Keeyword Search in Databases (original) (raw)
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Efficient Query Processing For Imprecise Data
International Journal of Advanced Trends in Computer Science and Engineering , 2022
In real world applications we often need to test the queries based on fuzzy data. For example, some one can specify as "find students' whose age is around 17 years old."; "find tall person". "find employee with high salary"; "find country with low population" etc. This fuzziness in measurement is captured in this paper. To test such fuzzy queries, we have developed an algorithm that is applicable universally to any type of database. In this paper first we have designed architecture to test fuzzy query. In the architecture we have defined an algorithm to find the membership value for each tuple of the relation based on the fuzzy attributes on which fuzzy query is made. Next Decision Maker (DM) will supply a threshold value or -cut based on which corresponding SQL of the given fuzzy query will be generated. This SQL will retrieve the resultant tuples from the database. Finally we have tested our algorithm with an example.
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FBR System: User Directed Filtering of Imprecise Queries
International Journal of Advance Research and Innovative Ideas in Education, 2017
The rapid expansion of World Wide Web has made a large number of databases like the bibliographies, scientific databases etc. So user not able to express their need explicitly and it results in to queries that lead to unsatisfactory results. The FBR (Feature Based Retrieval) system allows user to use imprecise queries to express their uncertainty. The traditional way of searching the data requires specifying the queries clearly. More time is needed to retrieve the data with traditional approach. FBR system computes the sensitivity of the output if user modifies certain conditions. The new conditions to improve the quality of result will also be explored by the FBR system. FBR system is designed in such a way that it can handle the probabilistic queries containing uncertainty. To support interactive response time, FBR system allows user to set threshold value. In large databases, to reduce the searching time there is need to search database scientifically which will lead to faster in...
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Imprecise Queries Using Approximate Dependencies and Concept Similarities
2009
Abstract—Most of the proposed systems to process queries over web databases require the user to provide some information regarding the relative importance of attributes and the similarities between nominal values. Recently, a new system called AIMQ has been proposed, which is based on measuring concept similarities. This system is end-user independent and can answer imprecise queries. The main drawback of this system is that it is not incremental. All computations must be repeated when a tuple is added to the database. As a solution to this problem, in this article, we propose an incremental and efficient system called IQPI, which can be considered as the incremental version of AIMQ. In IQPI, the set of approximate dependencies between attributes are mined, first (using our new efficient approach). Using this set of dependencies, the user's imprecise query is converted into some precise queries. Each of the precise queries is then fed into the system and the results are filtere...
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An algorithmic approach to Approximate Query
2014
As data collection continues to grow rapidly the ability to efficiently carry out exploratory searches on the data is becoming more important. An exploratory search can be modeled as an approximate query in a database: retrieve all database elements which are similar to the query. Different forms of approximate queries (using domain-dependent notions of similarity) are already popular in many applications including data cleansing, pattern recognition, bioinformatics, address matching, and Internet search. Currently, the most popular approach for approximate query processing consists of a two-step (phase) process. The first phase is called the filter phase and consists of enumerating a set of q-grams or substrings in a database. The q-grams form the inverted index and the query will use the inverted index to prune those records that are unlikely to match the query. In the second refinement phase, all database records which passed through the first phase are validated to produce the final answer. Despite showing improvement over a full table scan, the two-phase approach for approximate querying is still not practical and is not part of any well-known database management system. This is partly because the index size can be very large - sometimes bigger than the size of the database. In this thesis, we propose an algorithmic approach to selecting q-grams which will constitute the inverted index. We model the q-gram selection problem as an optimization problem and explore several models including vertex cover and feedback vertex set and discuss their trade-offs. We will also evaluate several algorithm design patterns like greedy and primal-dual and LP relaxation to solve the optimization problems. Our particular focus is on evaluating techniques not just for approximate guarantees but also on how easily (or gracefully) they be implemented and integrated into a modern relational database management system. We will demonstrate that our approach results in an index size that is bounded above the size of the database and provides no false dismissals and a low false-positive rate. We have implemented our approaches in a database management system and demonstrate how approximate queries can be posed using SQL and be efficiently processed by the system.
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Text Retrieval through Corrupted Queries
Advances in Artificial Intelligence - IBERAMIA 2008, volume 5290 of Lecture Notes in Artificial Intelligence, pp. 362-371, Springer-Verlag, Berlin-Heidelberg-New York, 2008. ISSN 0302-9743 / ISBN 978-3-540-88308-1. DOI 10.1007/978-3-540-88309-8_37, 2008
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