000			03819nas a22003975i 4500
001			2364-1541
003			DE-He213
005			20180511105620.0
007			cr nn 008mamaa
008			150723s||||||||gw uu poo|||||| b|EN |d
022			_a2364-1541
024	7		_a41019 _2local
210	1	0	_aData Sci. Eng.
245	1	0	_aData Science and Engineering _h[electronic resource] / _cedited by Elisa Bertino, Xiaoyang Wang.
264		1	_aBerlin/Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer.
300			_bonline resource.
520			_aThe journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing, (c) new methods for the design and analysis of the algorithms for solving problems with big data input, (d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data, (f)  storage, transmission, and management of big data, (g) methods and algorithms of  data intensive computing, such as mining big data, online analysis processing of big data, big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and   big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and (j) novel applications of big data.  .
650		0	_aComputer science.
650		0	_aComputer security.
650		0	_aAlgorithms.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aDatabase Management.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aSystems and Data Security.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
700	1		_aBertino, Elisa. _eeditor.
700	1		_aWang, Xiaoyang. _eeditor.
710	2		_aSpringerLink (Online service)
776	0	8	_iPrinted version: _x2364-1185
856	4	0	_uhttp://link.springer.com/journal/41019 _zOpen Access
999			_c189677 _d189677