Popular Query Language Research Articles

Application of Harmony Search Algorithm to Optimize SPARQL Protocol and Resource Description Framework Query Language Queries in Healthcare Data

The rapid developing international of internet, Semantic Web has become a platform for intelligent agents mainly in the healthcare sector. Inside the beyond few years there is a widening in the Semantic web data field in the healthcare industry. With a growth in the quantity of Semantic web data field in health industry, there exist some challenges to be resolved. One such challenge is to provide an efficient querying mechanism that can handle large number of Semantic web data. Consider many query languages; especially SPARQL (SPARQL Protocol and RDF Query Language) is the most popular query language. Each of these query languages has their own design strategy and it was identified in research that it is difficult to handle and query large quantity of RDF data efficiently using these languages. In the proposed process, Harmony search identify met heuristic algorithm to optimize the SPARQL queries in the healthcare data in the applicable manner. The application of Harmony search algorithm is evaluated with large Resource Description Framework (RDF) datasets and SPARQL queries. To assess performance, the algorithm’s implementation is compared to existing nature-inspired algorithms. The performance analysis shows that the proposed application performs well for large RDF datasets.

Error messages in relational database management systems: A comparison of effectiveness, usefulness, and user confidence

The database and the database management system (DBMS) are two of the main components of any information system. Structured Query Language (SQL) is the most popular query language for retrieving data from the database, as well as for many other data management tasks. During system development and maintenance, software developers use a considerable amount of time to interpret compiler error messages. The quality of these error messages has been demonstrated to affect software development effectiveness, and correctly formulating queries and fixing them when needed is an important task for many software developers. In this study, we set out to investigate how participants (N=152) experienced the qualities of error messages of four popular DBMSs in terms of error message effectiveness, perceived usefulness for finding and fixing errors, and error recovery confidence. Our results show differences between the DBMSs by three of the four metrics, and indicate a discrepancy between objective effectiveness and subjective usefulness. The results suggest that although error messages have perceived differences in terms of usefulness for finding and fixing errors, these differences may not necessarily result in differences in query fixing success rates.

An Ontology Based Expert System for Lung Cancer : OBESLC

Lung Cancer is the second most recurrent cancer in both men and women and which is the leading cause of cancer death worldwide. The American cancer Society (ACS) in US estimates nearly 228,150 new cases of lung cancer and 142,670 deaths from lung cancer for the year 2019. This paper proposes to build an ontology based expert system to diagnose Lung Cancer Disease and to identify the stage of Lung Cancer. Ontology is defined as a specification of conceptualization and describes knowledge about any domain in the form of concepts and relationships among them. It is a framework for representing shareable and reusable knowledge across a domain. The advantage of using ontology for knowledge representation of a particular domain is they are machine readable. We designed a System named OBESLC (Ontology Based Expert System for Lung Cancer) for lung cancer diagnosis, in that to construct an ontology we make use of Ontology Web Language (OWL) and Resource Description Framework (RDF) .The design of this system depends on knowledge about patient’s symptoms and the state of lung nodules to build knowledge base of Lung Cancer Disease. We verified our ontology OBESLC by querying it using SPARQL query language, a popular query language for extracting required information from Semantic web. We validate our ontology by developing reasoning rules using semantic Web Rule Language (SWRL).To provide the user interface, we implemented our approach in java using Jena API and Eclipse Editor.

Complexity of Two-Variable Logic on Finite Trees

Verification of properties expressed in the two-variable fragment of first-order logic FO 2 has been investigated in a number of contexts. The satisfiability problem for FO 2 over arbitrary structures is known to be NEXPTIME-complete, with satisfiable formulas having exponential-sized models. Over words, where FO 2 is known to have the same expressiveness as unary temporal logic, satisfiability is again NEXPTIME-complete. Over finite labelled ordered trees, FO 2 has the same expressiveness as navigational XPath, a popular query language for XML documents. Prior work on XPath and FO 2 gives a 2EXPTIME bound for satisfiability of FO 2 over trees. This work contains a comprehensive analysis of the complexity of FO 2 on trees, and on the size and depth of models. We show that different techniques are required depending on the vocabulary used, whether the trees are ranked or unranked, and the encoding of labels on trees. We also look at a natural restriction of FO 2 , its guarded version, GF 2 . Our results depend on an analysis of types in models of FO 2 formulas, including techniques for controlling the number of distinct subtrees, the depth, and the size of a witness to satisfiability for FO 2 sentences over finite trees.