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022			_a2524-6364
024	7		_a42488 _2local
210	1	0	_aJ. of Data, Inf. and Manag.
245	1	0	_aJournal of Data, Information and Management _h[electronic resource] / _cedited by El-Sayed M. El-Alfy, Michael Segal, Witold Pedrycz, Xiande Zhao, Xiang Li.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer.
300			_bonline resource.
520			_aOwing to the numerous advances in computer and the Internet technology, and now cloud computing services and big data, we are experiencing major changes in our life. Nowadays, the number of factors that influence management is so huge and complex that neither sophisticated theories nor elegant advanced models can offer complete and effective solutions. The challenges are in many ways fundamental that require closer collaboration to draw upon a collective wisdom across different disciplines. Data can enable, encourage and enhance such collaborative linkages. Data Sciences, Information Sciences and Management Sciences are by their very nature interdisciplinary. In our information-intensive era, to remain competitive, maintain constant vigilance and safeguard integrity and security in modern management, we have to rely on big data. Beyond any doubt data analytics has become an integral part of and is integrating information and management sciences. The Journal of Data, Information and Management (JDIM) is a unique and premiere platform for disseminating most up-to-date research and development in the data-information-management interdisciplinary problems. JDIM will offer the business world a venue that prudently balances practical applicability and theoretical rigour. Key research areas of JDIM include, but are not restricted to: Data Technology and Methodology Sensoring and identification technology Data processing and storage (hiding, fusion, compression, etc.) Data mining Data quality management Granular computing Deep learning Machine learning Neural networks Other technologies and methodologies Applications Data privacy and security Digital marketing and consumer behavior Data-driven production design and development Data-driven quality improvement Data-driven supply chain/logistics optimization Supply chain finance Data-driven intelligent manufacturing Data-driven reliability and maintenance management Intelligent transportation with big data Online decision-making Other applications (Healthcare, government, education, building, etc.).
650		0	_aComputational intelligence.
650		0	_aBusiness.
650		0	_aManagement science.
650		0	_aArtificial intelligence.
650	1	4	_aComputational Intelligence.
650	2	4	_aBusiness and Management.
650	2	4	_aArtificial Intelligence.
700	1		_aEl-Alfy, El-Sayed M. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSegal, Michael. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPedrycz, Witold. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aZhao, Xiande. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLi, Xiang. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
776	0	8	_iPrinted version: _x2524-6356
856	4	0	_uhttp://link.springer.com/journal/42488 _zHybrid
999			_c3643 _d3643