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An Improved Performance for Keyword Query Routing for Searching in Linked Databases

An Improved Performance for Keyword Query Routing for Searching in Linked Databases

Holger Bast Alexandru Chitea [10] ESTER is a modular and highly capable system for combined full-text and ontology search. ESTER build on a query engine that supports two basic operations: prefix search and join. Both operations can be implemented very efficiently with a compact index, in combination it provide powerful querying capabilities. Also shows how ESTER can answer basic SPARQL graph pattern queries on the ontology by reducing them to a small number of these two basic operations. ESTER further supports a natural blend of such semantic queries with ordinary full-text queries. Moreover, the prefix search operation allows for a fully interactive and proactive user interface, which after every keystroke suggests to the user possible semantic interpretations of his or her query, and speculatively executes the most likely of these interpretations.
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An Amended Keyword Cover Search Approaches

An Amended Keyword Cover Search Approaches

enters the whole keyword, otherwise it used to be shown incomplete output. It is complex to enter the complete keyword on cellular contraptions for getting the correct relative outcomes. To prevent this problem, proposed process studied in [1] concerning the place-conscious search. It can be back search solutions because the user enters in queries letter valeted. In this paper, the main assignment is to furnish the central answers swiftly. Creator uses a brand new index structure, prefix- vicinity tree (known as PR-tree), that may be support to provide the speedily outcome to the users. PR-Tree is a tree based index constitution which seamlessly integrates the textual description and spatial expertise to index the spatial knowledge. Utilizing the PR Tree, authors increase efficient algorithms to help single prefix queries and multi-key phrase queries. Experiments exhibit that our system achieves excessive performance and drastically outperforms today’s approaches. The region-aware keyword question returns ranked objects that are close a query location and that have textual descriptions that fit query key terms[2][3]. There are numerous cell applications and ordinary services makes use of this kind of question, e.g. Yellow pages and Maps services. In previous work, ranked question returns unbiased potential results. Rating could be very important in determination making. Nonetheless, a crucial outcomes object with nearby objects which are additionally central to the question is likely to be prime over a crucial object without vital regional objects. The paper proposes [2] the suggestion of prestige-based relevance to seize both the textual relevance of an object to a question and the results of regional objects. Headquartered on this, a brand new sort of query, the vicinity-aware high-ok status-founded textual content retrieval (Lip) question, is proposed that retrieves the highest-k spatial net objects ranked in keeping with both status -founded relevance and area proximity. They suggest two algorithms that compute Lip queries. Empirical experiences with actual-world spatial data demonstrate that Lip queries are mightier in retrieving net objects than a prior procedure that doesn't consider the consequences of regional objects; and they show that the proposed algorithms are scalable and outperform a baseline technique vastly.
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Keyword Query Routing

Keyword Query Routing

Effective Keyword Search in Relational Database With the amount of available text data in relational databases growing rapidly, the need for ordinary users to search such information is dramatically increasing. Even though the major RDBMSs have provided full-text search capabilities, they still require users to have knowledge of the database schemas and use a structured query language to search information. This search model is complicated for most ordinary users. Inspired by the big success of information retrieval (IR) style keyword search on the web, keyword search in relational databases has recently emerged as a new research topic. The differences between text databases and relational databases result in three new challenges:(1) Answers needed by users are not limited to individual tuples, but results assembled from joining tuples from multiple tables are used to form answers in the form of tuple trees. (2) A single score for each answer (i.e. a tuple tree) is needed to estimate its relevance to a given query. These scores are used to rank the most relevant answers as high as possible. (3) Relational databases have much richer structures than text databases. Existing IR strategies are inadequate in ranking relational outputs. This paper, propose a novel IR ranking strategy for effective keyword search. The first that conducts comprehensive experiments on search effectiveness using a real world database and a set of keyword queries collected by a major search companies. This strategy is significantly better than existing strategies. This approach can be used both at the application level and be incorporated into a
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A Metadata Search Approach to Keyword Query in Relational Databases

A Metadata Search Approach to Keyword Query in Relational Databases

DataSpot [16] is a database search system using free-form queries similar to our approach. It represents database content in form of schema-less semi-structured graph called hyperbase. Nodes in hyperbase represent data objects (e.g., relations, tuples, and attributes) and edges represent associations between data objects. Query results are connected subgraphs of hyperbase containing all query keywords. Goldman et al. [17] proposed a simple query language with two sets of keywords in form of find x near y. Two sets of objects in a database are found and the result set is ranked based on distance between these two sets. A similar system is proposed by Yin et al. [18]. Their concept is to find the target objects related to source objects with AND and OR semantics. The system converts a database schema to a graph. At the query time, it extends shortest join paths to measure the strengths of their relationships. Mragyati [19] is the system to keyword searching and browsing on relational databases. The system maps query keywords to a database schema using metadata as four-level trees and translates answer trees to SQL. The ranking function can be based on user-specified criteria but the default ranking is based on the number of foreign-key constraints. It is similar to our work in supporting synonyms and metadata. However, the implementation does not handle queries with more than 2 solution paths. Dissimilar to the other approaches, Wheeldon et al. [6] proposed a system to keyword search over relational databases which indexed a relational database as virtual documents to querying and navigation. Their approach indexes textual content of each tuple as a web page and their foreign-key constraints are
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Keyword Search In Top Down Xml

Keyword Search In Top Down Xml

Our baseline ELCA algorithm recursively gets all CA nodes in a top-down way, then checks the satisfiability of each CA node, which works on the traditional inverted lists of labels w.r.t. Dewey or one of its variants. To do so, it needs to solve two problems: ðP1Þ identify the set of child CA nodes for each CA node v, ðP2Þ check v’s satisfiability w.r.t. ELCA semantics. For P1, given a query Q with m keywords, we know that 8i 2 ½1;m_; ScaðvÞ SiðvÞ. Thus given a CA node v and its subtree set Fv ¼ fTv1; Tv2 ; . . . ; Tvng, to get ScaðvÞ, we do not need to check whether each subtree contains all query keywords; instead, we just need to check whether each node in SminvÞ, which contains least number of child nodes of v w. r.t. kmin, appears in SiðvÞði 2 ½1;m_ ^ i 6¼ minÞ. Even if we know the lengths of all child lists, it’s difficult to know which one is SminðvÞ. Fortunately, as all node IDs in each child list of v are sorted in ascending order, our newly proposed set intersection algorithm guarantees that the number of processed child nodes for each CA node v is bounded by jSminðvÞj. For P2, we use the following Lemma to check the satisfiability of v, which is
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Evaluation of Keyword Search System with Ranking

Evaluation of Keyword Search System with Ranking

The table contains the name of the file and file key based on user searched system and uploaded details are presented in the table. The ranks of the keyword search are present at the end of the table. Each and every time based on keyword search and downloaded file based the rank will get increased. Because the ranking based top most product will be send to user. Overall we will study all the presented techniques which is available in market. Each organization has some advantages and some issues, then compare all the techniques and tartan the routine. So lastly end that any existing system cannot execute all the condition of keyword uncertainty search. They require more legroom and time and also some techniques are narrow for fussy dataset.
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Keyword Search In Top Down Xml

Keyword Search In Top Down Xml

nodes, of which no LCA is the ancestor of any other LCA. As a comparison, ELCA tries to capture more meaningful results, it may take some LCAs that are not SLCAs as meaningful results. Assume that for a given query Q ¼ fk1; k2…..kmg, each keyword appears at least once in the given XML document. Intuitively, to get all CA nodes of Q, our method takes all nodes in the set of inverted IDDewey label lists as leaf nodes of an XML tree Tv rooted at node v, and checks whether each node of Tv contains all keywords of Q in a “top-down” way. The “top- down” means that if Tv contains all keywords of Q, then v must be a CA node. We then remove v and get a forest Fv ¼ fTv1; Tv2 ; . . . ; Tvng of subtrees rooted at the n child nodes of v. Based on Fv, we further find the set of subtrees FCA v Fv, where each subtree Tvi 2 FCA v contains every keyword of Q at least once, i.e., node vi is a CA node. If FCA v ¼ ;, it means that for Tv, only v is a CA node, then we can safely skip all nodes of Tv from being processed; otherwise, for each subtree Tvi 2 FCA v , we recursively compute its subtree set FCA vi until FCA vi ¼ ;. Let SiðvÞ denote, for v, the set of child nodes that contain ki, ScaðvÞ the set of child CA nodes of v, and CAðTvÞ the set of CA nodes in Tv. Formula 2 means that the set of CA nodes of Q equals the set of CA nodes in Tr, where r is the document root node. CAðTrÞ can be recursively
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A New Approach for Keyword Cover Search

A New Approach for Keyword Cover Search

that the distance-first spatial keyword question, where objects ar graded by distance and keywords are applied as a conjunctive filter to eliminate objects that don't contain them. That is our running example, displays a dataset of fictitious hotels with their spatial coordinates and a set of descriptive attributes (name, amenities)? AN example of a spatial keyword question is “find the closest hotels to purpose that contain keywords net and pool”. The highest results of this query is that the edifice object. Sadly there's no economical support for top-k spatial keyword queries, where a prefix of the results list is required. Instead, current systems use ad-hoc combinations of nearest neighbor (NN) and keyword search techniques to tackle the problem. There are easy ways to support queries that combine spatial and text features. For example, for the above query, we could first fetch all the restaurants whose menus contain the set of keywords, and then from the retrieved restaurants, find the nearest one. Similarly, one could also do it reversely by targeting first the spatial conditions – browse all the restaurants in ascending order of their distances to the query point until encountering one whose menu has all the keywords. The major drawback of these straightforward approaches is that they will fail to provide real time answers on difficult inputs. A typical example is that the real nearest neighbor lies quite far away from the query point, while all the closer neighbors are missing at least one of the query keywords. For better decision making, concept of keyword rating was introduced along with its features other than distance. For such search, query will take form of feature of objects. It search for nearest neighbor based on a new similarity measure, named weighted average of index rating which combine keyword rating, keyword search and nearest neighbour search.
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A Review on Scalable Keyword Cover Search

A Review on Scalable Keyword Cover Search

This issue has remarkable esteem in different applications since clients' prerequisites are often communicated as various keywords. For instance, a vacationer who arrangements to visit a city may have specific shopping, feasting and convenience needs. It is attractive that every one of these necessities can be fulfilled without long separation voyaging. Because of the astounding quality practically speaking, a few variations of spatial keyword search issue have been examined. The works mean to locate various individual protests, each of which is near a query location and the associated keywords (or called document) are exceptionally important to a set of query keywords (or called query document).
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Privacy-enhanced keyword search in clouds

Privacy-enhanced keyword search in clouds

The above limitations are addressed by Goh [10], Chang and Mitzenmacher [6] and also Curtmola, Garay, Kamara and Ostrovsky [7], etc. In [10], they built an index of keywords for each file using a Bloom filter with pseudo-random functions used as hash functions. One inherent problem with this Bloom- filter-based approach is that Bloom filters can induce false positives, which would potentially cause mobile users to download extra files not containing the keyword. In [6], Chang and Mitzenmacher achieved the notion of security to IND2- CKA for chosen keyword attack, except that it also tries to guarantee that the trapdoors do not leak any information about the words being queried. In [7], they proposed a multi-user construction that is efficient on server’s side, however every
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EFFICIENT KEYWORD SEARCH IN RELATIONAL DATABASES

EFFICIENT KEYWORD SEARCH IN RELATIONAL DATABASES

Every organization has data that needs to managed, analyzed and collected. A relational database system completes these needs. Along with these features of a relational database system come requirement for maintaining and developing the database. Database administrators, data analysts, and database designers need to be able to convert the data in a database into useful data for both day-to-day long-term planning and operations. RDBMS a database system made up of various files with data elements in two-dimensional array. It has the capability to recalculate data elements to from various relations resulting in a very great flexibility of data usage. Relational database management system is a DBMS in which data is saved in tables and the relationships among the data are saved in tables. The data can be reassembled and accessed in many different ways without change the table forms. It is a program that lets we administer, create, and update a relational database. Most commercial relational database management system uses the (SQL) Structured Query Language to access the database, although SQL was changed after the development of the relational database model and is not necessary for its use. Another important feature of relational database systems is that a each single database can be spread across different
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Keyword Generation for Search Engine Advertising

Keyword Generation for Search Engine Advertising

Proximity-based tools issue queries to a search engine to get highly ranked webpages for the seed keyword and expand the seed with words found in its proximity. For example for the seed keyword ‘hawaii vacations’, this tool will find keywords like: ‘hawaii family vacations’, ‘discount hawaii vacations’, etc. Though this tool finds a large number of keywords, it cannot find relevant keywords not containing the exact seed query words. The Google Adwords Tool [2] relies on query log mining for keyword generation. In Specific Matches, it presents frequent queries that contain the entire search term. Similarly, Overture’s Keyword Selection Tool lists frequent queries of recent past containing the seed terms. Both these techniques suffer from drawbacks like proximity-based searches, i.e., failure to generate relevant keywords not containing search terms. To generate Additional Keywords, Adwords mines advertiser logs. When searching for keyword ‘A’ to advertise, it presents other keywords which were searched for by other advertisers searching for ‘A’, i.e., it exploits co- occurrence relationships in advertiser query logs. Though this generates a large number of keywords, they are not always relevant. Also, keywords generated by this technique are limited to those words that occur frequently in advertiser search logs. Such frequent words have a good chance of being among expensive keywords, as they are already popular in the advertising community.
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A Survey on Best Keyword Cover Search

A Survey on Best Keyword Cover Search

ABSTRACT: Spatial databases are stores the information about the spatial objects which are associated with the keywords to show the information such as its business/services/features. Very important problem known as closest keywords search is to query objects, called keyword cover. In nearest keyword search, it covers a set of query keywords and minimum distance between objects. From last few years, keyword rating increases its availability and importance in object evaluation for the decision making. This is the main reason for developing this new algorithm called Best keyword cover which is considers inter distance as well as the rating provided by the customers through the online business review sites. Closest keyword search algorithm combines the objects from various query keywords toa generate candidate keyword cover. Two algorithms k-means clustering and keyword nearest-neighbor expansion algorithms are used to finding best keyword cover. K-means clustering algorithms are used to find out the similarity of different classes. The performance of the closest keyword algorithm drops dramatically, when the number of query keyword increases.
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Probabilistic RDF Graphs for Keyword Search

Probabilistic RDF Graphs for Keyword Search

ABSTRACT: In many real applications, RDF (Resource Description Framework) has been widelyusedto describe data in the Semantic Web in a W3C standard. RDF data may oftensuffer from the independency of their data sources, and exhibit errors or contrariety.Such unreliable RDF data by probabilistic RDF graphs, and study an importantproblem, probabilistic RDF graphs for keyword search query (namely, the pg-KWSquery). To retrieve meaningful keyword search answers, propose system design thescore rankings for sub graph answers specific for RDF data.The keyword searching technique over uncertain graph is introduced.The Keyword routing method is used to route the keywords to applicable source. In this Approachtwo methods are included. The keyword relationship graph concludes the relationship between keywords and the element mentioning them. Thescoring mechanism computes the score of keywords at each level which reduces the imprecision. The result will include the sub tree of the entire graph which includesall keywords of input query having high score and it retrieves the most significant data.
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An Efficient Method of Query Processing for Top-down XML Keyword

An Efficient Method of Query Processing for Top-down XML Keyword

Abstract: Proficiently noting XML watchword inquiries has pulled in much research exertion in the most recent decade. The key variables bringing about the wastefulness of existing strategies are the basic predecessor reiteration (CAR) and visitinguseless-nodes (VUN) problems. To address the CAR problem, we propose a non specific best down handling procedure to answer a given watchword question w.r.t. LCA/SLCA/ELCA semantics. By "top-down", we imply that we visit all regular precursor (CA) nodes in a profundity to start with, left-to-correct request; by "non specific", we imply that our strategy is free of the question semantics. To address the VUN problem, we propose to utilize kid nodes, as opposed to relative nodes to test the satisfiability of a node v w.r.t. the given semantics. We propose two algorithms that depend on either conventional modified records or our recently proposed LLists to enhance the general execution. We additionally propose a few algorithms that depend on hash search to improve the task of discovering CA nodes from all included LLists. The test comes about check the advantages of our strategies as indicated by different assessment measurements.
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Location Aware Keyword Query Suggestion Based On Document Proximity

Location Aware Keyword Query Suggestion Based On Document Proximity

ABSTRACT:Keyword suggestion in web search helps user to access relevant information without having to known how to precisely express their queries Exiting keyword suggestion techniques do not consider the location of user and the query result the spatial proximity of user to the retrieved result is not taken as a factored in the recommendation. However the relevance’s of search result in many application location based services is known to be correlated with proximity to the query issuer. Each query is related to one of topics identified in the conversion fragments preceding the recommendation and is submitted to a search engine over the English we propose in this paper an algorithm foe diverse merging of these lists using a sub modular reward function that reward the topical similar of documents to the conversation words as well as there diversity. We evaluates the proposed method through crowd sourcing the result superiority of the diverse merging technique over Several other which enforce the diversity of topics
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A Technic Based On Baseline Algorithm for Processing the Query with XML Keyword

A Technic Based On Baseline Algorithm for Processing the Query with XML Keyword

3. Collaborative synthetic clever (CAI). Non synthetic or conventional indexing strategies are tree based totally indexing, bitmap indexing, graph question processing, hashing, B tree, R tree which makes use of classifiers for indexing. artificial indexing strategies are extra accurate approach in constructing hybrid indexing mechanism. The strategies used underneath AI are fuzzy choice tree (FDT) and system mastering which produces green end result [10]. wellknown disk based shape and algorithms which incorporates B+ bushes, heap documents, disk based totally prefix timber, inverted indexes, binary massive item (BLOB) files, m manner posting list intersection, LRU buffer manager and external sorting used to build storage engine. Wook-Shin Han et al. carried out graph indexing techniques and confirmed diverse datasets and workload to expose diverse precise functions consisting of performance evaluation. They showed that tool supported for uploading dataset, choosing index algorithm, constructing index structure, specifying question workload, executing query and navigating through the results [11]. Indexing techniques are used to speed up the information retrieval. A. John et al. studied diverse tactics used to reduce the records information. update on trade and sampling are the main two approaches that are based on spatiotemporal indexing technique. statistics is represented in kinds:
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Document Proximity: Keyword Query Suggestion Based On User Location

Document Proximity: Keyword Query Suggestion Based On User Location

are most likely to refine the search of the user. Effective keyword suggestion methods are based on clicks information from query logs [2], [3], [4], [5], [6], [7], [8], and sessions. [9], [10], [11] or query subject models [12]. New keyword suggestions can be determined based on their semantic relevance to the original keyword query. The semantic relevance between two keyword queries can be determined (i) based on the overlap of their URLs clicked in a query log [2], [3], [4], (ii) by their proximity in a bipartite graph queries and their URLs clicked in the query log [5], [6], [7], [8], (iii) based on their co-occurrence in query sessions [13], and (iv) based on their similarity in the subject distribution space [12]. However, none of the existing methods provide a location keyword query suggestion, so that suggested keyword queries can retrieve documents not only related to the user's information needs, but also located near the location of the user. This requirement emerges because of the popularity of the spatial keyword search that takes a user location and a user-supplied keyword query as arguments and returns spatially close and textually relevant objects for those arguments. Google has processed an average of 4.7 billion queries per day in 20111, a substantial portion of which has
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Keyword Search and Geographic aspect Spatial Top k Search

Keyword Search and Geographic aspect Spatial Top k Search

There are number of location based services available on the sites. In which the number of keywords are used to retrieve the related information from those sites. Chengyuan Zhang et.al. The nearest neighbor search techniques used to find the nearest places from the database. Nearest neighbor finds based on the road distance. To enhance the data retrieval the problem of diversity of the data retrieval studied. Proposed the signature based indexing technique t0 search the spatial keyword by query retrieval system [3].

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An Efficient Nearest Keyword Set Query Search in Multi Dimensional Datasets
Adina Karunasri & B S N Murthy

An Efficient Nearest Keyword Set Query Search in Multi Dimensional Datasets Adina Karunasri & B S N Murthy

Maps. These objects are mainly connected with a set of tags capturing the embedded semantic and a set of coordinates showing their geographical locations. Traditional web resource searching strategies are not effective in such an environment due to the lack of the gazetteer context in the tags. In place of, a better alternative approach is to locate an object by tag matching. However, the number of tags associated with each object is typically small, making it difficult for an object to capture the complete semantics in the query objects. In this report, we concentrate on the basic application of locating geographical resources and propose an efficient tag-centric query processing strategy. In particular, we aim to find a set of nearest co-located objects which together match the query tags. Given the fact that there could be large number of data objects and tags, we develop an efficient search algorithm that can scale up in terms of the number of objects and tags. Further, to ensure that the results are relevant, we also propose a geographical context sensitive geo-tf-idf ranking mechanism. Our experiments on synthetic data sets show its scalability while the experiments using the real life data set confirm its utility.
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