China
Africa
Explore chapters and articles related to this topic
Data Science—Analytics, Context, and Strategies
Published in Unhelkar Bhuvan, Big Data Strategies for Agile Business, 2017
The following are the underlying technology aspects supporting data management in the Big Data domain:Hadoop Distributed File System (HDFS) architecture: Provides the distributed, redundant, fail-safe base for massive parallel processingMapReduce algorithms: Enable manipulation of dataSpark: Enhances the basics of Hadoop by making it in memory and thereby fastPig, Hive, Hbase, and Zookeeper: Apache projects that enable further handling and manipulation of data within the Hadoop technical ecosystemNoSQL data storage: Enables storing of unstructured data that can come in multiple mixed formats and that needs to interface with the enterprise structured data for analytical purposesR and Python: Data processing languages that are particularly suitable for manipulating Big Data storages
Criteria, Factors, and Models
Published in Christian Tominski, Heidrun Schumann, Interactive Visual Data Analysis, 2020
Christian Tominski, Heidrun Schumann
One such format that is universally applicable is the data table. A data table consists of rows and columns. The columns represent data variables. Each variable is associated with a data domain that specifies the values that can possibly appear in a column. The values that actually do appear in a column define the value range.
Reliability and Fatigue Life
Published in Srinivasan Chandrasekaran, Offshore Semi-Submersible Platform Engineering, 2020
Select the input data domain as Time History and the data type as Stress. Select the Rainflow Counting algorithm in the analysis and Fatigue in the toolboxes.
Interoperability in cloud manufacturing: a case study on private cloud structure for SMEs
Published in International Journal of Computer Integrated Manufacturing, 2018
Xi Vincent Wang, Lihui Wang, Reinhold Gördes
As illustrated in Figure 1, Sheth (1999) identified three generations of interoperability in semantic level, syntax level and system level from the data’s perspective. Semantic interoperability focuses on the domain-specific semantic, which can be achieved based on the comprehensive use of metadata, i.e. semantics- and ontology-based approaches. Syntax interoperability emphasises on structured data types and formats, schematic, query languages and interfaces. It is essential to understand a variety of metadata and schematic heterogeneity. System interoperability concentrates on communications within and between computer systems. Limited aspects of syntax and structure are considered at this level. In Sheth’s research, it was also predicted that the information would be accessed in media-independent methods in multi-media views, which was proven correct and already realised and developed in CM research. Similarly, Bishr (1998) provided a more detailed classification of interoperability, i.e. semantics, data, database management system, file, hardware, protocol and system interoperability. In the data domain, the syntax is divided into data model, database and data file issues. Meanwhile, the system interoperability is also considered from a more detailed component perspective, which leads to hardware, protocol and system interoperability at three levels.