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 Database using SQL

A type ahead search system computes answers on the fly as a user types in a keyword query character by character. We are going to study how to support type ahead search on data in a relational DBMS. We focus on how to help this type of search using the SQL. A prominent task that tests is how to influence existing database functionalities to meet the high performance to achieve an interactive speed. We extended the efficient way to the case of fuzzy queries, and suggested various techniques to improve query performance. We suggested incremental computation method to answer multi keyword queries, and calculated how to support first N queries and incremental updates. Our experimental results on large and real data sets showed that the proposed techniques can enables DBMS systems to support search as you type on large tables. DOI: 10.17762/ijritcc2321-8169.15024

An Extensible "SCHEMA-LESS" Database Framework for Managing High-Throughput Semi-Structured Documents

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semistructured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac

Keyword Search on RDF Graphs - A Query Graph Assembly Approach

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is $O(k^{2l} \cdot l^{3l})$, where $l$ is the number of the keywords and $k$ is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both $l$ and $k$ are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency