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IoT and Big Data Using Intelligence
Published in Vijayalakshmi Saravanan, Alagan Anpalagan, T. Poongodi, Firoz Khan, Securing IoT and Big Data, 2020
We all are very well aware of the database management system (DBMS), which works on three ideologies: data acquisition, data processing, and data storage and management. However, when we work with real-time data, streaming processing, which is the key for data analytics, is enabled by a data streaming management system (DSMS). The differences between DBMS and DSMS are given in Table 3.4.Various real-time devices are capable of producing streaming data. For any real-time data streaming process, Rakusen has recommended the use of techniques like assessment, aggregation, correlation, and temporal analysis [4]. Because big data is capable of collecting enormous amounts of data, there is a chance of irrelevant data. Assessment of data could help exclude such uninteresting data sets from further processing and help save bandwidth by simply discarding them.
Big Data Stream Processing
Published in Vivek Kale, Parallel Computing Architectures and APIs, 2019
Database management systems (DBMS) were originally developed to support business applications. However, this model of a DBMS as a repository of relatively static data, which is queried as a result of human interaction, does not meet the challenges posed by streaming applications. Traditional DBMS are inadequate to meet the requirements of a new class of applications data stream management systems (DSMS), which require evaluating persistent long-running queries against a continuous, real-time stream of data items. A comparison of the capabilities of traditional DBMS and SDMS is shown in Table 20.1.
Data Stream Management for CPS-based Healthcare: A Contemporary Review
Published in IETE Technical Review, 2022
Sadhana Tiwari, Sonali Agarwal
Data stream management (DSM) for cyber-physical systems (CPSs)-based healthcare is in focus these days and considered as an accelerating field of research. To develop an intelligent and smart healthcare system, DSM using CPS is very important [1–3]. Although several research had been carried out in this area, managing uncertainties in the data stream is still a challenging problem in healthcare [4–6]. Data streams can be defined as continuous and rapid growing sequences of data with respect to time or real-time sequences of data. DSMs are responsible for managing continuous queries over data stream in real time. Major applications of DSM include healthcare, network traffic and fault management. The principal role of DSM is to give quality of service (QoS) and few QoS supported by DSM include load shedding, capacity planning and scheduling.
A review on big data real-time stream processing and its scheduling techniques
Published in International Journal of Parallel, Emergent and Distributed Systems, 2020
Nicoleta Tantalaki, Stavros Souravlas, Manos Roumeliotis
A stream processing system or data stream management system (DSMS), is designed to handle data streams and manage continuous queries. It executes continuous queries that are not only once performed, but are continuously executed until they are explicitly uninstalled. It produces results as long as new data arrives in the system and data is processed on the fly without the need for storing it. Data is usually stored after processing. Stream processing systems differ from batch processing systems, due to the requirement of real-time data processing. The term ‘real-time processing system’ refers to a system that responds within ‘real-world’ time deadlines. It guarantees that a certain process will be executed within a given period, maybe a few seconds, depending on the quality of service constraints. The term ‘real-time’ is a bit redundant but many systems use the term to describe themselves as low latency systems. Elaborate and agile systems have been proposed for these new demands.
Vertical accuracy evaluation of freely available latest high-resolution (30 m) global digital elevation models over Cameroon (Central Africa) with GPS/leveling ground control points.
Published in International Journal of Digital Earth, 2019
Loudi Yap, Ludovic Houetchak Kandé, Robert Nouayou, Joseph Kamguia, Nasser Abdou Ngouh, Marie Brigitte Makuate
DEMs are generally presented as 2D regular gridded arrays of elevations relative to a datum, and referenced to a geographic coordinate system (Forkuo 2010) and can be provided in the form of ASCII, text or image file (Burrough 1986; Shingare and Kale 2013). Generally, the southwest corner of the grid is the starting point of the values containing in the DEMs, with following values in the northward of previous ones. After the coverage of the first row in the south-northern direction, the next in the eastward is displayed in the same manner. Nowadays, some DEMs have origins in the grid top-left-hand corner and are provided in raster or in 2-byte integer binary formats. Some earth features as mountain peaks, lake surface, confluences of streams and landmarks (geodetic control points) are represented by points or spots. It is also the case of digitized contour line segments considered as vector lines that can also represent them and sounding depth estimates (for bathymetry). There exists some confusions between DSM, DTM and DEM. DSM depicts heights of vegetation canopy (e.g. the tops of trees with their leaves) and of man-made structures elevated above the bare earth (Hirt 2014; Croneborg et al. 2015). DSMs are particularly useful for telecommunications management, forest management, air safety, 3-D modeling, and simulation (Croneborg et al. 2015). DTMs represent the bare ground of the terrain. In a DTM, the distinctive terrain features defined by a set of discrete points with unique height values over 2D points are more clearly visible, and contours generated from DTMs more closely approximate the real shape of the terrain (Hirt 2014; Croneborg et al. 2015). In some countries, DEMs are synonymous with DTMs but often as an umbrella term to describe both DTM and DSM (Hutchinson and Gallant 2005; Wood 2008; Shingare and Kale 2013; Hirt 2014).