China
Africa
Explore chapters and articles related to this topic
Databases
Published in Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, Big Data and Social Science, 2020
The need to support extremely large quantities of data and numbers of concurrent clients has led to the development of a range of alternative database technologies that relax consistency and thus these ACID properties in order to increase scalability and/or availability. These systems are commonly referred to as NoSQL (for “not SQL”—or, more recently, “not only SQL,” to communicate that they may support SQL-like query languages) because they usually do not require a fixed table schema nor support joins and other SQL features. Such systems are sometimes referred to as BASE (Fox et al., 1997): Basically Available (the system seems to work all of the time), Soft state (it does not have to be consistent all of the time), and Eventually consistent (it becomes consistent at some later time). The data systems used in essentially all large Internet companies (Google, Yahoo!, Facebook, Amazon, eBay) are BASE.
Application of graph databases in the communication and information asset management in power grid
Published in Lin Liu, Automotive, Mechanical and Electrical Engineering, 2017
Xuming Lv, Shanqi Zheng, Zhao Li, Siyan Liu, Yue Wang
Neo4j, a graph database management system developed by Neo Technology Inc, is one of the most popular graph databases according to www.db-engines.com. The first version is released in 2010, and the latest version, i.e., the third version is released in 2016. Neo4j supports graph model called “Property graph”, which includes nodes, edged and attribute (i.e., properties). Neo4j is written in Java, and provides APIs which are exposed through a whole range of various languages, e.g. Java, Python, Ruby, JavaScript, PHP, .NET, etc. It is noteworthy that C and C++ are not in the above list. Cypher is a declarative graph query language for the graph database Neo4j, and is roughly equivalent to SQL querying language in relational databases. The database system also implements the Blue-prints interface and a native REST interface to further expand the ways to communicate with the database. Neo4j provides three editions: Community, Enterprise, and Government. Community version is provided for individuals to learn graph databases and conduct smaller projects that do not require high levels of scaling. However, it excludes professional services and support. The Neo4j Enterprise edition offers incredible power and flexibility, with enterprise-grade availability, management and scale-up & scale-out capabilities.
m-Health: Community-Based Android Application for Medical Services
Published in Adwitiya Sinha, Megha Rathi, Smart Healthcare Systems, 2019
Mahima Narang, Charu Nigam, Nisha Chaurasia
To maintain the frequently changing database that stores the information about the token IDs (generated by Firebase as the app is installed on the device) and health units, it is important to maintain a real-time database called Firebase. The advantages of using Firebase in the proposed work are as follows: Latency: To overcome latency and get an optimized result for providing the best health unit in a fast and efficient manner, Firebase is used. It is a real-time database that follows the concept of NoSQL that does not hold database and tables like Structured Query Language (SQL); instead, it stores data in key/value pairs. The data is stored in the form of JavaScript Object Notation (JSON) (Firebase Realtime Database, n.d.). In contrast to the relational database framework, for example SQL, NoSQL does not have relationships between tables, which engage in frequent change in values (Leavitt, 2010). The data can be synced with all the end users in realtime, and the data remains stored and its functions work even if there is no internet connection (Alsalemi et al., 2017). This attribute permits agile development of the database as the location of the user can be changed from time to time, accordingly his/her nearby health units are changed. All this need quick data modification, deletion, insertion, and quick information retrieval for the best results.Compatibility: Firebase database, owned by Google, can support various platforms and devices, such as Android, iOS, Linux, and Arduino firmware (Su et al., 2011), which adds another factor to use Firebase.
Semantic rules for capability matchmaking in the context of manufacturing system design and reconfiguration
Published in International Journal of Computer Integrated Manufacturing, 2023
Eeva Järvenpää, Niko Siltala, Otto Hylli, Hasse Nylund, Minna Lanz
The resource functionalities and their related parameters are formalised by the Capability Model (Järvenpää et al. 2019a). This model defines simple and combined capabilities and formalises their relationships through the cm:hasInputCapability object property. As an example, a robot can have the simple capability ‘Moving’, and similarly, a gripper can have simple capabilities ‘Grasping’ and ‘Releasing’. If a robot and gripper are combined, they can have combined capabilities ‘Pick and Place’ and ‘Transporting’. The instances of cm:Capability are linked to the instances of rm:Device through the rm:hasCapability object property. The formalised relations between the simple and combined capabilities allow computer programs to form potential resource combinations having specific combined capabilities by utilizing information queried from the ontology by SPARQL. SPARQL is a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework (RDF) format and OWL ontologies (W3C SPARQL Working Group 2013). The following section will discuss the rules that allow automatic inference of the parameters of the combined capabilities. The rm:hasCalculatedCapability object property links this combined capability information to the specific device combination.
Co-simulation of complex engineered systems enabled by a cognitive twin architecture
Published in International Journal of Production Research, 2022
Yuanfu Li, Jinwei Chen, Zhenchao Hu, Huisheng Zhang, Jinzhi Lu, Dimitris Kiritsis
The simulation results of multiple FMUs in one specific simulation scenario are usually stored in one data set, resulting in some obstacles to the extract and analysis of data. Since the digital entity creator usually names a variable with a simplified name, it is difficult for the non-related sub-system creator to comprehend several variables. Therefore, it is necessary to both obtain the simulation results and analyze the simulation result of a specific variable. The ontology can store the knowledge about the model variables and realise the correspondence and description of the simulation results. Thus, the reasoning capability in the CT is fulfilled by reasoning with ontology. The SPARQL is used to reason about simulation results, which are time-series data. SPARQL (SPARQL Protocol and RDF Query Language) is the standard language for querying RDF data (Pérez, Arenas, and Gutierrez 2009).
A generic knowledge management approach towards the development of a decision support system
Published in International Journal of Production Research, 2021
Oussama Meski, Farouk Belkadi, Florent Laroche, Mathieu Ritou, Benoit Furet
The Global Framework is developed using the Java language. The technological issue was to find the most suitable API to communicate with the ontology developed using the Web Ontology Language (OWL). The required solution is to use the Jena API. Apache Jena, as stated on the Jena website, is a Java framework to construct Semantic Web Applications. It provides a programmatic environment for: Resource Description Framework (RDF), a graph model for describing web resources and their metadata, so that such descriptions can be processed automatically. Developed by the W3C, RDF is the basic language of the Semantic Web.The Web Ontology Language (OWL), a knowledge representation language built on the RDF data model. It provides the means to define structured web ontologies.SPARQL Protocol and RDF Query Language, a protocol that allows searching, adding, modifying or deleting RDF data available through the Internet.The Jena inference subsystem, designed to allow a range of inference engines or reasoners to be plugged into Jena. Such engines are used to derive additional RDF assertions, which are entailed from some base RDF together with any optional ontology information, and the axioms and rules associated with the reasoner.