Magento 2 and Elasticsearch Integration Kan, Andy Sukanto; Boots, Peter; Delvaux, Bart
JIRAE (International Journal of Industrial Research and Applied Engineering) (Online),
08/2020, Volume:
4, Issue:
1
Journal Article
Peer reviewed
Open access
The purpose of this research is to extend the default integration of Elasticsearch in Magento 2.3 to search for other content which is category and content pages. The current search engine in Magento ...uses the default MySQL search engine, with the new update of Magento 2.3, Elasticsearch is introduced as an optional search engine. Elasticsearch provides a better and optimized full text search, however this new integration can only search the catalog products, therefore a way of extending the integration of Elasticsearch in Magento is searched for. The features that wanted to be implemented is Category search using Elasticsearch in Magento 2.
Data Benchmark pada Google BigQuery dan Elasticsearch Nisrina Akbar Rizky Putri; Widyawan; Teguh Bharata Adji
JNTETI (Jurnal Nasional Teknik Elektro dan Teknologi Informasi) (Online),
08/2021, Volume:
10, Issue:
3
Journal Article
Peer reviewed
Open access
Cloud di masa kini tidak hanya berfungsi sebagai media penyimpanan data, tetapi dapat digunakan juga sebagai media untuk mengelola ataupun menganalisis suatu data. Google menawarkan Google BigQuery ...sebagai platform yang mampu mengelola dan menganalisis data, sedangkan Elasticsearch merupakan mesin pencari dan analisis yang dapat digunakan untuk menganalisis data dengan menggunakan Kibana. Dengan menggunakan dataset berupa cuitan hasil proses crawling melalui http://netlytic.org/ yang mengandung tagar #COVID19 dan #coronavirus, data tersebut dianalisis dan digunakan untuk membandingkan kinerjanya dengan benchmark. Benchmark merupakan proses yang digunakan untuk mengukur dan membandingkan kinerja terhadap sebuah aktivitas, sehingga tercapai tingkat kinerja yang diinginkan. Data benchmark dilakukan pada kedua platfrom untuk menghasilkan atau mengetahui beban kerja dari platfrom. Hasil akhir yang didapatkan menunjukkan bahwa Google BigQuery memiliki hasil yang lebih unggul, baik dari wadah upload untuk dataset yang lebih besar dibandingkan Elasticsearch dan dengan dua model pengujian kueri. Waktu pengelolaan kueri pada Google BigQuery juga lebih singkat dan cepat dibandingkan dengan Elasticsearch. Selain itu, hasil visualisasi dari kedua platform ini memiliki jumlah persentase yang sama.
Currently, humans live in an era of data oceans, where the amount of data production is increasing from time to time, which is followed by severe challenges in terms of processing, storing, and ...analyzing data, especially big data. The increase in the number of large data production can affect the speed of access to the database, effectiveness, and speed of response time in the data processing. Relational databases have been the leading model for data storage, analysis, processing, and retrieval for more than forty years. However, due to the increasing need for large-scale data storage, the scalability and performance of a data processing system, as well as the constant growth of the amount of data, another alternative to databases emerged, namely NoSQL technology. Based on previous studies regarding the comparison of response time and database performance, the average concludes that NoSQL performance is more effective and efficient than relational databases. Based on the implementation and testing, it can be concluded that the NoSQL database application MongoDB is proven to be superior in every command of CRUD tested compared to the Elasticsearch NoSQL database application, where in testing the create data command with a JSON file, the MongoDB database application is 42.5 times faster than the Elasticsearch database application. In testing the command to create data into a database containing different amounts of data, the MongoDB database application is 333.9 times faster than the average response time of the Elasticsearch database application. In testing the read command for data in a database containing different amounts of data, the MongoDB database application is 35.5 times faster than the Elasticsearch database application. In testing the update operation of data in a database containing different amounts of data, the MongoDB database application is 9.8 times faster than the Elasticsearch database application. in testing the delete operation of data in a database containing different amounts of data, the MongoDB database application is 58.9 times faster than the Elasticsearch database application.
Resumen: Los repositorios científicos almacenan cantidades de documentos publicados con información útil para la comunidad, por lo que recuperar información de manera manual es casi imposible, y la ...extracción automática de los contenidos es cada día un reto ya que la mayor parte de los documentos no están estructurados. Palabras-clave: ontologías, procesamiento de lenguaje natural, Internet de las cosas, recuperación de información, elasticsearch Abstract: Scientific repositories storages a big amount of published documents with useful information for the community, that is why retrieving information manually is almost impossible, and the automatic extraction of content is a daily challenge since most of the documents are not structured. In this research we have proposed the construction of a domain ontology in the Internet of things, which allows the optimization of the information retrieval in scientific documents, using natural language processing techniques based on ontologies, giving as a result more efficient relevance and precision than other methods. Keywords: Ontology, information retrieval, natural languaje process, Internet of Things, Elasticsearch 1.Introducción A lo largo de los años se han hecho muchos estudios acerca del papel que juegan las ontología en el proceso de recuperación de la información, por lo que existen diferentes herramientas, métodos y técnicas que permiten mostrar mejores resultados así como los documentos que contienen los conceptos ontológicos dentro del dominio y que es de vital importancia mantener una relación de sinonimia y parentesco (Zhou et al., 2021).
Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope ...and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present.
To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall.
We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.
Abstract
IoT platforms are in charge of extracting and processing the data that come from IoT networks, generating additional value, and providing access to the user through usable interfaces. ...However, the ever growing number of devices, networks, services and applications within the IoT ecosystem, and the recently adopted edge/cloud architecture, increase the complexity. Therefore, IoT platforms should integrate monitoring and visualization tools to facilitate deployment, management and maintenance tasks. In this work, we present the implementation and performance evaluation of an IoT modular platform for distributed architectures that combines the use of Elastic Stack tools (Elasticsearch, Kibana and Beats) and Apache Kafka. We have developed a monitoring framework based on Beats agents that supervise the platform performance attending to different metrics; and adapted the Kibana visualization tools to provide friendly and accessible information to platform administrators and users. Finally, we have deployed and evaluated the IoT platform in four real use cases, identifying the factors that affect the performance of the different modules: Edge Node, Data Streaming, Cloud Server and Search Engine.
Real-time online data processing is quickly becoming an essential tool in the analysis of social media for political trends, advertising, public health awareness programs and policy making. ...Traditionally, processes associated with offline analysis are productive and efficient only when the data collection is a one-time process. Currently, cutting edge research requires real-time data analysis that comes with a set of challenges, particularly the efficiency of continuous data fetching within the context of present NoSQL and relational databases. In this paper, we demonstrate a solution to effectively adsress the challenges of real-time analysis using a configurable
Elasticsearch
search engine. We are using a distributed database architecture, pre-build indexing and standardizing the Elasticsearch framework for large scale text mining. The results from the query engine are visulized in almost real-time.
Mayo Clinic (MC) healthcare generates a large number of HL7 V2 messages-0.7-1.1 million on weekends and 1.7-2.2 million on business days at present. With multiple RDBMS-based systems, such a large ...volume of HL7 messages still cannot be real-time or near-real-time stored, analyzed, and retrieved for enterprise-level clinic and nonclinic usage. To determine if Big Data technology coupled with ElasticSearch technology can satisfy MC daily healthcare needs for HL7 message processing, a BigData platform was developed to contain two identical Hadoop clusters (TDH1.3.2 version)-each containing an ElasticSearch cluster and instances of a storm topology-MayoTopology for processing HL7 messages on MC ESB queues into an ElasticSearch index and the HDFS. The implemented BigData platform can process 62 ± 4 million HL7 messages per day while the ElasticSearch index can provide ultrafast free-text searching at a speed level of 0.2-s per query on an index containing a dataset of 25 million HL7-derived-JSON-documents. The results suggest that the implemented BigData platform exceeds MC enterprise-level patient-care needs.
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