continues to grow the users of searching the data also increases. By this development of web information and scientific databases it is not able to get user require results with the static query forms. The Dynamic Query forms are able to dynamically generate query forms with the selection of user interested components and rank the query form components. The creation of query forms is an iteration process and user selects interested components in iteration process. The form components ranking are based on the user interest and Fmeasure is used to measure the query form goodness. The incremental hierarchical data clustering framework is used to capture the user2019;s interest based on click feedback to input some keyword queries

    Optimization Technique for Efficient Dynamic Query Forms with Keyword Search and NoSQL

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    Modern web database as well as scientific database maintains tremendous and heterogeneous that is unstructured data. In order to mine this data traditional data mining technologies cannot work properly. These real word databases may contain hundreds or even thousands of relations and attributes. Latest trends like Big data and cloud computing that leads to the adoption of NoSQL which simply means Not Only SQL. In current scenario Most of the web applications are hosted in cloud and that available through internet. This create explosion in number of concurrent users. So the technique to handle unstructured data is proposed in our work named as Dynamic query forms with nosql. This system presents dynamic query form interface for database exploration of an organization. In this system document oriented NoSQL database is used for that purpose MONGODB is used which support dynamic queries that do not require predefined map reduce function. And further the process generation of a query form is an iterative process guided by user. At each step system automatically generate ranking list of form components and user adds the desired form component into query form and submit queries to view query result. Two traditional measures to evaluate the quality of query result i.e, precision and recall is presented. Quality measures can be derived using overall performance measure as Fscore DOI: 10.17762/ijritcc2321-8169.15070

    Fast Nearest Neighbor Search with Keywords in Spatial Databases

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    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

    Fast Nearest Neighbor Search with Keywords Using IR2-Tree

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    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

    A unified view of data-intensive flows in business intelligence systems : a survey

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    Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
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