A Query Language for Workflow Logs

A business process (workflow) is an assembly of tasks to accomplish a business goal. Real-world workflow models often demanded to change due to new laws and policies, changes in the environment, and so on. To understand the inner workings of a business process to facilitate changes, workflow logs have the potential to enable inspecting, monitoring, diagnosing, analyzing, and improving the design of a complex workflow. Querying workflow logs, however, is still mostly an ad hoc practice by workflow managers. In this article, we focus on the problem of querying workflow log concerning both control flow and dataflow properties. We develop a query language based on “incident patterns” to allow the user to directly query workflow logs instead of having to transform such queries into database operations. We provide the formal semantics and a query evaluation algorithm of our language. By deriving an accurate cost model, we develop an optimization mechanism to accelerate query evaluation. Our experiment results demonstrate the effectiveness of the optimization and achieves up to 50× speedup over an adaption of existing evaluation method.

Driving cycle tracking device big data storing and management

<span>Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing.</span>

A unified ontology-based data integration approach for the internet of things

<span lang="EN-US">Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.</span>

Building Quranic stories ontology using MappingMaster domain-specific language

<p>The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL)technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well.</p>

Raif-Semantics: A Robust Automated Interlinking Framework for Semantic Web Using MapReduce and Multi-node Data Processing

The era of the web has evolved and the industry strives to work better every day, the constant need for data to be accessible at a random moment is expanding, and with this expansion, the need to create a meaningful query technique in the web is a major concerns. To transmit meaningful data or rich semantics, machines/projects need to have the ability to reach the correct information and make adequate connections, this problem is addressed after the emergence of Web 3.0, the semantic web is developing and being collected an immense. Information to prepare, this passes the giant data management test, to provide an ideal result at any time needed. Accordingly, in this article, we present an ideal system for managing huge information using MapReduce structures that internally help an engine bring information using the strength of fair preparation using smaller map occupations and connection disclosure measures. Calculations for similarity can be challenging, this work performs five similarity detection algorithms and determines the time it takes to address the patterns that has to be a better choice in the calculation decision. The proposed framework is created using the most recent and widespread information design, that is, the JSON design, the HIVE query language to obtain and process the information planned according to the customer’s needs and calculations for the disclosure of the interface. Finally, the results on a web page is made available that helps a user stack json information and make connections somewhere in the range of dataset 1 and dataset 2. The results are examined in 2 different sets, the results show that the proposed approach helps to interconnect significantly faster; Regardless of how large the information is, the time it takes is not radically extended. The results demonstrate the interlinking of the dataset 1 and dataset 2 is most notable using LD and JW, the time required is ideal in both calculations, this paper has mechanized the method involved with interconnecting via a web page, where customers can merge two sets of data that should be associated and used.

Graph Database to Enhance Supply Chain Resilience for Industry 4.0

Supply chain network in the automotive industry has complex, interconnected, multiple-depth relationships. Recently, the volume of supply chain data increases significantly with Industry 4.0. The complex relationships and massive volume of supply chain data can cause visibility and scalability issues in big data analysis and result in less responsive and fragile inventory management. The authors develop a graph data modeling framework to address the computational problem of big supply chain data analysis. In addition, this paper introduces Time-to-Stockout analysis for supply chain resilience and shows how to compute it through a labeled property graph model. The computational result shows that the proposed graph data model is efficient for recursive and variable-length data in supply chain, and relationship-centric graph query language has capable of handling a wide range of business questions with impressive query time.

A Comparative study of SQL Databases and NoSQL Databases for E-Commerce

With the advent of E-Commerce, businesses persistently examine various ways to improvise and accomplish their demands with web engineering that provide notable resolution. The progress in economic status demands colossal databases that store the data efficiently. The databases currently used are relational or non-relational. Both these types have their benefits and limitations that influence the overall processing of data. Non-relational databases are referred to as NoSQL-not only SQL, and Relational databases are known as SQL-Structured Query Language. It has been suggested in many studies that NoSQL databases surpass SQL databases. Our paper aims to evaluate these claims by analyzing the CRUD [Create, Read, Update, Delete] operations executed by both database types. Keywords: NoSQL, SQL, Non-relational Databases, MySQL, E-Commerce, MongoDb , Relational Databases

Diagnósticos de enfermagem prevalentes de pacientes em cuidados paliativos: uma mineração de dados

Introdução: o cuidado paliativo é centrado no paciente e inclui abordagens para o alívio dos sintomas e redução fisiológica e sofrimento psíquico associado à doença. Neste contexto, o diagnóstico de enfermagem (DE) estabelece bases para a seleção de intervenções de enfermagem para alcançar resultados nesta população, pelos quais o enfermeiro é responsável. Objetivo: identificar os DE prevalentes em pacientes em cuidados paliativos, perfil sociodemográfico e clínico de pacientes adultos hospitalizados que receberam consultorias em cuidados paliativos em unidades clínicas e cirúrgicas registrados em prontuário eletrônico. Método: estudo observacional retrospectivo com uso secundário de dados. A população do estudo foi composta por todos os adultos internados nas unidades clínicas e cirúrgicas de um hospital universitário entre junho de 2014 e julho de 2019, totalizando aproximadamente 51.000 prontuários únicos. A amostra compreendeu os pacientes que receberam consultorias em cuidados paliativos durante a internação. A análise dos dados foi realizada por meio do Structured Query Language (SQL). Resultados: foram eleitos 91 diferentes diagnósticos de enfermagem distintos para a amostra do estudo. Destes, três DE foram prevalentes: Risco de quedas esteve presente na prescrição de 1350 pacientes, Integridade tissular prejudicada em 1073 prescrições e Dor aguda em 1032. Conclusão: espera-se que a metodologia adotada nesta pesquisa apoie o processo de tomada de decisão dos profissionais de saúde, a fim de melhorar a eficácia no cuidado paliativo e otimizar o processo de segurança do paciente.