A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

QoS–Aware Web Service Composition using Weight Improved Particle Swarm Optimization

Lots of Web Services are available which differ in their QoS values but can perform a similar task. Discovery mechanism selects the best Web Service according to their QoS values and functional attributes. Cases arise, where the discovery mechanism fails, as a user’s complex query cannot be satisfied by a single Web Service. This can be solved by Web Service composition where multiple Web Services are combined to give a composite Web Service which meet user’s complex query. Our work is mainly focused on composition of Web Services that efficiently meets the user’s query. Different algorithms have been discussed and used by different researchers in this field. One of the most blooming topics is the use of evolutionary algorithms in optimization problems. In our work, we have chosen Particle Swarm Optimization Algorithm approach to discover the best efficient composition. Then, Weight Improved Particle Swarm Optimization Algorithm is used to improve the results which were found to be quite satisfying and efficient.

Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

Efficient Processing of Queries using Filtered Bitmap Index with Multi-Join Multiple Set Predicates

Efficient query process is an essential task in numerous environments that relate large sum of information. The performance degradation occurs when the sum of information is increased. It will further degrade when the amount of joins in the queries is increased. These problems emphasize a need for good query processing approach. Thus, in this report, we take a various method to optimize the multi-join query with multiple set predicates in Data warehousing environment. So we have proposed an effective algorithm as Filtered Bitmap Index with multi-join multiple set predicates processing approach and examine the time complexity on huge data set with multiple tables. In this approach, the multi-join query is processed by selecting the tabular array based on their level number from lower to higher. A simple rewritten query was created from the given complex query exploitation uses the lowest level table and executed. If the result exists then only continue the join processing in the rewritten query, by taking the next lower level table from the complex query and do the execution. The ratio of our technique is to demonstrated with moving experiment using WorldCup98 and TPC-H benchmark datasets

A Novel IR for Relational Database using Optimize Query Building

The different type search engine like Google, binge, AltaVista is used to fetch the information from the database by easy language. The non-technical employee they don’t understand the database and query cannot access the database. The proposed system is performing work as a search engine where users can fetch the information from the database by natural human sounding language. The previous existing system doesn’t able to solve queries in one easy statement. The structured query approach, while expressive and powerful, is not easy for naive users. The keyword-based approach is very friendly to use, but cannot express complex query intent accurately. This paper emphasis on Natural Language based query processor. We have proposed the use of query optimization approach to convert complex NLP query to SQL query, SPAM word removal, POS tagger applied over NL query and concluded that execution time lesser when query size increases.