ISO/IEC TR 20547-2:2018 pdf download – Information technology — Big data reference architecture — Part 2: Use cases and derived requirements.
4.2 Current solution Current solutions describe current approach to processing big data at the hardware and software infrastructure and analytics level. — Compute (System): Computing component of the data analysis system — Storage: Storage component of the data analysis system — Networking: Networking component of the data analysis system — Software: Software component of the data analysis system 4.3 Big data characteristics Big data Characteristics describe the properties of the (raw) data including the four major ‘V’s’ of big data. — Data source: The origin of data, which could be from instruments, Internet of Things, Web, Surveys, Commercial activity, or from simulations. The source(s) can be distributed, centralized, local, or remote. — Data destination: If data transformed in use case, where the final results end up. — Volume: The characteristic of datasets that is most associated with big data. Volume represents the extensive amount of data available for analysis to extract valuable information. The assumption that you can extract the most value by analysing as much of the volume of data as possible was one of the primary drivers for the creation of the new scaling technologies. — Velocity: The rate of flow at which the data is created, stored, analysed, or visualized. Big data velocity means a large quantity of data needs to be processed in a short amount of time. Dealing with high velocity data is commonly referred to as techniques for streaming data. — Variety: The need to analyse data from a number of domains and a number of data types. The variety of data was handled through transformations or pre-analytics to extract features that would allow integration with other data. The wider range of data formats, logical models, timescales, and semantics, which is desirous to be used in analytics, complicates the integration of the variety of data. Metadata is increasingly used to aid in the integration.
4.5 Overall big data issues — Other big data issues: Did we miss something important that your use case highlights? Your chance to address questions which we should have asked. — User Interface and mobile access issues: Refers to issues in accessing or generating big data from clients including smart phones and tablets. — List key features and related use cases: Put use case in context of related use cases. What features generalize and what are idiosyncratic to this use case. — Project future: How do you expect application, and approach (hardware, software, analytics) to change in future? — More project information (URLs): Put a collection of useful links. 4.6 Big data use case Template This clause provides one blank use case template. The below blank use case template was used for the purpose of capturing use cases to derived technical consideration. NOTE The terms used in this template may or may not match with ISO/IEC 20546 and other parts of the ISO/IEC 20547-series.
5 Use cases summaries 5.1 Use case development process A use case is a typical application stated at a high level for the purposes of extracting technical considerations or comparing usages across fields. In order to develop a consensus list of big data technical considerations across all stakeholders, publicly available information was collected for various big data architectures. After collection of use cases, application domains were identified to better organize the collection of use cases. NOTE 1 The list of application domains reflects the use cases submitted and is not intended to be exhaustive. The nine application domains were as follows: — Government operation (4): National Archives and Records Administration, Census Bureau; — Commercial (8): Finance in Cloud, Cloud Backup, Citations, Multi-media streaming, Web Search, Digital Materials, Cargo Shipping; — Defense (3): Sensors, Image Surveillance, Situation Assessment ; — Healthcare and life sciences (10): Medical Records, Graph and Probabilistic Analysis, Pathology, Bioimaging, Genomics, Epidemiology, People Activity Models, Biodiversity; — Deep learning and social media (6) Self-driving cars, Geolocate Images, SNS, Crowd Sourcing, Network Science, Benchmark Datasets;